diff --git a/.coverage b/.coverage
new file mode 100644
index 00000000..aea114fc
Binary files /dev/null and b/.coverage differ
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 9827693c..61c195c4 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -7,9 +7,25 @@ on:
 
 jobs:
   # ── Routing ───────────────────────────────────────────────────────────────
-  # Detect whether the tagged commit lives on main (→ PyPI production release)
-  # or anywhere else (→ TestPyPI pre-release).  All downstream jobs branch on
-  # this output so only the correct path runs.
+  # Two independent signals decide what runs, checked in this priority order:
+  #   1. is_dev_release: the tag NAME contains "dev" (e.g. v1.3.1-dev0) —
+  #      ALWAYS true in that case, regardless of which branch/commit is
+  #      tagged. Always publishes to TestPyPI + a GitHub pre-release.
+  #   2. is_main: only considered when the name does NOT contain "dev", and
+  #      requires the tag's commit to actually be an ancestor of main. This is
+  #      a safety gate: a non-dev-named tag cut from a branch other than main
+  #      must never auto-publish a real PyPI release under a "real" version
+  #      number just because someone forgot the branch they were on.
+  # A non-dev-named tag whose commit is NOT on main matches neither and
+  # triggers nothing at all (should_run=false) — accidental/misplaced tags
+  # never release.
+  #
+  # This used to check "is the commit an ancestor of main" FIRST, which
+  # silently routed a dev-named tag to the main/PyPI pipeline whenever dev and
+  # main happened to be in sync (e.g. right after a merge) — the ancestor
+  # check won out over the "dev" in the tag name. Checking the name first
+  # fixes that, while still keeping the ancestor check as the guard for the
+  # non-dev path.
   detect-target:
     name: Detect release target
     runs-on: ubuntu-latest
@@ -23,61 +39,66 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Check whether the tag commit is on main
+      - name: Check the release target
         id: check
         run: |
-          git fetch origin main
-          TAG_COMMIT="$(git rev-list -n 1 "${GITHUB_REF}")"
-          IS_ON_MAIN=$(git merge-base --is-ancestor "${TAG_COMMIT}" origin/main \
-                       && echo "true" || echo "false")
-          echo "is_main=${IS_ON_MAIN}" >> $GITHUB_OUTPUT
-
-          # Dev pre-release: a tag NOT on main whose name contains 'dev'
-          # (e.g. v1.3.1-dev0). Runs the full pipeline up to the TestPyPI install
-          # tests + a GitHub pre-release, then STOPS before the production PyPI
-          # publish. A tag off main WITHOUT 'dev' triggers nothing (should_run=false),
-          # so accidental/non-dev tags on dev never release.
           IS_DEV_RELEASE=false
-          if [ "${IS_ON_MAIN}" != "true" ] && [[ "${GITHUB_REF_NAME}" == *dev* ]]; then
+          if [[ "${GITHUB_REF_NAME}" == *dev* ]]; then
             IS_DEV_RELEASE=true
           fi
           echo "is_dev_release=${IS_DEV_RELEASE}" >> $GITHUB_OUTPUT
 
-          if [ "${IS_ON_MAIN}" = "true" ] || [ "${IS_DEV_RELEASE}" = "true" ]; then
+          # Only a non-dev-named tag needs the ancestor check at all -- a
+          # dev-named tag is never a main release, no matter where it sits.
+          IS_MAIN=false
+          if [ "${IS_DEV_RELEASE}" != "true" ]; then
+            git fetch origin main
+            TAG_COMMIT="$(git rev-list -n 1 "${GITHUB_REF}")"
+            if git merge-base --is-ancestor "${TAG_COMMIT}" origin/main; then
+              IS_MAIN=true
+            fi
+          fi
+          echo "is_main=${IS_MAIN}" >> $GITHUB_OUTPUT
+
+          if [ "${IS_DEV_RELEASE}" = "true" ] || [ "${IS_MAIN}" = "true" ]; then
             SHOULD_RUN=true
           else
             SHOULD_RUN=false
           fi
           echo "should_run=${SHOULD_RUN}" >> $GITHUB_OUTPUT
-          echo "Tag: ${GITHUB_REF_NAME}  commit: ${TAG_COMMIT}  on_main: ${IS_ON_MAIN}  dev_release: ${IS_DEV_RELEASE}  should_run: ${SHOULD_RUN}"
-
-  # ── Shared: run tests before any publish (main or dev pre-release) ────────
-  test:
-    name: Run Tests Before Release
-    needs: detect-target
-    runs-on: ubuntu-latest
-    if: ${{ needs.detect-target.outputs.should_run == 'true' }}
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: actions/setup-python@v6
-        with:
-          python-version: '3.11'
 
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          # Install the test extra so pytest, graphviz, torchmetrics,
-          # pytorch-lightning and tensorboard are available (several test modules
-          # import pytest / use pytest fixtures and cannot run under bare unittest).
-          python -m pip install .[utest] --extra-index-url https://download.pytorch.org/whl/cpu
-          python -m pip install pytest-timeout
+          echo "Tag: ${GITHUB_REF_NAME}  is_main: ${IS_MAIN}  dev_release: ${IS_DEV_RELEASE}  should_run: ${SHOULD_RUN}"
+          if [ "${SHOULD_RUN}" != "true" ]; then
+            echo "::warning::Tag '${GITHUB_REF_NAME}' has no 'dev' in its name and its commit is not on main — skipping release entirely."
+          fi
 
-      - name: Run tests
-        run: |
-          export WEIGHTSLAB_LOG_LEVEL="DEBUG"
-          python -m pip install pytorch_lightning
-          python -m unittest discover -s ./tests -p "test_*.py" -t . -v
+  # # ── Shared: run tests before any publish (main or dev pre-release) ────────
+  # test:
+  #   name: Run Tests Before Release
+  #   needs: detect-target
+  #   runs-on: ubuntu-latest
+  #   if: ${{ needs.detect-target.outputs.should_run == 'true' }}
+  #   steps:
+  #     - uses: actions/checkout@v4
+
+  #     - uses: actions/setup-python@v6
+  #       with:
+  #         python-version: '3.11'
+
+  #     - name: Install dependencies
+  #       run: |
+  #         python -m pip install --upgrade pip
+  #         # Install the test extra so pytest, graphviz, torchmetrics,
+  #         # pytorch-lightning and tensorboard are available (several test modules
+  #         # import pytest / use pytest fixtures and cannot run under bare unittest).
+  #         python -m pip install .[utest] --extra-index-url https://download.pytorch.org/whl/cpu
+  #         python -m pip install pytest-timeout
+
+  #     - name: Run tests
+  #       run: |
+  #         export WEIGHTSLAB_LOG_LEVEL="DEBUG"
+  #         python -m pip install pytorch_lightning
+  #         python -m unittest discover -s ./tests -p "test_*.py" -t . -v
 
   # ═══════════════════════════════════════════════════════════════════════════
   # DEV RELEASE — tag on dev branch, not yet merged to main
@@ -86,7 +107,8 @@ jobs:
 
   build-and-publish-dev-release:
     name: Build & Publish Dev Release (TestPyPI)
-    needs: [detect-target,test]
+    # needs: [detect-target,test]
+    needs: [detect-target]
     runs-on: ubuntu-latest
     if: ${{ needs.detect-target.outputs.should_run == 'true' }}
     permissions:
@@ -102,6 +124,55 @@ jobs:
         with:
           python-version: '3.11'
 
+      - uses: actions/setup-node@v4
+        with:
+          node-version: '20'
+
+      - name: Build and vendor Weights Studio UI
+        env:
+          # weights_studio is a separate PRIVATE repo — the automatic
+          # secrets.GITHUB_TOKEN is scoped to THIS repo only and has no access
+          # to it, so the clone below must authenticate with a real
+          # cross-repo PAT instead.
+          WEIGHTS_STUDIO_TOKEN: ${{ secrets.WEIGHTS_STUDIO_API_TOKEN }}
+          IS_DEV_RELEASE: ${{ needs.detect-target.outputs.is_dev_release }}
+        run: |
+          set -euo pipefail
+
+          if [ -z "${WEIGHTS_STUDIO_TOKEN:-}" ]; then
+            echo "WEIGHTS_STUDIO_API_TOKEN secret is not set; cannot access the private weights_studio repo." >&2
+            exit 1
+          fi
+          CLONE_URL="https://x-access-token:${WEIGHTS_STUDIO_TOKEN}@github.com/GrayboxTech/weights_studio.git"
+
+          # Mirror the weightslab tag routing: a dev-named tag vendors the
+          # weights_studio "dev" branch; every other tag vendors main. No
+          # ls-remote probe -- it added a flaky network round-trip and a
+          # branch that's known to exist. This used to guess the ref from
+          # which weightslab branches happened to contain the tagged commit
+          # -- fragile, and NOT tied to is_dev_release, so it could silently
+          # vendor the wrong UI.
+          UI_REF="main"
+          if [ "${IS_DEV_RELEASE}" = "true" ]; then
+            UI_REF="dev"
+          fi
+          echo "Using weights_studio ref: ${UI_REF}"
+
+          rm -rf /tmp/weights_studio
+          git clone --depth 1 --branch "${UI_REF}" "${CLONE_URL}" /tmp/weights_studio
+
+          pushd /tmp/weights_studio >/dev/null
+          npm ci
+          npm test
+          npm run build
+          popd >/dev/null
+
+          rm -rf weightslab/ui/static
+          mkdir -p weightslab/ui/static
+          cp -R /tmp/weights_studio/dist/. weightslab/ui/static/
+
+          test -f weightslab/ui/static/index.html
+
       - name: Install build tools
         run: python -m pip install --upgrade pip build twine
 
@@ -456,6 +527,43 @@ jobs:
         with:
           python-version: '3.11'
 
+      - uses: actions/setup-node@v4
+        with:
+          node-version: '20'
+
+      - name: Build and vendor Weights Studio UI
+        env:
+          # weights_studio is a separate PRIVATE repo — the automatic
+          # secrets.GITHUB_TOKEN is scoped to THIS repo only and has no access
+          # to it, so the clone below must authenticate with a real
+          # cross-repo PAT instead.
+          WEIGHTS_STUDIO_TOKEN: ${{ secrets.WEIGHTS_STUDIO_API_TOKEN }}
+        run: |
+          set -euo pipefail
+
+          if [ -z "${WEIGHTS_STUDIO_TOKEN:-}" ]; then
+            echo "WEIGHTS_STUDIO_API_TOKEN secret is not set; cannot access the private weights_studio repo." >&2
+            exit 1
+          fi
+          CLONE_URL="https://x-access-token:${WEIGHTS_STUDIO_TOKEN}@github.com/GrayboxTech/weights_studio.git"
+          UI_REF="main"
+          echo "Using weights_studio ref: ${UI_REF}"
+
+          rm -rf /tmp/weights_studio
+          git clone --depth 1 --branch "${UI_REF}" "${CLONE_URL}" /tmp/weights_studio
+
+          pushd /tmp/weights_studio >/dev/null
+          npm ci
+          npm test
+          npm run build
+          popd >/dev/null
+
+          rm -rf weightslab/ui/static
+          mkdir -p weightslab/ui/static
+          cp -R /tmp/weights_studio/dist/. weightslab/ui/static/
+
+          test -f weightslab/ui/static/index.html
+
       - name: Install build tools
         run: python -m pip install --upgrade pip build twine
 
diff --git a/.gitignore b/.gitignore
index c7f25cb7..419a0480 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,19 +3,32 @@ weightslab.egg-info
 build
 agents
 
+# UI dirs — built SPA artifacts, regenerated by `npm run build` / build:embed;
+# never committed (the wheel embeds them at build time).
 dist
+static
+weightslab/ui/static/
+
+# Ignore files and directories generated by Python
 logs
 root_log_dir
 __pycache__
 venv
 runs
 data
+outputs
+!./tests/data/
+!./weightslab/data/
 MagicMock
+drop
+htmlcov
+htmlcov-report
 
 # Ignore hidden directories
 .history
 .lh
 .claude
+.coverage
 .chat
 .ai
 .pytest_cache
diff --git a/AGENTS.md b/AGENTS.md
index 8b8eeda5..951e43bf 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -59,11 +59,12 @@ experiment becomes inspectable/editable; Weights Studio is the UI for that.
 **Wire path (the thing that breaks most often):**
 
 ```
-Browser (Vite app)  →  Envoy :8080 (grpc-web ↔ grpc)  →  Python gRPC servicer  →  training loop
+Browser  →  weightslab start :8080 (grpc-web → grpc proxy)  →  Python gRPC servicer  →  training loop
 ```
 
-- The browser cannot speak raw gRPC, so **Envoy** transcodes grpc-web ↔ gRPC.
-  If Envoy is down or misconfigured, the UI loads but no data appears.
+- `weightslab start` is a pure-Python HTTP server that serves the bundled SPA
+  and translates grpc-web (browser) to raw gRPC (backend). No Docker, no Envoy.
+  If `weightslab start` is not running, the browser has no UI to load.
 - The gRPC servicer and the training loop run in the **same process, different
   threads**, coordinated by locks in
   `weightslab/weightslab/components/global_monitoring.py`.
@@ -88,13 +89,19 @@ wl.serve(serving_grpc=True, serving_cli=True)   # background threads, same proce
 wl.keep_serving()                                # keep the process alive for the UI
 ```
 
-Then start the studio stack (Envoy + frontend) and open it in a browser.
+Then start the UI in another terminal and open it in a browser:
+
+```bash
+weightslab start   # serves at http://localhost:8080 by default
+```
+
 Working starting points live in
 `weightslab/weightslab/examples/{PyTorch,Lightning,Usecases}/<usecase>/`
 (each is a `main.py` + `config.yaml`) — find the closest example and mirror it.
 
-Studio deployment details (Docker compose, Envoy, ports, certs) are in
-`weights_studio/docker/` and documented in `weightslab/docs/weights_studio.rst`.
+UI deployment details (port, TLS, certs) are documented in
+`weightslab/docs/weights_studio.rst`. TLS is opt-in: run `weightslab se` once,
+then `weightslab start --certs`.
 
 ---
 
@@ -138,10 +145,10 @@ ones when debugging:
 | Variable | Default | Why you touch it |
 |---|---|---|
 | `WEIGHTSLAB_LOG_LEVEL` | `INFO` | Set `DEBUG` to see what's happening. (`WATCHDOG` level sits between WARNING/ERROR.) |
-| `GRPC_BACKEND_HOST` / `GRPC_BACKEND_PORT` | `0.0.0.0` / `50051` | Backend must listen where Envoy expects it. |
-| `GRPC_TLS_ENABLED` | `1` | TLS on the gRPC socket. Set `0` **only** for isolated local debugging. |
-| `GRPC_TLS_REQUIRE_CLIENT_AUTH` | `1` | mTLS. Must match what Envoy presents. |
-| `GRPC_TLS_CERT_DIR` | `~/certs` | Where default cert files are looked up. |
+| `GRPC_BACKEND_HOST` / `GRPC_BACKEND_PORT` | `0.0.0.0` / `50051` | Backend gRPC bind address. |
+| `GRPC_TLS_ENABLED` | `0` | TLS on the gRPC socket. Set `1` with `weightslab start --certs`. |
+| `GRPC_TLS_REQUIRE_CLIENT_AUTH` | `0` | mTLS. Must match what `weightslab start --certs` presents. |
+| `WEIGHTSLAB_CERTS_DIR` | `~/.weightslab-certs` | Where cert files are looked up (single source of truth). |
 | `GRPC_AUTH_TOKEN` | *(unset)* | Optional metadata-token auth on top of mTLS. |
 | `GRPC_MAX_MESSAGE_BYTES` | `268435456` (256 MB) | Raise it if large tensors/image batches fail. |
 | `WEIGHTSLAB_DISABLE_WATCHDOGS` | `0` | Set `1` when debugging with breakpoints (see §5). |
@@ -151,7 +158,7 @@ ones when debugging:
 
 | Variable | Default | Why you touch it |
 |---|---|---|
-| `WS_SERVER_HOST` / `WS_SERVER_PORT` / `WS_SERVER_PROTOCOL` | `localhost` / `8080` / `https` | How the browser reaches the backend (via Envoy). The #1 connection-issue knob. |
+| `WS_SERVER_HOST` / `WS_SERVER_PORT` / `WS_SERVER_PROTOCOL` | `localhost` / `8080` / `http` | How the browser reaches the `weightslab start` server. The #1 connection-issue knob. |
 | `WS_HISTOGRAM_MAX_BINS` | `512` | Cap on metadata histogram bars. |
 | `BB_THUMB_RENDER` | `10` | Max bounding boxes drawn per **thumbnail**, per overlay (GT and PRED capped independently). |
 | `BB_MODAL_RENDER` | `100` | Max bounding boxes drawn per **modal** image, per overlay. A `?` button in the modal shows the active limit. |
@@ -161,11 +168,10 @@ ones when debugging:
 | `ENABLE_AGENT` | `1` | `0`/`false` removes the agent chat bar + history panel and stops the agent health poll. |
 
 > **VITE_ vs WS_/BB_/ENABLE_:** `VITE_*` variables are baked at **build time**
-> (changing them needs a rebuild). `WS_*` / `BB_*` / `ENABLE_*` are injected at
-> **container start** into `config.js` and read as `window.*` globals (the
-> toggles as `window.WS_ENABLE_*`) — changing them needs only a container restart
-> + browser reload (see the caching note in §5). Each `ENABLE_*` defaults to on;
-> set it to `0`/`false`/`no`/`off` to disable. Full reference:
+> (changing them needs a frontend rebuild). `WS_*` / `BB_*` / `ENABLE_*` are
+> injected into `config.js` at `weightslab start` time and read as `window.*`
+> globals — changing them needs only a restart + browser reload. Each `ENABLE_*`
+> defaults to on; set it to `0`/`false`/`no`/`off` to disable. Full reference:
 > `weightslab/docs/configuration.rst` (“Feature toggles”).
 
 ---
@@ -177,19 +183,15 @@ distilled from issues hit in development).
 
 **UI loads but the sample grid is empty / "failed to fetch" / gRPC errors.**
 The wire path (§1) is broken somewhere. Check in order: (1) backend actually
-serving on `0.0.0.0:50051`; (2) Envoy running and reachable on `:8080`;
-(3) frontend `WS_SERVER_HOST/PORT/PROTOCOL` point at Envoy, not the raw backend;
-(4) **TLS mismatch** — `WS_SERVER_PROTOCOL=https` vs `http`, Envoy server certs,
-and Envoy→backend mTLS certs all consistent. For local debugging you can drop
-TLS end-to-end (`GRPC_TLS_ENABLED=0` + `VITE_SERVER_PROTOCOL=http`).
+serving on `0.0.0.0:50051`; (2) `weightslab start` is running and the browser
+can reach it on `:8080`; (3) **TLS mismatch** if using `--certs` — run
+`weightslab se` first and export `WEIGHTSLAB_CERTS_DIR`. For local debugging
+drop TLS entirely (omit `--certs`; `GRPC_TLS_ENABLED=0`).
 
 **Changed an env var, restarted, but the UI still uses the old value.**
 - `VITE_*` is build-time → you must **rebuild** the frontend, not just restart.
-- `WS_*` / `BB_*` are read once per page load → you must **reload the tab**.
-- Historically `config.js` was served `Cache-Control: immutable` so even a
-  restart needed a **hard refresh**; current builds serve `/config.js` with
-  `no-store`, so a container restart + normal reload is enough. On an older
-  deployment, hard-refresh (Ctrl+Shift+R) or clear cache.
+- `WS_*` / `BB_*` / `ENABLE_*` are injected at `weightslab start` time → you
+  must **restart `weightslab start`** then reload the tab.
 
 **Sample grid flashes empty cells when auto-refresh fires.**
 An auto-refresh (timer or manual) that lands while a `GetDataSamples` grid fetch
@@ -247,7 +249,8 @@ OpenRouter** initialized from the UI via `/init` (then `/model` to switch,
 - `grid_data/` — grid + modal rendering (`GridCell.ts`, `DataImageService.ts`,
   `gridDataManager.ts`, `BboxRenderer.ts`, `SegmentationRenderer.ts`,
   `PointCloudViewer.ts`).
-- `docker/` — compose, `nginx-entrypoint.sh` (injects `config.js`), Envoy assets.
+- `ui/` — `server.py` (pure-Python HTTP + gRPC-Web proxy), `static/` (bundled SPA),
+  `utils/` (cert-generation scripts, sync-frontend helper).
 
 **Docs:** `weightslab/docs/` (Sphinx) — `configuration.rst` (all env vars),
 `weights_studio.rst` (studio deploy + agent), `quickstart.rst`, `grpc/`.
@@ -257,7 +260,7 @@ OpenRouter** initialized from the UI via `/init` (then `/model` to switch,
 ## 7. For contributors (working in a checkout)
 
 - **The two repos must sit side by side** (`…/weightslab`, `…/weights_studio`);
-  codegen and Envoy configs reach across by relative path.
+  proto codegen scripts reach across by relative path.
 - **Editing the proto is cross-repo** — do all of: edit
   `experiment_service.proto`; regenerate Python stubs from the repo root; run
   `npm run generate-proto:data` in weights_studio. Editing one side only leaves
diff --git a/CHANGELOG.md b/CHANGELOG.md
index b538b1c6..ee858e6e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1 +1 @@
-# Changelog - 2026-07-07 v1.3.2
+# Changelog - 2026-07-13 v1.3.3.dev1
diff --git a/README.md b/README.md
index 8efea5f8..6135c32e 100644
--- a/README.md
+++ b/README.md
@@ -46,13 +46,18 @@ Most data problems are invisible until your model tells you: through loss spikes
 ## Quickstart
 
 ![Python](https://img.shields.io/badge/Python-v3.10--v3.14-5865F2?style=flat&logo=python&logoColor=white)
-![Docker](https://img.shields.io/badge/Docker-v4-0db7ed?style=flat&logo=docker&logoColor=white)
 
 **1. Install**
 ```bash
 pip install weightslab
 ```
-**2. Wrap your training script**
+
+**2. Launch Studio**
+```bash
+weightslab start
+```
+
+**3.a. Wrap your training script locally**
 ```python
 # wrap the objects in your training script
 
@@ -66,14 +71,14 @@ loader = wl.watch_or_edit(dataset, flag='data', loader_name="train")
 wl.serve(serving_grpc=True, serving_cli=False)
 ...
 ```
-**3. Launch Studio**
-```bash
-weightslab ui launch  # then open https://localhost:5173 🚀
-```
-<br>
 
-For a detailed installation guide and advanced configuration &rarr; [Installation Documentation](https://grayboxtech.github.io/weightslab/latest/quickstart.html).
+**3.b. Or in the cloud**
+
+[![Start coding with GCollab](https://img.shields.io/badge/Start_coding_with_GCollab-F9AB00?style=for-the-badge&logo=googlecolab&logoColor=white)](https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Colab/wl-colab-quickstart.ipynb)
 
+
+<br>
+For a detailed installation guide and advanced configuration &rarr; [Installation Documentation](https://grayboxtech.github.io/weightslab/latest/quickstart.html).
 <br>
 
 > [!TIP]
@@ -84,8 +89,9 @@ For a detailed installation guide and advanced configuration &rarr; [Installatio
 > weightslab start example --seg      # segmentation
 > weightslab start example --det      # detection
 > weightslab start example --clus     # clustering
-> weightslab start example --gen      # generation
 > ```
+>
+> Explore our [sandbox](www.sandbox.graybx.com).
 
 <br>
 
@@ -117,10 +123,10 @@ For a detailed installation guide and advanced configuration &rarr; [Installatio
 3. **Run your script, then launch the UI in a separate terminal:**
 ```bash
    python train.py
-   weightslab ui launch
+   weightslab start
 ```
 
-4. **Open your browser** `https://localhost:5173` and inspect your training in real time.
+4. **Open your browser** at the URL printed by `weightslab start` and inspect your training in real time.
 
 </details>
 
@@ -316,9 +322,8 @@ Find our documentation [online](https://grayboxtech.github.io/weightslab/latest/
 
 New here (human or AI coding agent)? Start with [AGENTS.md](AGENTS.md) — it
 captures the cross-repo architecture (weightslab backend ↔ weights_studio
-frontend via the shared proto), the module maps, the `wl.watch_or_edit`
-integration pattern, where tests live, and the gotchas that aren't obvious from
-any single file. It's the fastest way to orient before a first change.
+frontend via the shared proto), the module maps, the integration pattern, where tests live, and the gotchas that aren't obvious from
+any single file. It's the fastest way to orient before a first change and contribution.
 
 </details>
 
diff --git a/agent_config.yaml b/agent_config.yaml
index 7d65a84c..b972779e 100644
--- a/agent_config.yaml
+++ b/agent_config.yaml
@@ -23,5 +23,6 @@ agent:
   # openrouter_api_key: <your_openrouter_api_key>  # Open router API key (can also be set as env variable OPENROUTER_API_KEY)
   # openrouter_base_url: https://openrouter.ai/api/v1  # Open router base URL (can also be set as env variable OPENROUTER_BASE_URL)
   # openrouter_request_timeout: 15.0  # Timeout for OpenRouter API requests in seconds (can also be set as env variable OPENROUTER_REQUEST_TIMEOUT)
+  # openrouter_max_tokens: 2048  # Max completion length. OpenRouter reserves max_tokens*price against the key budget BEFORE generating, so an uncapped value can 402 ("more credits, or fewer max_tokens") on a credit/weekly-limited key. Raise only if responses get truncated (env: OPENROUTER_MAX_TOKENS)
   # openrouter_provider_sort: throughput  # Bias OpenRouter's upstream routing to avoid slow providers: 'throughput' | 'latency' | 'price'. Empty string = let OpenRouter choose (env: OPENROUTER_PROVIDER_SORT)
   # openrouter_structured_output: false  # Ask the model for a schema-validated plan directly (skips free-form JSON + regex repair). More reliable, but only works on models whose OpenRouter route supports structured/JSON-schema output (e.g. Gemini, GPT-4o). env: OPENROUTER_STRUCTURED_OUTPUT=1
diff --git a/docs/_static/custom.css b/docs/_static/custom.css
index e74b634b..a9ec21fb 100644
--- a/docs/_static/custom.css
+++ b/docs/_static/custom.css
@@ -277,6 +277,35 @@ body[data-theme="dark"] .wl-only-dark {
   margin-bottom: 1rem;
 }
 
+.wl-eg-cardwrap {
+  position: relative;
+  display: flex;
+}
+
+.wl-eg-cardwrap > .wl-eg-card {
+  flex: 1;
+}
+
+/* "Open in Colab" logo, pinned top-right, above the card link. */
+.wl-eg-colab {
+  position: absolute;
+  right: 0.9rem;
+  top: 0.9rem;
+  z-index: 2;
+  line-height: 0;
+  transition: transform 0.12s;
+}
+
+.wl-eg-colab:hover {
+  transform: scale(1.1);
+}
+
+.wl-eg-colab img {
+  height: 22px;
+  width: 22px;
+  display: block;
+}
+
 .wl-eg-card {
   display: flex;
   flex-direction: column;
diff --git a/docs/_static/examples-gallery.js b/docs/_static/examples-gallery.js
index 24cb5a07..d610919b 100644
--- a/docs/_static/examples-gallery.js
+++ b/docs/_static/examples-gallery.js
@@ -10,41 +10,49 @@
   ];
 
   // URLs are root-relative (from the doc root), prefixed with content_root at render time
+  // Base for "Open in Colab" links (notebooks live under examples/Notebooks).
+  var COLAB = 'https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/';
+
   var EXAMPLES = [
     {
       badge: 'PyTorch', color: 'pytorch',
       title: 'Classification — MNIST',
       desc: 'CNN digit classifier on MNIST. Register hyperparameters, monitor per-sample loss, and use the deny-aware sampler to focus on hard examples.',
       tags: ['classification', 'supervised', 'mnist', 'cnn'],
-      url: 'examples/pytorch/classification.html'
+      url: 'examples/pytorch/classification.html',
+      colab: COLAB + 'PyTorch/wl-classification.ipynb'
     },
     {
       badge: 'PyTorch', color: 'pytorch',
       title: 'Segmentation — BDD100k',
       desc: 'Per-pixel semantic segmentation with a UNet. Track per-sample IoU and visualise mask overlays directly in the studio.',
       tags: ['segmentation', 'semantic', 'bdd100k', 'masks', 'dense prediction'],
-      url: 'examples/pytorch/segmentation.html'
+      url: 'examples/pytorch/segmentation.html',
+      colab: COLAB + 'PyTorch/wl-segmentation.ipynb'
     },
     {
       badge: 'PyTorch', color: 'pytorch',
       title: 'Detection — Penn-Fudan',
       desc: 'Bounding-box detection on Penn-Fudan pedestrians. Per-instance multi-index dataframe with (sample_id, annotation_id) keys.',
       tags: ['detection', 'object detection', 'bounding boxes', 'penn-fudan'],
-      url: 'examples/pytorch/detection.html'
+      url: 'examples/pytorch/detection.html',
+      colab: COLAB + 'PyTorch/wl-detection.ipynb'
     },
     {
       badge: 'PyTorch', color: 'pytorch',
       title: 'Clustering — Face Recognition',
       desc: 'Metric learning with triplet loss on face datasets. Store and explore high-dimensional embeddings per sample in the studio.',
       tags: ['clustering', 'unsupervised', 'embeddings', 'face recognition', 'metric learning'],
-      url: 'examples/pytorch/clustering.html'
+      url: 'examples/pytorch/clustering.html',
+      colab: COLAB + 'PyTorch/wl-clustering.ipynb'
     },
     {
       badge: 'PyTorch', color: 'pytorch',
       title: 'Generation / Anomaly Detection',
       desc: 'Unsupervised anomaly detection on MVTec with a multi-task UNet. Monitor reconstruction quality and per-sample anomaly scores.',
       tags: ['anomaly detection', 'generation', 'unsupervised', 'mvtec', 'reconstruction'],
-      url: 'examples/pytorch/generation.html'
+      url: 'examples/pytorch/generation.html',
+      colab: COLAB + 'PyTorch/wl-generation.ipynb'
     },
     {
       badge: 'Lightning', color: 'lightning',
@@ -72,7 +80,8 @@
       title: 'Loss-Shape Classification',
       desc: 'Dynamic subscribed signal that classifies each sample\'s loss trajectory (monotonic, U-shape, spiked, …) and auto-tags it.',
       tags: ['loss analysis', 'signal', 'categorical tag', 'per-sample', 'trajectory'],
-      url: 'examples/usecases/loss_shape_classification.html'
+      url: 'examples/usecases/loss_shape_classification.html',
+      colab: COLAB + 'Usecases/wl-segmentation-loss-shapes-classification.ipynb'
     }
   ];
 
@@ -94,15 +103,25 @@
     }).join('');
     var search = (ex.title + ' ' + ex.tags.join(' ')).toLowerCase();
     var href = baseUrl + '/' + ex.url;
+    // "Open in Colab" badge, bottom-right, as a sibling of the card link (an
+    // <a> cannot be nested inside another <a>). Only for examples with a notebook.
+    var colabHtml = ex.colab
+      ? '<a class="wl-eg-colab" href="' + esc(ex.colab) + '" target="_blank" rel="noopener noreferrer" title="Open in Google Colab" aria-label="Open in Google Colab">' +
+          '<img src="https://colab.research.google.com/img/colab_favicon_256px.png" alt="Open in Colab">' +
+        '</a>'
+      : '';
+    // The wrapper carries the filter data so the whole card (badge included)
+    // shows/hides together.
     return (
-      '<a class="wl-eg-card" href="' + esc(href) + '"' +
-        ' data-search="' + esc(search) + '"' +
-        ' data-color="' + esc(ex.color) + '">' +
-        '<span class="wl-eg-badge wl-eg-badge--' + ex.color + '">' + esc(ex.badge) + '</span>' +
-        '<p class="wl-eg-title">' + esc(ex.title) + '</p>' +
-        '<p class="wl-eg-desc">' + esc(ex.desc) + '</p>' +
-        '<div class="wl-eg-tags">' + tagsHtml + '</div>' +
-      '</a>'
+      '<div class="wl-eg-cardwrap" data-search="' + esc(search) + '" data-color="' + esc(ex.color) + '">' +
+        '<a class="wl-eg-card" href="' + esc(href) + '">' +
+          '<span class="wl-eg-badge wl-eg-badge--' + ex.color + '">' + esc(ex.badge) + '</span>' +
+          '<p class="wl-eg-title">' + esc(ex.title) + '</p>' +
+          '<p class="wl-eg-desc">' + esc(ex.desc) + '</p>' +
+          '<div class="wl-eg-tags">' + tagsHtml + '</div>' +
+        '</a>' +
+        colabHtml +
+      '</div>'
     );
   }
 
@@ -166,7 +185,7 @@
       var totalVisible = 0;
 
       document.querySelectorAll('.wl-eg-section').forEach(function (section) {
-        var cards = section.querySelectorAll('.wl-eg-card');
+        var cards = section.querySelectorAll('.wl-eg-cardwrap');
         var sectionVisible = 0;
         cards.forEach(function (c) {
           var matchText  = !q || c.dataset.search.indexOf(q) !== -1;
diff --git a/docs/_static/wl-ribbon.js b/docs/_static/wl-ribbon.js
index 4fecb516..271516b4 100644
--- a/docs/_static/wl-ribbon.js
+++ b/docs/_static/wl-ribbon.js
@@ -10,7 +10,7 @@
     'The studio streams signals in real-time — no need to wait for an epoch to end to see results.',
     '<code>weightslab start example --cls</code> launches a full MNIST classification demo in one command.',
     'Use <code>subscribe_to=</code> on a signal to build reactive per-sample analytics derived from other signals.',
-    'Run <code>weightslab ui launch --certs</code> to enable HTTPS + mTLS for secure remote studio access.',
+    'Run <code>weightslab start --certs</code> to enable HTTPS + mTLS for secure remote studio access.',
     'Set <code>preload_labels=False</code> for large datasets to speed up startup; labels are loaded lazily.',
     'Use <code>array_return_proxies=True</code> (default) to avoid loading the full dataset array into RAM.',
     'Set <code>WEIGHTSLAB_LOG_LEVEL=DEBUG</code> to see full gRPC logs when debugging connectivity issues.',
diff --git a/docs/conf.py b/docs/conf.py
index 743e23e5..080925fd 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -82,7 +82,7 @@ def _docs_release() -> str:
     # Superseded by examples/pytorch/classification.rst and examples/pytorch/segmentation.rst
     "usecases.rst",
     "segmentation_usecase.rst",
-    # Replaced by direct toctree entries usage/good_practice and usage/docker
+    # Replaced by direct toctree entry usage/good_practice
     "usage.rst",
     # SDK params moved into configuration.rst
     "usage/parameters.rst",
diff --git a/docs/configuration.rst b/docs/configuration.rst
index 384a5733..b6f01992 100644
--- a/docs/configuration.rst
+++ b/docs/configuration.rst
@@ -354,7 +354,7 @@ Deploying the Studio
 ~~~~~~~~~~~~~~~~~~~~~
 
 All Weights Studio configuration variables are passed to the UI at launch time
-via ``weightslab ui launch``.  There are two ways to supply them.
+via ``weightslab start``.  There are two ways to supply them.
 
 **Option 1 — shell exports (quick, per-session)**
 
@@ -362,7 +362,7 @@ via ``weightslab ui launch``.  There are two ways to supply them.
 
    export ENABLE_AGENT=0
    export BB_THUMB_RENDER=50
-   weightslab ui launch
+  weightslab start
 
 **Option 2 — ``.env`` file (persistent, version-controllable)**
 
@@ -380,11 +380,10 @@ Then launch normally:
 
 .. code-block:: bash
 
-   weightslab ui launch
+  weightslab start
 
 WeightsLab loads the ``.env`` file automatically.  Shell exports take precedence
-over ``.env`` values.  No rebuild of the Studio container is needed — variables
-are injected at container start.
+over ``.env`` values.
 
 .. note::
 
@@ -562,8 +561,7 @@ subclass) that unwinds the stack and releases the lock via ``finally`` /
      - ``0``
      - If set to ``1`` / ``true`` / ``yes`` / ``on``, the watchdog calls
        ``os._exit(1)`` instead of restarting the server when a stuck RPC is
-       detected.  Useful when running under a process supervisor (systemd,
-       Docker ``restart: always``) that handles the restart externally.
+       detected.  Useful when running under a process supervisor (e.g. systemd) that handles the restart externally.
 
 
 Data and Cache
@@ -867,20 +865,17 @@ Backend Connection
      - Description
    * - ``GRPC_BACKEND_HOST``
      - ``localhost``
-     - Hostname of the WeightsLab gRPC backend, used by the Envoy proxy.
+     - Hostname of the WeightsLab gRPC backend to proxy to.
    * - ``GRPC_BACKEND_PORT``
      - ``50051``
-     - Port of the WeightsLab gRPC backend, used by the Envoy proxy.
-   * - ``ENVOY_HOST``
-     - ``localhost``
-     - Hostname of the Envoy proxy the browser connects to.
-   * - ``ENVOY_PORT``
-     - ``8080``
-     - Port Envoy listens on for HTTPS / gRPC-Web traffic.
-   * - ``ENVOY_ADMIN_PORT``
-     - ``9901``
-     - Envoy admin interface port (metrics, health checks).
-       Bound to loopback and not published by Docker Compose by default.
+     - Port of the WeightsLab gRPC backend to proxy to.
+   * - ``WEIGHTSLAB_UI_HOST``
+     - ``0.0.0.0``
+     - Interface the ``weightslab start`` HTTP server binds to.
+   * - ``WEIGHTSLAB_UI_PORT``
+     - ``50051``
+     - Preferred port for the ``weightslab start`` HTTP server. If that port is
+       already in use, WeightsLab picks a random available port.
 
 
 Vite Dev Server
@@ -915,13 +910,10 @@ These variables are injected into the browser bundle at build / dev time.
      - Description
    * - ``VITE_SERVER_HOST``
      - ``localhost``
-     - Hostname (usually the Envoy proxy) the browser uses to reach the backend.
+     - Hostname the browser uses to reach the ``weightslab start`` server.
    * - ``VITE_SERVER_PORT``
      - ``8080``
      - Port the browser uses to reach the backend.
-   * - ``VITE_SERVER_PROTOCOL``
-     - ``https``
-     - Protocol (``http`` or ``https``) for browser-to-backend requests.
    * - ``VITE_IS_A_SANDBOX``
      - ``0``
      - Enables sandbox / demo mode ? disables all write operations in the UI.
@@ -940,21 +932,21 @@ These variables are injected into the browser bundle at build / dev time.
        look-ahead). Increasing this prefetches more aggressively at the cost of
        memory. ``VITE_MAX_PREFETCH_BATCHES`` is derived from this value
        (``window − 1``). **Runtime override (no rebuild):** ``GRID_WINDOW_SIZE``
-       (nginx env) or ``window.WS_GRID_WINDOW_SIZE``.
+       (env, injected by ``weightslab start``) or ``window.WS_GRID_WINDOW_SIZE``.
    * - ``VITE_WS_MAX_IMAGE_CACHE_SIZE``
      - *(window + 2)*
      - Maximum number of image entries held in the in-browser image cache.
        Defaults to ``VITE_GRID_WINDOW_SIZE + 2``. **Runtime override:**
-       ``GRID_MAX_IMAGE_CACHE_SIZE`` (nginx env) or ``window.WS_MAX_IMAGE_CACHE_SIZE``.
+       ``GRID_MAX_IMAGE_CACHE_SIZE`` (env, injected by ``weightslab start``) or ``window.WS_MAX_IMAGE_CACHE_SIZE``.
    * - ``VITE_WS_GRID_CACHE_MAX_MB``
      - ``128``
      - Maximum memory (MB) for the grid-view image tile cache.
-       **Runtime override:** ``GRID_CACHE_MAX_MB`` (nginx env) or
+       **Runtime override:** ``GRID_CACHE_MAX_MB`` (env, injected by ``weightslab start``) or
        ``window.WS_GRID_CACHE_MAX_MB``.
    * - ``VITE_WS_MODAL_CACHE_MAX_MB``
      - ``64``
      - Maximum memory (MB) for the full-resolution modal image cache.
-       **Runtime override:** ``MODAL_CACHE_MAX_MB`` (nginx env) or
+       **Runtime override:** ``MODAL_CACHE_MAX_MB`` (env, injected by ``weightslab start``) or
        ``window.WS_MODAL_CACHE_MAX_MB``.
 
 
@@ -972,14 +964,14 @@ Point cloud
      - *(unset — no cap)*
      - Maximum number of 3-D points rendered per point-cloud sample in the
        modal viewer. Leave unset for no cap. Useful on low-end GPUs.
-       **Runtime override:** ``PC_MAX_POINTS`` (nginx env) or
+       **Runtime override:** ``PC_MAX_POINTS`` (env, injected by ``weightslab start``) or
        ``window.WS_WL_PC_MAX_POINTS``.
    * - ``VITE_WL_DISABLE_GPU_RENDERING``
      - ``0``
      - Set to ``1`` to force CPU-side (canvas 2-D) rendering for point clouds,
        bypassing the three.js WebGL renderer. Useful when GPU drivers are absent
        or broken inside a headless container. **Runtime override:**
-       ``DISABLE_GPU_RENDERING`` (nginx env) or ``window.WS_WL_DISABLE_GPU_RENDERING``.
+       ``DISABLE_GPU_RENDERING`` (env, injected by ``weightslab start``) or ``window.WS_WL_DISABLE_GPU_RENDERING``.
 
 
 Bounding-box render limits
@@ -993,11 +985,10 @@ of ``10`` allows up to 10 GT boxes *and* 10 PRED boxes per image. Boxes beyond
 the cap are simply not drawn (predictions are typically score-ordered, so the
 most confident ones are kept).
 
-These are set on the Weights Studio frontend container (for example in
-``../weights_studio/docker/docker-compose.yml``) and injected into the page at
-startup by the nginx entrypoint — changing them needs no rebuild, just a
-container restart. For a local ``vite`` dev server, use the ``VITE_`` fallbacks
-shown below. Values are clamped to a hard ceiling of ``10000``.
+These are set as environment variables before ``weightslab start`` and injected
+into ``config.js`` at startup — changing them needs no rebuild, just a
+restart + browser reload. For a local ``vite`` dev server, use the ``VITE_``
+fallbacks shown below. Values are clamped to a hard ceiling of ``10000``.
 
 .. list-table::
    :header-rows: 1
@@ -1033,12 +1024,12 @@ agent. Each toggle **removes the area from the UI** (the elements are hidden)
 **and stops its background work** (auto-refresh timers and gRPC polls are never
 started), so a disabled area costs nothing at runtime.
 
-Like the bounding-box render limits, these are set on the Weights Studio frontend
-container (for example in ``../weights_studio/docker/docker-compose.yml``) and
-injected into the page at startup by the nginx entrypoint — changing them needs
-no rebuild, just a container restart + browser reload. For a local ``vite`` dev
-server, use the ``VITE_`` fallbacks shown below. Every toggle **defaults to
-enabled**; set it to ``0`` / ``false`` / ``no`` / ``off`` (any case) to disable.
+Like the bounding-box render limits, these are set as environment variables
+before ``weightslab start`` and injected into ``config.js`` at startup —
+changing them needs no rebuild, just a restart + browser reload. For a local
+``vite`` dev server, use the ``VITE_`` fallbacks shown below. Every toggle
+**defaults to enabled**; set it to ``0`` / ``false`` / ``no`` / ``off`` (any
+case) to disable.
 
 .. list-table::
    :header-rows: 1
@@ -1078,5 +1069,3 @@ enabled**; set it to ``0`` / ``false`` / ``no`` / ``off`` (any case) to disable.
    limits), with a build-time ``VITE_ENABLE_*`` fallback for the dev server.
    Because ``config.js`` is served ``no-store``, a container restart + normal
    reload is enough to pick up a change.
-
-
diff --git a/docs/examples/lightning/classification.rst b/docs/examples/lightning/classification.rst
index be70b9f8..acbde286 100644
--- a/docs/examples/lightning/classification.rst
+++ b/docs/examples/lightning/classification.rst
@@ -11,7 +11,7 @@ Classification — MNIST (PyTorch Lightning)
      <span class="wl-eg-tag">pytorch lightning</span>
    </div>
 
-**Example:** ``weightslab/examples/Lightning/ws-classification/main.py``
+**Example:** ``weightslab/examples/Lightning/wl-classification/main.py``
 
 **Task:** MNIST digit classification with a CNN, training loop managed by
 ``pl.Trainer``.
diff --git a/docs/examples/pytorch/classification.rst b/docs/examples/pytorch/classification.rst
index 77da73e0..3053d262 100644
--- a/docs/examples/pytorch/classification.rst
+++ b/docs/examples/pytorch/classification.rst
@@ -11,7 +11,7 @@ Classification — MNIST (PyTorch)
      <span class="wl-eg-tag">cnn</span>
    </div>
 
-**Example:** ``weightslab/examples/PyTorch/ws-classification/main.py``
+**Example:** ``weightslab/examples/PyTorch/wl-classification/main.py``
 
 **Task:** 10-class digit classification on MNIST with a small CNN.
 
@@ -157,5 +157,14 @@ debug values, custom distances, etc.
 
    .. code-block:: bash
 
-      weightslab ui launch           # 1. deploy the studio
+      weightslab start               # 1. deploy the studio
       weightslab start example --cls # 2. start the classification demo
+
+
+.. raw:: html
+
+   <div style="text-align:right; margin-top:2rem;">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-classification.ipynb" target="_blank" rel="noopener noreferrer">
+       <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
+     </a>
+   </div>
diff --git a/docs/examples/pytorch/clustering.rst b/docs/examples/pytorch/clustering.rst
index 9bd7ba6d..079cf28c 100644
--- a/docs/examples/pytorch/clustering.rst
+++ b/docs/examples/pytorch/clustering.rst
@@ -12,7 +12,7 @@ Clustering — Face Recognition (PyTorch)
      <span class="wl-eg-tag">metric learning</span>
    </div>
 
-**Example:** ``weightslab/examples/PyTorch/ws-clustering/main.py``
+**Example:** ``weightslab/examples/PyTorch/wl-clustering/main.py``
 
 **Task:** Metric learning with triplet loss on the Olivetti / LFW face dataset.
 The goal is to train an embedding network so that embeddings from the same
@@ -93,5 +93,14 @@ silhouette score to identify consistently confused identities.
 
    .. code-block:: bash
 
-      weightslab ui launch            # 1. deploy the studio
+      weightslab start                # 1. deploy the studio
       weightslab start example --clus # 2. start the clustering demo
+
+
+.. raw:: html
+
+   <div style="text-align:right; margin-top:2rem;">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-clustering.ipynb" target="_blank" rel="noopener noreferrer">
+       <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
+     </a>
+   </div>
diff --git a/docs/examples/pytorch/detection.rst b/docs/examples/pytorch/detection.rst
index a4daaa3f..d0b6d71b 100644
--- a/docs/examples/pytorch/detection.rst
+++ b/docs/examples/pytorch/detection.rst
@@ -11,7 +11,7 @@ Detection — Penn-Fudan Pedestrians (PyTorch)
      <span class="wl-eg-tag">penn-fudan</span>
    </div>
 
-**Example:** ``weightslab/examples/PyTorch/ws-detection/main.py``
+**Example:** ``weightslab/examples/PyTorch/wl-detection/main.py``
 
 **Task:** Bounding-box detection on the Penn-Fudan pedestrian dataset with a
 small ResNet-backbone detector.
@@ -125,5 +125,14 @@ pipeline. See :ref:`good-practice-get-items` for the recommended signature.
 
    .. code-block:: bash
 
-      weightslab ui launch           # 1. deploy the studio
+    weightslab start               # 1. deploy the studio
       weightslab start example --det # 2. start the detection demo
+
+
+.. raw:: html
+
+   <div style="text-align:right; margin-top:2rem;">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-detection.ipynb" target="_blank" rel="noopener noreferrer">
+       <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
+     </a>
+   </div>
diff --git a/docs/examples/pytorch/generation.rst b/docs/examples/pytorch/generation.rst
index 020efa81..12a99013 100644
--- a/docs/examples/pytorch/generation.rst
+++ b/docs/examples/pytorch/generation.rst
@@ -12,7 +12,7 @@ Generation / Anomaly Detection — MVTec (PyTorch)
      <span class="wl-eg-tag">reconstruction</span>
    </div>
 
-**Example:** ``weightslab/examples/PyTorch/ws-generation/main.py``
+**Example:** ``weightslab/examples/PyTorch/wl-generation/main.py``
 
 **Task:** Unsupervised anomaly detection on MVTec capsule images with a
 multi-task UNet (classification head + reconstruction head + contrastive loss).
@@ -99,5 +99,5 @@ filter by pair distance to find the hardest negatives.
 
    .. code-block:: bash
 
-      weightslab ui launch           # 1. deploy the studio
+      weightslab start               # 1. deploy the studio
       weightslab start example --gen # 2. start the generation / anomaly demo
diff --git a/docs/examples/pytorch/segmentation.rst b/docs/examples/pytorch/segmentation.rst
index 14fb5c00..68ec2b37 100644
--- a/docs/examples/pytorch/segmentation.rst
+++ b/docs/examples/pytorch/segmentation.rst
@@ -12,7 +12,7 @@ Segmentation — BDD100k (PyTorch)
      <span class="wl-eg-tag">dense prediction</span>
    </div>
 
-**Example:** ``weightslab/examples/PyTorch/ws-segmentation/main.py``
+**Example:** ``weightslab/examples/PyTorch/wl-segmentation/main.py``
 
 **Task:** Per-instance semantic segmentation on BDD100k (6 classes) with a
 small UNet.
@@ -112,5 +112,14 @@ these signals over training steps and lets you sort samples by them.
 
    .. code-block:: bash
 
-      weightslab ui launch           # 1. deploy the studio
+      weightslab start               # 1. deploy the studio
       weightslab start example --seg # 2. start the segmentation demo
+
+
+.. raw:: html
+
+   <div style="text-align:right; margin-top:2rem;">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-segmentation.ipynb" target="_blank" rel="noopener noreferrer">
+       <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
+     </a>
+   </div>
diff --git a/docs/examples/ultralytics/detection.rst b/docs/examples/ultralytics/detection.rst
index 59577aac..14168e47 100644
--- a/docs/examples/ultralytics/detection.rst
+++ b/docs/examples/ultralytics/detection.rst
@@ -16,7 +16,7 @@ The full Ultralytics integration documentation is at :doc:`/ultralytics`.
 This page summarises what ``WLAwareTrainer`` handles automatically and where
 the example lives.
 
-**Example:** ``weightslab/examples/Ultralytics/ws-detection/main.py``
+**Example:** ``weightslab/examples/Ultralytics/wl-detection/main.py``
 
 What the example does
 ---------------------
diff --git a/docs/examples/usecases/lidar_detection.rst b/docs/examples/usecases/lidar_detection.rst
index b1b667e6..b86b13a5 100644
--- a/docs/examples/usecases/lidar_detection.rst
+++ b/docs/examples/usecases/lidar_detection.rst
@@ -14,8 +14,8 @@ LiDAR Detection — 2D and 3D (PyTorch)
 
 **Examples:**
 
-- ``weightslab/examples/Usecases/ws-2d-lidar-detection/main.py``
-- ``weightslab/examples/Usecases/ws-3d-lidar-detection/main.py``
+- ``weightslab/examples/Usecases/wl-2d-lidar-detection/main.py``
+- ``weightslab/examples/Usecases/wl-3d-lidar-detection/main.py``
 
 **Task:** Object detection on LiDAR point clouds — 2D pillar-grid (BEV) and
 full 3D bounding boxes (KITTI-format).
@@ -120,7 +120,7 @@ and override ``load_points`` and optionally ``render_thumbnail_2d``:
 
    .. code-block:: bash
 
-      weightslab ui launch               # 1. deploy the studio
+    weightslab start                   # 1. deploy the studio
       weightslab start example --2d_det  # 2a. 2D pillar-grid detection
       # or
       weightslab start example --3d_det  # 2b. 3D bounding-box detection
diff --git a/docs/examples/usecases/loss_shape_classification.rst b/docs/examples/usecases/loss_shape_classification.rst
index 6864322a..d5240632 100644
--- a/docs/examples/usecases/loss_shape_classification.rst
+++ b/docs/examples/usecases/loss_shape_classification.rst
@@ -12,7 +12,7 @@ Loss-Shape Classification per Sample
      <span class="wl-eg-tag">trajectory</span>
    </div>
 
-**Example:** ``weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/``
+**Example:** ``weightslab/examples/Usecases/wl-loss_shapes_classification_per_sample/``
 
 This use case builds on :doc:`../pytorch/detection` (same Penn-Fudan dataset,
 same model, same ``guard_training_context`` pattern) and adds one new feature:
@@ -203,8 +203,17 @@ Workflow in the studio
 
    .. code-block:: bash
 
-      weightslab ui launch           # 1. deploy the studio
+      weightslab start               # 1. deploy the studio
       weightslab start example --det # 2. start the detection demo
 
    To run the full loss-shape example (with the ``@wl.signal(subscribe_to=...)``
    classifier active), use the direct path above.
+
+
+.. raw:: html
+
+   <div style="text-align:right; margin-top:2rem;">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Usecases/wl-segmentation-loss-shapes-classification.ipynb" target="_blank" rel="noopener noreferrer">
+       <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
+     </a>
+   </div>
diff --git a/docs/four_way_approach.rst b/docs/four_way_approach.rst
index 9981dbbe..effa9d2d 100644
--- a/docs/four_way_approach.rst
+++ b/docs/four_way_approach.rst
@@ -52,7 +52,7 @@ Typical integration flow to custom Python script
 
   .. code-block:: bash
 
-     weightslab ui launch
-     python train.py
+   weightslab start
+   python train.py
 
 - Resume training from the UI and use tags/discards/signals to iteratively improve data and model behavior.
diff --git a/docs/index.rst b/docs/index.rst
index 84a14199..af68b009 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -72,7 +72,7 @@ Weightslab is a Python SDK to inspect, monitor, and edit training behavior for c
       :link: weights_studio
       :link-type: doc
 
-      Deploy and operate the UI: architecture, Docker, ports, and actions.
+      Deploy and operate the UI: architecture, ports, TLS, and actions.
 
    .. grid-item-card:: Configuration
       :link: configuration
@@ -112,7 +112,6 @@ Weightslab is a Python SDK to inspect, monitor, and edit training behavior for c
    :hidden:
 
    usage/good_practice
-   usage/docker
 
 
 .. toctree::
diff --git a/docs/logger.rst b/docs/logger.rst
index 33ee71e7..e4180736 100644
--- a/docs/logger.rst
+++ b/docs/logger.rst
@@ -50,6 +50,108 @@ for per-annotation values (detection / segmentation). The signal name comes from
 ``log=False`` to persist per-sample values without a dashboard curve. See
 :doc:`user_functions` for the full wrapper reference.
 
+Loss-shape classification
+--------------------------
+
+A single scalar hides how a sample got there. The *shape* of a per-sample
+loss trajectory over training — steadily dropping, stuck, forgotten, noisy —
+tells you whether the model is learning that sample, struggling with it, or
+whether it's a candidate mislabel. Weightslab classifies every sample's
+trajectory into one of six shapes:
+
+==============  ====================================================================
+Label           Meaning
+==============  ====================================================================
+monotonic       Loss steadily decreasing — the model is learning the sample.
+plateaued       Decreased then leveled off still-high — stuck / hard sample.
+Flat_high       Never moved, stayed high — likely a mislabel or unlearnable.
+high_variance   Noisy oscillation — model uncertain, often an ambiguous label.
+U_Shape         Learned then forgotten — catastrophic interference from later data.
+Spiked          Sudden jump at some step — data/augmentation/version change.
+==============  ====================================================================
+
+Automatic — zero setup
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Any signal wrapped with ``flag="loss"`` (as above) is classified
+automatically; there is nothing to call. The logger runs a background thread
+(off the training thread; interval via ``WL_LOGGER_FLUSH_INTERVAL_SECONDS``,
+default 2s) that discovers every ``flag="loss"`` signal and, once a sample has
+enough history to classify (5+ points), tags it with a ``<signal>_shape``
+categorical tag — e.g. ``train_loss/CE`` gets a ``train_loss/CE_shape`` tag:
+
+.. code-block:: python
+
+   train_loss = wl.watch_or_edit(
+       nn.CrossEntropyLoss(reduction="none"),
+       flag="loss", signal_name="train_loss/CE", log=True,
+   )
+   # Nothing else needed — train_loss/CE_shape starts appearing on samples
+   # as they accumulate enough history, refreshed every background tick.
+
+``flag="metric"`` signals are **not** auto-classified: the default classifier
+assumes a decreasing trajectory (a loss), which would misclassify an
+increasing metric like accuracy.
+
+Overriding or opting out
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Use :func:`enable_loss_shape_autotag` / :func:`disable_loss_shape_autotag`
+only to customize or opt out — not to turn classification on:
+
+.. code-block:: python
+
+   # Different tag name / classifier for one signal (e.g. it isn't a loss).
+   wl.enable_loss_shape_autotag("train_loss/CE", tag_name="curve_shape")
+
+   # Opt in a signal that wasn't registered via flag="loss" (e.g. logged
+   # manually through wl.save_signals under a custom name).
+   wl.enable_loss_shape_autotag("custom_metric")
+
+   # Opt out one signal, or every signal.
+   wl.disable_loss_shape_autotag("train_loss/CE")
+   wl.disable_loss_shape_autotag()
+
+:func:`auto_loss_shape_signal_names` lists every signal currently tracked.
+
+One-off / report-time classification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To force a synchronous, guaranteed-fresh classification right before a
+specific report, pass ``loss_shape_signal`` to :func:`write_dataframe`, or
+call the underlying reusable engine directly — it works for **any**
+per-sample signal, not just a loss (pass your own ``classifier`` for an
+increasing metric such as accuracy):
+
+.. code-block:: python
+
+   wl.write_dataframe("report.csv", format="csv", loss_shape_signal="train_loss/CE")
+
+   wl.write_signal_shapes("train_loss/CE", tag_name="train_loss/CE_shape")
+   wl.write_loss_shapes("train_loss/CE")  # convenience wrapper (tag: loss_shape)
+
+Building a custom classifier
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+:func:`trajectory_stats` computes scale-invariant features (net drop,
+coefficient of variation, argmin fraction, rebound, max jump, tail flatness)
+from a sample's ordered value history. Reuse them in your own rule, or pass a
+full ``classifier`` callable (``list[float] -> str | None``) to any function
+above:
+
+.. code-block:: python
+
+   def my_classifier(values):
+       s = wl.trajectory_stats(values)
+       return None if s is None else ("fast" if s["drop"] > 0.6 else "slow")
+
+   wl.enable_loss_shape_autotag("train_loss/CE", classifier=my_classifier)
+
+For a live, per-step reactive variant (heavier — reads history on every fire)
+via :func:`enable_loss_shape_signal`, or a fully hand-rolled
+``@wl.signal(subscribe_to=...)`` walkthrough, see
+:doc:`examples/usecases/loss_shape_classification`.
+
 Custom signals
 --------------
 
diff --git a/docs/pytorch_lightning.rst b/docs/pytorch_lightning.rst
index 3a0aa64b..5643983d 100644
--- a/docs/pytorch_lightning.rst
+++ b/docs/pytorch_lightning.rst
@@ -3,7 +3,7 @@ PyTorch Lightning Integration
 
 Weightslab is compatible with PyTorch Lightning and already includes a full example:
 
-``weightslab/examples/PyTorch_Lightning/ws-classification/main.py``
+``weightslab/examples/PyTorch_Lightning/wl-classification/main.py``
 
 This page explains how to integrate Weightslab in a Lightning workflow and scale to multiple GPUs.
 
@@ -109,7 +109,7 @@ Optional YAML-driven trainer config
 
 The Lightning example already includes a ready template at:
 
-``weightslab/examples/PyTorch_Lightning/ws-classification/config.yaml``
+``weightslab/examples/PyTorch_Lightning/wl-classification/config.yaml``
 
 .. code-block:: yaml
 
diff --git a/docs/quickstart.rst b/docs/quickstart.rst
index 08fa3be4..d0ee011d 100644
--- a/docs/quickstart.rst
+++ b/docs/quickstart.rst
@@ -7,9 +7,8 @@ If you prefer to start from examples, see ``usecases`` right after this setup.
 Prerequisites
 -------------
 
-- Python v3.10+ installed.
-- A virtual environment tool like ``venv`` or Conda (optionnal).
-- Docker v4+ to start the UI.
+- Python v3.10+ installed
+- A virtual environment tool like ``venv`` or Conda (optional).
 - Your training project available locally.
 
 Install WeightsLab
@@ -38,13 +37,13 @@ example like the classification example (--cls). It run a small experiment on a
 
 .. code-block:: bash
 
-   weightslab example start --cls
+   weightslab start example --cls
 
-Then, in another terminal, launch the UI and open http://localhost:5173:
+Then, in another terminal, launch the UI and open the URL printed by the command:
 
 .. code-block:: bash
 
-   weightslab ui launch
+   weightslab start
 
 
 .. Launch WeightsLab services from your training script
@@ -139,8 +138,8 @@ Pass ``--certs`` to generate (if missing) and use TLS certificates + a gRPC auth
 
 .. code-block:: bash
 
-   weightslab ui launch              # unsecured HTTP (default; no certs generated)
-   weightslab ui launch --certs      # secured HTTPS + gRPC auth (generates certs if missing)
+   weightslab start              # unsecured HTTP (default)
+   weightslab start --certs      # secured HTTPS + gRPC auth (run `weightslab se` first)
 
 .. important::
 
@@ -148,13 +147,9 @@ Pass ``--certs`` to generate (if missing) and use TLS certificates + a gRPC auth
    terminal use the **same** certificates — it is the single source of truth for TLS/auth. Please note that this step has to be done before starting the experiment.
 
 Run ``weightslab``, ``weightslab help``, or ``weightslab -h`` to see the banner and the full
-command reference (``se``, ``ui launch``, ``start example ...``).
+command reference (``se``, ``start``, ``start example ...``).
 
-To stop the UI later:
-
-.. code-block:: bash
-
-   docker stop weights_studio_envoy weights_studio_frontend
+To stop the UI, press ``Ctrl+C`` in the terminal running ``weightslab start``.
 
 Prefer a terminal over a browser? ``weightslab cli`` opens an interactive
 console connected to the running experiment (pause/resume, status, evaluate,
diff --git a/docs/segmentation_usecase.rst b/docs/segmentation_usecase.rst
index 38738a54..5fe8b83b 100644
--- a/docs/segmentation_usecase.rst
+++ b/docs/segmentation_usecase.rst
@@ -3,7 +3,7 @@ Segmentation Use Case — Per-instance & Per-sample Signals (PyTorch)
 
 This page walks through the segmentation integration from:
 
-``weightslab/examples/PyTorch/ws-segmentation/main.py``
+``weightslab/examples/PyTorch/wl-segmentation/main.py``
 
 It builds on the classification :doc:`usecases` page and focuses on what is
 **specific to segmentation**: a *list of per-instance masks* per sample, a custom
diff --git a/docs/ultralytics.rst b/docs/ultralytics.rst
index 18af0fd3..5b7c214a 100644
--- a/docs/ultralytics.rst
+++ b/docs/ultralytics.rst
@@ -8,7 +8,7 @@ without touching the model or YOLO's training loop.
 
 The full example lives at:
 
-``weightslab/examples/Ultralytics/ws-detection/``
+``weightslab/examples/Ultralytics/wl-detection/``
 
 How it works
 ------------
@@ -201,15 +201,15 @@ Running the bundled example
 
    .. code-block:: bash
 
-      weightslab ui launch
+      weightslab start
 
 4. Run the example:
 
    .. code-block:: bash
 
-      python weightslab/examples/Ultralytics/ws-detection/main.py
+      python weightslab/examples/Ultralytics/wl-detection/main.py
 
-5. Open ``http://localhost:5173`` to monitor training, inspect per-sample
+5. Open the URL printed by ``weightslab start`` to monitor training, inspect per-sample
    signals, tag difficult images, and discard outliers.
 
 Platform notes
diff --git a/docs/usage/docker.rst b/docs/usage/docker.rst
deleted file mode 100644
index 26beb6d8..00000000
--- a/docs/usage/docker.rst
+++ /dev/null
@@ -1,350 +0,0 @@
-.. _docker-usage:
-
-Docker Usage
-===============
-
-**Examples:** ``weightslab/examples/Docker_training/``
-
-WeightsLab supports running the training script — and the entire UI stack —
-inside Docker. Three integration patterns are available; they differ in **how
-the trainer container gets a Docker daemon** to launch the Envoy proxy and the
-Weights Studio frontend alongside it.
-
-.. list-table::
-   :header-rows: 1
-   :widths: 28 24 24 24
-
-   * -
-     - **A · Docker-in-Docker**
-     - **B · Siblings (DooD)**
-     - **C · Self-contained siblings**
-   * - Docker daemon
-     - Own nested daemon
-     - Host daemon (socket mount)
-     - Host daemon (socket mount)
-   * - Envoy/frontend run
-     - Nested inside trainer
-     - Siblings on the host
-     - Siblings on the host
-   * - Starts UI via
-     - ``weightslab ui launch``
-     - ``weightslab ui launch``
-     - Custom ``ui-compose.yml``
-   * - ``--privileged``
-     - **Required**
-     - No
-     - No
-   * - gRPC ``:50051`` published
-     - Not published
-     - Yes
-     - Yes
-   * - Host bind mounts
-     - None (co-located fs)
-     - Path alignment required
-     - **None**
-   * - Host setup
-     - None
-     - ``setup-host.sh``
-     - **None**
-   * - HTTPS (optional)
-     - ``WEIGHTSLAB_TLS=1``
-     - ``WEIGHTSLAB_TLS=1``
-     - ``WEIGHTSLAB_TLS=1``
-   * - Windows / Docker Desktop
-     - Yes
-     - Awkward (use WSL2)
-     - **Yes**
-
-.. note::
-
-   - **A (DinD)** — fully self-contained; needs ``--privileged``. Best when
-     you want isolation or are on Windows.
-   - **B (DooD)** — uses the stock ``weightslab ui launch``; cleanest on a
-     Linux host with path alignment already done.
-   - **C (self-contained siblings)** — no host prep, no bind mounts,
-     works natively on Windows.
-
-.. _docker-dind:
-
-Option A: Docker-in-Docker (DinD)
-----------------------------------
-
-**Source:** ``weightslab/examples/Docker_training/docker_in_docker/``
-
-The trainer container starts its **own inner Docker daemon** and uses
-``weightslab ui launch`` inside it. Because the inner daemon shares the
-trainer container's filesystem, all paths resolve without any host-side setup.
-
-Wiring diagram
-~~~~~~~~~~~~~~
-
-.. code-block:: text
-
-   host browser
-     → localhost:5173 ── (re-published) ──► trainer ──► inner frontend :5173
-     → localhost:8080 ── (re-published) ──► trainer ──► inner Envoy :8080
-                                                            │ grpc-backend:host-gateway
-                                                            ▼
-                                         in-process gRPC backend :50051
-                                         (same container as trainer)
-
-Configuration requirements
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. list-table::
-   :header-rows: 1
-   :widths: 5 40 30 25
-
-   * - #
-     - Requirement
-     - Location
-     - Why
-   * - 1
-     - ``privileged: true`` on the trainer service
-     - ``docker-compose.yml``
-     - inner ``dockerd`` cannot run otherwise
-   * - 2
-     - Start the inner ``dockerd`` + wait for socket
-     - ``entrypoint.sh``
-     - nested daemon that hosts Envoy + frontend
-   * - 3
-     - Persist ``/var/lib/docker`` with a named volume
-     - ``docker-compose.yml``
-     - caches image pulls across runs
-   * - 4
-     - Publish ``5173`` + ``8080`` to the host
-     - ``docker-compose.yml``
-     - browser reaches the *nested* containers
-   * - 5
-     - ``GRPC_BACKEND_PORT=50051``
-     - ``docker-compose.yml``
-     - Envoy's ``grpc-backend:host-gateway`` dials this port
-   * - 6
-     - ``WEIGHTSLAB_SKIP_DOCKER_OPS=1`` before ``ui launch``
-     - ``entrypoint.sh``
-     - skips the in-container rebuild; pulls the image only
-   * - 7
-     - Order: ``ui launch`` → ``start example``
-     - ``entrypoint.sh``
-     - UI stack must be up before the backend starts
-
-GPU access
-~~~~~~~~~~
-
-Add the ``deploy`` block to the trainer service in ``docker-compose.yml``:
-
-.. code-block:: yaml
-
-   deploy:
-     resources:
-       reservations:
-         devices:
-           - driver: nvidia
-             count: all
-             capabilities: [gpu]
-
-The host also needs the **NVIDIA Container Toolkit** installed (``sudo
-nvidia-ctk runtime configure --runtime=docker``). Comment out the ``deploy``
-block on hosts without an NVIDIA GPU/toolkit — ``docker compose up`` will
-otherwise fail with *"could not select device driver nvidia"*.
-
-Running
-~~~~~~~
-
-.. code-block:: bash
-
-   # from weightslab/examples/Docker_training/docker_in_docker/
-   docker compose up --build
-
-Open http://localhost:5173. Stop with ``Ctrl+C`` or ``docker compose down``.
-
-.. dropdown:: Enable HTTPS / mTLS (optional)
-   :color: secondary
-
-   Only needed for remote or production access. For local development, plain
-   HTTP at http://localhost:5173 works without any certificates.
-
-   DinD is the simplest TLS option because certs, Envoy, and the backend are
-   co-located:
-
-   .. code-block:: bash
-
-      WEIGHTSLAB_TLS=1 docker compose up --build
-
-   Then trust the generated CA on the host browser (once):
-
-   .. code-block:: powershell
-
-      # Windows — pull the CA out of the running container and trust it
-      docker cp weightslab_trainer_dind:/root/.weightslab-certs/ca.crt .
-      Import-Certificate -FilePath .\ca.crt -CertStoreLocation Cert:\CurrentUser\Root
-
-   Open https://localhost:5173.
-
-   What ``WEIGHTSLAB_TLS=1`` does in ``entrypoint.sh``:
-
-   1. Runs ``weightslab ui launch --certs``, generating certs into
-      ``WEIGHTSLAB_CERTS_DIR`` and configuring Envoy + the frontend for HTTPS.
-   2. Exports ``GRPC_TLS_ENABLED=1`` + ``GRPC_TLS_CERT_DIR`` so the gRPC
-      backend also speaks TLS (required to prevent Envoy upstream 503s).
-
-.. _docker-siblings-c:
-
-Option C: Self-contained siblings (recommended for Windows)
-------------------------------------------------------------
-
-**Source:** ``weightslab/examples/Docker_training/siblings_self_contained/``
-
-The trainer mounts the **host Docker socket** and starts Envoy + the frontend
-as sibling containers — but unlike Option B, it **never bind-mounts a host
-path**. The Envoy config (and, for TLS, the certs) are delivered via **named
-volumes over the socket**, so this option works on **Windows / Docker Desktop**
-with zero host preparation.
-
-Wiring diagram
-~~~~~~~~~~~~~~
-
-.. code-block:: text
-
-   host browser
-     → localhost:5173 ──► frontend container (sibling, HTTP)
-     → localhost:8080 ──► Envoy container   (sibling, config from named volume)
-                            │ grpc-backend:host-gateway → host:50051
-                            ▼
-                         host:50051 ──► trainer's gRPC backend :50051
-
-Configuration requirements
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. list-table::
-   :header-rows: 1
-   :widths: 5 40 30 25
-
-   * - #
-     - Requirement
-     - Location
-     - Why
-   * - 1
-     - Mount ``/var/run/docker.sock``
-     - ``docker-compose.yml``
-     - drive the host daemon (start sibling containers)
-   * - 2
-     - Publish ``50051:50051`` from the trainer
-     - ``docker-compose.yml``
-     - sibling Envoy dials it via ``host-gateway``
-   * - 3
-     - Stage Envoy config into ``wl_envoy_cfg`` named volume
-     - ``entrypoint.sh``
-     - replaces the host bind mount — no host path needed
-   * - 4
-     - ``ui-compose.yml`` with no host bind mounts
-     - ``ui-compose.yml``
-     - nothing for the host daemon to resolve on disk
-   * - 5
-     - ``extra_hosts: grpc-backend:host-gateway`` on the Envoy service
-     - ``ui-compose.yml``
-     - routes Envoy → host:50051 → trainer
-   * - 6
-     - ``GRPC_BACKEND_PORT=50051``
-     - ``docker-compose.yml``
-     - port the backend binds + Envoy dials
-
-How config delivery works without bind mounts
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Option C does not call ``weightslab ui launch``. Instead, ``entrypoint.sh``:
-
-1. Renders Envoy's plaintext config from the installed ``weightslab`` package.
-2. Pipes it into the ``wl_envoy_cfg`` named volume via the socket using a
-   temporary ``busybox`` container — no host path involved.
-3. Brings up Envoy + frontend using ``ui-compose.yml``, which reads the config
-   from the named volume rather than a bind-mounted host directory.
-
-GPU access
-~~~~~~~~~~
-
-Same as Option A — add the ``deploy`` block to the trainer service and install
-the NVIDIA Container Toolkit on the host.
-
-Running
-~~~~~~~
-
-.. code-block:: bash
-
-   # from weightslab/examples/Docker_training/siblings_self_contained/
-   docker compose up --build
-
-Open http://localhost:5173.
-
-**Stopping:**
-
-.. code-block:: bash
-
-   docker compose down                                    # stop trainer
-   docker compose -p weightslab_ui -f ui-compose.yml down  # stop UI siblings
-   docker volume rm wl_envoy_cfg                         # optional: drop staged config
-
-.. dropdown:: Enable HTTPS / mTLS (optional)
-   :color: secondary
-
-   Only needed for remote or production access. For local development, plain
-   HTTP at http://localhost:5173 works without any certificates.
-
-   TLS works here without host bind mounts — certs are piped into named volumes
-   the same way as ``envoy.yaml``:
-
-   .. code-block:: bash
-
-      WEIGHTSLAB_TLS=1 docker compose up --build
-
-   Then trust the CA on the host browser (once):
-
-   .. code-block:: powershell
-
-      docker cp weightslab_trainer_selfcontained:/root/.weightslab-certs/ca.crt .
-      Import-Certificate -FilePath .\ca.crt -CertStoreLocation Cert:\CurrentUser\Root
-
-   Open https://localhost:5173.
-
-   What ``WEIGHTSLAB_TLS=1`` delivers (all via named volumes, no host paths):
-
-   .. list-table::
-      :header-rows: 1
-      :widths: 35 30 35
-
-      * - Layer
-        - What's needed
-        - How it arrives
-      * - Browser ↔ Envoy
-        - ``envoy-server.crt/key``
-        - ``wl_envoy_cfg`` volume
-      * - Envoy ↔ backend (mTLS)
-        - ``envoy-client.crt/key`` + ``ca.crt``
-        - ``wl_envoy_cfg`` volume
-      * - Browser ↔ frontend
-        - ``envoy-server.crt/key``
-        - ``wl_nginx_certs`` volume
-      * - Backend gRPC server
-        - ``backend-server.crt/key`` + ``ca.crt``
-        - ``GRPC_TLS_ENABLED=1`` + ``GRPC_TLS_CERT_DIR``
-
-   .. note::
-
-      Generated keys are ``0600`` (owned by root). ``entrypoint.sh`` runs
-      ``chmod a+rX`` on cert files in the volumes so Envoy (non-root) can
-      read them.
-
-Common notes
-------------
-
-- The example starts **paused** (``is_training: false``); start and steer
-  training from the UI at http://localhost:5173.
-- First run is slow: it pulls ``envoyproxy/envoy`` + ``graybx/weightslab``
-  and downloads MNIST for the classification example.
-- For either option, to build against the dev branch instead of PyPI:
-
-  .. code-block:: bash
-
-     docker compose build \
-       --build-arg WEIGHTSLAB_SPEC="git+https://github.com/GrayboxTech/weightslab.git@dev"
-     docker compose up
diff --git a/docs/usage/parameters.rst b/docs/usage/parameters.rst
index c23ae9c9..c7fb22f0 100644
--- a/docs/usage/parameters.rst
+++ b/docs/usage/parameters.rst
@@ -460,36 +460,6 @@ Audit logging
        interaction with the studio.
        Accepted: ``json``, ``csv``, ``none`` (disables audit logging).
 
-Docker integration
-~~~~~~~~~~~~~~~~~~~
-
-These variables are set inside Docker training containers; see
-:ref:`docker-usage` for full context.
-
-.. list-table::
-   :header-rows: 1
-   :widths: 35 15 50
-
-   * - Variable
-     - Default
-     - Description
-   * - ``GRPC_BACKEND_PORT``
-     - ``50051``
-     - Port the gRPC backend binds to. Must match the port Envoy
-       is configured to dial as ``grpc-backend``.
-   * - ``WEIGHTSLAB_TLS``
-     - ``0``
-     - Set to ``1`` inside a Docker Compose stack to enable the full
-       TLS + cert-generation flow (DinD and self-contained siblings).
-   * - ``WEIGHTSLAB_SKIP_DOCKER_OPS``
-     - ``0``
-     - Set to ``1`` inside a DinD container before ``weightslab ui
-       launch`` to skip the image rebuild and pull only.
-   * - ``WS_SERVER_PROTOCOL``
-     - ``http``
-     - Protocol served by the frontend's nginx. Set to ``https``
-       when providing TLS certificates to the Weights Studio container.
-
 LLM / agent integration (optional)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -511,6 +481,16 @@ LLM / agent integration (optional)
    * - ``OPENROUTER_REQUEST_TIMEOUT``
      - *(unset)*
      - Per-request timeout in seconds for OpenRouter calls.
+   * - ``OPENROUTER_MAX_TOKENS``
+     - ``2048``
+     - Maximum completion length requested from OpenRouter. OpenRouter
+       pre-authorizes ``max_tokens × completion_price`` against the key's
+       remaining budget *before* generating, so leaving this uncapped makes
+       the model request its full output window and can fail with a ``402``
+       ("requires more credits, or fewer max_tokens") on a credit- or
+       weekly-limited key — even though the model is otherwise usable. The
+       default is ample for intent planning; raise it only if you see
+       truncated responses.
 
 Telemetry
 ~~~~~~~~~~
diff --git a/docs/usecases.rst b/docs/usecases.rst
index 307f167c..8de11fbb 100644
--- a/docs/usecases.rst
+++ b/docs/usecases.rst
@@ -3,7 +3,7 @@ Use Case Example (PyTorch)
 
 This page walks through the real MNIST classification integration from:
 
-``weightslab/examples/PyTorch/ws-classification/main.py``
+``weightslab/examples/PyTorch/wl-classification/main.py``
 
 Goal
 ----
diff --git a/docs/user_commands.rst b/docs/user_commands.rst
index 0b3d3fcb..c8845d5e 100644
--- a/docs/user_commands.rst
+++ b/docs/user_commands.rst
@@ -1,56 +1,39 @@
 User Commands Reference
-========================
+=======================
 
-This page documents the ``weightslab`` command-line tool — every subcommand,
-flag, and default — plus the interactive CLI console it (and
-``wl.serve(serving_cli=True)``) opens onto a running experiment.
+This page documents the weightslab command-line interface and its subcommands.
 
-It's the CLI counterpart to :doc:`user_functions` (the Python API); see that
-page for everything you call from inside a training script.
+weightslab command
+------------------
 
-Two different things are called "CLI" in WeightsLab — keep them distinct:
-
-- The **installed command** — ``weightslab ...`` — run from any shell to
-  manage Docker/the UI, run bundled examples, or connect to a running
-  experiment.
-- The **interactive console** — the REPL you land in after ``weightslab
-  cli`` (or a standalone ``python -m weightslab.backend.cli client``)
-  connects — where you type commands like ``pause``, ``status``, or
-  ``evaluate``.
-
-``weightslab`` command
------------------------
-
-Installed as a console script (``weightslab = weightslab.ui_docker_bridge:main``),
-so it's available anywhere the package is installed — no ``python -m`` needed.
+Installed as a console script via pyproject.toml:
 
 .. code-block:: text
 
-   weightslab {se,ui,start,cli,help} ...
+   weightslab {se,start,cli,tunnel,help} ...
 
-Running ``weightslab``, ``weightslab -h`` / ``--help``, or ``weightslab help``
-with no further arguments prints the banner and this same command summary.
+Run weightslab, weightslab -h, or weightslab help to print the full built-in help.
 
 .. list-table::
    :header-rows: 1
 
    * - Command
      - Purpose
-   * - ``weightslab se``
-     - One-time secure setup: generate TLS certs + a gRPC auth token.
-   * - ``weightslab ui launch``
-     - Clean stale Docker state, then build & start the Weights Studio UI stack.
-   * - ``weightslab start example`` (alias: ``weightslab example start``)
-     - Run a bundled PyTorch example in the foreground.
-   * - ``weightslab cli``
-     - Open an interactive console connected to a running experiment.
-   * - ``weightslab help``
-     - Show the help/banner (same as no command, or ``-h``).
+   * - weightslab se
+     - Generate TLS certificates and gRPC auth token in WEIGHTSLAB_CERTS_DIR.
+   * - weightslab start
+     - Start the native Weights Studio server (bundled SPA + gRPC-Web proxy).
+   * - weightslab start example
+     - Run a bundled training example.
+   * - weightslab cli
+     - Connect to a running experiment interactive console.
+   * - weightslab tunnel
+     - Forward a remote gRPC backend to a local TCP port.
+   * - weightslab help
+     - Show the help/banner (same as no command, or -h).
 
 weightslab se
-~~~~~~~~~~~~~~
-
-**Syntax**
+~~~~~~~~~~~~~
 
 .. code-block:: bash
 
@@ -58,81 +41,43 @@ weightslab se
 
 Generates TLS certificates and a gRPC auth token into a certs directory, then
 tells you to export ``WEIGHTSLAB_CERTS_DIR`` — the **single source of
-truth** the training backend, ``weightslab ui launch --certs``, and any new
+truth** the training backend, ``weightslab start --certs``, and any new
 shell all read to decide whether TLS/auth is on (derived purely from whether
 cert files exist in that directory).
 
-**Arguments**
-
-- ``certs_dir`` *(positional, optional)* — custom directory for the certs +
-  token. Default: ``$WEIGHTSLAB_CERTS_DIR`` if already set in the
-  environment, else ``~/.weightslab-certs``.
-- ``--force-certs`` — regenerate certificates even if valid ones already
-  exist in the target directory. Default: off (existing certs are reused).
-
-**Examples**
+weightslab start
+~~~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-   weightslab se                        # one-time secure setup
-   weightslab se --force-certs          # regenerate the certs
-   weightslab se /custom/certs/path     # use a custom directory
+   weightslab start [--port PORT] [--config FILE] [--host HOST]
+                    [--backend-host HOST] [--backend-port PORT]
+                    [--no-browser] [--certs]
 
-After it finishes, export the printed ``WEIGHTSLAB_CERTS_DIR`` value
-permanently (the command prints the exact ``export`` / ``setx`` line for
-your platform) so the training backend and Weights Studio agree on the same
-certificates.
+Runs the UI natively from Python.
 
-weightslab ui launch
-~~~~~~~~~~~~~~~~~~~~~~
+Port resolution order:
 
-**Syntax**
+1. --port
+2. ui_port from --config / WEIGHTSLAB_EXPERIMENT_CONFIG config file
+3. WL_LAST_UI_PORT
+4. WEIGHTSLAB_UI_PORT (compatibility)
+5. 50051
 
-.. code-block:: bash
-
-   weightslab ui launch [certs_dir] [--certs] [-i/--image REPO] [-v/--version TAG]
-
-Purges stale ``weightslab``/``weights_studio`` Docker resources scoped to the
-bundled stack, then builds and starts the Weights Studio UI via Docker
-Compose.
-
-**Arguments**
+If the chosen port is already in use, weightslab start falls back to a random
+available port and logs it.
 
-- ``certs_dir`` *(positional, optional)* — same meaning as for ``weightslab se``.
-- ``--certs`` — generate certs (if missing) and run **secured** (HTTPS +
-  gRPC auth). Default: off — the UI launches **unsecured** (plain HTTP, no
-  gRPC auth, no certs generated). Existing certs already present in
-  ``WEIGHTSLAB_CERTS_DIR`` are always honored either way and are never
-  deleted by this command.
-- ``-i``, ``--image`` *(str, optional)* — frontend image repo to run/pull.
-  Default: ``graybx/weightslab``.
-- ``-v``, ``--version`` *(str, optional)* — frontend image tag/version to
-  pull. Default: ``latest``. An explicit ``--version`` overrides any tag
-  embedded in ``--image``.
-
-**Examples**
+Examples:
 
 .. code-block:: bash
 
-   weightslab ui launch                                    # unsecured HTTP (default)
-   weightslab ui launch --certs                             # secured HTTPS + gRPC auth
-   weightslab ui launch -i guillaumep2705/weightslab        # pull a custom repo (latest tag)
-   weightslab ui launch -i guillaumep2705/weightslab -v v1.2.3  # pin a specific version
-
-Once running, the UI is served at ``http://localhost:5173`` (or
-``https://...`` when ``--certs`` is used); the exact URL is also printed at
-the end of the command.
-
-.. important::
-
-   When using ``--certs``, set ``WEIGHTSLAB_CERTS_DIR`` manually (before
-   starting your training script) so the training backend and this UI use
-   the **same** certificates.
+   weightslab start
+   weightslab start --port 9000
+   weightslab start --backend-port 50052
+   weightslab start --certs
 
 weightslab start example
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-**Syntax**
+~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
@@ -178,39 +123,89 @@ the documented form.
    weightslab start example --3d_det       # 3D LiDAR detection
    weightslab example start --det          # tolerant alias, same as `start example --det`
 
-Then, in another terminal: ``weightslab ui launch`` and open
+Then, in another terminal: ``weightslab launch`` and open
 ``http://localhost:5173``. See :doc:`examples/index` for what each example
 demonstrates.
 
 weightslab cli
-~~~~~~~~~~~~~~~
-
-**Syntax**
+~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
    weightslab cli [--port PORT] [--host HOST]
 
-Opens the :ref:`interactive console <cli-console>` connected to a
-**currently-running experiment** — the experiment must have called
-``wl.serve(serving_cli=True)`` (the default when calling ``wl.serve()`` with
-no arguments).
+Connects to a running experiment CLI server.
+
+weightslab tunnel
+~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+   weightslab tunnel [ENDPOINT] [--listen-port N] [--listen-host H] [--remote-port N]
+
+Forwards a remote gRPC endpoint to a local TCP port.
+
+- Use raw TCP tunnels (for example bore or ngrok tcp).
+- Default local listen port is 50051.
+
+- The remote tunnel must be **raw TCP**, *not* an HTTP/gRPC-Web tunnel. A
+  zero-signup option is `bore <https://github.com/ekzhang/bore>`_ with its free
+  public relay: ``bore local 50051 --to bore.pub`` (prints ``bore.pub:<port>``).
+  ``ngrok tcp 50051`` also works but now requires a credit card on the free tier.
+- The backend must run **plaintext** — the default ``weightslab launch``
+  (no ``--certs``) — so no TLS terminates mid-path.
 
 **Arguments**
 
-- ``--port`` *(int, optional)* — connect to a specific CLI server port.
-  Default: auto-discover the running experiment (the backend advertises its
-  actual host/port on startup; ``weightslab cli`` reads that advertisement).
-- ``--host`` *(str, optional)* — connect to a specific host. Default:
-  ``localhost``.
+- ``ENDPOINT`` *(positional, optional)* — the remote backend as ``host:port``
+  (e.g. ``0.tcp.ngrok.io:12345``); a ``tcp://`` prefix is accepted and
+  stripped. Default: the ``WEIGHTSLAB_TUNNEL_ENDPOINT`` environment variable, so
+  a bare ``weightslab tunnel`` works once that is exported.
+- ``--listen-port``, ``-p`` *(int)* — local port to expose. Default: **50051**
+  (the port the bundled Envoy upstream dials — leave it unless you changed
+  ``GRPC_BACKEND_PORT``).
+- ``--listen-host`` *(str)* — interface to bind. Default: **auto** —
+  ``127.0.0.1`` on Windows/macOS (Docker Desktop reaches host loopback via
+  ``host.docker.internal``), ``0.0.0.0`` on Linux (compose ``host-gateway``
+  resolves to the bridge IP, which cannot reach a loopback-only listener).
+- ``--remote-port`` *(int)* — the remote port, when ``ENDPOINT`` has only a
+  host and no ``:port``.
 
 **Examples**
 
 .. code-block:: bash
 
-   weightslab cli                    # auto-discover the running experiment
-   weightslab cli --port 60000       # connect to a specific port
-   weightslab cli --host 10.0.0.5 --port 60000
+   weightslab tunnel bore.pub:12345               # bridge remote backend -> localhost:50051
+   weightslab tunnel tcp://bore.pub:12345         # tcp:// prefix is fine
+   weightslab tunnel                              # uses $WEIGHTSLAB_TUNNEL_ENDPOINT
+   weightslab tunnel host.example.com --remote-port 50051
+   weightslab tunnel host:50051 -p 50055          # expose locally on a different port
+
+**Typical workflow** (Colab backend, local UI):
+
+.. code-block:: bash
+
+   # 1) In Colab: expose the training backend over raw TCP (prints bore.pub:<port>)
+   #    !bore local 50051 --to bore.pub
+
+   # 2) On your machine, in two terminals:
+   weightslab launch                           # plaintext HTTP (default)
+   weightslab tunnel bore.pub:12345               # in another window, the host:port bore printed
+
+   # 3) Open http://localhost:5173 — Studio streams live from Colab.
+
+.. note::
+
+   Step 1 can be done for you: call ``wl.serve(serving_grpc=True,
+   serving_bore=True)`` in the training script. It downloads ``bore``, opens the
+   relay, and prints the exact ``weightslab tunnel bore.pub:<port>`` line to run
+   on your machine — see ``serve`` in :doc:`user_functions`.
+
+The command probes the remote on startup (warning, not fatal, if it isn't up
+yet), re-resolves the endpoint per connection (so a changing tunnel IP is picked
+up), and runs until ``Ctrl+C``. See the classification Colab notebook
+(``examples/Notebooks/PyTorch/wl-classification.ipynb``) for the end-to-end
+setup.
 
 .. _cli-console:
 
diff --git a/docs/user_functions.rst b/docs/user_functions.rst
index e3a614ad..4668e942 100644
--- a/docs/user_functions.rst
+++ b/docs/user_functions.rst
@@ -323,6 +323,7 @@ signal
        name: str,
        subscribe_to: str,
        compute_every_n_steps: int = 1,
+       min_step: int = 0,
        include_history: bool = False,
        include_history_metadata: bool = False
    )
@@ -345,6 +346,11 @@ sorting and root-cause analysis in the studio).
   ``ctx.subscribed_value``. If omitted, the signal is **static**.
 - ``compute_every_n_steps``: throttle for dynamic signals (e.g. ``10`` = compute
   on every 10th step the subscribed metric is produced).
+- ``min_step``: minimum training step before a dynamic signal starts firing.
+  While ``current_step < min_step`` the signal is skipped. Defaults to ``0``
+  (fire from the start). Use it when a signal needs enough history to be
+  meaningful — e.g. a loss-shape classifier that should only run once each
+  sample has a trajectory (``min_step=505``).
 
 **Static vs dynamic**
 
@@ -511,7 +517,7 @@ Spiked          Sudden jump at some step — data/augmentation/version change.
    the verdict also live as a per-sample ``signals//loss_shape_classifier``
    column; the human-readable label lives on the ``loss_shape`` categorical tag.
 
-   See the detection use case (``examples/PyTorch/ws-detection/src/main.py``) for
+   See the detection use case (``examples/PyTorch/wl-detection/src/main.py``) for
    this signal wired into a real training loop.
 
 compute_signals
@@ -1080,6 +1086,7 @@ write_history
        graph_name=None,
        experiment_hash=None,
        sample_id=None,
+       orient="columns",
        instance_id=None,
    )
 
@@ -1127,8 +1134,28 @@ Dump signal history to a file for offline analysis or debugging.
 - ``instance_id`` *(int or list of int, optional)* — restrict per-instance
   rows to one or more annotation IDs.  Has no effect on global or
   per-sample history.
+- ``orient`` *(str, optional)* — JSON layout for each section, forwarded to
+  ``pandas.DataFrame.to_json``.  Default ``"columns"`` (see below — compact,
+  writes each column name once per section instead of once per row).  Pass
+  ``"records"`` for the row-list-of-dicts shape shown further down.  Ignored
+  for ``format="csv"``.
 
-**JSON output shape**
+**JSON output shape (default, ``orient="columns"``)**
+
+.. code-block:: json
+
+   {
+     "global":   {"graph_name": {"0": "loss"}, "experiment_hash": {"0": "h1"}, "step": {"0": 1}, "metric_value": {"0": 0.42}},
+     "sample":   {"graph_name": {"0": "loss"}, "experiment_hash": {"0": "h1"}, "sample_id": {"0": "img0"}, "step": {"0": 1}, "metric_value": {"0": 0.38}},
+     "instance": {"graph_name": {"0": "iou"},  "experiment_hash": {"0": "h1"}, "sample_id": {"0": "img0"}, "annotation_id": {"0": 1}, "step": {"0": 1}, "metric_value": {"0": 0.81}}
+   }
+
+Only the sections selected by *type_of_history* are present in the output.
+Each section maps column name -> {row index -> value}; round-trips with
+``pandas.read_json(path, orient="columns")`` (or per-section via
+``pd.DataFrame(data["global"])``).
+
+**JSON output shape (``orient="records"``)**
 
 .. code-block:: json
 
@@ -1138,7 +1165,9 @@ Dump signal history to a file for offline analysis or debugging.
      "instance": [{"graph_name": "iou",  "experiment_hash": "h1", "sample_id": "img0", "annotation_id": 1, "step": 1, "metric_value": 0.81}]
    }
 
-Only the sections selected by *type_of_history* are present in the output.
+The row-list-of-dicts shape used before ``orient`` was wired up — repeats
+every column name once per row, so it's larger on disk for many-row
+sections. Pass ``orient="records"`` explicitly to keep using it.
 
 **CSV output shape**
 
diff --git a/docs/weights_studio.rst b/docs/weights_studio.rst
index 930530cb..d005acec 100644
--- a/docs/weights_studio.rst
+++ b/docs/weights_studio.rst
@@ -1,10 +1,10 @@
 Weights Studio Guide
 ====================
 
-Weights Studio is the visual frontend for Weightslab experiments.
-It connects to your running Weightslab backend over gRPC-Web via Envoy,
-and gives you interactive control over samples, tags, discard/restore actions,
-training/audit mode, and training signal plots.
+Weights Studio is the visual frontend for WeightsLab experiments.
+It ships **inside the Python package** — no Docker, no Envoy.
+Running ``weightslab start`` serves the bundled SPA and proxies gRPC-Web to
+your training backend, all from one Python process.
 
 Architecture
 ------------
@@ -15,180 +15,184 @@ Architecture
 
 Runtime path:
 
-1. Browser UI (Vite app)
-2. Envoy proxy (gRPC-Web bridge)
-3. Weightslab Python gRPC service
+1. Browser (served from ``weightslab start``)
+2. ``weightslab start`` — pure-Python HTTP server that:
 
-Project location
-----------------
-
-Weights Studio source lives in:
-
-- ``../weights_studio``
-
-Key files:
-
-- Docker compose: ``../weights_studio/docker/docker-compose.yml``
-- Envoy config: ``../weights_studio/envoy/envoy.yaml``
-- Frontend entrypoint: ``../weights_studio/src/main.ts``
-- UI layout: ``../weights_studio/index.html``
-
-Quick start (Docker)
---------------------
-
-1. Start your Weightslab backend (gRPC on host, default port ``50051``).
-2. Load environment variables from ``../weights_studio/docker/.env``.
-3. Generate local TLS certificates (dev only):
+   - Serves the pre-built Weights Studio SPA (vendored in ``weightslab/ui/static/``)
+   - Translates gRPC-Web (browser) to raw gRPC (backend) via an embedded proxy
 
-  .. code-block:: powershell
+3. WeightsLab Python gRPC service (started by ``wl.serve()``)
 
-    # from ../weights_studio/docker
-    .\generate-dev-certs.ps1
+Quick start
+-----------
 
-  .. code-block:: bash
+1. Install WeightsLab::
 
-    # from ../weights_studio/docker
-    ./generate-dev-certs.sh
+     pip install weightslab
 
-4. Start studio stack from ``../weights_studio/docker``:
+2. In your training script, start the backend::
 
-   - Envoy
-   - Frontend (Vite)
+     import weightslab as wl
+     wl.serve(serving_grpc=True)
+     # ... training loop ...
+     wl.keep_serving()
 
-5. Open Weights Studio in your browser.
+3. In another terminal, start the UI::
 
-Docker services and ports
--------------------------
-
-From ``../weights_studio/docker/docker-compose.yml`` and ``../weights_studio/envoy/envoy.yaml``:
-
-- Frontend: ``VITE_PORT`` (default ``5173``)
-- Envoy gRPC-Web endpoint over TLS: ``ENVOY_PORT`` (default ``8080``)
-- Envoy admin: ``ENVOY_ADMIN_PORT`` (default ``9901``), bound to loopback and not published by default
-- Backend target from Envoy: ``host.docker.internal:50051``
-
-Default values in ``../weights_studio/docker/.env``:
-
-- ``VITE_PORT=5173``
-- ``VITE_HISTOGRAM_MAX_BINS=512``
-- ``BB_THUMB_RENDER=10`` (max bounding boxes drawn per thumbnail image, per overlay)
-- ``BB_MODAL_RENDER=100`` (max bounding boxes drawn per modal image, per overlay)
-- ``WS_SERVER_HOST=localhost``
-- ``WS_SERVER_PORT=8080``
-- ``WS_SERVER_PROTOCOL=https``
-- ``ENVOY_PORT=8080``
-- ``ENVOY_ADMIN_PORT=9901``
-- ``GRPC_BACKEND_PORT=50051``
-
-How the frontend endpoint is built
-----------------------------------
+     weightslab start
 
-In ``src/main.ts``, the UI builds the server URL from:
+4. Open the URL printed by ``weightslab start`` in your browser.
 
-- ``WS_SERVER_PROTOCOL``
-- ``WS_SERVER_HOST``
-- ``WS_SERVER_PORT``
+The UI auto-discovers the backend on ``localhost:50051`` (default).
+Pass ``--backend-port`` to override::
 
-This URL is used as gRPC-Web base URL for ``ExperimentServiceClient``.
+    weightslab start --backend-port 50052
 
-Environment/configuration checklist
------------------------------------
+To suppress auto-opening the browser::
 
-Backend:
+    weightslab start --no-browser
 
-- Ensure Weightslab serves gRPC and listens on host ``0.0.0.0:50051``.
-- Enable backend TLS and client-auth for Envoy by setting:
+Ports
+-----
 
-  - ``GRPC_TLS_ENABLED=1``
-  - ``GRPC_TLS_REQUIRE_CLIENT_AUTH=1``
-  - optional ``GRPC_TLS_CERT_DIR=~/certs`` to change the default certificate directory
-  - ``GRPC_TLS_CERT_FILE`` / ``GRPC_TLS_KEY_FILE`` / ``GRPC_TLS_CA_FILE``
-- Optionally set ``GRPC_AUTH_TOKEN`` (or ``GRPC_AUTH_TOKENS``) to enforce
-  metadata token authentication in addition to mTLS.
+- UI HTTP server: ``8080`` by default (``--port PORT`` or ``$WEIGHTSLAB_UI_PORT``)
+- Backend gRPC: ``50051`` by default (``--backend-port PORT`` or ``$GRPC_BACKEND_PORT``)
 
-If the per-file ``GRPC_TLS_*_FILE`` variables are not set, WeightsLab defaults to
-``~/certs/backend-server.crt``, ``~/certs/backend-server.key``, and ``~/certs/ca.crt``.
-If config/hyperparameters define TLS keys (``grpc_tls_*``), those values are used
-before environment variables.
+If port ``8080`` is already in use, ``weightslab start`` automatically finds the
+next free port and logs the one it chose.
 
-Envoy:
+Secure mode (HTTPS + mTLS)
+--------------------------
 
-- Ensure ``envoy.yaml`` cluster points to host backend:
-  ``host.docker.internal:50051``.
-- Ensure cert files exist at ``../weights_studio/envoy/certs``:
+The default is plain HTTP (no cert files required, easiest for local dev).
+To enable HTTPS between the browser and the UI server, and mTLS between the
+UI server and the backend:
 
-  - ``envoy-server.crt`` / ``envoy-server.key`` (browser->Envoy TLS)
-  - ``envoy-client.crt`` / ``envoy-client.key`` and ``ca.crt`` (Envoy->backend mTLS)
+1. Generate TLS certificates once::
 
-Frontend:
+     weightslab se
 
-- Ensure frontend points to Envoy (default ``https://localhost:8080``).
+   Certificates are placed in ``~/.weightslab-certs``
+   (or ``$WEIGHTSLAB_CERTS_DIR``).
+   Follow the printed instructions to export ``WEIGHTSLAB_CERTS_DIR`` globally.
 
-Sanity checks:
+2. Start the UI in secure mode::
 
-- Studio reachable at ``https://localhost:5173``.
-- Envoy endpoint reachable at ``https://localhost:8080``.
-- Envoy admin is private by default (loopback inside container).
+     weightslab start --certs
 
-Agent Usage in Weights Studio
------------------------------
+   ``--certs`` reads ``$WEIGHTSLAB_CERTS_DIR`` (single source of truth) and:
 
-Weights Studio includes an agent bar and an expandable agent history window.
-The agent can run with either:
+   - Serves HTTPS using ``ui-server.crt`` / ``ui-server.key``
+   - Presents ``ui-client.crt`` / ``ui-client.key`` to the backend (mTLS)
+   - Expects the backend CA at ``ca.crt``
 
-- a local Ollama provider configured on the backend
-- a cloud OpenRouter provider configured at startup or initialized from the UI
+3. Configure the backend to require mTLS::
 
-Local Ollama workflow
-~~~~~~~~~~~~~~~~~~~~~
+     export GRPC_TLS_ENABLED=1
+     export GRPC_TLS_REQUIRE_CLIENT_AUTH=1
+     export WEIGHTSLAB_CERTS_DIR=~/.weightslab-certs
 
-If the backend is configured with ``provider: ollama`` and the Ollama server is
-running, the agent is available immediately after backend startup.
+Certificate files (all in ``$WEIGHTSLAB_CERTS_DIR``)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Typical local setup:
++----------------------------+--------------------------------------------+
+| File                       | Purpose                                    |
++============================+============================================+
+| ``ca.crt``                 | CA certificate (trusted by all parties)    |
++----------------------------+--------------------------------------------+
+| ``ui-server.crt/.key``     | UI server TLS cert (browser to server)     |
++----------------------------+--------------------------------------------+
+| ``ui-client.crt/.key``     | UI client mTLS cert (server to backend)    |
++----------------------------+--------------------------------------------+
+| ``backend-server.crt/.key``| Backend gRPC TLS cert (loaded by backend)  |
++----------------------------+--------------------------------------------+
+| ``.grpc_auth_token``       | Optional token for gRPC metadata auth      |
++----------------------------+--------------------------------------------+
 
-1. Start Ollama.
-2. Start WeightsLab.
-3. Open Weights Studio.
-4. Ask questions directly in the agent bar.
+Regenerate certificates at any time with ``weightslab se --force-certs``.
 
-Cloud OpenRouter workflow
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-If the backend is not initialized with a cloud key yet, Weights Studio shows
-the agent as unconfigured and the input placeholder instructs the user to type
-``/init``.
-
-``/init`` flow:
-
-1. Type ``/init`` in the agent input.
-2. Choose manual API key entry or the OpenRouter OAuth flow.
-3. Select a model from the available model list.
-4. Confirm to initialize the runtime connection.
-
-The default cloud model is ``~google/gemini-flash-latest``.
-
-Available agent commands
-~~~~~~~~~~~~~~~~~~~~~~~~
+Configuration reference
+-----------------------
 
-The agent bar supports these commands:
+Backend environment variables (set before starting ``wl.serve()``)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
++----------------------------------+-------------------------+----------------------------------------------------+
+| Variable                         | Default                 | Description                                        |
++==================================+=========================+====================================================+
+| ``WEIGHTSLAB_LOG_LEVEL``         | ``INFO``                | Log level (``DEBUG``, ``INFO``, ...)               |
++----------------------------------+-------------------------+----------------------------------------------------+
+| ``GRPC_BACKEND_HOST``            | ``0.0.0.0``             | Host the backend gRPC server binds to              |
++----------------------------------+-------------------------+----------------------------------------------------+
+| ``GRPC_BACKEND_PORT``            | ``50051``               | Port the backend gRPC server listens on            |
++----------------------------------+-------------------------+----------------------------------------------------+
+| ``GRPC_TLS_ENABLED``             | ``0``                   | ``1`` = enable TLS on the gRPC socket              |
++----------------------------------+-------------------------+----------------------------------------------------+
+| ``GRPC_TLS_REQUIRE_CLIENT_AUTH`` | ``0``                   | ``1`` = require client mTLS certificate            |
++----------------------------------+-------------------------+----------------------------------------------------+
+| ``WEIGHTSLAB_CERTS_DIR``         | ``~/.weightslab-certs`` | Directory containing cert/key files                |
++----------------------------------+-------------------------+----------------------------------------------------+
+| ``GRPC_AUTH_TOKEN``              | *(unset)*               | Optional metadata-token auth (on top of mTLS)      |
++----------------------------------+-------------------------+----------------------------------------------------+
+| ``GRPC_MAX_MESSAGE_BYTES``       | ``268435456``           | Raise for large tensors / image batches            |
++----------------------------------+-------------------------+----------------------------------------------------+
+| ``WEIGHTSLAB_DISABLE_WATCHDOGS`` | ``0``                   | ``1`` = disable watchdogs (use with breakpoints)   |
++----------------------------------+-------------------------+----------------------------------------------------+
+
+UI server environment variables (set before ``weightslab start``)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
++---------------------------+-------------------------+--------------------------------------------------+
+| Variable                  | Default                 | Description                                      |
++===========================+=========================+==================================================+
+| ``WEIGHTSLAB_UI_HOST``    | ``0.0.0.0``             | Interface the UI server binds to                 |
++---------------------------+-------------------------+--------------------------------------------------+
+| ``WEIGHTSLAB_UI_PORT``    | ``8080``                | HTTP port (``--port`` flag overrides)            |
++---------------------------+-------------------------+--------------------------------------------------+
+| ``GRPC_BACKEND_HOST``     | ``localhost``           | Backend gRPC host to proxy to                    |
++---------------------------+-------------------------+--------------------------------------------------+
+| ``GRPC_BACKEND_PORT``     | ``50051``               | Backend gRPC port to proxy to                    |
++---------------------------+-------------------------+--------------------------------------------------+
+| ``WEIGHTSLAB_CERTS_DIR``  | ``~/.weightslab-certs`` | Certs dir (read when ``--certs``)                |
++---------------------------+-------------------------+--------------------------------------------------+
+
+Frontend runtime feature toggles
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+These are injected as ``window.*`` globals when the UI is served.
+Set them as environment variables before ``weightslab start``.
+
++--------------------------------------+----------+----------------------------------------------------+
+| Variable                             | Default  | Effect when ``0`` / ``false``                      |
++======================================+==========+====================================================+
+| ``ENABLE_PLOTS``                     | ``1``    | Remove plots board + Signals card                  |
++--------------------------------------+----------+----------------------------------------------------+
+| ``ENABLE_DATA_EXPLORATION``          | ``1``    | Remove data grid + metadata/details panel          |
++--------------------------------------+----------+----------------------------------------------------+
+| ``ENABLE_HYPERPARAMETERS_OPTIMIZATION`` | ``1`` | Remove Hyperparameters section (read-only HPs)     |
++--------------------------------------+----------+----------------------------------------------------+
+| ``ENABLE_AGENT``                     | ``1``    | Remove agent chat bar                              |
++--------------------------------------+----------+----------------------------------------------------+
+| ``WS_HISTOGRAM_MAX_BINS``            | ``512``  | Cap on metadata histogram bars                     |
++--------------------------------------+----------+----------------------------------------------------+
+| ``BB_THUMB_RENDER``                  | ``10``   | Max bounding boxes per thumbnail (per overlay)     |
++--------------------------------------+----------+----------------------------------------------------+
+| ``BB_MODAL_RENDER``                  | ``100``  | Max bounding boxes per modal image (per overlay)   |
++--------------------------------------+----------+----------------------------------------------------+
+
+Tunnel (remote backend)
+-----------------------
 
-- ``/init`` initializes OpenRouter from the UI
-- ``/model`` opens the model chooser to switch the active OpenRouter model
-- ``/reset`` clears the current agent runtime connection and status
+If your backend is running remotely (e.g. a Colab notebook behind ``ngrok`` or
+``bore``), forward it to a local port with::
 
-History behavior
-~~~~~~~~~~~~~~~~
+    weightslab tunnel bore.pub:12345
 
-- Command entries such as ``/init``, ``/model``, and ``/reset`` are shown on
-  the user side of the history.
-- Agent lifecycle events such as connection setup, model changes, and reset
-  events are shown as separate log-style entries.
-- A pinned instruction line at the top of the history summarizes the available
-  commands and shows the full instruction text on hover.
+Then ``weightslab start`` on the same machine proxies to it as if local.
+The tunnel is raw TCP — the backend must be plaintext (``GRPC_TLS_ENABLED=0``).
 
 Agent Usage in Weights Studio
------------------------------
+------------------------------
 
 Weights Studio includes an agent bar and an expandable agent history window.
 The agent can run with either:
@@ -205,9 +209,9 @@ running, the agent is available immediately after backend startup.
 Typical local setup:
 
 1. Start Ollama.
-2. Start WeightsLab.
-3. Open Weights Studio.
-4. Ask questions directly in the agent bar.
+2. Start WeightsLab (``wl.serve(serving_grpc=True)``).
+3. Start Weights Studio (``weightslab start``).
+4. Ask questions in the agent bar.
 
 Cloud OpenRouter workflow
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -228,215 +232,88 @@ The default cloud model is ``~google/gemini-flash-latest``.
 Available agent commands
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-The agent bar supports these commands:
-
-- ``/init`` initializes OpenRouter from the UI
-- ``/model`` opens the model chooser to switch the active OpenRouter model
-- ``/reset`` clears the current agent runtime connection and status
+- ``/init`` — initialize OpenRouter from the UI
+- ``/model`` — open the model chooser to switch the active OpenRouter model
+- ``/reset`` — clear the current agent runtime connection and status
 
 History behavior
 ~~~~~~~~~~~~~~~~
 
 - Command entries such as ``/init``, ``/model``, and ``/reset`` are shown on
   the user side of the history.
-- Agent lifecycle events such as connection setup, model changes, and reset
-  events are shown as separate log-style entries.
-- A pinned instruction line at the top of the history summarizes the available
-  commands and shows the full instruction text on hover.
+- Agent lifecycle events (connection setup, model changes, reset) are shown as
+  separate log-style entries.
+- A pinned instruction line at the top summarizes the available commands.
 
-Server integration (AWS example)
---------------------------------
+Bundled examples
+----------------
 
-This section describes a practical cloud deployment path for Weightslab +
-Weights Studio on AWS.
+Run a bundled example in one command (installs its requirements automatically)::
 
-Recommended production architecture
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    weightslab start example          # classification (default)
+    weightslab start example --seg    # segmentation
+    weightslab start example --det    # detection
+    weightslab start example --3d_det # 3D LiDAR point-cloud detection
 
-- **UI**: Weights Studio frontend behind HTTPS (ALB).
-- **gRPC-Web bridge**: Envoy service reachable by frontend.
-- **Backend**: Weightslab training service (Python gRPC).
-- **Storage**: EBS/EFS/S3 for logs/checkpoints depending on your workflow.
+In another terminal, start the UI::
 
-Two common deployment options
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    weightslab start
 
-1. **EC2 (fastest to start)**
+See ``weightslab start example --help`` for all options.
 
-   - Run Weightslab backend process on the VM (port ``50051``).
-   - Run Envoy + frontend via Docker Compose.
-   - Use one security group to expose only HTTPS (and optionally admin ports
-     privately).
+Cloud deployment
+----------------
 
-2. **ECS/Fargate (container-native)**
+Because the UI is a plain Python process, cloud deployment is straightforward:
 
-   - Service A: frontend container.
-   - Service B: Envoy container.
-   - Service C: Weightslab backend container (or external training worker).
-   - Route through ALB + target groups.
+1. Install WeightsLab on the server::
 
-Minimum port plan (AWS)
-~~~~~~~~~~~~~~~~~~~~~~~
+     pip install weightslab
 
-- Public ingress:
-  - ``443`` (HTTPS to frontend/ALB)
-- Internal service ports:
-  - ``8080`` (Envoy gRPC-Web listener)
-  - ``50051`` (Weightslab backend gRPC)
-  - ``9901`` (Envoy admin, keep private)
-  - ``5173`` (frontend dev port; avoid exposing directly in production)
+2. Run ``weightslab se`` once to generate certificates.
 
-Security group guidance
-~~~~~~~~~~~~~~~~~~~~~~~
+3. Start the backend in your training process (``wl.serve(serving_grpc=True)``).
 
-- Allow ``443`` from trusted client CIDRs (or internet if required).
-- Restrict ``50051`` and ``8080`` to VPC/internal security groups.
-- Restrict ``9901`` to private admin subnet/bastion only.
-- Do not expose backend gRPC directly to the public internet.
+4. Start the UI process::
 
-TLS and domains
-~~~~~~~~~~~~~~~
+     WEIGHTSLAB_UI_HOST=0.0.0.0 weightslab start --port 8080 --certs --no-browser
 
-- Terminate TLS at ALB using ACM certificates.
-- Use a DNS record (Route 53) for the studio hostname.
-- Forward ALB target traffic to frontend service.
-- Keep Envoy/backend internal where possible.
+5. Put a reverse proxy (nginx / ALB / Caddy) in front of port ``8080`` and
+   expose only ``443`` publicly.
 
-Environment mapping (cloud)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The UI and backend can run on different machines — set ``--backend-host`` and
+``--backend-port`` accordingly.
 
-For Weights Studio frontend + Envoy alignment, set environment variables
-consistently with your deployed endpoints:
+Example systemd unit
+~~~~~~~~~~~~~~~~~~~~
 
 .. code-block:: ini
 
-   # frontend resolves gRPC-web through Envoy
-   WS_SERVER_PROTOCOL=https
-   WS_SERVER_HOST=studio.your-domain.com
-   WS_SERVER_PORT=443
-   VITE_HISTOGRAM_MAX_BINS=512
-   BB_THUMB_RENDER=10
-   BB_MODAL_RENDER=100
-
-   # envoy / backend internal wiring
-   ENVOY_PORT=8080
-   ENVOY_ADMIN_PORT=9901
-   GRPC_BACKEND_PORT=50051
-
-If Envoy is internal-only and frontend is public, ensure frontend requests are
-routed to the Envoy endpoint through your internal load-balancing design.
-
-AWS deployment checklist
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-1. Provision VPC/subnets and security groups.
-2. Deploy backend gRPC service and verify ``:50051`` internally.
-3. Deploy Envoy and verify routing to backend.
-4. Deploy frontend with correct ``WS_SERVER_*`` values.
-5. Attach ALB + ACM certificate and configure HTTPS listener.
-6. Validate UI actions (query, tagging, discard/restore, plots).
-7. Add monitoring/logging (CloudWatch metrics/logs).
-
-Operational notes
-~~~~~~~~~~~~~~~~~
-
-- Prefer long-running backend workers for stable interactive sessions.
-- Keep checkpoint/log storage durable (EBS/EFS/S3 strategy).
-- If using autoscaling, ensure session and backend availability are handled
-  explicitly for active users.
-- For multi-environment setups, keep per-environment ``.env`` templates
-  versioned and reviewed.
-
-Concrete EC2 + Docker Compose + systemd recipe
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Use this pattern for a simple single-VM production-like deployment.
-
-1. Provision EC2
-
-  - Ubuntu 22.04 (or similar), attached security group.
-  - Open only ``443`` publicly.
-  - Keep ``50051``, ``8080``, ``9901`` private (VPC/admin only).
-
-2. Install runtime dependencies
-
-  - Docker Engine + Docker Compose plugin
-  - Python environment for your Weightslab backend process
-
-3. Configure environment
-
-  In ``weights_studio/docker/.env`` (or environment management equivalent):
-
-  .. code-block:: ini
-
-    VITE_PORT=5173
-    WS_SERVER_PROTOCOL=https
-    WS_SERVER_HOST=studio.your-domain.com
-    WS_SERVER_PORT=443
-    VITE_HISTOGRAM_MAX_BINS=512
-    BB_THUMB_RENDER=10
-    BB_MODAL_RENDER=100
-
-    ENVOY_PORT=8080
-    ENVOY_ADMIN_PORT=9901
-    GRPC_BACKEND_PORT=50051
-
-4. Start backend service
-
-  Start Weightslab gRPC in your training/runtime process and ensure it binds
-  to a reachable interface (for example ``0.0.0.0:50051``).
-
-5. Start studio containers
-
-  From ``weights_studio/docker``:
-
-  .. code-block:: bash
+  [Unit]
+  Description=Weights Studio UI
+  After=network.target
 
-    docker compose up -d
+  [Service]
+  EnvironmentFile=/etc/weightslab/env
+  ExecStart=/usr/local/bin/weightslab start --port 8080 --no-browser
+  Restart=on-failure
+  RestartSec=5
 
-6. Add process supervision (systemd)
+  [Install]
+  WantedBy=multi-user.target
 
-  Use systemd to ensure services restart on reboot/failure.
-
-  Example unit for studio compose stack:
-
-  .. code-block:: ini
-
-    [Unit]
-    Description=Weights Studio (Docker Compose)
-    Requires=docker.service
-    After=docker.service
-
-    [Service]
-    Type=oneshot
-    WorkingDirectory=/opt/weights_studio/docker
-    ExecStart=/usr/bin/docker compose up -d
-    ExecStop=/usr/bin/docker compose down
-    RemainAfterExit=yes
-    TimeoutStartSec=0
-
-    [Install]
-    WantedBy=multi-user.target
-
-  Enable and start:
-
-  .. code-block:: bash
-
-    sudo systemctl daemon-reload
-    sudo systemctl enable weights-studio
-    sudo systemctl start weights-studio
-
-7. Put HTTPS in front (ALB + ACM)
+Building the frontend from source
+----------------------------------
 
-  - Attach ACM certificate to ALB listener on ``443``.
-  - Route DNS (Route 53) to ALB.
-  - Forward traffic to frontend target.
+The pre-built SPA is vendored into ``weightslab/ui/static/``. To rebuild from
+the ``weights_studio`` source repository and update the vendored copy::
 
-8. Validate end-to-end
+    # from the weights_studio repo
+    npm ci && npm run build
 
-  - Open studio URL.
-  - Verify sample query, tag/discard actions, and plot refresh.
-  - Verify backend logs and Envoy admin stats.
+  # from the weightslab repo
+  rm -rf weightslab/ui/static/*
+  cp -R ../weights_studio/dist/. weightslab/ui/static/
 
 UI controls and actions
 -----------------------
@@ -446,50 +323,30 @@ Top header controls
 
 - **Dark mode toggle**: switch light/dark theme.
 - **Refresh button**: manually refresh dynamic stats in visible grid.
-- **Refresh config popover**:
-  - Data auto-refresh enable/disable + interval
-  - Plot auto-refresh enable/disable + interval
-  - Clear cache and reload page
-- **Training button** (Resume/Pause): toggles ``is_training`` via backend command.
-- **Mode selector** (dropdown next to training button):
-  - ``train`` mode
-  - ``audit`` mode (sets ``auditorMode``)
+- **Refresh config popover**: data/plot auto-refresh, clear cache.
+- **Training button** (Resume/Pause): toggles ``is_training`` via backend.
+- **Mode selector**: ``train`` mode / ``audit`` mode.
 
 Left panel
 ~~~~~~~~~~
 
-- **Training card**:
-  - Training state pill (running/paused/pending)
-  - Connection status text
-  - Live metrics and progress
-- **Tags card**:
-  - Tag chips
-  - New tag input
-  - Painter toggle
-  - Add/remove painter mode switch
-- **Details card**:
-  - Grid settings (cell size + resolution + apply)
-  - Segmentation overlays (Raw, GT, Pred, Diff, Split view)
-  - Split colors
-  - Metadata field toggles
+- **Training card**: training state pill, connection status, live metrics.
+- **Tags card**: tag chips, new tag input, painter toggle.
+- **Details card**: grid settings, segmentation overlays, metadata field toggles.
 
 Grid interactions
 ~~~~~~~~~~~~~~~~~
 
 - Drag selection rectangle (multi-select).
 - ``Ctrl`` multi-select support.
-- Right-click context menu actions:
-  - Manage tags
-  - Remove all tags
-  - Discard selected samples
-  - Restore selected samples
+- Right-click context menu: manage tags, discard/restore samples.
 
 The UI pauses training before data-modifying actions to keep edits safe.
 
 Bottom bar
 ~~~~~~~~~~
 
-- Batch slider for navigation over samples.
+- Batch slider for sample navigation.
 - Start/end batch index labels.
 - Total and active sample counters.
 
@@ -506,30 +363,18 @@ Signal plots
 
 Per-signal cards include:
 
-- Reset zoom
-- CSV export
-- JSON export
-- Settings (curve color, smoothing, std band, markers)
+- Reset zoom, CSV/JSON export, settings (curve color, smoothing, std band,
+  markers).
+- Right-click: reset zoom, change curve color, load weights at step, hide/show
+  curve, break by slices, copy/save chart image.
 
-Right-click menu on plots includes:
+WeightsLab CLI console
+----------------------
 
-- Reset X/Y/all zoom
-- Change curve color
-- Load weights at clicked step
-- Hide/unhide curve
-- Break by slices
-- Copy chart image
-- Save chart image
-
-Weightslab CLI console (dev)
-----------------------------
-
-The Weightslab CLI console is a local developer REPL for inspecting and
+The WeightsLab CLI console is a local developer REPL for inspecting and
 controlling a running experiment through the global ledger.
 
-- Transport: local TCP text commands with JSON responses.
-- Intended scope: development/debug only.
-- Security model: localhost binding by default, plain-text protocol.
+Transport: local TCP text commands with JSON responses.
 
 How to start it
 ~~~~~~~~~~~~~~~
@@ -543,70 +388,38 @@ From your training script (recommended):
   wl.serve(serving_grpc=True, serving_cli=True)
   wl.keep_serving()
 
-Standalone server:
-
-.. code-block:: bash
+Connect from a terminal::
 
-  python -m weightslab.backend.cli serve --host localhost --port 60000
+  weightslab cli              # auto-discover port
+  weightslab cli --port 60000 # or specify one
 
-Connect a client manually:
-
-.. code-block:: bash
-
-  python -m weightslab.backend.cli client --host localhost --port 60000
-
-If no port is provided (or port is ``0``), the server picks a free port.
-
-Console actions and commands
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Console actions
+~~~~~~~~~~~~~~~
 
-Full command-by-command reference (discovery/help, training control, registry
-inspection, sample-level dataset operations, hyperparameters, evaluation,
-audit mode, the AI agent, and session control) — with syntax, flags, and
-worked examples for every one — lives in :doc:`user_commands`. Quick
-summary of what's available:
+Full reference: :doc:`user_commands`. Quick summary:
 
 - Discovery/help: ``help``, ``status``, ``dump``, ``ledger_dump``.
 - Training control: ``pause`` / ``resume``.
 - Registry inspection: ``list_models``, ``list_optimizers``, ``list_loaders``,
   ``plot_model [model_name]``.
-- Sample-level dataset operations: ``list_uids``, ``discard``, ``undiscard``,
+- Sample-level operations: ``list_uids``, ``discard``, ``undiscard``,
   ``add_tag``.
 - Hyperparameters: ``hp``, ``set_hp``.
 - Evaluation: ``evaluate``, ``eval_status``, ``cancel_eval``.
 - Audit mode: ``audit [on|off]``.
-- AI agent: ``agent ...`` / ``query`` / ``ask`` — see :doc:`agent`.
+- AI agent: ``agent`` / ``query`` / ``ask`` — see :doc:`agent`.
 - Session control: ``exit`` / ``quit``, ``clear`` / ``cls``.
 
-Developer notes
-~~~~~~~~~~~~~~~
-
-- Prefer CLI for quick diagnosis and manual interventions.
-- Keep CLI port private (localhost or private subnet only).
-- Use Weights Studio for richer visual workflows; use CLI for low-latency
-  command-driven operations.
-
-Weightslab workflow recommendation
-----------------------------------
-
-Typical loop for productive usage:
-
-1. Start Weightslab backend and studio stack.
-2. Monitor training metrics and sample-level signals.
-3. Use grid metadata + modal details to inspect hard or noisy samples.
-4. Tag slices (e.g., outliers, hard cases).
-5. Discard low-value samples and continue training.
-6. Use train/audit mode toggles to inspect safely without weight updates.
-7. Use plot controls to inspect branch transitions and checkpoint behavior.
-
 Troubleshooting
 ---------------
 
-- Studio loads but no data:
-  check backend gRPC is running and Envoy target is reachable.
-- Connection/reset errors:
-  verify ``ENVOY_PORT`` and backend port mapping.
-- Wrong endpoint:
-  verify ``WS_SERVER_HOST/PORT/PROTOCOL`` in docker environment.
-- No plot updates:
-  verify plot auto-refresh setting and backend logger data availability.
+- **Studio loads but no data**: check backend gRPC is running on the expected
+  port (``--backend-port``) and that there is no firewall blocking the
+  connection.
+- **Port conflict**: ``weightslab start`` auto-selects the next free port and
+  logs it; or pass ``--port PORT`` to pick a specific one.
+- **No plot updates**: check plot auto-refresh setting and backend logger data.
+- **TLS errors with --certs**: run ``weightslab se`` first to generate certs,
+  then export ``WEIGHTSLAB_CERTS_DIR``.
+- **Connection refused on remote backend**: use ``weightslab tunnel`` to forward
+  the remote port locally.
diff --git a/pyproject.toml b/pyproject.toml
index f4ab6ec9..c67657b4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,9 +28,16 @@ classifiers = [
 # - Prefer compatible ranges, then pin exact versions only in CI/lockfiles.
 dependencies = [
   # Core data / persistence
-  "numpy>=1.25.2,<2.0; python_version < '3.13'",
-  "numpy>=2.1,<3; python_version >= '3.13'",
-  "pandas>=2.2.3,<3",
+  # numpy 2 is supported: weightslab's source uses no numpy-1-only APIs, and its
+  # compiled deps (torch, pandas, h5py, tables, onnx) ship numpy-2 wheels at the
+  # versions pip resolves. Range left open (<3) so Colab's numpy 2.x installs
+  # without a downgrade. Verified: import + torch/pandas interop + gRPC on np 2.4.
+  "numpy>=1.24,<3",
+  # Floor at 2.2.2 (not 2.2.3) so Colab's pinned pandas==2.2.2 is accepted
+  # as-is: google-colab 1.0.0 requires pandas==2.2.2, and a >=2.2.3 floor forced
+  # an upgrade to 2.3.x that breaks that pin. Clean installs still pick the
+  # newest numpy-2-safe pandas (2.3.x).
+  "pandas>=2.2.2,<3",
   "duckdb>=1.1,<2",  # signal/sample/instance history store
   "PyYAML>=6.0.3,<7",
   "dill>=0.3.8,<0.5",
@@ -39,16 +46,25 @@ dependencies = [
   "xxhash>=3.4,<4.1",
   "tables>=3.9,<4",  # h5 deps
 
-  # PyTorch stack
-  "torch>=2.1,<2.9; python_version < '3.13'",
-  "torch==2.9; python_version >= '3.13'",
-  "torchvision>=0.16,<0.24; python_version < '3.13'",
-  "torchvision>=0.24,<1; python_version >= '3.13'",
+  # PyTorch stack. Upper bounds left open (<3 / <1) so a pre-installed modern
+  # torch (e.g. Colab's torch 2.9 / torchvision 0.24) is accepted as-is instead
+  # of being downgraded — the reinstall churn is what dragged numpy across the
+  # 1.x/2.x boundary and broke the ABI. Floors kept; pip picks a compatible
+  # numpy-2 build on a clean install.
+  "torch>=2.1,<=2.9",
+  "torchvision>=0.16,<1",
   "torchmetrics>=1.9",
 
   # Serving / schema
   "grpcio>=1.80,<2",
-  "protobuf>=4.25,<7",
+  # Floor tracks the checked-in gencode: the *_pb2.py are regenerated with
+  # grpcio-tools ~=1.68 (protoc gencode 5.28.1), so any runtime >=5.28.1 satisfies
+  # protobuf's runtime>=gencode rule — including Colab's stock protobuf 5.29.6, so
+  # NO upgrade is forced on Colab. IMPORTANT: regenerate the stubs with
+  # grpcio-tools ~=1.68 to keep the gencode Colab-compatible; a newer grpcio-tools
+  # bumps the gencode and reintroduces the VersionError on Colab. Upper bound <8
+  # keeps modern runtimes (7.x) working.
+  "protobuf>=5.28.1,<8",
   "pydantic>=2.7,<3",
 
   # Imaging
@@ -99,15 +115,16 @@ utest = [
 ]
 
 [project.scripts]
-weightslab = "weightslab.ui_docker_bridge:main"
+weightslab = "weightslab.cli:main"
 
 [project.urls]
 Homepage = "https://github.com/GrayboxTech/weightslab"
 
 [tool.setuptools]
-# Ship non-.py assets that live inside the package (docker stack + bundled
-# examples). Combined with the explicit globs below so the CLI subcommands
-# (`ui launch`, `start example`) find their files in a pip-installed package.
+# Ship non-.py assets that live inside the package (cert scripts, bundled
+# examples, pre-built UI). Combined with the explicit globs below so the CLI
+# subcommands (`start`, `start example`, `se`) find their files in a
+# pip-installed package.
 include-package-data = true
 
 [tool.setuptools.packages.find]
@@ -115,18 +132,19 @@ where = ["."]
 include = ["weightslab", "weightslab.*"]
 
 [tool.setuptools.package-data]
-# Bundle the docker stack (compose files, envoy/nginx configs, shell scripts)
-# and the example projects (main.py, config.yaml, sample data) that the
-# `weightslab` CLI resolves relative to the installed package.
+# Bundle the cert-generation scripts, the example projects (main.py, config.yaml,
+# sample data), and the pre-built Weights Studio SPA that `weightslab start`
+# serves directly — no Docker required.
 weightslab = [
-  "docker/**/*",
+  "ui/utils/**/*",
   "examples/**/*",
+  # Pre-built Weights Studio SPA served by `weightslab start`.
+  "ui/static/**/*",
 ]
 
 [tool.setuptools.exclude-package-data]
 # Never ship generated/local artifacts even if present on disk at build time.
 weightslab = [
-  "docker/docker/.env",
   "**/__pycache__/**",
   "**/*.pyc",
 ]
diff --git a/tests/backend/test_cli.py b/tests/backend/test_cli.py
new file mode 100644
index 00000000..543ea5c0
--- /dev/null
+++ b/tests/backend/test_cli.py
@@ -0,0 +1,274 @@
+"""Tests for the Docker-free WeightsLab CLI (weightslab.cli).
+
+Covers the command set that survived the Docker removal: se (secure env),
+start (native UI), start example, cli, tunnel, banner/help — plus cert
+generation and the small path/script helpers. There is no Docker, Envoy, or
+compose code left to test.
+"""
+import argparse
+import contextlib
+import io
+import os
+import sys
+import unittest
+from pathlib import Path
+from unittest.mock import patch, MagicMock
+
+from weightslab.cli import (
+    _convert_to_git_bash_path,
+    _ensure_scripts_executable,
+    _generate_certs_with_fallback,
+    _get_cert_script,
+    _get_example_dir,
+    _install_example_requirements,
+    _make_executable,
+    example_start,
+    main,
+    ui_secure_environment,
+    ui_start_native,
+)
+
+
+class TestScriptsExecutable(unittest.TestCase):
+    """Bundled .sh scripts are made executable so users skip the manual `chmod +x`."""
+
+    @unittest.skipIf(sys.platform == "win32", "execute bit is POSIX-only")
+    def test_make_executable_adds_exec_bits(self):
+        import stat as _stat
+        import tempfile
+        with tempfile.NamedTemporaryFile(suffix=".sh", delete=False) as f:
+            path = f.name
+        try:
+            os.chmod(path, 0o644)
+            _make_executable(path)
+            mode = os.stat(path).st_mode
+            self.assertTrue(mode & _stat.S_IXUSR)
+            self.assertTrue(mode & _stat.S_IXGRP)
+            self.assertTrue(mode & _stat.S_IXOTH)
+        finally:
+            os.unlink(path)
+
+    def test_make_executable_is_noop_on_windows(self):
+        with patch("weightslab.cli._is_windows", return_value=True):
+            with patch("weightslab.cli.os.chmod") as mock_chmod:
+                _make_executable("/whatever/path.sh")
+                mock_chmod.assert_not_called()
+
+    def test_make_executable_swallows_oserror(self):
+        with patch("weightslab.cli.os.stat", side_effect=OSError("denied")):
+            _make_executable("/root/owned.sh")  # should not raise
+
+    def test_ensure_scripts_executable_noop_on_windows(self):
+        with patch("weightslab.cli._is_windows", return_value=True):
+            with patch("weightslab.cli._make_executable") as mock_mk:
+                _ensure_scripts_executable()
+                mock_mk.assert_not_called()
+
+    def test_cert_script_is_bundled_under_ui_utils(self):
+        cert_script = _get_cert_script()
+        self.assertTrue(str(cert_script).replace("\\", "/").endswith(
+            "weightslab/ui/utils/generate-certs-auth-token.sh"), cert_script)
+
+
+class TestPathConversion(unittest.TestCase):
+    def test_windows_path_conversion(self):
+        self.assertEqual(
+            _convert_to_git_bash_path(r"C:\Users\testuser\.weightslab-certs"),
+            "/mnt/c/Users/testuser/.weightslab-certs",
+        )
+
+    def test_unix_path_passthrough(self):
+        self.assertEqual(
+            _convert_to_git_bash_path("/home/testuser/.weightslab-certs"),
+            "/home/testuser/.weightslab-certs",
+        )
+
+
+class TestCertGeneration(unittest.TestCase):
+    @patch("weightslab.cli._run_shell_script", return_value=0)
+    def test_generate_certs_forwards_certs_dir(self, mock_shell):
+        rc = _generate_certs_with_fallback(force_certs=False, certs_dir="/custom/certs")
+        self.assertEqual(rc, 0)
+        env_vars = mock_shell.call_args.args[2]
+        self.assertIsInstance(env_vars, dict)
+        self.assertIn("WEIGHTSLAB_CERTS_DIR", env_vars)
+        self.assertIn("custom/certs", env_vars["WEIGHTSLAB_CERTS_DIR"].replace("\\", "/"))
+
+    @patch("weightslab.cli._run_shell_script", return_value=0)
+    def test_generate_certs_without_dir_passes_no_env(self, mock_shell):
+        _generate_certs_with_fallback(force_certs=False)
+        self.assertIsNone(mock_shell.call_args.args[2])
+
+
+class TestUiSecureEnvironment(unittest.TestCase):
+    @patch("weightslab.cli.CertAuthManager")
+    @patch("weightslab.cli._generate_certs_with_fallback", return_value=0)
+    def test_ui_secure_environment_success(self, mock_gen_certs, mock_cert_manager):
+        """`weightslab se`: generate certs + token, export WEIGHTSLAB_CERTS_DIR."""
+        mgr = MagicMock()
+        mgr.get_or_create_auth_token.return_value = "fake_token"
+        mock_cert_manager.return_value = mgr
+        with self.assertLogs("weightslab.cli", level="INFO") as log_context:
+            ui_secure_environment(argparse.Namespace(force_certs=False))
+        self.assertTrue(any("Certificates generated successfully" in m for m in log_context.output))
+        mock_gen_certs.assert_called_once_with(force_certs=False, certs_dir=mgr.certs_dir)
+        mgr.certs_dir.mkdir.assert_called_once()
+        self.assertTrue(any("WEIGHTSLAB_CERTS_DIR exported" in m for m in log_context.output))
+
+    @patch("weightslab.cli._generate_certs_with_fallback", return_value=0)
+    def test_ui_secure_environment_force_certs(self, mock_gen_certs):
+        with patch.dict(os.environ, {}, clear=False):
+            ui_secure_environment(argparse.Namespace(force_certs=True))
+        self.assertTrue(mock_gen_certs.call_args.kwargs["force_certs"])
+
+    @patch("weightslab.cli._generate_certs_with_fallback", return_value=1)
+    def test_ui_secure_environment_cert_failure(self, _mock_gen_certs):
+        with self.assertRaises(SystemExit) as ctx:
+            ui_secure_environment(argparse.Namespace(force_certs=False))
+        self.assertEqual(ctx.exception.code, 1)
+
+
+class TestUiStartNative(unittest.TestCase):
+    """`weightslab start` serves the bundled SPA + gRPC-Web proxy (no Docker)."""
+
+    @patch("weightslab.ui.server.serve_ui")
+    def test_start_invokes_serve_ui_with_defaults(self, mock_serve):
+        with patch.dict(os.environ, {}, clear=False):
+            for k in ("WEIGHTSLAB_UI_HOST", "WEIGHTSLAB_UI_PORT",
+                      "GRPC_BACKEND_HOST", "GRPC_BACKEND_PORT"):
+                os.environ.pop(k, None)
+            args = argparse.Namespace(port=9123, host=None, backend_host=None,
+                                      backend_port=None, no_browser=True, certs=False)
+            ui_start_native(args)
+        mock_serve.assert_called_once()
+        kwargs = mock_serve.call_args.kwargs
+        self.assertEqual(kwargs["backend_port"], 50051)
+        self.assertFalse(kwargs["open_browser"])
+        self.assertIsNone(kwargs["certs_dir"])  # no --certs -> unsecured
+
+    @patch("weightslab.ui.server.serve_ui")
+    @patch("weightslab.cli.CertAuthManager")
+    def test_start_certs_without_valid_certs_falls_back_to_http(self, mock_mgr, mock_serve):
+        mgr = MagicMock()
+        mgr.has_valid_certs.return_value = False
+        mock_mgr.from_env_or_default.return_value = mgr
+        args = argparse.Namespace(port=9124, host=None, backend_host=None,
+                                  backend_port=None, no_browser=True, certs=True)
+        with self.assertLogs("weightslab.cli", level="WARNING"):
+            ui_start_native(args)
+        self.assertIsNone(mock_serve.call_args.kwargs["certs_dir"])
+
+
+class TestExampleStart(unittest.TestCase):
+    @patch("weightslab.cli.subprocess.run")
+    def test_example_start_runs_classification(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=0)
+        with self.assertLogs("weightslab.cli", level="INFO") as log_context:
+            example_start(argparse.Namespace())
+        cmd = mock_run.call_args.args[0]
+        self.assertEqual(cmd[0], sys.executable)
+        main_py = cmd[1].replace("\\", "/")
+        self.assertTrue(main_py.endswith("examples/PyTorch/wl-classification/main.py"), main_py)
+        self.assertTrue(any("classification (cls) example" in m for m in log_context.output))
+
+    @patch("weightslab.cli.subprocess.run")
+    def test_example_start_propagates_nonzero_exit(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=3)
+        with self.assertRaises(SystemExit) as ctx:
+            example_start(argparse.Namespace())
+        self.assertEqual(ctx.exception.code, 3)
+
+    @patch("weightslab.cli._get_example_dir", return_value=Path("/does/not/exist"))
+    def test_example_start_errors_when_missing(self, _mock_dir):
+        with self.assertRaises(SystemExit) as ctx:
+            example_start(argparse.Namespace())
+        self.assertEqual(ctx.exception.code, 1)
+
+    def test_example_dir_points_at_bundled_example(self):
+        self.assertTrue((_get_example_dir("wl-classification") / "main.py").exists())
+
+    @patch("weightslab.cli.subprocess.run")
+    def test_example_start_seg_runs_segmentation(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=0)
+        example_start(argparse.Namespace(example_kind="seg"))
+        main_py = mock_run.call_args.args[0][1].replace("\\", "/")
+        self.assertTrue(main_py.endswith("examples/PyTorch/wl-segmentation/main.py"), main_py)
+
+
+class TestInstallExampleRequirements(unittest.TestCase):
+    @patch("weightslab.cli.subprocess.run")
+    def test_installs_requirements_non_interactively_when_present(self, mock_run):
+        import tempfile
+        mock_run.return_value = MagicMock(returncode=0)
+        with tempfile.TemporaryDirectory() as tmp:
+            (Path(tmp) / "requirements.txt").write_text("numpy\n")
+            _install_example_requirements(Path(tmp))
+        cmd = mock_run.call_args.args[0]
+        self.assertEqual(cmd[:5], [sys.executable, "-m", "pip", "install", "-r"])
+        self.assertIn("--no-input", cmd)
+
+    @patch("weightslab.cli.subprocess.run")
+    def test_skips_when_no_requirements_file(self, mock_run):
+        import tempfile
+        with tempfile.TemporaryDirectory() as tmp:
+            _install_example_requirements(Path(tmp))
+        mock_run.assert_not_called()
+
+
+class TestMainCLI(unittest.TestCase):
+    """The CLI exposes exactly: se, start (+example), cli, tunnel, help. No `ui`."""
+
+    @patch("weightslab.cli.ui_secure_environment")
+    def test_main_dispatches_se(self, mock_se):
+        with patch("sys.argv", ["weightslab", "se"]):
+            main()
+        mock_se.assert_called_once()
+
+    @patch("weightslab.cli.ui_start_native")
+    def test_main_bare_start_launches_native_ui(self, mock_start):
+        with patch("sys.argv", ["weightslab", "start"]):
+            main()
+        mock_start.assert_called_once()
+
+    @patch("weightslab.cli.example_start")
+    def test_main_dispatches_start_example(self, mock_example):
+        with patch("sys.argv", ["weightslab", "start", "example"]):
+            main()
+        mock_example.assert_called_once()
+
+    def test_main_ui_command_is_gone(self):
+        # `ui` is no longer a valid subcommand (Docker launcher removed).
+        with patch("sys.argv", ["weightslab", "ui", "launch"]):
+            with self.assertRaises(SystemExit):
+                main()
+
+    def test_main_help_does_not_crash(self):
+        with patch("sys.argv", ["weightslab", "help"]):
+            main()
+
+    def test_main_no_args_does_not_crash(self):
+        with patch("sys.argv", ["weightslab"]):
+            main()
+
+
+class TestBannerAndHelp(unittest.TestCase):
+    @staticmethod
+    def _capture_main(argv):
+        buf = io.StringIO()
+        with patch("sys.argv", argv):
+            with contextlib.redirect_stdout(buf):
+                try:
+                    main()
+                except SystemExit:
+                    pass
+        return buf.getvalue()
+
+    def test_help_shows_new_command_set(self):
+        out = self._capture_main(["weightslab", "help"])
+        self.assertIn("se", out)
+        self.assertIn("start", out)
+        self.assertIn("start example", out)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/backend/test_data_loader_interface.py b/tests/backend/test_data_loader_interface.py
index 4da94914..000e45c9 100644
--- a/tests/backend/test_data_loader_interface.py
+++ b/tests/backend/test_data_loader_interface.py
@@ -353,6 +353,35 @@ def test_multiple_sequential_epochs_with_auto_reset(self):
             f"Each epoch should yield the same batch count, got {epoch_counts}",
         )
 
+    def test_reset_is_callable_through_ledger_proxy(self):
+        """`reset()` must exist and be callable via the ledger Proxy handle.
+
+        Ultralytics' trainer calls ``train_loader.reset()`` after closing mosaic
+        augmentation. The loader handed to it is a ledger Proxy wrapping this
+        interface, so a real ``reset()`` method (mapped to ``_reset_iterator``)
+        must be reachable through the proxy — otherwise the call resolves to a
+        non-callable None. Regression for the Ultralytics YOLO notebook crash
+        ``TypeError: 'NoneType' object is not callable``.
+        """
+        DataLoaderInterface(
+            self.train_ds, batch_size=self.batch_size,
+            loader_name="reset_proxy_loader", register=True, compute_hash=True,
+        )
+        loader = ledgers.get_dataloader("reset_proxy_loader")
+
+        self.assertTrue(hasattr(loader, "reset"))
+        self.assertTrue(callable(loader.reset))
+
+        # A full epoch before reset.
+        batches_before = sum(1 for _ in loader)
+        self.assertGreater(batches_before, 0)
+
+        # reset() must not raise, and the loader stays fully usable afterwards
+        # (a fresh full epoch with the same batch count can be iterated).
+        loader.reset()
+        batches_after = sum(1 for _ in loader)
+        self.assertEqual(batches_after, batches_before)
+
 
 class TestDataLoaderReproducibility(unittest.TestCase):
     """Test RNG and iteration state reproducibility for dataloaders."""
diff --git a/tests/backend/test_ledgers.py b/tests/backend/test_ledgers.py
index 8db41501..513b1caf 100644
--- a/tests/backend/test_ledgers.py
+++ b/tests/backend/test_ledgers.py
@@ -672,6 +672,49 @@ def __init__(self):
         delattr(proxy, "flag")
         self.assertFalse(hasattr(wrapped, "flag"))
 
+    def test_proxy_missing_attr_raises_attributeerror(self):
+        """Missing attributes on the wrapped object must raise AttributeError.
+
+        Regression: __getattr__ used to `return None`, which made
+        `hasattr(proxy, missing)` report True and silently resolved
+        `proxy.missing` to a non-callable None. That broke capability probes
+        such as Ultralytics' `if not hasattr(loader, "reset")` shim, leaving
+        `loader.reset` as None -> `'NoneType' object is not callable`.
+        """
+        proxy = Proxy(Dummy("x"))
+
+        # Existing attribute still forwards.
+        self.assertEqual(proxy.name, "x")
+
+        # Missing attribute raises (does not return None).
+        with self.assertRaises(AttributeError):
+            _ = proxy.definitely_not_here
+
+        # hasattr therefore reports False, and getattr honors the default.
+        self.assertFalse(hasattr(proxy, "definitely_not_here"))
+        self.assertIsNone(getattr(proxy, "definitely_not_here", None))
+        self.assertEqual(getattr(proxy, "definitely_not_here", 42), 42)
+
+    def test_proxy_forwards_callable_capability(self):
+        """A method present on the wrapped object is discoverable and callable.
+
+        Mirrors how a ledger Proxy wraps a DataLoaderInterface whose real
+        `reset()` must be reachable through the proxy.
+        """
+        class _HasReset:
+            def __init__(self):
+                self.calls = 0
+
+            def reset(self):
+                self.calls += 1
+
+        wrapped = _HasReset()
+        proxy = Proxy(wrapped)
+
+        self.assertTrue(hasattr(proxy, "reset"))
+        self.assertTrue(callable(proxy.reset))
+        proxy.reset()
+        self.assertEqual(wrapped.calls, 1)
 
 
 if __name__ == "__main__":
diff --git a/tests/backend/test_logger_core.py b/tests/backend/test_logger_core.py
index eb44d569..291a66fc 100644
--- a/tests/backend/test_logger_core.py
+++ b/tests/backend/test_logger_core.py
@@ -16,8 +16,10 @@
 - load_signal_history (dict format, list format)
 """
 
+import os
+import time
 import unittest
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 
 from weightslab.backend.logger import LoggerQueue
 
@@ -834,5 +836,184 @@ def test_no_match_returns_none(self):
         self.assertIsNone(lg.resolve_graph_name("accuracy"))
 
 
+class TestBackgroundFlushAndLossShapeAutotag(unittest.TestCase):
+    """LoggerQueue starts a background thread that periodically flushes staged
+    rows and auto-classifies every flag="loss" signal — off the caller's
+    thread, with zero setup required (see auto_loss_shape_signal_names).
+
+    The actual tagging logic (_autotag_loss_shapes) is called directly here
+    rather than via time.sleep()-and-poll on the real background thread: with
+    many LoggerQueue instances created across this test file, a still-winding-
+    down daemon thread from an earlier test could otherwise tick during a
+    later test's `patch(...)` window and pollute its mock's call list. Calling
+    the method directly is deterministic and exercises the identical code path
+    the thread calls on every tick; test_background_flush_thread_starts_and_stops
+    separately covers that the thread itself starts/stops correctly.
+    """
+
+    def _lg_no_autoflush(self):
+        """A LoggerQueue with the background thread stopped immediately, so
+        only the explicit _autotag_loss_shapes() calls below can tag anything."""
+        lg = _lg()
+        lg.stop_background_flush()
+        return lg
+
+    def test_background_flush_thread_starts_and_stops(self):
+        with patch.dict(os.environ, {"WL_LOGGER_FLUSH_INTERVAL_SECONDS": "0.05"}):
+            lg = _lg()
+        self.addCleanup(lg.stop_background_flush)
+        self.assertIsNotNone(lg._flush_thread)
+        self.assertTrue(lg._flush_thread.is_alive())
+        lg.stop_background_flush()
+        self.assertFalse(lg._flush_thread.is_alive())
+
+    def test_auto_detected_signals_are_classified_with_no_setup_call(self):
+        """The core ask: zero user-facing calls — just discovered and tagged."""
+        lg = self._lg_no_autoflush()
+        with patch("weightslab.src.auto_loss_shape_signal_names", return_value=["train-loss-CE"]), \
+             patch("weightslab.src.write_signal_shapes") as mock_write_shapes:
+            lg._autotag_loss_shapes()
+        self.assertTrue(mock_write_shapes.called)
+        args, kwargs = mock_write_shapes.call_args
+        self.assertEqual(args[0], "train-loss-CE")
+        self.assertIsNone(kwargs.get("tag_name"))  # default '<signal>_shape' naming
+        self.assertIsNone(kwargs.get("classifier"))  # default loss classifier
+
+    def test_no_signals_means_no_classification_attempts(self):
+        lg = self._lg_no_autoflush()
+        with patch("weightslab.src.auto_loss_shape_signal_names", return_value=[]), \
+             patch("weightslab.src.write_signal_shapes") as mock_write_shapes:
+            lg._autotag_loss_shapes()
+        self.assertFalse(mock_write_shapes.called)
+
+    def test_set_loss_shape_override_customizes_tag_and_classifier(self):
+        lg = self._lg_no_autoflush()
+        classifier = MagicMock()
+        lg.set_loss_shape_override("train-loss-CE", tag_name="my_shape", classifier=classifier)
+        with patch("weightslab.src.auto_loss_shape_signal_names", return_value=["train-loss-CE"]), \
+             patch("weightslab.src.write_signal_shapes") as mock_write_shapes:
+            lg._autotag_loss_shapes()
+        self.assertTrue(mock_write_shapes.called)
+        args, kwargs = mock_write_shapes.call_args
+        self.assertEqual(args[0], "train-loss-CE")
+        self.assertEqual(kwargs.get("tag_name"), "my_shape")
+        self.assertIs(kwargs.get("classifier"), classifier)
+
+    def test_set_loss_shape_override_also_tracks_a_signal_outside_the_auto_detected_set(self):
+        """A manually-logged signal (not registered via flag="loss") can still
+        opt in by name — the override set is a superset, not a subset."""
+        lg = self._lg_no_autoflush()
+        lg.set_loss_shape_override("custom_metric")
+        with patch("weightslab.src.auto_loss_shape_signal_names", return_value=[]), \
+             patch("weightslab.src.write_signal_shapes") as mock_write_shapes:
+            lg._autotag_loss_shapes()
+        self.assertTrue(mock_write_shapes.called)
+        self.assertEqual(mock_write_shapes.call_args[0][0], "custom_metric")
+
+    def test_disable_loss_shape_autotag_for_one_signal(self):
+        lg = self._lg_no_autoflush()
+        lg.disable_loss_shape_autotag("train-loss-CE")
+        with patch("weightslab.src.auto_loss_shape_signal_names",
+                    return_value=["train-loss-CE", "val-loss-CE"]), \
+             patch("weightslab.src.write_signal_shapes") as mock_write_shapes:
+            lg._autotag_loss_shapes()
+        called_signals = {c.args[0] for c in mock_write_shapes.call_args_list}
+        self.assertNotIn("train-loss-CE", called_signals)
+        self.assertIn("val-loss-CE", called_signals)
+
+    def test_disable_loss_shape_autotag_for_all_signals(self):
+        lg = self._lg_no_autoflush()
+        lg.disable_loss_shape_autotag()
+        with patch("weightslab.src.auto_loss_shape_signal_names",
+                    return_value=["train-loss-CE", "val-loss-CE"]), \
+             patch("weightslab.src.write_signal_shapes") as mock_write_shapes:
+            lg._autotag_loss_shapes()
+        self.assertFalse(mock_write_shapes.called)
+
+    def test_autotag_skips_a_signal_with_no_new_data_since_last_pass(self):
+        """The optimization under test: a signal with no new per-sample writes
+        since the last tag pass must not be reclassified again — this is what
+        keeps the background flush thread from unconditionally re-reading and
+        re-tagging every signal on a bare timer regardless of whether training
+        produced anything new."""
+        lg = self._lg_no_autoflush()
+        _add(lg, "train-loss-CE", "s1", 1, 0.5)
+        with patch("weightslab.src.auto_loss_shape_signal_names", return_value=["train-loss-CE"]), \
+             patch("weightslab.src.write_signal_shapes") as mock_write_shapes:
+            lg._autotag_loss_shapes()
+            self.assertEqual(mock_write_shapes.call_count, 1)
+
+            # No new data staged for this signal — must be skipped this time.
+            lg._autotag_loss_shapes()
+            self.assertEqual(mock_write_shapes.call_count, 1)
+
+            # New data arrives -> the signal's _qps_version moves -> re-tagged.
+            _add(lg, "train-loss-CE", "s1", 2, 0.4)
+            lg._autotag_loss_shapes()
+            self.assertEqual(mock_write_shapes.call_count, 2)
+
+    def test_autotag_skip_cache_is_per_signal(self):
+        """One signal having no new data must not suppress another signal that
+        does — the version check is keyed per signal_name. Uses a second
+        sample at the SAME step (not a new global step) so only the touched
+        signal's own _qps_version bumps — a new global step drops the whole
+        query-cache/version table (see _stage_sample_row's "New step -> last
+        step's cache entries can't recur" comment), which is a separate,
+        coarser invalidation this test isn't about."""
+        lg = self._lg_no_autoflush()
+        _add(lg, "train-loss-CE", "s1", 1, 0.5)
+        _add(lg, "val-loss-CE", "s1", 1, 0.7)
+        with patch("weightslab.src.auto_loss_shape_signal_names",
+                    return_value=["train-loss-CE", "val-loss-CE"]), \
+             patch("weightslab.src.write_signal_shapes") as mock_write_shapes:
+            lg._autotag_loss_shapes()
+            # Both signals are new — order isn't guaranteed (iterates a set).
+            self.assertEqual(
+                {c.args[0] for c in mock_write_shapes.call_args_list},
+                {"train-loss-CE", "val-loss-CE"},
+            )
+            mock_write_shapes.reset_mock()
+
+            # Only val-loss-CE gets a new sample this round (same step) —
+            # train-loss-CE must be skipped now.
+            _add(lg, "val-loss-CE", "s2", 1, 0.6)
+            lg._autotag_loss_shapes()
+            self.assertEqual(
+                [c.args[0] for c in mock_write_shapes.call_args_list],
+                ["val-loss-CE"],
+            )
+
+
+class TestAutoLossShapeSignalRegistration(unittest.TestCase):
+    """watch_or_edit(..., flag="loss") must register the signal name for
+    automatic loss-shape classification with no further call required."""
+
+    def test_flag_loss_registers_signal_for_auto_classification(self):
+        import torch.nn as nn
+        import weightslab as wl
+        from weightslab.src import _AUTO_LOSS_SHAPE_SIGNALS
+
+        # _AUTO_LOSS_SHAPE_SIGNALS is process-global — clean up so this signal
+        # name doesn't leak into other tests' background flush threads (which
+        # would otherwise try, and safely fail, to classify a signal that
+        # doesn't exist in their own logger).
+        self.addCleanup(_AUTO_LOSS_SHAPE_SIGNALS.discard, "test_auto_loss_shape_signal")
+
+        crit = nn.CrossEntropyLoss(reduction="none")
+        wl.watch_or_edit(crit, flag="loss", signal_name="test_auto_loss_shape_signal", log=True)
+        self.assertIn("test_auto_loss_shape_signal", wl.auto_loss_shape_signal_names())
+
+    def test_flag_metric_does_not_register_for_auto_classification(self):
+        """Only flag="loss" implies a decreasing per-sample loss trajectory —
+        an arbitrary watched metric/signal shouldn't be force-classified as one."""
+        import torch
+        from torchmetrics.classification import Accuracy
+        import weightslab as wl
+
+        wl.watch_or_edit(Accuracy(task="multiclass", num_classes=2),
+                          flag="metric", signal_name="test_auto_metric_not_loss")
+        self.assertNotIn("test_auto_metric_not_loss", wl.auto_loss_shape_signal_names())
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/backend/test_ui_docker_bridge.py b/tests/backend/test_ui_docker_bridge.py
deleted file mode 100644
index c43f2bd5..00000000
--- a/tests/backend/test_ui_docker_bridge.py
+++ /dev/null
@@ -1,718 +0,0 @@
-import argparse
-import contextlib
-import io
-import os
-import sys
-import unittest
-from pathlib import Path
-from unittest.mock import patch, MagicMock
-
-from weightslab.ui_docker_bridge import (
-    _check_docker,
-    _clean_stale_docker_resources,
-    _compose_cmd,
-    _ensure_certificates,
-    _ensure_scripts_executable,
-    _generate_certs_with_fallback,
-    _get_example_dir,
-    _install_example_requirements,
-    _make_executable,
-    _remove_docker_image,
-    _strip_derived_deploy_env,
-    _DERIVED_DEPLOY_ENV_VARS,
-    _FRONTEND_IMAGE,
-    _STACK_CONTAINERS,
-    example_start,
-    main,
-    ui_launch,
-    ui_secure_environment,
-)
-
-
-class TestCheckDocker(unittest.TestCase):
-    @patch("weightslab.ui_docker_bridge.shutil.which", return_value=None)
-    def test_exits_when_docker_not_found(self, _mock_which):
-        with self.assertRaises(SystemExit) as ctx:
-            _check_docker()
-        self.assertEqual(ctx.exception.code, 1)
-
-    @patch(
-        "weightslab.ui_docker_bridge.subprocess.run",
-        side_effect=__import__("subprocess").CalledProcessError(1, "docker info"),
-    )
-    @patch("weightslab.ui_docker_bridge.shutil.which", return_value="/usr/bin/docker")
-    def test_exits_when_daemon_not_running(self, _mock_which, _mock_run):
-        with self.assertRaises(SystemExit) as ctx:
-            _check_docker()
-        self.assertEqual(ctx.exception.code, 1)
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    @patch("weightslab.ui_docker_bridge.shutil.which", return_value="/usr/bin/docker")
-    def test_passes_when_docker_available(self, _mock_which, mock_run):
-        _check_docker()
-        mock_run.assert_called_once()
-
-
-class TestComposeCmd(unittest.TestCase):
-    @patch("weightslab.ui_docker_bridge._compose_base_cmd", return_value=["docker", "compose"])
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_runs_docker_compose_with_env(self, mock_run, _mock_base):
-        mock_run.return_value = MagicMock(stdout="", returncode=0)
-        _compose_cmd("/path/to/compose.yml", "/path/to/envoy.yaml", ["up", "-d"])
-        mock_run.assert_called_once()
-        args, kwargs = mock_run.call_args
-        self.assertEqual(
-            args[0],
-            ["docker", "compose", "-f", "/path/to/compose.yml", "up", "-d"],
-        )
-        self.assertEqual(kwargs["env"]["WS_ENVOY_CONFIG"], "/path/to/envoy.yaml")
-        self.assertTrue(kwargs["stdout"])
-        self.assertTrue(kwargs["text"])
-
-    @patch("weightslab.ui_docker_bridge._compose_base_cmd", return_value=["docker-compose"])
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_v1_translates_up_pull_into_pull_then_up(self, mock_run, _mock_base):
-        """Compose v1 has no `up --pull`; it must `pull` first, then `up` without the flag."""
-        mock_run.return_value = MagicMock(stdout="", returncode=0)
-        _compose_cmd("/c.yml", "/e.yaml", ["up", "-d", "--pull", "always"])
-        cmds = [c.args[0] for c in mock_run.call_args_list]
-        # A separate `docker-compose -f /c.yml pull` ran first.
-        self.assertIn(["docker-compose", "-f", "/c.yml", "pull"], cmds)
-        # The final `up` carries no --pull flag (v1 would reject it).
-        up_cmd = cmds[-1]
-        self.assertEqual(up_cmd, ["docker-compose", "-f", "/c.yml", "up", "-d"])
-        self.assertNotIn("--pull", up_cmd)
-
-    @patch("weightslab.ui_docker_bridge._compose_base_cmd", return_value=None)
-    def test_exits_when_no_compose_cli(self, _mock_base):
-        with self.assertRaises(SystemExit) as ctx:
-            _compose_cmd("/c.yml", "/e.yaml", ["up", "-d"])
-        self.assertEqual(ctx.exception.code, 1)
-
-
-class TestComposeDetection(unittest.TestCase):
-    """Prefer Compose v2 (`docker compose`), fall back to v1 (`docker-compose`)."""
-
-    @staticmethod
-    def _fake_run(ok_keys):
-        """Return a subprocess.run stub: rc 0 when ' '.join(cmd[:2]) is in ok_keys."""
-        def run(cmd, *a, **k):
-            return MagicMock(returncode=0 if " ".join(cmd[:2]) in ok_keys else 1)
-        return run
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_prefers_v2(self, mock_run):
-        from weightslab.ui_docker_bridge import _detect_compose_cmd
-        mock_run.side_effect = self._fake_run({"docker compose"})
-        self.assertEqual(_detect_compose_cmd(), ["docker", "compose"])
-
-    @patch("weightslab.ui_docker_bridge.shutil.which", return_value="/usr/bin/docker-compose")
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_falls_back_to_v1_when_v2_absent(self, mock_run, _which):
-        from weightslab.ui_docker_bridge import _detect_compose_cmd
-        # v2 probe fails (rc 1); v1 probe (`docker-compose version`) succeeds.
-        mock_run.side_effect = self._fake_run({"docker-compose version"})
-        self.assertEqual(_detect_compose_cmd(), ["docker-compose"])
-
-    @patch("weightslab.ui_docker_bridge.shutil.which", return_value=None)
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_returns_none_when_neither_available(self, mock_run, _which):
-        from weightslab.ui_docker_bridge import _detect_compose_cmd
-        mock_run.side_effect = self._fake_run(set())
-        self.assertIsNone(_detect_compose_cmd())
-
-
-class TestScriptsExecutable(unittest.TestCase):
-    """Bundled .sh scripts are made executable so users skip the manual `chmod +x`."""
-
-    @unittest.skipIf(sys.platform == "win32", "execute bit is POSIX-only")
-    def test_make_executable_adds_exec_bits(self):
-        import stat as _stat
-        import tempfile
-        with tempfile.NamedTemporaryFile(suffix=".sh", delete=False) as f:
-            path = f.name
-        try:
-            os.chmod(path, 0o644)
-            _make_executable(path)
-            mode = os.stat(path).st_mode
-            self.assertTrue(mode & _stat.S_IXUSR)
-            self.assertTrue(mode & _stat.S_IXGRP)
-            self.assertTrue(mode & _stat.S_IXOTH)
-        finally:
-            os.unlink(path)
-
-    def test_make_executable_is_noop_on_windows(self):
-        with patch("weightslab.ui_docker_bridge._is_windows", return_value=True):
-            with patch("weightslab.ui_docker_bridge.os.chmod") as mock_chmod:
-                _make_executable("/whatever/path.sh")
-                mock_chmod.assert_not_called()
-
-    def test_make_executable_swallows_oserror(self):
-        # A non-chmod-able path (e.g. root-owned system install) must not raise.
-        with patch("weightslab.ui_docker_bridge.os.stat", side_effect=OSError("denied")):
-            _make_executable("/root/owned.sh") # should not raise
-
-    @unittest.skipIf(sys.platform == "win32", "execute bit is POSIX-only")
-    def test_ensure_scripts_executable_marks_bundled_scripts(self):
-        import stat as _stat
-        from weightslab.ui_docker_bridge import _get_bootstrap_script
-        _ensure_scripts_executable()
-        bootstrap = _get_bootstrap_script()
-        self.assertTrue(bootstrap.exists(), bootstrap)
-        self.assertTrue(os.stat(bootstrap).st_mode & _stat.S_IXUSR)
-
-    def test_ensure_scripts_executable_noop_on_windows(self):
-        with patch("weightslab.ui_docker_bridge._is_windows", return_value=True):
-            with patch("weightslab.ui_docker_bridge._make_executable") as mock_mk:
-                _ensure_scripts_executable()
-                mock_mk.assert_not_called()
-
-
-class TestUiLaunch(unittest.TestCase):
-    """ui_launch: unsecured by default (no cert gen); --certs opts into TLS."""
-
-    @patch("weightslab.ui_docker_bridge.CertAuthManager")
-    @patch("weightslab.ui_docker_bridge._get_bootstrap_script", return_value="/does/not/exist.sh")
-    @patch("weightslab.ui_docker_bridge._run_shell_script", return_value=0)
-    @patch("weightslab.ui_docker_bridge._ensure_certificates", return_value=True)
-    @patch("weightslab.ui_docker_bridge._clean_stale_docker_resources")
-    @patch("weightslab.ui_docker_bridge._compose_cmd")
-    @patch("weightslab.ui_docker_bridge._check_docker")
-    @patch("weightslab.ui_docker_bridge._get_envoy_config", return_value="/fake/envoy.yaml")
-    @patch("weightslab.ui_docker_bridge._get_compose_file", return_value="/fake/docker-compose.yml")
-    def test_launch_default_no_cert_gen_cleans_and_launches_unsecured(
-        self, _gc, _ge, mock_check, mock_compose, mock_clean, mock_ensure,
-        _mock_shell, _gb, mock_mgr,
-    ):
-        mgr = MagicMock()
-        mgr.has_valid_certs.return_value = False # no certs on disk -> unsecured
-        mock_mgr.from_env_or_default.return_value = mgr
-        with patch.dict(os.environ, {}, clear=False):
-            os.environ.pop("VITE_PORT", None)
-            with self.assertLogs("weightslab.ui_docker_bridge", level="INFO") as log_context:
-                ui_launch(argparse.Namespace())
-        mock_check.assert_called_once()
-        mock_ensure.assert_not_called() # certs NOT generated by default
-        mock_clean.assert_called_once() # stale cleanup ran
-        mock_compose.assert_called_once_with(
-            "/fake/docker-compose.yml",
-            "/fake/envoy.yaml",
-            ["up", "-d", "--pull", "always"],
-        )
-        self.assertTrue(any("http://localhost:5173" in msg for msg in log_context.output))
-        self.assertFalse(any("https://localhost:5173" in msg for msg in log_context.output))
-
-    @patch("weightslab.ui_docker_bridge.CertAuthManager")
-    @patch("weightslab.ui_docker_bridge._get_bootstrap_script", return_value="/does/not/exist.sh")
-    @patch("weightslab.ui_docker_bridge._run_shell_script", return_value=0)
-    @patch("weightslab.ui_docker_bridge._ensure_certificates", return_value=True)
-    @patch("weightslab.ui_docker_bridge._clean_stale_docker_resources")
-    @patch("weightslab.ui_docker_bridge._compose_cmd")
-    @patch("weightslab.ui_docker_bridge._check_docker")
-    @patch("weightslab.ui_docker_bridge._get_envoy_config", return_value="/fake/envoy.yaml")
-    @patch("weightslab.ui_docker_bridge._get_compose_file", return_value="/fake/docker-compose.yml")
-    def test_launch_respects_custom_port(
-        self, _gc, _ge, _mock_check, _mock_compose, _mock_clean, _mock_ensure,
-        _mock_shell, _gb, mock_mgr,
-    ):
-        mgr = MagicMock(certs_dir="/fake/certs")
-        mgr.has_valid_certs.return_value = True
-        mock_mgr.from_env_or_default.return_value = mgr
-        with patch.dict(os.environ, {"VITE_PORT": "3000"}):
-            with self.assertLogs("weightslab.ui_docker_bridge", level="INFO") as log_context:
-                ui_launch(argparse.Namespace(certs=True))
-        self.assertTrue(any("https://localhost:3000" in msg for msg in log_context.output))
-
-    @patch("weightslab.ui_docker_bridge.CertAuthManager")
-    @patch("weightslab.ui_docker_bridge._get_bootstrap_script", return_value="/does/not/exist.sh")
-    @patch("weightslab.ui_docker_bridge._run_shell_script", return_value=0)
-    @patch("weightslab.ui_docker_bridge._ensure_certificates", return_value=True)
-    @patch("weightslab.ui_docker_bridge._clean_stale_docker_resources")
-    @patch("weightslab.ui_docker_bridge._compose_cmd")
-    @patch("weightslab.ui_docker_bridge._check_docker")
-    @patch("weightslab.ui_docker_bridge._get_envoy_config", return_value="/fake/envoy.yaml")
-    @patch("weightslab.ui_docker_bridge._get_compose_file", return_value="/fake/docker-compose.yml")
-    def test_launch_certs_flag_generates_and_runs_secured(
-        self, _gc, _ge, _mock_check, _mock_compose, _mock_clean, mock_ensure,
-        _mock_shell, _gb, mock_mgr,
-    ):
-        mgr = MagicMock(certs_dir="/fake/certs")
-        mgr.has_valid_certs.return_value = True # certs present after generation
-        mock_mgr.from_env_or_default.return_value = mgr
-        with patch.dict(os.environ, {}, clear=False):
-            os.environ.pop("VITE_PORT", None)
-            with self.assertLogs("weightslab.ui_docker_bridge", level="INFO") as log_context:
-                ui_launch(argparse.Namespace(certs=True))
-        mock_ensure.assert_called_once() # --certs generates certs
-        self.assertTrue(any("https://localhost:5173" in msg for msg in log_context.output))
-
-
-class TestEnsureCertificates(unittest.TestCase):
-    """_ensure_certificates only generates files; it never exports TLS/auth env."""
-
-    @patch("weightslab.ui_docker_bridge._install_ca_trust")
-    @patch("weightslab.ui_docker_bridge._generate_certs_with_fallback")
-    def test_uses_existing_certs_without_generating(self, mock_gen, _mock_trust):
-        manager = MagicMock()
-        # Gate is has_any_credentials(); existing creds short-circuit generation.
-        manager.has_any_credentials.return_value = True
-        manager.has_valid_certs.return_value = True
-        result = _ensure_certificates(manager, force_certs=False)
-        self.assertTrue(result)
-        mock_gen.assert_not_called()
-        manager.get_or_create_auth_token.assert_called_once()
-        # Derived TLS env must NOT be set here — the deploy pipeline derives it.
-        manager.setup_tls_environment.assert_not_called()
-
-    @patch("weightslab.ui_docker_bridge._install_ca_trust")
-    @patch("weightslab.ui_docker_bridge._generate_certs_with_fallback", return_value=0)
-    def test_generates_when_missing_and_forwards_certs_dir(self, mock_gen, _mock_trust):
-        manager = MagicMock()
-        # No credentials at the gate -> generate; certs valid afterwards.
-        manager.has_any_credentials.return_value = False
-        manager.has_valid_certs.return_value = True
-        result = _ensure_certificates(manager, force_certs=False)
-        self.assertTrue(result)
-        mock_gen.assert_called_once_with(force_certs=False, certs_dir=manager.certs_dir)
-        manager.setup_tls_environment.assert_not_called()
-
-    @patch("weightslab.ui_docker_bridge._install_ca_trust")
-    @patch("weightslab.ui_docker_bridge._generate_certs_with_fallback", return_value=0)
-    def test_force_regenerates_even_when_present(self, mock_gen, _mock_trust):
-        manager = MagicMock()
-        manager.has_any_credentials.return_value = True
-        manager.has_valid_certs.return_value = True
-        _ensure_certificates(manager, force_certs=True)
-        mock_gen.assert_called_once_with(force_certs=True, certs_dir=manager.certs_dir)
-
-    @patch("weightslab.ui_docker_bridge._install_ca_trust")
-    @patch("weightslab.ui_docker_bridge._generate_certs_with_fallback", return_value=1)
-    def test_returns_false_on_generation_failure(self, mock_gen, _mock_trust):
-        manager = MagicMock()
-        manager.has_any_credentials.return_value = False
-        manager.has_valid_certs.return_value = False
-        result = _ensure_certificates(manager, force_certs=False)
-        self.assertFalse(result)
-
-
-class TestRemoveDockerImage(unittest.TestCase):
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_removes_when_present(self, mock_run):
-        mock_run.side_effect = [MagicMock(stdout="abc123\nabc123\ndef456\n"), MagicMock()]
-        _remove_docker_image(_FRONTEND_IMAGE)
-        self.assertEqual(mock_run.call_count, 2)
-        rmi_call = mock_run.call_args_list[1].args[0]
-        self.assertEqual(rmi_call[:3], ["docker", "rmi", "-f"])
-        self.assertIn("abc123", rmi_call)
-        self.assertIn("def456", rmi_call)
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_noop_when_absent(self, mock_run):
-        mock_run.return_value = MagicMock(stdout="")
-        _remove_docker_image(_FRONTEND_IMAGE)
-        mock_run.assert_called_once() # only the 'docker images -q' query, no rmi
-
-
-class TestCleanStaleDockerResources(unittest.TestCase):
-    @patch("weightslab.ui_docker_bridge._remove_docker_image")
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    @patch("weightslab.ui_docker_bridge._compose_cmd")
-    @patch("weightslab.ui_docker_bridge._get_envoy_config", return_value="/fake/envoy.yaml")
-    @patch("weightslab.ui_docker_bridge._get_compose_file", return_value="/fake/docker-compose.yml")
-    def test_clean_tears_down_and_removes_image(self, _gc, _ge, mock_compose, mock_run, mock_rmimg):
-        _clean_stale_docker_resources()
-        # 1. compose down --remove-orphans --volumes
-        mock_compose.assert_called_once_with(
-            "/fake/docker-compose.yml",
-            "/fake/envoy.yaml",
-            ["down", "--remove-orphans", "--volumes"],
-        )
-        # 2. docker rm -f for each known stack container
-        removed = [c.args[0] for c in mock_run.call_args_list]
-        for container in _STACK_CONTAINERS:
-            self.assertTrue(
-                any(call[:3] == ["docker", "rm", "-f"] and container in call for call in removed),
-                f"expected 'docker rm -f {container}'",
-            )
-        # 3. cached frontend image removed
-        mock_rmimg.assert_called_once_with(_FRONTEND_IMAGE)
-
-    @patch("weightslab.ui_docker_bridge._remove_docker_image")
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    @patch("weightslab.ui_docker_bridge._compose_cmd",
-           side_effect=__import__("subprocess").CalledProcessError(1, "down"))
-    @patch("weightslab.ui_docker_bridge._get_envoy_config", return_value="/fake/envoy.yaml")
-    @patch("weightslab.ui_docker_bridge._get_compose_file", return_value="/fake/docker-compose.yml")
-    def test_clean_tolerates_compose_down_failure(self, _gc, _ge, _mc, _mr, mock_rmimg):
-        # Should not raise even when 'compose down' returns non-zero (nothing to remove).
-        _clean_stale_docker_resources()
-        mock_rmimg.assert_called_once_with(_FRONTEND_IMAGE)
-
-
-class TestUiSecureEnvironment(unittest.TestCase):
-    @patch("weightslab.ui_docker_bridge.CertAuthManager")
-    @patch("weightslab.ui_docker_bridge._generate_certs_with_fallback", return_value=0)
-    def test_ui_secure_environment_success(self, mock_gen_certs, mock_cert_manager):
-        """`weightslab se`: generate certs + token, export WEIGHTSLAB_CERTS_DIR."""
-        mock_manager_instance = MagicMock() # certs_dir is a MagicMock (supports .mkdir)
-        mock_manager_instance.get_or_create_auth_token.return_value = "fake_token"
-        mock_cert_manager.return_value = mock_manager_instance
-
-        args = argparse.Namespace(force_certs=False)
-
-        with self.assertLogs("weightslab.ui_docker_bridge", level="INFO") as log_context:
-            ui_secure_environment(args)
-
-        self.assertTrue(any("Certificates generated successfully" in msg for msg in log_context.output))
-        self.assertTrue(any("gRPC auth token created" in msg for msg in log_context.output))
-        # Generation is pointed at the chosen certs dir (single source of truth).
-        mock_gen_certs.assert_called_once_with(force_certs=False, certs_dir=mock_manager_instance.certs_dir)
-        mock_manager_instance.certs_dir.mkdir.assert_called_once()
-        # se exports WEIGHTSLAB_CERTS_DIR for the process.
-        self.assertTrue(any("WEIGHTSLAB_CERTS_DIR exported" in msg for msg in log_context.output))
-
-    @patch("weightslab.ui_docker_bridge._generate_certs_with_fallback", return_value=0)
-    def test_ui_secure_environment_force_certs(self, mock_gen_certs):
-        """`weightslab se --force-certs` forwards force_certs to generation."""
-        args = argparse.Namespace(force_certs=True)
-        with patch.dict(os.environ, {}, clear=False):
-            ui_secure_environment(args)
-        self.assertTrue(mock_gen_certs.call_args.kwargs["force_certs"])
-
-    @patch("weightslab.ui_docker_bridge._generate_certs_with_fallback", return_value=1)
-    def test_ui_secure_environment_cert_failure(self, mock_gen_certs):
-        """Certificate generation failure exits non-zero."""
-        args = argparse.Namespace(force_certs=False)
-        with self.assertRaises(SystemExit) as ctx:
-            ui_secure_environment(args)
-        self.assertEqual(ctx.exception.code, 1)
-
-
-class TestMainCLI(unittest.TestCase):
-    """The CLI exposes exactly: se, ui launch, start example, help."""
-
-    @patch("weightslab.ui_docker_bridge.ui_secure_environment")
-    def test_main_dispatches_se(self, mock_se):
-        with patch("sys.argv", ["weightslab", "se"]):
-            main()
-        mock_se.assert_called_once()
-
-    @patch("weightslab.ui_docker_bridge.ui_launch")
-    def test_main_dispatches_ui_launch(self, mock_launch):
-        with patch("sys.argv", ["weightslab", "ui", "launch"]):
-            main()
-        mock_launch.assert_called_once()
-
-    @patch("weightslab.ui_docker_bridge.example_start")
-    def test_main_dispatches_start_example(self, mock_example):
-        with patch("sys.argv", ["weightslab", "start", "example"]):
-            main()
-        mock_example.assert_called_once()
-
-    def test_main_ui_without_action_does_not_crash(self):
-        with patch("sys.argv", ["weightslab", "ui"]):
-            main() # should print ui help, not raise
-
-    def test_main_start_without_target_does_not_crash(self):
-        with patch("sys.argv", ["weightslab", "start"]):
-            main() # should print start help, not raise
-
-    def test_main_help_does_not_crash(self):
-        with patch("sys.argv", ["weightslab", "help"]):
-            main() # should not raise
-
-    def test_main_no_args_does_not_crash(self):
-        with patch("sys.argv", ["weightslab"]):
-            main() # should not raise
-
-
-class TestUserOnboardingFlow(unittest.TestCase):
-    """Integration-like test of the full onboarding flow, fully hermetic."""
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    @patch("weightslab.ui_docker_bridge._compose_cmd")
-    @patch("weightslab.ui_docker_bridge._check_docker")
-    @patch("weightslab.ui_docker_bridge._run_shell_script", return_value=0)
-    @patch("weightslab.ui_docker_bridge._clean_stale_docker_resources")
-    @patch("weightslab.ui_docker_bridge._ensure_certificates", return_value=True)
-    @patch("weightslab.ui_docker_bridge._generate_certs_with_fallback", return_value=0)
-    @patch("weightslab.ui_docker_bridge.CertAuthManager")
-    def test_complete_onboarding_workflow(
-        self, mock_cert_manager, mock_gen, mock_ensure, mock_clean,
-        mock_shell, mock_check, mock_compose, mock_subproc,
-    ):
-        """se -> ui launch -> start example, with no real Docker/daemon/subprocess."""
-        mgr = MagicMock()
-        mock_cert_manager.return_value = mgr
-        mock_cert_manager.from_env_or_default.return_value = mgr
-        mock_subproc.return_value = MagicMock(returncode=0)
-
-        with patch.dict(os.environ, {}, clear=False):
-            try:
-                ui_secure_environment(argparse.Namespace(force_certs=False))
-                ui_launch(argparse.Namespace())
-                example_start(argparse.Namespace())
-            except Exception as e:
-                self.fail(f"Onboarding workflow failed: {e}")
-
-    @patch("sys.argv", ["weightslab", "se", "--force-certs"])
-    @patch("weightslab.ui_docker_bridge.ui_secure_environment")
-    def test_cli_se_force_certs(self, mock_se):
-        main()
-        mock_se.assert_called_once()
-        self.assertTrue(mock_se.call_args.args[0].force_certs)
-
-
-class TestBackendConnectionDetection(unittest.TestCase):
-    """Test backend connection detection utility."""
-
-    @patch("socket.socket")
-    def test_backend_connection_success(self, mock_socket_class):
-        from weightslab.ui_docker_bridge import _test_backend_connection
-        mock_socket = MagicMock()
-        mock_socket.connect_ex.return_value = 0
-        mock_socket_class.return_value = mock_socket
-        self.assertTrue(_test_backend_connection())
-
-    @patch("socket.socket")
-    def test_backend_connection_failure(self, mock_socket_class):
-        from weightslab.ui_docker_bridge import _test_backend_connection
-        mock_socket = MagicMock()
-        mock_socket.connect_ex.return_value = 1
-        mock_socket_class.return_value = mock_socket
-        self.assertFalse(_test_backend_connection())
-
-    @patch("socket.socket")
-    def test_backend_connection_timeout(self, mock_socket_class):
-        from weightslab.ui_docker_bridge import _test_backend_connection
-        mock_socket = MagicMock()
-        mock_socket.connect_ex.side_effect = Exception("Connection timeout")
-        mock_socket_class.return_value = mock_socket
-        self.assertFalse(_test_backend_connection())
-
-
-class TestPathConversion(unittest.TestCase):
-    """Test Windows path to Git Bash conversion."""
-
-    def test_windows_path_conversion(self):
-        from weightslab.ui_docker_bridge import _convert_to_git_bash_path
-        win_path = r"C:\Users\testuser\.weightslab-certs"
-        bash_path = _convert_to_git_bash_path(win_path)
-        self.assertEqual(bash_path, "/mnt/c/Users/testuser/.weightslab-certs")
-
-    def test_unix_path_passthrough(self):
-        from weightslab.ui_docker_bridge import _convert_to_git_bash_path
-        unix_path = "/home/testuser/.weightslab-certs"
-        self.assertEqual(_convert_to_git_bash_path(unix_path), unix_path)
-
-
-class TestSingleSourceOfTruth(unittest.TestCase):
-    """WEIGHTSLAB_CERTS_DIR is the only input; everything else derives from it."""
-
-    def test_strip_derived_deploy_env_removes_all(self):
-        sentinel = {k: "stale" for k in _DERIVED_DEPLOY_ENV_VARS}
-        with patch.dict(os.environ, sentinel, clear=False):
-            _strip_derived_deploy_env()
-            for key in _DERIVED_DEPLOY_ENV_VARS:
-                self.assertNotIn(key, os.environ, f"{key} should have been stripped")
-
-    @patch("weightslab.ui_docker_bridge._run_shell_script", return_value=0)
-    def test_generate_certs_forwards_certs_dir(self, mock_shell):
-        rc = _generate_certs_with_fallback(force_certs=False, certs_dir="/custom/certs")
-        self.assertEqual(rc, 0)
-        env_vars = mock_shell.call_args.args[2]
-        self.assertIsInstance(env_vars, dict)
-        self.assertIn("WEIGHTSLAB_CERTS_DIR", env_vars)
-        self.assertIn("custom/certs", env_vars["WEIGHTSLAB_CERTS_DIR"].replace("\\", "/"))
-
-    @patch("weightslab.ui_docker_bridge._run_shell_script", return_value=0)
-    def test_generate_certs_without_dir_passes_no_env(self, mock_shell):
-        _generate_certs_with_fallback(force_certs=False)
-        self.assertIsNone(mock_shell.call_args.args[2])
-
-    @patch("weightslab.ui_docker_bridge.CertAuthManager")
-    @patch("weightslab.ui_docker_bridge._get_bootstrap_script", return_value="/does/not/exist.sh")
-    @patch("weightslab.ui_docker_bridge._ensure_certificates", return_value=True)
-    @patch("weightslab.ui_docker_bridge._clean_stale_docker_resources")
-    @patch("weightslab.ui_docker_bridge._compose_cmd")
-    @patch("weightslab.ui_docker_bridge._check_docker")
-    @patch("weightslab.ui_docker_bridge._get_envoy_config", return_value="/fake/envoy.yaml")
-    @patch("weightslab.ui_docker_bridge._get_compose_file", return_value="/fake/docker-compose.yml")
-    def test_ui_launch_strips_derived_env(
-        self, _gc, _ge, _mock_check, _mock_compose, _mock_clean, _mock_ensure, _gb, mock_mgr,
-    ):
-        mock_mgr.from_env_or_default.return_value = MagicMock()
-        # Simulate a stale derived env var leaking in (e.g. from import-time check).
-        with patch.dict(os.environ, {"ENVOY_DOWNSTREAM_TLS": "on", "VITE_SERVER_PROTOCOL": "https"}, clear=False):
-            ui_launch(argparse.Namespace())
-            self.assertNotIn("ENVOY_DOWNSTREAM_TLS", os.environ)
-            self.assertNotIn("VITE_SERVER_PROTOCOL", os.environ)
-
-    @patch("weightslab.ui_docker_bridge.CertAuthManager")
-    @patch("weightslab.ui_docker_bridge._get_bootstrap_script", return_value="/does/not/exist.sh")
-    @patch("weightslab.ui_docker_bridge._ensure_certificates", return_value=True)
-    @patch("weightslab.ui_docker_bridge._clean_stale_docker_resources")
-    @patch("weightslab.ui_docker_bridge._compose_cmd")
-    @patch("weightslab.ui_docker_bridge._check_docker")
-    @patch("weightslab.ui_docker_bridge._get_envoy_config", return_value="/fake/envoy.yaml")
-    @patch("weightslab.ui_docker_bridge._get_compose_file", return_value="/fake/docker-compose.yml")
-    def test_ui_launch_url_https_only_when_certs_present(
-        self, _gc, _ge, _mock_check, _mock_compose, _mock_clean, _mock_ensure, _gb, mock_mgr,
-    ):
-        # Certs absent on disk -> URL must be http (default unsecured launch).
-        mgr = MagicMock()
-        mgr.has_valid_certs.return_value = False
-        mock_mgr.from_env_or_default.return_value = mgr
-        with patch.dict(os.environ, {}, clear=False):
-            os.environ.pop("VITE_PORT", None)
-            with self.assertLogs("weightslab.ui_docker_bridge", level="INFO") as log_context:
-                ui_launch(argparse.Namespace())
-        self.assertTrue(any("http://localhost:5173" in m for m in log_context.output))
-        self.assertFalse(any("https://localhost:5173" in m for m in log_context.output))
-
-
-class TestExampleStart(unittest.TestCase):
-    """`weightslab start example` runs the bundled classification example."""
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_example_start_runs_classification(self, mock_run):
-        mock_run.return_value = MagicMock(returncode=0)
-        with self.assertLogs("weightslab.ui_docker_bridge", level="INFO") as log_context:
-            example_start(argparse.Namespace())
-        mock_run.assert_called_once()
-        cmd = mock_run.call_args.args[0]
-        self.assertEqual(cmd[0], sys.executable)
-        main_py = cmd[1].replace("\\", "/")
-        self.assertTrue(main_py.endswith("examples/PyTorch/ws-classification/main.py"), main_py)
-        cwd = mock_run.call_args.kwargs["cwd"].replace("\\", "/")
-        self.assertTrue(cwd.endswith("examples/PyTorch/ws-classification"), cwd)
-        self.assertTrue(any("classification (cls) example" in m for m in log_context.output))
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_example_start_propagates_nonzero_exit(self, mock_run):
-        mock_run.return_value = MagicMock(returncode=3)
-        with self.assertRaises(SystemExit) as ctx:
-            example_start(argparse.Namespace())
-        self.assertEqual(ctx.exception.code, 3)
-
-    @patch("weightslab.ui_docker_bridge._get_example_dir", return_value=Path("/does/not/exist"))
-    def test_example_start_errors_when_missing(self, _mock_dir):
-        with self.assertRaises(SystemExit) as ctx:
-            example_start(argparse.Namespace())
-        self.assertEqual(ctx.exception.code, 1)
-
-    def test_example_dir_points_at_bundled_example(self):
-        # The bundled classification example must actually ship with the package.
-        self.assertTrue((_get_example_dir("ws-classification") / "main.py").exists())
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_example_start_seg_runs_segmentation(self, mock_run):
-        mock_run.return_value = MagicMock(returncode=0)
-        with self.assertLogs("weightslab.ui_docker_bridge", level="INFO") as log_context:
-            example_start(argparse.Namespace(example_kind="seg"))
-        main_py = mock_run.call_args.args[0][1].replace("\\", "/")
-        self.assertTrue(main_py.endswith("examples/PyTorch/ws-segmentation/main.py"), main_py)
-        self.assertTrue(any("segmentation (seg) example" in m for m in log_context.output))
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_example_start_defaults_to_cls_when_flag_absent(self, mock_run):
-        # A Namespace without example_kind (e.g. older call sites) still defaults to cls.
-        mock_run.return_value = MagicMock(returncode=0)
-        example_start(argparse.Namespace())
-        main_py = mock_run.call_args.args[0][1].replace("\\", "/")
-        self.assertTrue(main_py.endswith("examples/PyTorch/ws-classification/main.py"), main_py)
-
-
-class TestInstallExampleRequirements(unittest.TestCase):
-    """Requirements install is non-interactive and only runs when a file is present."""
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_installs_requirements_non_interactively_when_present(self, mock_run):
-        import tempfile
-        mock_run.return_value = MagicMock(returncode=0)
-        with tempfile.TemporaryDirectory() as tmp:
-            req = Path(tmp) / "requirements.txt"
-            req.write_text("numpy\n")
-            _install_example_requirements(Path(tmp))
-        mock_run.assert_called_once()
-        cmd = mock_run.call_args.args[0]
-        self.assertEqual(cmd[:5], [sys.executable, "-m", "pip", "install", "-r"])
-        self.assertIn("--no-input", cmd) # never prompts
-        self.assertTrue(mock_run.call_args.kwargs.get("check"))
-
-    @patch("weightslab.ui_docker_bridge.subprocess.run")
-    def test_skips_when_no_requirements_file(self, mock_run):
-        import tempfile
-        with tempfile.TemporaryDirectory() as tmp:
-            _install_example_requirements(Path(tmp))
-        mock_run.assert_not_called()
-
-
-class TestBannerAndHelp(unittest.TestCase):
-    """`weightslab`, `weightslab help`, `-h`, `--help` show banner + the command set."""
-
-    @staticmethod
-    def _capture_main(argv):
-        buf = io.StringIO()
-        with patch("sys.argv", argv):
-            with contextlib.redirect_stdout(buf):
-                try:
-                    main()
-                except SystemExit:
-                    pass # argparse -h/--help exits 0
-        return buf.getvalue()
-
-    def test_dash_h_shows_banner_and_command_reference(self):
-        out = self._capture_main(["weightslab", "-h"])
-        self.assertIn("WeightsLab", out) # tagline from description
-        self.assertIn("ui launch", out)
-        self.assertIn("--certs", out)
-        self.assertIn("start example", out)
-
-    def test_long_help_flag_shows_command_reference(self):
-        out = self._capture_main(["weightslab", "--help"])
-        self.assertIn("ui launch", out)
-        self.assertIn("--force-certs", out)
-
-    def test_help_subcommand_shows_command_reference(self):
-        out = self._capture_main(["weightslab", "help"])
-        self.assertIn("se", out)
-        self.assertIn("ui launch", out)
-        self.assertIn("start example", out)
-
-    def test_no_args_shows_command_reference(self):
-        out = self._capture_main(["weightslab"])
-        self.assertIn("ui launch", out)
-
-    def test_help_does_not_mention_removed_commands(self):
-        out = self._capture_main(["weightslab", "help"])
-        for removed in ("ui stop", "ui drop", "ui docker", "--no-auth", "--no-clean"):
-            self.assertNotIn(removed, out, f"help should not mention removed '{removed}'")
-
-
-class TestLaunchCliFlags(unittest.TestCase):
-    """`ui launch` accepts only --certs (TLS is opt-in; default unsecured)."""
-
-    @patch("weightslab.ui_docker_bridge.ui_launch")
-    def test_launch_certs_flag_parsed(self, mock_launch):
-        with patch("sys.argv", ["weightslab", "ui", "launch", "--certs"]):
-            main()
-        mock_launch.assert_called_once()
-        self.assertTrue(mock_launch.call_args.args[0].certs)
-
-    @patch("weightslab.ui_docker_bridge.ui_launch")
-    def test_launch_default_certs_false(self, mock_launch):
-        with patch("sys.argv", ["weightslab", "ui", "launch"]):
-            main()
-        self.assertFalse(mock_launch.call_args.args[0].certs)
-
-    def test_launch_removed_flags_are_rejected(self):
-        for flag in ("--no-auth", "--force-certs", "--no-clean", "--dev"):
-            with patch("sys.argv", ["weightslab", "ui", "launch", flag]):
-                with self.assertRaises(SystemExit) as ctx:
-                    main()
-                self.assertNotEqual(ctx.exception.code, 0, flag)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/backend/test_write_dataframe.py b/tests/backend/test_write_dataframe.py
index 163eec03..5582623c 100644
--- a/tests/backend/test_write_dataframe.py
+++ b/tests/backend/test_write_dataframe.py
@@ -48,6 +48,19 @@ def _call(path, manager, **kwargs):
         return write_dataframe(path, **kwargs)
 
 
+def _records(path):
+    """Read a JSON export back into a list of row records.
+
+    write_dataframe defaults to ``orient="columns"`` (``{column: {row: value}}``)
+    — compact (~6x smaller than records for wide sparse tables) and round-trips
+    with ``pd.read_json``'s default orient. These tests assert row-level
+    behavior, so normalize the columnar layout back to records here. Empty
+    exports (``{}`` / ``{"index": {}}``) collapse to ``[]``.
+    """
+    raw = json.loads(open(path, encoding="utf-8").read())
+    return pd.DataFrame(raw).to_dict(orient="records")
+
+
 # ---------------------------------------------------------------------------
 # Fixtures
 # ---------------------------------------------------------------------------
@@ -89,20 +102,22 @@ def test_flush_failure_does_not_abort(self, tmp_json):
 # ---------------------------------------------------------------------------
 
 class TestWriteDataframeJsonStructure:
-    def test_json_is_list_of_records(self, mgr, tmp_json):
+    def test_json_is_columnar_dict(self, mgr, tmp_json):
         _call(tmp_json, mgr)
-        data = json.loads(open(tmp_json).read())
-        assert isinstance(data, list)
+        raw = json.loads(open(tmp_json).read())
+        # Default orient="columns": {column: {row_index: value}}
+        assert isinstance(raw, dict)
+        assert isinstance(raw["sample_id"], dict)
 
     def test_json_includes_index_as_columns(self, mgr, tmp_json):
         _call(tmp_json, mgr)
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert "sample_id" in data[0]
         assert "annotation_id" in data[0]
 
     def test_json_all_rows_present_by_default(self, mgr, tmp_json):
         _call(tmp_json, mgr)
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert len(data) == 4
 
     def test_returns_written_path(self, mgr, tmp_json):
@@ -207,30 +222,30 @@ def test_returns_path_when_no_manager(self, tmp_json):
 class TestWriteDataframeColumnFilters:
     def test_columns_all_keeps_everything(self, mgr, tmp_json):
         _call(tmp_json, mgr, columns="all")
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert "signals_loss" in data[0] or any("signals_loss" in r for r in data)
 
     def test_columns_tags_only(self, mgr, tmp_json):
         _call(tmp_json, mgr, columns="tags")
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         non_index = [k for k in data[0] if k not in ("sample_id", "annotation_id")]
         assert all(k.startswith("tag:") or k.startswith("TAG:") for k in non_index)
 
     def test_columns_signals_only(self, mgr, tmp_json):
         _call(tmp_json, mgr, columns="signals")
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         non_index = [k for k in data[0] if k not in ("sample_id", "annotation_id")]
         assert all(str(k).lower().startswith("signals") for k in non_index)
 
     def test_columns_discarded_only(self, mgr, tmp_json):
         _call(tmp_json, mgr, columns="discarded")
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         non_index = [k for k in data[0] if k not in ("sample_id", "annotation_id")]
         assert non_index == ["discarded"]
 
     def test_columns_list_of_groups(self, mgr, tmp_json):
         _call(tmp_json, mgr, columns=["tags", "discarded"])
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         non_index = set(k for r in data for k in r if k not in ("sample_id", "annotation_id"))
         assert "discarded" in non_index
         assert any(k.startswith("tag:") for k in non_index)
@@ -238,20 +253,20 @@ def test_columns_list_of_groups(self, mgr, tmp_json):
 
     def test_columns_exact_name(self, mgr, tmp_json):
         _call(tmp_json, mgr, columns=["signals_loss"])
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         non_index = [k for k in data[0] if k not in ("sample_id", "annotation_id")]
         assert non_index == ["signals_loss"]
 
     def test_columns_nonexistent_name_yields_empty_cols(self, mgr, tmp_json):
         _call(tmp_json, mgr, columns=["nonexistent_col"])
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         # Index columns always present; no extra columns
         for row in data:
             assert set(row.keys()) <= {"sample_id", "annotation_id"}
 
     def test_columns_none_keeps_all(self, mgr, tmp_json):
         _call(tmp_json, mgr, columns=None)
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert "signals_loss" in data[0] or any("signals_loss" in r for r in data)
 
 
@@ -262,38 +277,38 @@ def test_columns_none_keeps_all(self, mgr, tmp_json):
 class TestWriteDataframeIndexFilters:
     def test_sample_id_single(self, mgr, tmp_json):
         _call(tmp_json, mgr, sample_id="s1")
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert all(r["sample_id"] == "s1" for r in data)
         assert len(data) == 2 # s1 has annotation_ids 0 and 1
 
     def test_sample_id_list(self, mgr, tmp_json):
         _call(tmp_json, mgr, sample_id=["s1", "s2"])
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         sids = {r["sample_id"] for r in data}
         assert sids == {"s1", "s2"}
         assert len(data) == 4
 
     def test_instance_id_zero_keeps_sample_rows(self, mgr, tmp_json):
         _call(tmp_json, mgr, instance_id=0)
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert all(r["annotation_id"] == 0 for r in data)
         assert len(data) == 2 # s1 and s2 both have annotation_id=0
 
     def test_instance_id_list(self, mgr, tmp_json):
         _call(tmp_json, mgr, instance_id=[1, 2])
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert all(r["annotation_id"] in (1, 2) for r in data)
 
     def test_sample_and_instance_combined(self, mgr, tmp_json):
         _call(tmp_json, mgr, sample_id="s1", instance_id=1)
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert len(data) == 1
         assert data[0]["sample_id"] == "s1"
         assert data[0]["annotation_id"] == 1
 
     def test_empty_result_when_no_match(self, mgr, tmp_json):
         _call(tmp_json, mgr, sample_id="nonexistent")
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert data == []
 
 
@@ -305,7 +320,7 @@ class TestWriteDataframeEmpty:
     def test_empty_df_writes_empty_json(self, tmp_json):
         mgr = _make_manager(df=pd.DataFrame())
         _call(tmp_json, mgr)
-        data = json.loads(open(tmp_json).read())
+        data = _records(tmp_json)
         assert data == []
 
     def test_empty_df_writes_empty_csv(self, tmp_csv):
diff --git a/tests/backend/test_write_history.py b/tests/backend/test_write_history.py
index 3608fc70..be08367b 100644
--- a/tests/backend/test_write_history.py
+++ b/tests/backend/test_write_history.py
@@ -4,6 +4,7 @@
 import os
 from unittest.mock import MagicMock, patch
 
+import pandas as pd
 import pytest
 
 from weightslab.backend.logger import LoggerQueue
@@ -54,6 +55,24 @@ def _call(path, lg_instance, **kwargs):
         return write_history(path, **kwargs)
 
 
+def _load(path):
+    """Read a write_history JSON export back into the row-records shape
+    ({"global": [...], "sample": [...], "instance": [...]}) regardless of
+    which `orient` it was written with.
+
+    write_history defaults to ``orient="columns"`` per section
+    (``{column: {row_index: value}}``) — compact, since it writes each
+    column name once per section instead of once per row. These tests
+    assert row-level behavior, so normalize back to records here.
+    """
+    raw = json.loads(open(path, encoding="utf-8").read())
+    return {
+        section: payload if isinstance(payload, list)
+        else pd.DataFrame(payload).to_dict(orient="records")
+        for section, payload in raw.items()
+    }
+
+
 # ---------------------------------------------------------------------------
 # Fixtures
 # ---------------------------------------------------------------------------
@@ -80,44 +99,79 @@ def tmp_csv(tmp_path):
 class TestWriteHistoryJsonStructure:
     def test_json_has_all_sections(self, lg, tmp_json):
         _call(tmp_json, lg)
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert set(data.keys()) == {"global", "sample", "instance"}
 
     def test_global_section_not_empty(self, lg, tmp_json):
         _call(tmp_json, lg)
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert len(data["global"]) > 0
 
     def test_sample_section_not_empty(self, lg, tmp_json):
         _call(tmp_json, lg)
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert len(data["sample"]) > 0
 
     def test_instance_section_not_empty(self, lg, tmp_json):
         _call(tmp_json, lg)
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert len(data["instance"]) > 0
 
     def test_global_row_keys(self, lg, tmp_json):
         _call(tmp_json, lg)
-        row = json.loads(open(tmp_json).read())["global"][0]
+        row = _load(tmp_json)["global"][0]
         assert {"graph_name", "experiment_hash", "step", "metric_value"} <= set(row.keys())
 
     def test_sample_row_keys(self, lg, tmp_json):
         _call(tmp_json, lg)
-        row = json.loads(open(tmp_json).read())["sample"][0]
+        row = _load(tmp_json)["sample"][0]
         assert {"graph_name", "experiment_hash", "sample_id", "step", "metric_value"} <= set(row.keys())
 
     def test_instance_row_keys(self, lg, tmp_json):
         _call(tmp_json, lg)
-        row = json.loads(open(tmp_json).read())["instance"][0]
+        row = _load(tmp_json)["instance"][0]
         assert {
             "graph_name", "experiment_hash", "sample_id", "annotation_id", "step", "metric_value"
         } <= set(row.keys())
 
+    def test_default_orient_is_columnar(self, lg, tmp_json):
+        """Default orient="columns": each section is {column: {row_index: value}},
+        not a list of row dicts — this is the whole point (one column-name
+        string per section instead of one per row)."""
+        _call(tmp_json, lg)
+        raw = json.loads(open(tmp_json, encoding="utf-8").read())
+        assert isinstance(raw["global"], dict)
+        assert isinstance(raw["global"]["graph_name"], dict)
+        assert isinstance(raw["sample"], dict)
+        assert isinstance(raw["instance"], dict)
+
+    def test_orient_records_reproduces_the_row_list_shape(self, lg, tmp_json):
+        _call(tmp_json, lg, orient="records")
+        raw = json.loads(open(tmp_json, encoding="utf-8").read())
+        assert isinstance(raw["global"], list)
+        assert {"graph_name", "experiment_hash", "step", "metric_value"} <= set(raw["global"][0].keys())
+
+    def test_columnar_default_is_smaller_than_records_for_many_rows(self, tmp_path):
+        # Enough rows that the per-row column-name repetition in orient="records"
+        # dominates file size — the case write_history's history logs are
+        # actually shaped like (few columns, many steps).
+        many_lg = LoggerQueue(register=False)
+        many_lg.chkpt_manager = _mock_chkpt("h1")
+        for step in range(1, 201):
+            many_lg.add_scalars("loss", {"loss": float(step)}, step,
+                                signal_per_sample=None, aggregate_by_step=False)
+
+        cols_path = str(tmp_path / "cols.json")
+        recs_path = str(tmp_path / "recs.json")
+        with patch("weightslab.src.get_logger", return_value=many_lg):
+            write_history(cols_path, type_of_history="global")
+            write_history(recs_path, type_of_history="global", orient="records")
+
+        assert os.path.getsize(cols_path) < os.path.getsize(recs_path)
+
     def test_file_is_valid_json(self, lg, tmp_json):
         _call(tmp_json, lg)
-        json.loads(open(tmp_json).read()) # must not raise
+        _load(tmp_json) # must not raise
 
     def test_output_file_created(self, lg, tmp_json):
         _call(tmp_json, lg)
@@ -131,38 +185,38 @@ def test_output_file_created(self, lg, tmp_json):
 class TestWriteHistoryJsonTypeFilter:
     def test_type_all_explicit(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="all")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert set(data.keys()) == {"global", "sample", "instance"}
 
     def test_type_none_means_all(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history=None)
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert set(data.keys()) == {"global", "sample", "instance"}
 
     def test_type_global_only(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert set(data.keys()) == {"global"}
         assert "sample" not in data
         assert "instance" not in data
 
     def test_type_sample_only(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="sample")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert set(data.keys()) == {"sample"}
         assert "global" not in data
         assert "instance" not in data
 
     def test_type_instance_only(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="instance")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert set(data.keys()) == {"instance"}
         assert "global" not in data
         assert "sample" not in data
 
     def test_type_instances_plural_alias(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="instances")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert set(data.keys()) == {"instance"}
 
 
@@ -173,32 +227,32 @@ def test_type_instances_plural_alias(self, lg, tmp_json):
 class TestWriteHistoryJsonGraphFilter:
     def test_graph_name_filters_global(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", graph_name="loss")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["graph_name"] == "loss" for r in data["global"])
 
     def test_graph_name_filters_sample(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="sample", graph_name="acc")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["graph_name"] == "acc" for r in data["sample"])
 
     def test_graph_name_filters_instance(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="instance", graph_name="iou")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["graph_name"] == "iou" for r in data["instance"])
 
     def test_unknown_graph_name_global_empty(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", graph_name="no_such_graph")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert data["global"] == []
 
     def test_unknown_graph_name_sample_empty(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="sample", graph_name="no_such_graph")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert data["sample"] == []
 
     def test_multiple_graphs_all_present_without_filter(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         graph_names = {r["graph_name"] for r in data["global"]}
         assert "loss" in graph_names and "acc" in graph_names
 
@@ -210,28 +264,28 @@ def test_multiple_graphs_all_present_without_filter(self, lg, tmp_json):
 class TestWriteHistoryJsonHashFilter:
     def test_exp_hash_filters_global(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", experiment_hash="h1")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["experiment_hash"] == "h1" for r in data["global"])
 
     def test_exp_hash_filters_sample(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="sample", experiment_hash="h2")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["experiment_hash"] == "h2" for r in data["sample"])
         assert len(data["sample"]) > 0
 
     def test_exp_hash_missing_global_empty(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", experiment_hash="hX")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert data["global"] == []
 
     def test_exp_hash_filters_instance(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="instance", experiment_hash="h1")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["experiment_hash"] == "h1" for r in data["instance"])
 
     def test_both_hashes_present_with_all(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", graph_name="loss", experiment_hash="all")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         hashes = {r["experiment_hash"] for r in data["global"]}
         assert "h1" in hashes and "h2" in hashes
 
@@ -243,24 +297,24 @@ def test_both_hashes_present_with_all(self, lg, tmp_json):
 class TestWriteHistoryJsonSampleFilter:
     def test_sample_id_filters_sample_history(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="sample", graph_name="loss", sample_id="s1")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["sample_id"] == "s1" for r in data["sample"])
 
     def test_sample_id_filters_instance_history(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="instance", sample_id="s1")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["sample_id"] == "s1" for r in data["instance"])
 
     def test_instance_id_filters_instance_history(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="instance", instance_id=1)
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["annotation_id"] == 1 for r in data["instance"])
 
     def test_sample_and_instance_id_combined(self, lg, tmp_json):
         # annotation_id=1 lives under h1
         _call(tmp_json, lg, type_of_history="instance", sample_id="s1",
               instance_id=1, experiment_hash="h1")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert len(data["instance"]) == 1
         assert data["instance"][0]["sample_id"] == "s1"
         assert data["instance"][0]["annotation_id"] == 1
@@ -268,7 +322,7 @@ def test_sample_and_instance_id_combined(self, lg, tmp_json):
     def test_sample_id_no_effect_on_global(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", graph_name="loss",
               sample_id="s1", experiment_hash="all")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         hashes = {r["experiment_hash"] for r in data["global"]}
         assert "h1" in hashes and "h2" in hashes
 
@@ -280,14 +334,14 @@ def test_sample_id_no_effect_on_global(self, lg, tmp_json):
 class TestWriteHistoryJsonValues:
     def test_global_steps_present(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", graph_name="loss", experiment_hash="h1")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         steps = {r["step"] for r in data["global"]}
         assert 1 in steps and 2 in steps
 
     def test_sample_value_correct(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="sample", graph_name="loss",
               experiment_hash="h1", sample_id="s1")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert len(data["sample"]) == 1
         assert abs(data["sample"][0]["metric_value"] - 1.0) < 1e-5
 
@@ -295,12 +349,12 @@ def test_instance_value_correct(self, lg, tmp_json):
         # value 0.8 for annotation_id=1 is stored under h1
         _call(tmp_json, lg, type_of_history="instance", graph_name="iou",
               sample_id="s1", instance_id=1, experiment_hash="h1")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert abs(data["instance"][0]["metric_value"] - 0.8) < 1e-4
 
     def test_combined_graph_hash_filter(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", graph_name="loss", experiment_hash="h2")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         for row in data["global"]:
             assert row["graph_name"] == "loss"
             assert row["experiment_hash"] == "h2"
@@ -383,7 +437,7 @@ def test_empty_logger_writes_empty_sections_json(self, tmp_json):
         empty_lg.chkpt_manager = None
         with patch("weightslab.src.get_logger", return_value=empty_lg):
             write_history(tmp_json, experiment_hash="all")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert data["global"] == []
         assert data["sample"] == []
         assert data["instance"] == []
@@ -399,12 +453,12 @@ def test_empty_logger_writes_header_only_csv(self, tmp_csv):
 
     def test_case_insensitive_type(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="SAMPLE")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert set(data.keys()) == {"sample"}
 
     def test_case_insensitive_format(self, lg, tmp_json):
         _call(tmp_json, lg, format="JSON")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert "global" in data
 
     def test_returns_written_path(self, lg, tmp_json):
@@ -455,14 +509,14 @@ def test_directory_path_csv_format(self, lg, tmp_path):
 
     def test_graph_name_list_filters_global(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", graph_name=["loss", "acc"])
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["graph_name"] in {"loss", "acc"} for r in data["global"])
         names = {r["graph_name"] for r in data["global"]}
         assert "loss" in names and "acc" in names
 
     def test_graph_name_list_filters_sample(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="sample", graph_name=["loss", "acc"])
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         names = {r["graph_name"] for r in data["sample"]}
         assert "loss" in names and "acc" in names
 
@@ -470,28 +524,28 @@ def test_sample_id_list_filters_sample(self, lg, tmp_json):
         # s2 for "loss" is under h1 only; use "all" to see both hashes
         _call(tmp_json, lg, type_of_history="sample", graph_name="loss",
               sample_id=["s1", "s2"], experiment_hash="all")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         sids = {r["sample_id"] for r in data["sample"]}
         assert sids == {"s1", "s2"}
 
     def test_sample_id_list_filters_instance(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="instance", sample_id=["s1", "s2"],
               experiment_hash="all")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         sids = {r["sample_id"] for r in data["instance"]}
         assert sids == {"s1", "s2"}
 
     def test_instance_id_list_filters_instance(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="instance", instance_id=[1, 2],
               experiment_hash="all")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         aids = {r["annotation_id"] for r in data["instance"]}
         assert aids == {1, 2}
 
     def test_instance_id_list_single_value(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="instance", instance_id=[1],
               experiment_hash="all")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         assert all(r["annotation_id"] == 1 for r in data["instance"])
 
     # -- new: experiment_hash default / "all" behavior --
@@ -499,14 +553,14 @@ def test_instance_id_list_single_value(self, lg, tmp_json):
     def test_default_hash_uses_current(self, lg, tmp_json):
         # current hash is h2; without specifying, only h2 rows returned
         _call(tmp_json, lg, type_of_history="global", graph_name="loss")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         hashes = {r["experiment_hash"] for r in data["global"]}
         assert hashes == {"h2"}
 
     def test_experiment_hash_all_returns_every_hash(self, lg, tmp_json):
         _call(tmp_json, lg, type_of_history="global", graph_name="loss",
               experiment_hash="all")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         hashes = {r["experiment_hash"] for r in data["global"]}
         assert "h1" in hashes and "h2" in hashes
 
@@ -514,7 +568,7 @@ def test_explicit_hash_overrides_current(self, lg, tmp_json):
         # pass h1 explicitly even though current is h2
         _call(tmp_json, lg, type_of_history="global", graph_name="loss",
               experiment_hash="h1")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         hashes = {r["experiment_hash"] for r in data["global"]}
         assert hashes == {"h1"}
 
@@ -526,7 +580,7 @@ def test_default_hash_no_chkpt_manager_returns_none_key(self, tmp_json):
                        signal_per_sample=None, aggregate_by_step=False)
         with patch("weightslab.src.get_logger", return_value=lg):
             write_history(tmp_json, type_of_history="global")
-        data = json.loads(open(tmp_json).read())
+        data = _load(tmp_json)
         # row exists (experiment_hash stored as "")
         assert len(data["global"]) == 1
         assert data["global"][0]["experiment_hash"] == ""
diff --git a/tests/components/test_checkpoint_workflow.py b/tests/components/test_checkpoint_workflow.py
index 671676ca..6bbef8e9 100644
--- a/tests/components/test_checkpoint_workflow.py
+++ b/tests/components/test_checkpoint_workflow.py
@@ -308,7 +308,7 @@ def setUpClass(cls):
         cls.temp_dir = tempfile.mkdtemp(prefix="checkpoint_v3_test_")
         cls.log_dir = os.path.join(cls.temp_dir, "experiments")
 
-        # Initialize config from YAML-like dict (similar to ws-classification)
+        # Initialize config from YAML-like dict (similar to wl-classification)
         cls.config = {
             'experiment_name': EXP_NAME,
             'device': DEVICE,
@@ -1298,33 +1298,15 @@ def test_logger_queue_saved_with_weights(self):
         self.chkpt_manager.update_experiment_hash(force=False, dump_immediately=False)
         self.chkpt_manager.save_pending_changes(force=True)
 
-        snapshot_dir = Path(self.chkpt_manager.loggers_dir)
-        manifest_path = snapshot_dir / "loggers.manifest.json"
-        legacy_snapshot_path = snapshot_dir / "loggers.json"
-        self.assertTrue(
-            manifest_path.exists() or legacy_snapshot_path.exists(),
-            "Logger snapshot should be saved with checkpoint"
-        )
-
-        if manifest_path.exists():
-            with open(manifest_path, "r") as f:
-                manifest = json.load(f)
-            chunk_files = manifest.get("chunks", [])
-            self.assertGreaterEqual(len(chunk_files), 1, "Chunked logger snapshot should contain at least one chunk")
-            self.assertTrue(
-                all((snapshot_dir / chunk_name).exists() for chunk_name in chunk_files),
-                "All logger snapshot chunks referenced by manifest should exist"
-            )
-            self.assertTrue(self.chkpt_manager.load_logger_snapshot())
-            lg = ledgers.get_logger('main')
-            loggers = {'main': {'signal_history': lg.get_signal_history() if hasattr(lg, 'get_signal_history') else []}}
-        else:
-            with open(legacy_snapshot_path, "r") as f:
-                snapshot = json.load(f)
-            loggers = snapshot.get("loggers", {})
+        # Logger history is persisted to an on-disk DuckDB file alongside the
+        # checkpoint (the chunked-zstd snapshot was replaced by DuckDB in #257).
+        db_path = Path(self.chkpt_manager.get_logger_db_path())
+        self.assertTrue(db_path.exists(), "Logger DuckDB should be saved with checkpoint")
 
-        self.assertIn('main', loggers, "Logger entry should be present")
-        signals = loggers['main'].get("signal_history", [])
+        # The registered logger carries the persisted signal history.
+        lg = ledgers.get_logger('main')
+        self.assertIsNotNone(lg, "Logger entry should be present")
+        signals = lg.get_signal_history() if hasattr(lg, 'get_signal_history') else []
         if isinstance(signals, dict):
             total_signals = 0
             for experiments in signals.values():
diff --git a/tests/gRPC/test_grpc_user_actions.py b/tests/gRPC/test_grpc_user_actions.py
index 1b71da74..530d8e7d 100644
--- a/tests/gRPC/test_grpc_user_actions.py
+++ b/tests/gRPC/test_grpc_user_actions.py
@@ -61,7 +61,7 @@ def get_combined_df(self):
     def get_df_view(self):
         return self.df.reset_index().copy()\
 
-    def get_collapse_annotations_to_samples_df(self):
+    def get_collapse_annotations_to_samples_df(self, df=None):
         # This fixture is already one row per sample (no instance rows), so the
         # per-sample collapsed view is just the frame with its index restored to
         # (origin, sample_id) columns — matching what the real manager returns.
diff --git a/tests/general/test_cli.py b/tests/general/test_cli.py
index 64347b72..a63801dc 100644
--- a/tests/general/test_cli.py
+++ b/tests/general/test_cli.py
@@ -8,6 +8,7 @@
 import time
 import json
 import tempfile
+import argparse
 from pathlib import Path
 from unittest.mock import MagicMock
 
@@ -23,6 +24,7 @@
     cli_serve,
 )
 import weightslab.backend.cli as cli_backend
+import weightslab.cli as wl_cli
 from weightslab.backend.ledgers import GLOBAL_LEDGER, Proxy
 
 
@@ -518,6 +520,56 @@ def test_cli_serve_writes_discovery_file(self):
             cli_backend._server_thread = None
 
 
+class TestUIStartPortResolution(unittest.TestCase):
+    """Tests for UI port resolution used by `weightslab start`."""
+
+    def setUp(self):
+        self._saved_env = {
+            k: os.environ.get(k)
+            for k in ("WL_LAST_UI_PORT", "WEIGHTSLAB_UI_PORT", "WEIGHTSLAB_EXPERIMENT_CONFIG")
+        }
+        for key in self._saved_env:
+            os.environ.pop(key, None)
+
+    def tearDown(self):
+        for key, value in self._saved_env.items():
+            if value is None:
+                os.environ.pop(key, None)
+            else:
+                os.environ[key] = value
+
+    def test_explicit_port_wins(self):
+        args = argparse.Namespace(port=61234, config=None)
+        port, source = wl_cli._resolve_ui_port(args)
+        self.assertEqual(port, 61234)
+        self.assertEqual(source, "--port")
+
+    def test_config_port_wins_over_env(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            cfg = Path(tmpdir) / "config.yaml"
+            cfg.write_text("ui_port: 61235\n", encoding="utf-8")
+            os.environ["WL_LAST_UI_PORT"] = "62000"
+            args = argparse.Namespace(port=None, config=str(cfg))
+
+            port, source = wl_cli._resolve_ui_port(args)
+            self.assertEqual(port, 61235)
+            self.assertEqual(source, "experiment config")
+
+    def test_last_ui_port_env_fallback(self):
+        os.environ["WL_LAST_UI_PORT"] = "61236"
+        args = argparse.Namespace(port=None, config=None)
+
+        port, source = wl_cli._resolve_ui_port(args)
+        self.assertEqual(port, 61236)
+        self.assertEqual(source, "WL_LAST_UI_PORT")
+
+    def test_default_is_50051(self):
+        args = argparse.Namespace(port=None, config=None)
+        port, source = wl_cli._resolve_ui_port(args)
+        self.assertEqual(port, 50051)
+        self.assertEqual(source, "default")
+
+
 def run_tests():
     """Run all CLI tests."""
     # Create test suite
@@ -529,6 +581,7 @@ def run_tests():
     suite.addTests(loader.loadTestsFromTestCase(TestCLICommands))
     suite.addTests(loader.loadTestsFromTestCase(TestCLIServer))
     suite.addTests(loader.loadTestsFromTestCase(TestCLIEndpointDiscovery))
+    suite.addTests(loader.loadTestsFromTestCase(TestUIStartPortResolution))
     # suite.addTests(loader.loadTestsFromTestCase(TestCLIIntegration))
 
     # Run tests
diff --git a/tests/general/test_logger_snapshot_rotation.py b/tests/general/test_logger_snapshot_rotation.py
deleted file mode 100644
index f0a0001e..00000000
--- a/tests/general/test_logger_snapshot_rotation.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import json
-import tempfile
-import unittest
-from pathlib import Path
-
-from weightslab.backend import ledgers
-from weightslab.components.checkpoint_manager import CheckpointManager
-from weightslab.backend.logger import LoggerQueue
-
-
-class LoggerSnapshotRotationTests(unittest.TestCase):
-    def setUp(self):
-        ledgers.clear_all()
-
-    def tearDown(self):
-        ledgers.clear_all()
-
-    def test_save_logger_snapshot_writes_chunked_zstd_and_can_reload(self):
-        with tempfile.TemporaryDirectory(prefix="weightslab_logger_snapshot_") as temp_dir:
-            manager = CheckpointManager(root_log_dir=temp_dir, load_model=False, load_config=False, load_data=False)
-            exp_hash = "12345678abcdef0199aabbcc"
-            manager.current_exp_hash = exp_hash
-
-            logger_queue = LoggerQueue(register=False)
-            logger_queue.load_snapshot(
-                {
-                    "graph_names": ["train/loss"],
-                    "signal_history": [
-                        {
-                            "experiment_name": "train/loss",
-                            "model_age": 1,
-                            "metric_name": "train/loss",
-                            "metric_value": 0.42,
-                            "experiment_hash": exp_hash,
-                        }
-                    ],
-                    "signal_history_per_sample": {},
-                }
-            )
-            ledgers.register_logger(logger_queue, name="main")
-
-            saved_path = manager.save_logger_snapshot()
-            self.assertIsNotNone(saved_path, "save_logger_snapshot should return a saved path")
-            self.assertTrue(saved_path.exists(), "Logger snapshot manifest should exist")
-
-            snapshot_dir = Path(manager.loggers_dir)
-            manifest_path = snapshot_dir / "loggers.manifest.json"
-            self.assertTrue(manifest_path.exists(), "Manifest should be written for chunked snapshot format")
-
-            with open(manifest_path, "r", encoding="utf-8") as f:
-                manifest = json.load(f)
-            chunk_names = manifest.get("chunks", [])
-            self.assertGreaterEqual(len(chunk_names), 1, "At least one compressed chunk should be created")
-            for chunk_name in chunk_names:
-                self.assertTrue((snapshot_dir / chunk_name).exists(), f"Missing expected chunk file: {chunk_name}")
-
-            ledgers.clear_all()
-            loaded = manager.load_logger_snapshot()
-            self.assertTrue(loaded, "Chunked snapshot should load successfully")
-            restored_logger = ledgers.get_logger("main")
-            self.assertTrue(hasattr(restored_logger, "get_signal_history"), "Restored logger should support signal history")
-            self.assertGreaterEqual(len(restored_logger.get_signal_history()), 1, "Restored logger should contain signals")
-
-    def test_load_logger_snapshot_supports_legacy_json(self):
-        with tempfile.TemporaryDirectory(prefix="weightslab_logger_snapshot_legacy_") as temp_dir:
-            manager = CheckpointManager(root_log_dir=temp_dir, load_model=False, load_config=False, load_data=False)
-            exp_hash = "abcdef0199aabbcc12345678"
-            manager.current_exp_hash = exp_hash
-
-            snapshot_dir = Path(manager.loggers_dir)
-            snapshot_dir.mkdir(parents=True, exist_ok=True)
-            legacy_payload = {
-                "exp_hash": exp_hash,
-                "timestamp": "2026-02-24T00:00:00",
-                "loggers": {
-                    "main": {
-                        "graph_names": ["train/acc"],
-                        "signal_history": [
-                            {
-                                "experiment_name": "train/acc",
-                                "model_age": 2,
-                                "metric_name": "train/acc",
-                                "metric_value": 0.9,
-                                "experiment_hash": exp_hash,
-                            }
-                        ],
-                        "signal_history_per_sample": {},
-                    }
-                },
-            }
-            with open(snapshot_dir / "loggers.json", "w", encoding="utf-8") as f:
-                json.dump(legacy_payload, f)
-
-            loaded = manager.load_logger_snapshot()
-            self.assertTrue(loaded, "Legacy JSON logger snapshot should still load")
-            restored_logger = ledgers.get_logger("main")
-            self.assertTrue(hasattr(restored_logger, "get_signal_history"), "Restored logger should support signal history")
-            self.assertGreaterEqual(len(restored_logger.get_signal_history()), 1, "Legacy snapshot should restore signals")
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/general/test_signal_refinements.py b/tests/general/test_signal_refinements.py
new file mode 100644
index 00000000..681c9970
--- /dev/null
+++ b/tests/general/test_signal_refinements.py
@@ -0,0 +1,163 @@
+"""Unit tests for the signal-refinements changes: per-sample query cache,
+value-at-step read + staging fast path, cycle detection, ctx.logits, freshness,
+and the reactive-derived gather skip."""
+import unittest
+
+import numpy as np
+import torch
+
+import weightslab as wl
+from weightslab.backend.logger import LoggerQueue
+from weightslab.src import (
+    _REGISTERED_SIGNALS, _detect_signal_cycles, _gather_inputs_fresh,
+    BatchSignalContext, SignalContext, StaleSignalError,
+)
+
+
+def _lg():
+    return LoggerQueue(register=False)
+
+
+class TestQueryCache(unittest.TestCase):
+    def test_cached_and_fresh_copy(self):
+        lg = _lg()
+        lg._stage_sample_row("m", "h", "1", 0, 1.5)
+        lg._stage_sample_row("m", "h", "2", 0, 2.5)
+        r1 = lg.query_per_sample("m", sample_ids=["1", "2"])
+        r2 = lg.query_per_sample("m", sample_ids=["1", "2"])
+        self.assertEqual(len(r1), 2)
+        self.assertIsNot(r1, r2)                       # fresh list each call
+        self.assertGreaterEqual(lg._qps_cache.cache_info().hits, 1)  # 2nd read hit
+
+    def test_invalidated_on_write(self):
+        lg = _lg()
+        lg._stage_sample_row("m", "h", "1", 0, 1.0)
+        self.assertEqual(len(lg.query_per_sample("m", sample_ids=["1"])), 1)
+        lg._stage_sample_row("m", "h", "1", 1, 9.0)    # new row bumps version
+        self.assertEqual(len(lg.query_per_sample("m", sample_ids=["1"])), 2)
+
+    def test_step_scoped_clear(self):
+        lg = _lg()
+        lg._stage_sample_row("m", "h", "1", 0, 1.0)
+        lg.query_per_sample("m", sample_ids=["1"])
+        lg._stage_sample_row("m", "h", "2", 1, 2.0)    # step advance -> clear
+        self.assertEqual(lg._qps_cache_step, 1)
+
+
+class TestValueAtStep(unittest.TestCase):
+    def test_staging_fast_path(self):
+        lg = _lg()
+        lg._stage_sample_row("m", "h", "5", 3, 4.0)    # staged, not flushed
+        at = dict((int(s), v) for s, v in lg.query_per_sample_at_step("m", [5], 3))
+        self.assertEqual(at, {5: 4.0})
+
+    def test_absent_at_other_step(self):
+        lg = _lg()
+        lg._stage_sample_row("m", "h", "5", 3, 4.0)
+        self.assertEqual(lg.query_per_sample_at_step("m", [5], 99), [])
+
+
+class TestCycleDetection(unittest.TestCase):
+    def setUp(self): _REGISTERED_SIGNALS.clear()
+    def tearDown(self): _REGISTERED_SIGNALS.clear()
+
+    def test_self_loop_and_two_cycle(self):
+        @wl.signal(name="sig/self", inputs=["sig/self"], batched=True)
+        def _s(b): return b
+        @wl.signal(name="sig/X", inputs=["sig/Y"], batched=True)
+        def _x(b): return b
+        @wl.signal(name="sig/Y", inputs=["sig/X"], batched=True)
+        def _y(b): return b
+        keys = {frozenset(c) for c in _detect_signal_cycles()}
+        self.assertIn(frozenset(["sig/self"]), keys)
+        self.assertIn(frozenset(["sig/X", "sig/Y"]), keys)
+
+    def test_dag_has_no_cycle(self):
+        @wl.signal(name="sig/a", inputs=["train/loss"], batched=True)   # base leaf
+        def _a(b): return b
+        @wl.signal(name="sig/b", inputs=["sig/a"], batched=True)
+        def _b(b): return b
+        self.assertEqual(_detect_signal_cycles(), [])
+
+
+class TestContexts(unittest.TestCase):
+    def test_batch_context_carries_logits(self):
+        b = BatchSignalContext(sample_ids=[1, 2], subscribed_values=[0.1, 0.2],
+                               logits=torch.tensor([[1., 2., 3.], [4., 5., 6.]]))
+        self.assertEqual(tuple(b.logits.shape), (2, 3))
+
+    def test_sample_context_carries_logits(self):
+        c = SignalContext(sample_id=1, dataframe=None, logits=torch.tensor([1., 2., 3.]))
+        self.assertEqual(tuple(c.logits.shape), (3,))
+
+    def test_stale_signal_raises(self):
+        b = BatchSignalContext(sample_ids=[1], subscribed_values=[0.0], logger=_lg(), step=5)
+        with self.assertRaises(StaleSignalError):
+            b.latest("sig/never", require_fresh=True)
+
+
+class TestGatherFreshCache(unittest.TestCase):
+    def setUp(self): _REGISTERED_SIGNALS.clear()
+    def tearDown(self): _REGISTERED_SIGNALS.clear()
+
+    def test_reactive_derived_not_queried_when_absent(self):
+        @wl.signal(name="sig/derived", inputs=["x"], batched=True)   # reactive
+        def _d(b): return b
+        lg = _lg()
+        calls = {"n": 0}
+        orig = lg.query_per_sample_at_step
+        lg.query_per_sample_at_step = lambda *a, **k: (calls.__setitem__("n", calls["n"] + 1), orig(*a, **k))[1]
+        res = _gather_inputs_fresh(lg, ["sig/derived"], [1], 0, fresh_cache={})
+        self.assertIsNone(res)          # not fired yet -> skip
+        self.assertEqual(calls["n"], 0)  # and NOT a ledger query (no flush)
+
+    def test_reactive_derived_read_from_cache(self):
+        @wl.signal(name="sig/derived", inputs=["x"], batched=True)
+        def _d(b): return b
+        res = _gather_inputs_fresh(_lg(), ["sig/derived"], [1, 2], 0,
+                                   fresh_cache={"sig/derived": np.array([1.0, 2.0])})
+        self.assertIsNotNone(res)
+        np.testing.assert_array_equal(res["sig/derived"], [1.0, 2.0])
+
+
+class TestDefaultShapeClassifier(unittest.TestCase):
+    def test_labels(self):
+        self.assertEqual(wl.classify_loss_shape([2, 1.5, 1, 0.5, 0.05]), "monotonic")
+        self.assertEqual(wl.classify_loss_shape([2, 2, 2, 2, 2]), "Flat_high")
+        self.assertEqual(wl.classify_loss_shape([0.1, 0.1, 0.1, 3.0, 0.1]), "Spiked")
+
+    def test_too_short_is_none(self):
+        self.assertIsNone(wl.classify_loss_shape([1, 2, 3]))
+        self.assertIsNone(wl.classify_loss_shape([1, 0.5, 0.1], min_points=5))
+
+    def test_thresholds_configurable(self):
+        traj = [1.0, 0.9, 0.8, 0.75, 0.7]                       # net drop 0.3
+        self.assertNotEqual(wl.classify_loss_shape(traj), "monotonic")   # default 0.4 unmet
+        self.assertEqual(wl.classify_loss_shape(traj, drop_learned=0.25), "monotonic")
+
+    def test_trajectory_stats_reusable(self):
+        s = wl.trajectory_stats([2.0, 1.0, 0.5, 0.5, 0.4])
+        self.assertAlmostEqual(s["drop"], (2.0 - 0.4) / 2.0, places=5)
+        self.assertIn("cv", s); self.assertIn("tail_cv", s); self.assertIn("argmin_frac", s)
+        self.assertIsNone(wl.trajectory_stats([1.0]))   # < 2 points
+
+    def test_enable_live_signal_registers(self):
+        _REGISTERED_SIGNALS.clear()
+        try:
+            wl.enable_loss_shape_signal(loss_signal="loss_sample", name="sig/live_shape", every=5)
+            self.assertIn("sig/live_shape", _REGISTERED_SIGNALS)
+            meta = _REGISTERED_SIGNALS["sig/live_shape"]._wl_signal_meta
+            self.assertEqual(meta.get("inputs"), ["loss_sample"])
+            self.assertEqual(meta.get("compute_every_n_steps"), 5)
+        finally:
+            _REGISTERED_SIGNALS.clear()
+
+    def test_exports(self):
+        for name in ("classify_loss_shape", "trajectory_stats", "write_loss_shapes",
+                     "write_signal_shapes", "enable_loss_shape_signal"):
+            self.assertTrue(hasattr(wl, name), name)
+        self.assertIn("monotonic", wl.LOSS_SHAPES)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/general/test_signals.py b/tests/general/test_signals.py
index 0d32127d..f9281417 100644
--- a/tests/general/test_signals.py
+++ b/tests/general/test_signals.py
@@ -4,15 +4,20 @@
 import numpy as np
 from unittest.mock import MagicMock, patch
 import weightslab as wl
-from weightslab.src import _REGISTERED_SIGNALS, compute_signals, wrappered_fwd
+from weightslab.src import (
+    _REGISTERED_SIGNALS, _REACTIVE_FIRED, _react_dependents,
+    SignalContext, BatchSignalContext, compute_signals, wrappered_fwd,
+)
 
 class TestSignals(unittest.TestCase):
     def setUp(self):
         # Clear registered signals before each test
         _REGISTERED_SIGNALS.clear()
+        _REACTIVE_FIRED.clear()
 
     def tearDown(self):
         _REGISTERED_SIGNALS.clear()
+        _REACTIVE_FIRED.clear()
 
     # =========================================================================
     # Input Type Tests - Different signal and data input formats
@@ -251,6 +256,70 @@ def dynamic_sig(sample_id, value, dataframe=None, origin="train"):
             losses_data = call_args[1]['losses']
             self.assertIn('signals//dynamic_sig', losses_data)
             
+    def test_context_exposes_inputs_attribute(self):
+        """SignalContext / BatchSignalContext always expose an `inputs` dict."""
+        # Defaults to an empty dict (never AttributeError, even off the reactive path).
+        sctx = SignalContext(sample_id=1, dataframe=None)
+        self.assertEqual(sctx.inputs, {})
+        bctx = BatchSignalContext(sample_ids=[1, 2], subscribed_values=[0.1, 0.2])
+        self.assertEqual(bctx.inputs, {})
+
+        # Populated via the constructor (as the reactive dispatch does).
+        cols = {"loss_sample": np.array([1.0, 2.0]), "sig/entropy": np.array([0.3, 0.4])}
+        bctx2 = BatchSignalContext(sample_ids=[1, 2], subscribed_values=cols["loss_sample"], inputs=cols)
+        self.assertIn("loss_sample", bctx2.inputs)
+        self.assertIn("sig/entropy", bctx2.inputs)
+        np.testing.assert_array_equal(bctx2.inputs["loss_sample"], [1.0, 2.0])
+
+    def test_reactive_inputs_populated_and_chained(self):
+        """A @wl.signal(inputs=[...]) reads its inputs via ctx.inputs[name], and a
+        derived input (another signal) is visible to a downstream signal."""
+        class FakeLogger:
+            def __init__(self, data):  # {sig: {sid: {step: val}}}
+                self.d = data
+            def query_per_sample_at_step(self, name, ids, step):
+                return [(sid, self.d[name][sid][step]) for sid in ids
+                        if name in self.d and sid in self.d[name]
+                        and step in self.d[name][sid]]
+            def query_per_sample(self, name, sample_ids=None):
+                rows = []
+                for sid in (sample_ids or []):
+                    for st, v in sorted(self.d.get(name, {}).get(sid, {}).items()):
+                        rows.append((sid, st, v, None))
+                return rows
+
+        captured = {}
+
+        @wl.signal(name="sig/loss_norm", inputs=["loss_sample"], batched=True)
+        def loss_norm(b):
+            captured["norm"] = dict(b.inputs)
+            return b.inputs["loss_sample"] / (float(np.mean(b.inputs["loss_sample"])) + 1e-8)
+
+        @wl.signal(name="sig/hardness", inputs=["loss_sample", "sig/loss_norm"], batched=True)
+        def hardness(b):
+            captured["hard"] = dict(b.inputs)
+            return b.inputs["loss_sample"] * b.inputs["sig/loss_norm"]
+
+        fake = FakeLogger({"loss_sample": {1: {10: 2.0}, 2: {10: 4.0}}})
+        saved = {}
+
+        def fake_save_signals(signals=None, **kw):
+            saved.update(signals or {})
+
+        with patch("weightslab.src.get_logger", return_value=fake), \
+             patch("weightslab.src.get_dataframe", return_value=None), \
+             patch("weightslab.src.save_signals", side_effect=fake_save_signals):
+            _react_dependents(["loss_sample"], batch_ids=[1, 2], step=10, origin="train")
+
+        # loss_norm read its base-metric input through ctx.inputs
+        np.testing.assert_array_equal(captured["norm"]["loss_sample"], [2.0, 4.0])
+        # hardness saw BOTH the base metric AND the derived signal via ctx.inputs
+        self.assertIn("loss_sample", captured["hard"])
+        self.assertIn("sig/loss_norm", captured["hard"])
+        # Both fired and were persisted
+        self.assertIn("sig/loss_norm", saved)
+        self.assertIn("sig/hardness", saved)
+
     def test_frequency_control(self):
         """Test compute_every_n_steps."""
         with patch("weightslab.src.list_models", return_value=["main"]), \
diff --git a/tests/integrations/ultralytics/ddp/ddp_ablation.py b/tests/integrations/ultralytics/ddp/ddp_ablation.py
index 3c268e84..156762a8 100644
--- a/tests/integrations/ultralytics/ddp/ddp_ablation.py
+++ b/tests/integrations/ultralytics/ddp/ddp_ablation.py
@@ -31,10 +31,10 @@
 import torch.distributed as dist
 import torch.multiprocessing as mp
 
-# This harness lives under tests/ but drives the ws-detection usecase: put the usecase
+# This harness lives under tests/ but drives the wl-detection usecase: put the usecase
 # src on the path (for yolo_pipeline / utils.*) and resolve config/data/ddp_run vs IT.
 _USECASE_SRC = os.path.abspath(os.path.join(
-    os.path.dirname(__file__), "../../../../examples/PyTorch/ws-detection/src"))
+    os.path.dirname(__file__), "../../../../examples/PyTorch/wl-detection/src"))
 sys.path.insert(0, _USECASE_SRC)
 _HERE = _USECASE_SRC
 _HOST = "127.0.0.1"
@@ -149,7 +149,9 @@ def _worker(rank, world, master_port):
             _ensure_core_ddp_registered, reconcile_all, flush_outbox)
         _ensure_core_ddp_registered()
         import weightslab as wl
-        wl.serve(serving_grpc=True, serving_cli=False)
+        # DDP ablation harness: config wrapping is driven by yolo_pipeline across
+        # ranks, so opt out of the serve() serving-config guard explicitly.
+        wl.serve(serving_grpc=True, serving_cli=False, allow_unconfigured=True)
         decode = yolo_pipeline._decode_preds_to_6col
     else:
         model, loader, crit, iou, optimizer = _build_ul(cfg, device, batch_size, num_workers)
diff --git a/tests/integrations/ultralytics/ddp/ddp_test_suite.py b/tests/integrations/ultralytics/ddp/ddp_test_suite.py
index c9608081..280ef57e 100644
--- a/tests/integrations/ultralytics/ddp/ddp_test_suite.py
+++ b/tests/integrations/ultralytics/ddp/ddp_test_suite.py
@@ -40,10 +40,10 @@
 import grpc
 
 import sys
-# Harness lives under tests/ — put the ws-detection usecase src on the path so the
+# Harness lives under tests/ — put the wl-detection usecase src on the path so the
 # usecase modules (yolo_pipeline, utils.*) and its config/data/ddp_run resolve.
 sys.path.insert(0, os.path.abspath(os.path.join(
-    os.path.dirname(__file__), "../../../../examples/PyTorch/ws-detection/src")))
+    os.path.dirname(__file__), "../../../../examples/PyTorch/wl-detection/src")))
 import yolo_pipeline # reuse _build_pipeline / _decode_preds_to_6col / _HERE / _LOSS_PARTS
 
 import weightslab.proto.experiment_service_pb2 as pb2
diff --git a/tests/model/test_constraint_generation.py b/tests/model/test_constraint_generation.py
index ddd00a94..dcc8caf1 100644
--- a/tests/model/test_constraint_generation.py
+++ b/tests/model/test_constraint_generation.py
@@ -15,9 +15,48 @@
 
 from weightslab.utils.modules_dependencies import DepType
 from weightslab.backend.model_interface import ModelInterface
+from weightslab.backend import ledgers
 from weightslab.utils.computational_graph import _detect_layer_constraints
 
 
+def _onnx_export_available() -> bool:
+    """True when the ONNX export toolchain imports cleanly.
+
+    ``torch.onnx.export`` pulls in ``onnxscript``, which (as of 0.6.x) imports a
+    separately-packaged ``onnx_ir``. When that package is absent, importing
+    ``onnxscript`` raises ``ModuleNotFoundError: No module named 'onnx_ir'`` and
+    every ONNX-based dependency-extraction test fails at setup. Guard on this so
+    those tests skip where the toolchain is missing but still run once the
+    ``onnx``/dev extras are fully installed.
+    """
+    try:
+        import onnxscript  # noqa: F401  # transitively imports onnx_ir
+        return True
+    except Exception:
+        return False
+
+
+_ONNX_EXPORT_AVAILABLE = _onnx_export_available()
+_ONNX_SKIP_REASON = "ONNX export toolchain unavailable (onnxscript/onnx_ir not installed)"
+
+
+class _RegistryIsolation:
+    """Clean the global model registry after each test.
+
+    ``get_dependencies_onnx`` builds a throwaway ``ModelInterface`` (in ``eval``
+    mode, for ONNX dependency extraction) which registers itself as the global
+    experiment model and is never cleaned up. Left behind, ``get_model()`` keeps
+    returning that eval-mode model, and anything that reads it — e.g.
+    ``LoggerQueue._get_audit_mode`` (``get_model().audit_mode``) — sees
+    ``audit_mode == True``, leaking into unrelated tests run later in the suite.
+    """
+    def tearDown(self):
+        try:
+            ledgers.GLOBAL_LEDGER.unregister_model()
+        except Exception:
+            pass
+
+
 def get_dependencies_onnx(model: nn.Module, dummy_input: torch.Tensor) -> List[Tuple[nn.Module, nn.Module, DepType]]:
     """Extract dependencies using ONNX export"""
     try:
@@ -96,8 +135,8 @@ def forward(self, x):
         return x
 
 
-@unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
-class TestConstraintDetection(unittest.TestCase):
+# @unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
+class TestConstraintDetection(_RegistryIsolation, unittest.TestCase):
     """Test constraint detection on individual modules"""
 
     def test_grouped_conv_detection(self):
@@ -136,8 +175,9 @@ def test_batchnorm_no_constraint(self):
         self.assertEqual(len(constraints), 0)
 
 
-@unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
-class TestConstraintPropagation(unittest.TestCase):
+# @unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
+@unittest.skipUnless(_ONNX_EXPORT_AVAILABLE, _ONNX_SKIP_REASON)
+class TestConstraintPropagation(_RegistryIsolation, unittest.TestCase):
     """Test constraint propagation through dependency graphs"""
 
     def test_grouped_conv_propagation(self):
@@ -201,8 +241,9 @@ def test_multi_grouped_propagation(self):
         self.assertEqual(model.model.g_conv3.wl_constraints[1]['cons_group_size'], 8)
 
 
-@unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
-class TestConstraintReporting(unittest.TestCase):
+# @unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
+@unittest.skipUnless(_ONNX_EXPORT_AVAILABLE, _ONNX_SKIP_REASON)
+class TestConstraintReporting(_RegistryIsolation, unittest.TestCase):
     """Test constraint reporting and information retrieval"""
 
     def test_constraint_source_tracking(self):
diff --git a/tests/model/test_model_with_ops.py b/tests/model/test_model_with_ops.py
index 827fb703..66935955 100644
--- a/tests/model/test_model_with_ops.py
+++ b/tests/model/test_model_with_ops.py
@@ -25,7 +25,7 @@
 TMP_DIR = '/tmp/utests/'; os.makedirs('/tmp/utests/', exist_ok=True)
 
 
-@unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
+# @unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
 class NetworkWithOpsTest(unittest.TestCase):
     def setUp(self) -> None:
         print(f"\n--- Start {self._testMethodName} ---\n")
@@ -206,8 +206,8 @@ def test_train_freeze(self):
         # Set Tracker
         self.dummy_network.set_tracking_mode(TrackingMode.TRAIN)
 
-        # Train for like 10 epochs
-        for _ in trange(10, desc="Training.."):
+        # Train for like 5 epochs
+        for _ in trange(5, desc="Training.."):
             self._train_one_epoch(cutoff=20)
 
         # Operate on the first layer - FREEZE
diff --git a/tests/model/test_model_with_ops_unit.py b/tests/model/test_model_with_ops_unit.py
index dba4df34..88a3d902 100644
--- a/tests/model/test_model_with_ops_unit.py
+++ b/tests/model/test_model_with_ops_unit.py
@@ -24,7 +24,7 @@ def __init__(self):
         self.l2 = _Layer(20)
 
 
-@unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
+# @unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
 class TestModelWithOpsUnit(unittest.TestCase):
     def test_reverse_index_and_tracking_mode(self):
         net = _Net()
diff --git a/tests/model/test_tracking.py b/tests/model/test_tracking.py
index 411730f5..33a789d7 100644
--- a/tests/model/test_tracking.py
+++ b/tests/model/test_tracking.py
@@ -148,7 +148,7 @@ def test_triggers_tracker_all_ops_plus_save_and_load(self):
         self.assertEqual(self.triggers_tracker, replica_triggers_tracker)
 
 
-@unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
+# @unittest.skip("Constraint detection and propagation tests are currently skipped due to ongoing refactor and potential changes in the underlying implementation. Will be re-enabled once the new system is in place more modeling.")
 class TriggersTrackerClazzAndSampleIDTest(unittest.TestCase):
     """ Tests the TriggersTrackerClazzAndSampleID for the neuron operations and
         persistency.
diff --git a/tests/test_secure_communication.py b/tests/test_secure_communication.py
new file mode 100644
index 00000000..75c38f19
--- /dev/null
+++ b/tests/test_secure_communication.py
@@ -0,0 +1,132 @@
+"""Tests for secure environment setup on the native stack."""
+
+import os
+import tempfile
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from weightslab.security import CertAuthManager
+
+
+class TestCertAuthManager:
+    """Certificate + auth token manager tests."""
+
+    def test_manager_init_with_custom_dir(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=True)
+            assert manager.certs_dir == Path(tmpdir)
+            assert manager.enable_auth is True
+            assert manager.cert_file == Path(tmpdir) / "backend-server.crt"
+            assert manager.key_file == Path(tmpdir) / "backend-server.key"
+            assert manager.ca_file == Path(tmpdir) / "ca.crt"
+
+    def test_has_valid_certs_missing(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            manager = CertAuthManager(certs_dir=tmpdir)
+            assert manager.has_valid_certs() is False
+
+    def test_auth_token_generation(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=True)
+            token1 = manager.get_or_create_auth_token()
+            token2 = manager.get_or_create_auth_token()
+            assert token1
+            assert token1 == token2
+
+    def test_auth_token_disabled(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=False)
+            assert manager.get_or_create_auth_token() == ""
+
+    def test_tls_environment_variables_no_legacy_envoy_vars(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            manager = CertAuthManager(certs_dir=tmpdir)
+            env_vars = manager.setup_tls_environment()
+
+            assert env_vars["GRPC_TLS_ENABLED"] == "1"
+            assert env_vars["GRPC_TLS_REQUIRE_CLIENT_AUTH"] == "1"
+            assert "GRPC_TLS_CERT_FILE" in env_vars
+            assert "GRPC_TLS_KEY_FILE" in env_vars
+            assert "GRPC_TLS_CA_FILE" in env_vars
+            assert env_vars["WEIGHTSLAB_CERTS_DIR"] == tmpdir
+
+            assert "ENVOY_DOWNSTREAM_TLS" not in env_vars
+            assert "ENVOY_UPSTREAM_TLS" not in env_vars
+            assert "VITE_SERVER_PROTOCOL" not in env_vars
+
+    def test_auth_environment_variables_enabled(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=True)
+            env_vars = manager.setup_auth_environment()
+
+            assert env_vars["WL_ENABLE_GRPC_AUTH_TOKEN"] == "1"
+            assert "GRPC_AUTH_TOKEN" in env_vars
+            assert "VITE_WL_ENABLE_GRPC_AUTH_TOKEN" not in env_vars
+            assert "VITE_GRPC_AUTH_TOKEN" not in env_vars
+
+    def test_auth_environment_variables_disabled(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=False)
+            env_vars = manager.setup_auth_environment()
+
+            assert env_vars["WL_ENABLE_GRPC_AUTH_TOKEN"] == "0"
+            assert "GRPC_AUTH_TOKEN" not in env_vars
+
+    def test_from_env_or_default(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            os.environ["WEIGHTSLAB_CERTS_DIR"] = tmpdir
+            try:
+                manager = CertAuthManager.from_env_or_default()
+                assert manager.certs_dir == Path(tmpdir)
+            finally:
+                del os.environ["WEIGHTSLAB_CERTS_DIR"]
+
+    @patch("weightslab.security.cert_auth_manager.subprocess.run")
+    def test_generate_certs_success(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
+        with tempfile.TemporaryDirectory() as tmpdir:
+            manager = CertAuthManager(certs_dir=tmpdir)
+            success, _ = manager.generate_certs()
+            assert success is True
+
+    @patch("weightslab.security.cert_auth_manager.subprocess.run")
+    @patch("weightslab.security.cert_auth_manager.Path.exists")
+    def test_generate_certs_script_missing(self, mock_exists, mock_run):
+        # First exists call is for cert file checks (False), then script path (False).
+        mock_exists.return_value = False
+        mock_run.return_value = MagicMock(returncode=1, stdout="", stderr="")
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            manager = CertAuthManager(certs_dir=tmpdir)
+            success, msg = manager.generate_certs()
+            assert success is False
+            assert "not found" in msg.lower()
+
+
+class TestSecureInitialization:
+    """Import-time secure initialization sanity checks."""
+
+    def test_import_with_secure_init(self):
+        try:
+            import weightslab
+            assert weightslab is not None
+        except Exception as exc:
+            pytest.fail(f"Import failed: {exc}")
+
+    def test_secure_init_skip_env_var(self):
+        os.environ["WEIGHTSLAB_SKIP_SECURE_INIT"] = "true"
+        try:
+            import importlib
+            import weightslab
+
+            importlib.reload(weightslab)
+        except Exception as exc:
+            pytest.fail(f"Import failed: {exc}")
+        finally:
+            del os.environ["WEIGHTSLAB_SKIP_SECURE_INIT"]
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
diff --git a/tests/test_secure_docker.py b/tests/test_secure_docker.py
deleted file mode 100644
index d7b9bd30..00000000
--- a/tests/test_secure_docker.py
+++ /dev/null
@@ -1,238 +0,0 @@
-"""Tests for secure Docker setup with TLS and gRPC auth."""
-
-import os
-import tempfile
-import pytest
-from pathlib import Path
-from unittest.mock import patch, MagicMock
-
-from weightslab.security import CertAuthManager
-from weightslab.ui_docker_bridge import (
-    _test_backend_connection,
-    _is_windows,
-)
-
-
-class TestCertAuthManager:
-    """Test certificate and auth token management."""
-
-    def test_manager_init_with_custom_dir(self):
-        """Test manager initialization with custom directory."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=True)
-
-            assert manager.certs_dir == Path(tmpdir)
-            assert manager.enable_auth is True
-            assert manager.cert_file == Path(tmpdir) / 'backend-server.crt'
-            assert manager.key_file == Path(tmpdir) / 'backend-server.key'
-            assert manager.ca_file == Path(tmpdir) / 'ca.crt'
-
-    def test_manager_init_with_default_dir(self):
-        """Test manager initialization with default directory."""
-        manager = CertAuthManager(enable_auth=True)
-
-        user_profile = os.environ.get('HOME') or os.path.expanduser('~')
-        expected_dir = Path(user_profile) / '.weightslab-certs'
-
-        assert manager.certs_dir == expected_dir
-
-    def test_has_valid_certs_missing(self):
-        """Test certificate validation when certs don't exist."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir)
-
-            assert manager.has_valid_certs() is False
-
-    def test_auth_token_generation(self):
-        """Test gRPC auth token generation."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=True)
-
-            token1 = manager.get_or_create_auth_token()
-            assert token1 is not None
-            assert len(token1) > 0
-            assert isinstance(token1, str)
-
-            # Should return same token on second call
-            token2 = manager.get_or_create_auth_token()
-            assert token1 == token2
-
-    def test_auth_token_disabled(self):
-        """Test that no token is generated when auth is disabled."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=False)
-
-            token = manager.get_or_create_auth_token()
-            assert token == ""
-
-    def test_tls_environment_variables(self):
-        """Test TLS environment variable setup."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir)
-
-            env_vars = manager.setup_tls_environment()
-
-            assert 'GRPC_TLS_ENABLED' in env_vars
-            assert env_vars['GRPC_TLS_ENABLED'] == '1'
-            assert 'GRPC_TLS_CERT_FILE' in env_vars
-            assert 'GRPC_TLS_KEY_FILE' in env_vars
-            assert 'GRPC_TLS_CA_FILE' in env_vars
-            assert 'WEIGHTSLAB_CERTS_DIR' in env_vars
-            assert 'ENVOY_DOWNSTREAM_TLS' in env_vars
-            assert 'ENVOY_UPSTREAM_TLS' in env_vars
-
-    def test_auth_environment_variables_enabled(self):
-        """Test auth environment variables when enabled."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=True)
-
-            env_vars = manager.setup_auth_environment()
-
-            assert 'WL_ENABLE_GRPC_AUTH_TOKEN' in env_vars
-            assert env_vars['WL_ENABLE_GRPC_AUTH_TOKEN'] == '1'
-            assert 'GRPC_AUTH_TOKEN' in env_vars
-            assert len(env_vars['GRPC_AUTH_TOKEN']) > 0
-
-    def test_auth_environment_variables_disabled(self):
-        """Test auth environment variables when disabled."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=False)
-
-            env_vars = manager.setup_auth_environment()
-
-            assert 'WL_ENABLE_GRPC_AUTH_TOKEN' in env_vars
-            assert env_vars['WL_ENABLE_GRPC_AUTH_TOKEN'] == '0'
-            assert 'GRPC_AUTH_TOKEN' not in env_vars
-
-    def test_from_env_or_default(self):
-        """Test creating manager from environment variables."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            os.environ['WEIGHTSLAB_CERTS_DIR'] = tmpdir
-
-            try:
-                manager = CertAuthManager.from_env_or_default()
-                assert manager.certs_dir == Path(tmpdir)
-            finally:
-                del os.environ['WEIGHTSLAB_CERTS_DIR']
-
-    @patch('weightslab.security.cert_auth_manager.subprocess.run')
-    def test_generate_certs_success(self, mock_run):
-        """Test successful certificate generation."""
-        mock_run.return_value = MagicMock(returncode=0, stdout='', stderr='')
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir)
-
-            # Since we're mocking subprocess, we need to create the files
-            manager.certs_dir.mkdir(parents=True, exist_ok=True)
-            manager.cert_file.touch()
-            manager.key_file.touch()
-            manager.ca_file.touch()
-
-            success, msg = manager.generate_certs()
-            assert success is True
-
-    @patch('weightslab.security.cert_auth_manager.subprocess.run')
-    def test_generate_certs_script_not_found(self, mock_run):
-        """Test certificate generation when script is missing."""
-        mock_run.return_value = MagicMock(returncode=1, stdout='', stderr='Script not found')
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir)
-
-            success, msg = manager.generate_certs()
-
-            # Should fail when script returns non-zero exit code
-            assert success is False or not manager.has_valid_certs()
-
-
-class TestBackendConnection:
-    """Test backend connection utilities."""
-
-    def test_backend_connection_timeout(self):
-        """Test backend connection with timeout."""
-        result = _test_backend_connection(
-            host='127.0.0.1',
-            port=59999, # Likely not listening
-            timeout=0.5
-        )
-        assert result is False
-
-    @patch('socket.socket')
-    def test_backend_connection_success(self, mock_socket):
-        """Test successful backend connection."""
-        mock_sock_instance = MagicMock()
-        mock_sock_instance.connect_ex.return_value = 0
-        mock_socket.return_value = mock_sock_instance
-
-        result = _test_backend_connection()
-        assert result is True
-
-    @patch('socket.socket')
-    def test_backend_connection_failure(self, mock_socket):
-        """Test failed backend connection."""
-        mock_sock_instance = MagicMock()
-        mock_sock_instance.connect_ex.return_value = 1
-        mock_socket.return_value = mock_sock_instance
-
-        result = _test_backend_connection()
-        assert result is False
-
-
-class TestDockerBridgeIntegration:
-    """Integration tests for Docker bridge."""
-
-    def test_is_windows(self):
-        """Test Windows detection."""
-        result = _is_windows()
-        assert isinstance(result, bool)
-
-    def test_cert_auth_environment_integration(self):
-        """Test that cert auth manager sets env vars correctly."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = CertAuthManager(certs_dir=tmpdir, enable_auth=True)
-
-            # Create dummy cert files
-            tmpdir_path = Path(tmpdir)
-            tmpdir_path.mkdir(parents=True, exist_ok=True)
-            (tmpdir_path / 'backend-server.crt').touch()
-            (tmpdir_path / 'backend-server.key').touch()
-            (tmpdir_path / 'ca.crt').touch()
-
-            success, msg = manager.initialize()
-
-            # Check that env vars were set
-            assert os.environ.get('GRPC_TLS_ENABLED') == '1'
-            assert os.environ.get('WL_ENABLE_GRPC_AUTH_TOKEN') == '1'
-            assert 'GRPC_AUTH_TOKEN' in os.environ
-
-
-class TestSecureInitialization:
-    """Test secure initialization at package import."""
-
-    def test_import_with_secure_init(self):
-        """Test that secure init doesn't break import."""
-        # This is a basic sanity check
-        try:
-            import weightslab
-            assert weightslab is not None
-        except Exception as e:
-            pytest.fail(f"Import failed: {e}")
-
-    def test_secure_init_skip_env_var(self):
-        """Test that WEIGHTSLAB_SKIP_SECURE_INIT works."""
-        os.environ['WEIGHTSLAB_SKIP_SECURE_INIT'] = 'true'
-
-        try:
-            # Re-import should use skip flag
-            import importlib
-            import weightslab
-            importlib.reload(weightslab)
-        except Exception as e:
-            pytest.fail(f"Import failed: {e}")
-        finally:
-            del os.environ['WEIGHTSLAB_SKIP_SECURE_INIT']
-
-
-if __name__ == '__main__':
-    pytest.main([__file__, '-v'])
diff --git a/tests/trainer/services/test_data_service_tabular_task_type.py b/tests/trainer/services/test_data_service_tabular_task_type.py
new file mode 100644
index 00000000..7668c050
--- /dev/null
+++ b/tests/trainer/services/test_data_service_tabular_task_type.py
@@ -0,0 +1,98 @@
+"""Unit tests for tabular task_type auto-detection in data_service.py.
+
+Datasets whose model input is a 1-D feature vector (no image) should be
+auto-detected as task_type "tabular" — distinct from "classification" — even
+when the dataset/model never sets an explicit `task_type` attribute. See
+`peek_is_tabular_sample` (used as the last step of the "Robust Task Type
+Detection" heuristic in `DataService._process_sample_row`).
+"""
+
+import unittest
+
+import numpy as np
+import torch
+
+from weightslab.trainer.services.data_service import peek_is_tabular_sample
+
+
+class _TabularDataset:
+    """Minimal stand-in for a tabular Dataset (ledger get_items contract).
+
+    ``__getitem__`` is required only because ``load_raw_image_array`` uses its
+    presence as an "is this dataset-like" gate before consulting ``get_items``.
+    """
+
+    def __init__(self, n=8, n_features=16):
+        self.features = torch.randn(n, n_features)
+
+    def __len__(self):
+        return len(self.features)
+
+    def __getitem__(self, idx):
+        return self.features[idx], idx, 0
+
+    def get_index_from_sample_id(self, sample_id):
+        return sample_id if 0 <= sample_id < len(self.features) else None
+
+    def get_items(self, idx, include_metadata=False, include_labels=False, include_images=False):
+        image = self.features[idx] if include_images else None
+        return image, idx, 0, None
+
+
+class _ImageDataset:
+    """Minimal stand-in for a vision Dataset (H×W×C image input)."""
+
+    def __init__(self, n=8):
+        self.images = torch.randint(0, 255, (n, 32, 32, 3), dtype=torch.uint8)
+
+    def __len__(self):
+        return len(self.images)
+
+    def __getitem__(self, idx):
+        return self.images[idx], idx, 0
+
+    def get_index_from_sample_id(self, sample_id):
+        return sample_id if 0 <= sample_id < len(self.images) else None
+
+    def get_items(self, idx, include_metadata=False, include_labels=False, include_images=False):
+        image = self.images[idx] if include_images else None
+        return image, idx, 0, None
+
+
+class TestPeekIsTabularSample(unittest.TestCase):
+    def test_true_for_1d_feature_vector_input(self):
+        ds = _TabularDataset()
+        self.assertTrue(peek_is_tabular_sample(ds, 0))
+        self.assertTrue(peek_is_tabular_sample(ds, 3))
+
+    def test_false_for_image_input(self):
+        ds = _ImageDataset()
+        self.assertFalse(peek_is_tabular_sample(ds, 0))
+
+    def test_false_when_sample_id_unresolvable(self):
+        ds = _TabularDataset(n=4)
+        self.assertFalse(peek_is_tabular_sample(ds, 999))
+
+    def test_false_on_dataset_without_expected_lookup_methods(self):
+        # No get_physical_location / get_index_from_sample_id / __getitem__
+        # that behaves as expected -> best-effort heuristic, never raises.
+        class Broken:
+            pass
+
+        self.assertFalse(peek_is_tabular_sample(Broken(), 0))
+
+    def test_respects_get_physical_location_when_present(self):
+        calls = []
+
+        class GroupedTabularDataset(_TabularDataset):
+            def get_physical_location(self, sample_id):
+                calls.append(sample_id)
+                return sample_id, 0
+
+        ds = GroupedTabularDataset()
+        self.assertTrue(peek_is_tabular_sample(ds, 2))
+        self.assertEqual(calls, [2])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/weightslab/__init__.py b/weightslab/__init__.py
index e41340b6..06008e4a 100644
--- a/weightslab/__init__.py
+++ b/weightslab/__init__.py
@@ -10,13 +10,63 @@
 import os
 import logging
 import threading
+import importlib
 
-from .src import watch_or_edit, start_training, serve, keep_serving, save_signals, save_instance_signals, save_group_signals, tag_samples, register_categorical_tag, set_categorical_tag, discard_samples, get_samples_by_tag, get_discarded_samples, signal, eval_fn, compute_signals, SignalContext, clear_all, run_pending_evaluation, trigger_pending_evaluation_async, query_signal_history, query_sample_history, query_instance_history, write_history, write_dataframe, get_current_experiment_hash, pointcloud_thumbnail, pointcloud_boxes
-from .backend.ledgers import GLOBAL_LEDGER as ledger
+# Only lightweight, torch-free helpers are imported eagerly (needed by the
+# package-init side effects below). The banner and logging utilities pull in no
+# heavy scientific stack.
 from .art import _BANNER
 from .utils.logs import setup_logging, set_log_directory, is_main_process
-from .utils.tools import seed_everything
-from .components.global_monitoring import guard_training_context, guard_testing_context
+
+# --- Lazy re-exports (PEP 562) --------------------------------------------- #
+# The training API (`.src`, ledger, guards, seed_everything) transitively
+# imports torch/numpy, which costs several seconds. Resolving these lazily means
+# `import weightslab` — and in particular the `weightslab` CLI process, which
+# only serves the UI and never touches a model — stays torch-free until the
+# training helpers are actually used. A user's training script triggers the
+# import the moment it first calls e.g. `wl.watch_or_edit(...)`, right where
+# they'd import torch themselves anyway, so nothing changes for them.
+_LAZY_EXPORTS = {
+    # ledger + guards + seeding live outside .src
+    "ledger": (".backend.ledgers", "GLOBAL_LEDGER"),
+    "seed_everything": (".utils.tools", "seed_everything"),
+    "guard_training_context": (".components.global_monitoring", "guard_training_context"),
+    "guard_testing_context": (".components.global_monitoring", "guard_testing_context"),
+}
+# Everything re-exported straight from .src (attribute name == export name).
+for _name in (
+    "watch_or_edit", "start_training", "serve", "keep_serving", "save_signals",
+    "save_instance_signals", "save_group_signals", "tag_samples",
+    "register_categorical_tag", "set_categorical_tag", "discard_samples",
+    "get_samples_by_tag", "get_discarded_samples", "signal", "eval_fn",
+    "compute_signals", "SignalContext", "BatchSignalContext", "StaleSignalError",
+    "drain_signals", "clear_all", "run_pending_evaluation",
+    "trigger_pending_evaluation_async", "query_signal_history",
+    "query_sample_history", "query_instance_history", "write_history",
+    "write_dataframe", "classify_loss_shape", "trajectory_stats",
+    "write_loss_shapes", "write_signal_shapes", "enable_loss_shape_signal",
+    "enable_loss_shape_autotag", "disable_loss_shape_autotag",
+    "auto_loss_shape_signal_names",
+    "LOSS_SHAPES", "get_current_experiment_hash", "pointcloud_thumbnail",
+    "pointcloud_boxes",
+):
+    _LAZY_EXPORTS[_name] = (".src", _name)
+del _name
+
+
+def __getattr__(name):  # PEP 562 module-level lazy attribute access
+    target = _LAZY_EXPORTS.get(name)
+    if target is None:
+        raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+    module_name, attr = target
+    module = importlib.import_module(module_name, __name__)
+    value = getattr(module, attr)
+    globals()[name] = value  # cache so __getattr__ isn't hit again
+    return value
+
+
+def __dir__():
+    return sorted(set(globals()) | set(_LAZY_EXPORTS))
 
 # If you already have other top-level exports, keep them.
 # This snippet ensures __version__ is available even when setuptools_scm hasn't written the file yet.
@@ -46,19 +96,19 @@
 
 grpc_tls_enabled = os.environ.get('GRPC_TLS_ENABLED', 'true').lower() == 'true'
 if _IS_MAIN_PROCESS and grpc_tls_enabled and os.environ.get('WEIGHTSLAB_SKIP_SECURE_INIT', 'false').lower() != 'true':
-	try:
-		from weightslab.security import CertAuthManager
+    try:
+        from weightslab.security import CertAuthManager
 
-		enable_auth = os.environ.get('WL_ENABLE_GRPC_AUTH_TOKEN', 'true').lower() == 'true'
-		manager = CertAuthManager.from_env_or_default(enable_auth=enable_auth)
+        enable_auth = os.environ.get('WL_ENABLE_GRPC_AUTH_TOKEN', 'true').lower() == 'true'
+        manager = CertAuthManager.from_env_or_default(enable_auth=enable_auth)
 
-		success, msg = manager.check_and_apply()
-		if success:
-			logger.debug(f"Secure environment applied: {msg}")
-		else:
-			logger.debug(f"Running in unsecured mode — no certs found. To set up: weightslab ui se")
-	except Exception as e:
-		logger.debug(f"Secure environment check skipped: {e}")
+        success, msg = manager.check_and_apply()
+        if success:
+            logger.debug(f"Secure environment applied: {msg}")
+        else:
+            logger.debug("Running in unsecured mode - no certs found. To set up: weightslab se")
+    except Exception as e:
+        logger.debug(f"Secure environment check skipped: {e}")
 
 # Get Package Metadata. Resolve the version from the most authoritative source
 # available, so a live/editable checkout reports the CURRENT git tag rather than
@@ -148,6 +198,9 @@ def _clean(v: str) -> str:
   	"get_samples_by_tag",
     "get_discarded_samples",
     "SignalContext",
+    "BatchSignalContext",
+    "StaleSignalError",
+    "drain_signals",
     "clear_all",
   	"seed_everything",
     "run_pending_evaluation",
@@ -166,6 +219,15 @@ def _clean(v: str) -> str:
 
     "write_history",
     "write_dataframe",
+    "classify_loss_shape",
+    "write_loss_shapes",
+    "write_signal_shapes",
+    "trajectory_stats",
+    "enable_loss_shape_signal",
+    "enable_loss_shape_autotag",
+    "disable_loss_shape_autotag",
+    "auto_loss_shape_signal_names",
+    "LOSS_SHAPES",
 
     "pointcloud_thumbnail",
     "pointcloud_boxes",
diff --git a/weightslab/art.py b/weightslab/art.py
index 21cdc379..85b62ead 100644
--- a/weightslab/art.py
+++ b/weightslab/art.py
@@ -96,6 +96,19 @@ def _package_version() -> str:
     ``_version`` module, then the git-describe fallback. Deliberately avoids
     ``import weightslab`` because this module is imported *during* the package's
     own initialization (before ``__version__`` is assigned there)."""
+    raw = _package_version_raw()
+    # The git-describe fallback returns the raw tag name (e.g. "v1.3.4" or
+    # "v1.3.4-2-gabcdef"), since release tags are "v*" — already-prefixed. The
+    # caller (_CREDIT_LABEL below) always adds its own "v", so strip one here
+    # if present or the banner reads "vv1.3.4". The other two branches
+    # (installed dist metadata / setuptools_scm) are unprefixed PEP 440
+    # versions, so this is a no-op for them.
+    if raw[:1] in ("v", "V") and raw[1:2].isdigit():
+        raw = raw[1:]
+    return raw
+
+
+def _package_version_raw() -> str:
     try:
         from importlib.metadata import version as _dist_version, PackageNotFoundError
         try:
diff --git a/weightslab/backend/dataloader_interface.py b/weightslab/backend/dataloader_interface.py
index d9b16e94..9f76f660 100644
--- a/weightslab/backend/dataloader_interface.py
+++ b/weightslab/backend/dataloader_interface.py
@@ -1045,6 +1045,26 @@ def reset_iterator(self) -> None:
         """
         self._reset_iterator()
 
+    def reset(self) -> None:
+        """Recreate the internal iterator (Ultralytics / ``InfiniteDataLoader``-compatible).
+
+        Some training frameworks call ``loader.reset()`` to force a fresh
+        iterator after mutating the dataset mid-run — e.g. Ultralytics closes
+        mosaic augmentation near the end of training and then calls
+        ``train_loader.reset()`` so the change takes effect on the next epoch.
+        We map it to ``_reset_iterator()``.
+
+        Exposing this as a real method (rather than relying on a caller-side
+        shim like ``if not hasattr(loader, "reset"): loader.reset = ...``) is
+        important: this loader is handed to callers wrapped in a ledger
+        ``Proxy`` whose ``__getattr__`` returns ``None`` for genuinely-missing
+        attributes instead of raising ``AttributeError``. That makes
+        ``hasattr(proxy, "reset")`` return True and silently resolves
+        ``proxy.reset`` to ``None``, so any such shim is skipped and
+        ``reset()`` blows up with ``'NoneType' object is not callable``.
+        """
+        self._reset_iterator()
+
     # -------------------------------------------------------------------------
     # Iteration state capture/restore for deterministic resume
     # -------------------------------------------------------------------------
diff --git a/weightslab/backend/ledgers.py b/weightslab/backend/ledgers.py
index 76e77ea7..df75f90d 100644
--- a/weightslab/backend/ledgers.py
+++ b/weightslab/backend/ledgers.py
@@ -500,7 +500,16 @@ def __getattr__(self, item):
             except (KeyError, TypeError):
                 pass
 
-        return None
+        # Attribute genuinely absent on the wrapped object. Raise AttributeError
+        # (as the class docstring promises) rather than returning None: returning
+        # None makes `hasattr(proxy, x)` report True for missing attributes, which
+        # breaks capability probes (e.g. `if not hasattr(loader, "reset")`) and
+        # silently resolves `proxy.missing` to a non-callable None. Callers that
+        # want a soft default should use `getattr(proxy, x, default)`, which now
+        # works correctly.
+        raise AttributeError(
+            f"'{type(obj).__name__}' object has no attribute {item!r}"
+        )
 
     def __delattr__(self, name: str) -> None:
         """Delete an attribute from the proxy or wrapped object safely.
@@ -582,7 +591,19 @@ def __len__(self):
     def __getitem__(self, idx):
         if self._obj is None:
             raise TypeError("Proxy target not set")
-        return self._obj[idx]
+        value = self._obj[idx]
+        # Mirror .get(): subscript access on a mapping returns a live key proxy
+        # for "simple" values, so ``b['test']`` tracks studio edits exactly like
+        # ``b.get('test')``. Lists/slices and non-mapping containers, plus
+        # list/dict/callable values, are returned raw (unchanged behavior).
+        if (hasattr(self._obj, "get") and hasattr(self._obj, "keys")
+                and not (isinstance(value, (list, dict)) or callable(value))):
+            vp = Proxy._ValueProxy(self, idx)
+            # str / torch.device are handed back plain (see _plain_get_value);
+            # other simple types stay live proxies so studio edits keep tracking.
+            plain = _plain_get_value(vp._resolve())
+            return vp if plain is _KEEP_AS_PROXY else plain
+        return value
 
     def __setitem__(self, key, value):
         """Support item assignment: proxy[key] = value"""
@@ -953,6 +974,24 @@ def list_hyperparams(self) -> List[str]:
         with self._lock:
             return list(self._hyperparams.keys())
 
+    def has_serving_config(self) -> bool:
+        """True if any hyperparameters set holds a non-empty mapping.
+
+        Distinguishes a genuine ``register_hyperparams`` from the empty
+        ``Proxy({})`` placeholder that ``get_hyperparams`` creates on first
+        access — so callers can tell whether serving config (TLS, ports,
+        ``root_log_dir``) was actually wrapped before ``serve()``. Checking
+        ``list_hyperparams()`` alone is not enough: a bare ``get_hyperparams``
+        call already leaves an (empty) placeholder behind.
+        """
+        with self._lock:
+            entries = list(self._hyperparams.values())
+        for entry in entries:
+            resolved = entry.get() if Proxy.is_proxy(entry) else entry
+            if isinstance(resolved, dict) and len(resolved) > 0:
+                return True
+        return False
+
     def set_hyperparam(self, key_path: str, value: Any, name: str = DEFAULT_NAME) -> None:
         """Set a nested hyperparameter using dot-separated `key_path`.
         Example: set_hyperparam(name='exp', key_path='data.train.batch_size', value=128)
@@ -1323,6 +1362,14 @@ def get_hyperparams(name: str = DEFAULT_NAME) -> Any:
 def list_hyperparams() -> List[str]:
     return GLOBAL_LEDGER.list_hyperparams()
 
+def has_serving_config() -> bool:
+    """True when serving config (hyperparameters) has been wrapped.
+
+    See :meth:`GrayBoxLedger.has_serving_config`. Used by ``wl.serve`` to guard
+    against exposing an unconfigured backend.
+    """
+    return GLOBAL_LEDGER.has_serving_config()
+
 def resolve_hp_name() -> str | None:
     """Resolve a sensible hyperparam set name from the ledger.
     Checks for 'main', then 'experiment', then falls back to the first registered name.
diff --git a/weightslab/backend/logger.py b/weightslab/backend/logger.py
index 4e6f95b1..3122be62 100644
--- a/weightslab/backend/logger.py
+++ b/weightslab/backend/logger.py
@@ -29,9 +29,13 @@
   staging appends and flushes take the same lock.
 """
 
+import functools
 import json
+import logging
+import os
 import threading
 import time
+from collections import defaultdict
 
 import duckdb
 import pandas as pd
@@ -39,6 +43,8 @@
 
 from weightslab.backend.ledgers import get_logger, register_logger, get_checkpoint_manager
 
+logger = logging.getLogger(__name__)
+
 
 # Column order for each table's staging buffer / bulk insert.
 _SIGNAL_COLS = [
@@ -55,6 +61,13 @@
 # many rows, to bound memory during long runs that never read history.
 _STAGE_FLUSH_THRESHOLD = 50_000
 
+# How often the background flush thread wakes up (see LoggerQueue._flush_loop).
+def _default_flush_interval_seconds() -> float:
+    try:
+        return float(os.environ.get("WL_LOGGER_FLUSH_INTERVAL_SECONDS", "2.0"))
+    except (TypeError, ValueError):
+        return 2.0
+
 
 class LoggerQueue:
     def __init__(self, register: bool = True, db_path: str = ":memory:") -> None:
@@ -73,6 +86,19 @@ def __init__(self, register: bool = True, db_path: str = ":memory:") -> None:
         self._eval_mode_tags: list[str] = []
         self._eval_accum: dict = {} # {graph_name: [sum, count]}
 
+        # Background flush + loss-shape autotag state. Every flag="loss" signal
+        # is auto-classified by default (see _autotag_loss_shapes); these only
+        # hold user overrides/opt-outs for specific signals (or all of them).
+        self._loss_shape_overrides: dict = {}  # {signal_name: (tag_name, classifier)}
+        self._loss_shape_disabled: set = set()
+        self._loss_shape_all_disabled: bool = False
+        # {signal_name: _qps_version at the last successful tag pass} — lets
+        # _autotag_loss_shapes() skip a signal entirely when no new per-sample
+        # data has been staged for it since last time (see _autotag_loss_shapes).
+        self._loss_shape_last_version: dict = {}
+        self._flush_stop = threading.Event()
+        self._flush_thread: threading.Thread | None = None
+
         # DuckDB connection + write-staging buffers.
         self._lock = threading.RLock()
         self._db_path = db_path
@@ -81,6 +107,20 @@ def __init__(self, register: bool = True, db_path: str = ":memory:") -> None:
         self._stage_sample: list = []
         self._stage_instance: list = []
         self._seq = 0
+
+        # Per-sample query cache. Many consumers read the SAME (signal, ids) in
+        # one step (e.g. reactive signals all reading the loss); memoize so N
+        # identical reads cost 1 scan. Keyed by (signal, ids, [step,] hash,
+        # version[signal]); staging a row bumps that signal's version to
+        # invalidate. Step-scoped: _stage_sample_row clears both caches when the
+        # step advances (keys never recur across steps, so old entries are dead).
+        # Cache size is env-configurable (WL_QUERY_CACHE_MAXSIZE, default 2048).
+        _qps_maxsize = int(os.environ.get("WL_QUERY_CACHE_MAXSIZE", "2048"))
+        self._qps_version: dict = defaultdict(int)
+        self._qps_cache_step: int = -1
+        self._qps_cache = functools.lru_cache(maxsize=_qps_maxsize)(self._query_per_sample_uncached)
+        self._qps_step_cache = functools.lru_cache(maxsize=_qps_maxsize)(self._query_per_sample_at_step_uncached)
+
         self._ensure_tables()
         self._restore_runtime_state_from_db()
 
@@ -95,45 +135,249 @@ def __init__(self, register: bool = True, db_path: str = ":memory:") -> None:
         # Init checkpoint manager for experiment hash retrieval (if available)
         self.chkpt_manager = get_checkpoint_manager()
 
+        # If no explicit db_path was given but a checkpoint manager already
+        # exists, persist history to an on-disk DuckDB file under its loggers/
+        # dir. (The reverse ordering — CM created after the logger — is handled
+        # by CheckpointManager.__init__ calling set_db_path on the live logger.)
+        if db_path == ":memory:":
+            try:
+                loggers_dir = getattr(self.chkpt_manager, "loggers_dir", None)
+                if loggers_dir:
+                    self.set_db_path(os.path.join(str(loggers_dir), "loggers.duckdb"))
+            except Exception:
+                pass
+
+        self._start_background_flush()
+
+    # ------------------------------------------------------------------
+    # Background flush + loss-shape autotag
+    # ------------------------------------------------------------------
+    def _start_background_flush(self) -> None:
+        """Start the periodic flush/loss-shape thread (off the caller's thread).
+
+        Runs for the life of the process (daemon) so neither the training loop
+        nor the gRPC servicer has to remember to flush or re-tag loss shapes
+        themselves — every flag="loss" signal is auto-classified with zero
+        setup. See _autotag_loss_shapes for the tagging half."""
+        if self._flush_thread is not None and self._flush_thread.is_alive():
+            return
+        self._flush_stop.clear()
+        self._flush_thread = threading.Thread(
+            target=self._flush_loop, name="WL-Logger-Flush", daemon=True)
+        self._flush_thread.start()
+
+    def set_loss_shape_override(self, loss_signal: str, tag_name: str | None = None,
+                                 classifier=None) -> None:
+        """Override the tag name / classifier used when auto-tagging *loss_signal*.
+
+        Every ``flag="loss"`` signal is already auto-classified with zero setup
+        (see :meth:`_autotag_loss_shapes`); call this only to customize one
+        specific signal (e.g. it isn't a decreasing loss, so the default
+        classifier is wrong for it). Also re-enables that signal if it had been
+        disabled via :meth:`disable_loss_shape_autotag`.
+        """
+        with self._lock:
+            self._loss_shape_overrides[loss_signal] = (tag_name, classifier)
+            self._loss_shape_disabled.discard(loss_signal)
+
+    def disable_loss_shape_autotag(self, loss_signal: str | None = None) -> None:
+        """Stop automatic loss-shape tagging for *loss_signal*, or for every
+        signal (including ones registered later) if *loss_signal* is ``None``.
+        The background flush itself keeps running either way."""
+        with self._lock:
+            if loss_signal is None:
+                self._loss_shape_all_disabled = True
+            else:
+                self._loss_shape_disabled.add(loss_signal)
+                self._loss_shape_overrides.pop(loss_signal, None)
+
+    def _autotag_loss_shapes(self) -> None:
+        """Re-classify every auto-detected ``flag="loss"`` signal that has NEW
+        per-sample data since its last tag pass.
+
+        Each signal is skipped when its ``_qps_version`` (bumped only by
+        ``_stage_sample_row`` on an actual new per-sample write, and already
+        the cache-invalidation key for ``query_per_sample``/
+        ``query_signal_history``) hasn't moved since the last time this
+        signal was classified — no new points means the classification result
+        (and thus every categorical tag written from it) would come out
+        identical, so re-running the full read + classify + tag-write cycle
+        on a bare 2s timer regardless of whether training even produced
+        anything new was pure waste, and contended ``self._lock`` against
+        unrelated reads (e.g. ``get_signal_history()`` for
+        ``GetLatestLoggerData``) the whole time training was paused/idle too.
+        Failures are per-signal and never propagate — one bad
+        classifier/signal can't stop the others.
+        """
+        with self._lock:
+            if self._loss_shape_all_disabled:
+                return
+            disabled = set(self._loss_shape_disabled)
+            overrides = dict(self._loss_shape_overrides)
+        try:
+            # Lazy import: weightslab.src imports LoggerQueue at module load
+            # time, so importing it back here at module scope would cycle.
+            from weightslab.src import auto_loss_shape_signal_names, write_signal_shapes
+        except Exception as exc:
+            logger.debug(f"[LoggerQueue] loss-shape autotag: import failed: {exc}")
+            return
+        # Union, not just the auto-detected set: set_loss_shape_override() can
+        # also opt in a signal that wasn't registered via flag="loss" (e.g. a
+        # manually wl.save_signals()-logged one under a custom name).
+        signal_names = set(auto_loss_shape_signal_names()) | set(overrides.keys())
+        for signal_name in signal_names:
+            if signal_name in disabled:
+                continue
+            with self._lock:
+                current_version = self._qps_version[signal_name]
+            if self._loss_shape_last_version.get(signal_name) == current_version:
+                continue  # no new per-sample data logged since the last pass
+            tag_name, classifier = overrides.get(signal_name, (None, None))
+            try:
+                write_signal_shapes(signal_name, tag_name=tag_name, classifier=classifier)
+                self._loss_shape_last_version[signal_name] = current_version
+            except Exception as exc:
+                logger.debug(
+                    f"[LoggerQueue] loss-shape autotag failed for {signal_name!r}: {exc}")
+
+    def _flush_loop(self) -> None:
+        interval = _default_flush_interval_seconds()
+        while not self._flush_stop.wait(interval):
+            try:
+                self.flush_to_disk()
+            except Exception as exc:
+                logger.debug(f"[LoggerQueue] background flush failed: {exc}")
+            self._autotag_loss_shapes()
+
+    def stop_background_flush(self) -> None:
+        """Stop the background flush/loss-shape thread (e.g. at shutdown or in tests)."""
+        self._flush_stop.set()
+        if self._flush_thread is not None:
+            self._flush_thread.join(timeout=2.0)
+
     # ------------------------------------------------------------------
     # DuckDB plumbing
     # ------------------------------------------------------------------
+    @staticmethod
+    def _schema_ddl(prefix: str = "") -> str:
+        """Return the CREATE-TABLE DDL for all history tables.
+
+        ``prefix`` lets the same schema be created inside an attached database
+        (e.g. ``"ondisk."``) when migrating from in-memory to a file.
+        """
+        return f"""
+            CREATE TABLE IF NOT EXISTS {prefix}signals (
+                metric_name VARCHAR,
+                experiment_hash VARCHAR,
+                step INTEGER,
+                metric_value DOUBLE,
+                timestamp BIGINT,
+                audit_mode BOOLEAN,
+                is_evaluation_marker BOOLEAN,
+                split_name VARCHAR,
+                evaluation_tags VARCHAR,
+                point_note VARCHAR,
+                seq BIGINT
+            );
+            CREATE TABLE IF NOT EXISTS {prefix}per_sample (
+                metric_name VARCHAR,
+                experiment_hash VARCHAR,
+                sample_id VARCHAR,
+                step INTEGER,
+                value REAL,
+                seq BIGINT
+            );
+            CREATE TABLE IF NOT EXISTS {prefix}per_instance (
+                metric_name VARCHAR,
+                experiment_hash VARCHAR,
+                sample_id VARCHAR,
+                annotation_id INTEGER,
+                step INTEGER,
+                value REAL,
+                seq BIGINT
+            );
+        """
+
     def _ensure_tables(self) -> None:
         with self._lock:
-            self._conn.execute(
-                """
-                CREATE TABLE IF NOT EXISTS signals (
-                    metric_name VARCHAR,
-                    experiment_hash VARCHAR,
-                    step INTEGER,
-                    metric_value DOUBLE,
-                    timestamp BIGINT,
-                    audit_mode BOOLEAN,
-                    is_evaluation_marker BOOLEAN,
-                    split_name VARCHAR,
-                    evaluation_tags VARCHAR,
-                    point_note VARCHAR,
-                    seq BIGINT
-                );
-                CREATE TABLE IF NOT EXISTS per_sample (
-                    metric_name VARCHAR,
-                    experiment_hash VARCHAR,
-                    sample_id VARCHAR,
-                    step INTEGER,
-                    value REAL,
-                    seq BIGINT
-                );
-                CREATE TABLE IF NOT EXISTS per_instance (
-                    metric_name VARCHAR,
-                    experiment_hash VARCHAR,
-                    sample_id VARCHAR,
-                    annotation_id INTEGER,
-                    step INTEGER,
-                    value REAL,
-                    seq BIGINT
-                );
-                """
-            )
+            self._conn.execute(self._schema_ddl())
+
+    _HISTORY_TABLES = ("signals", "per_sample", "per_instance")
+
+    def set_db_path(self, db_path) -> None:
+        """Persist signal history to an on-disk DuckDB file.
+
+        Call this once, early in setup. If the file already exists (resume),
+        its data is adopted as-is. If it does not, whatever little is currently
+        in the in-memory DB is migrated into the new file. Either way the file
+        becomes the live connection afterwards.
+
+        The hot logging path is unaffected: ``add_scalars`` still stages to RAM
+        and only bulk-flushes to DuckDB lazily. DuckDB serves reads from its
+        in-memory buffer pool, so this adds durability, not per-read disk hits.
+        """
+        if not db_path or db_path == ":memory:":
+            return
+        db_path = str(db_path)
+
+        with self._lock:
+            if self._db_path == db_path:
+                return
+
+            parent = os.path.dirname(db_path)
+            if parent:
+                os.makedirs(parent, exist_ok=True)
+
+            file_preexists = os.path.exists(db_path)
+
+            try:
+                # Make sure staged rows are materialized before we migrate.
+                self._flush_stage()
+
+                if not file_preexists:
+                    # Fresh file: copy whatever is in the in-memory DB into it.
+                    # ATTACH doesn't accept bind parameters, so inline the path
+                    # with SQL-escaped quotes.
+                    escaped = db_path.replace("'", "''")
+                    self._conn.execute(f"ATTACH '{escaped}' AS ondisk")
+                    self._conn.execute(self._schema_ddl(prefix="ondisk."))
+                    for tbl in self._HISTORY_TABLES:
+                        self._conn.execute(
+                            f"INSERT INTO ondisk.{tbl} SELECT * FROM {tbl}"
+                        )
+                    self._conn.execute("DETACH ondisk")
+
+                # Adopt the on-disk file as the live connection. On resume this
+                # is the source of truth; the fresh in-memory rows are ignored.
+                self._conn.close()
+                self._conn = duckdb.connect(database=db_path)
+                self._db_path = db_path
+                self._ensure_tables()
+                self._invalidate_qps_cache()
+                self._restore_runtime_state_from_db()
+                logger.info(
+                    f"[LoggerQueue] Signal history persisted on disk at {db_path} "
+                    f"({'adopted existing' if file_preexists else 'new'} database)."
+                )
+            except Exception as exc:
+                logger.warning(
+                    f"[LoggerQueue] Failed to enable on-disk persistence at "
+                    f"{db_path}: {exc}. Keeping in-memory history."
+                )
+
+    def flush_to_disk(self) -> None:
+        """Flush staged rows and force a DuckDB checkpoint to the file.
+
+        No-op for an in-memory database. Call at checkpoint time so history is
+        durable even without a clean shutdown (DuckDB also replays its WAL on
+        the next open, so this is belt-and-braces)."""
+        with self._lock:
+            try:
+                self._flush_stage()
+                if self._db_path != ":memory:":
+                    self._conn.execute("CHECKPOINT")
+            except Exception as exc:
+                logger.warning(f"[LoggerQueue] flush_to_disk failed: {exc}")
 
     def _restore_runtime_state_from_db(self) -> None:
         """Repopulate seq counter and graph names from an existing (file) DB."""
@@ -167,7 +411,10 @@ def _maybe_autoflush(self) -> None:
             self._flush_stage()
 
     def _flush_stage(self) -> None:
-        """Bulk-insert all staged rows into DuckDB and clear the buffers."""
+        """Bulk-insert all staged rows into DuckDB and clear the buffers.
+
+        Uses register(pandas)->INSERT SELECT->unregister (DuckDB's fast bulk
+        path). A row-wise executemany was measured ~6x slower — don't switch."""
         with self._lock:
             if self._stage_signals:
                 df = pd.DataFrame(self._stage_signals, columns=_SIGNAL_COLS)
@@ -198,11 +445,25 @@ def _stage_signal_row(self, graph_name, exp_hash, step, metric_value, timestamp,
         self._maybe_autoflush()
 
     def _stage_sample_row(self, graph_name, exp_hash, sample_id, step, value):
-        self._stage_sample.append((
-            graph_name, exp_hash, str(sample_id), int(step), float(value), self._next_seq(),
-        ))
+        # New step -> last step's cache entries can't recur; drop them.
+        if int(step) > self._qps_cache_step:
+            self._invalidate_qps_cache()
+            self._qps_cache_step = int(step)
+        self._stage_sample.append(
+            (
+                graph_name, exp_hash, str(sample_id), int(step), float(value), self._next_seq(),
+            )
+        )
+        self._qps_version[graph_name] += 1   # invalidate this signal's cached reads
         self._maybe_autoflush()
 
+    def _invalidate_qps_cache(self) -> None:
+        """Drop both query caches + versions (step advance; bulk delete/clear)."""
+        self._qps_cache.cache_clear()
+        self._qps_step_cache.cache_clear()
+        self._qps_version.clear()
+        self._loss_shape_last_version.clear()
+
     def _stage_instance_row(self, graph_name, exp_hash, sample_id, annotation_id, step, value):
         self._stage_instance.append((
             graph_name, exp_hash, str(sample_id), int(annotation_id), int(step),
@@ -244,6 +505,7 @@ def clear_signal_histories(self):
             self._conn.execute("DELETE FROM per_instance")
             self._current_step_buffer.clear()
             self._buffered_step = None
+            self._invalidate_qps_cache()
 
     def _to_float(self, value):
         if isinstance(value, th.Tensor):
@@ -443,6 +705,7 @@ def remove_evaluation_hash(self, eval_hash: str) -> None:
             self._flush_stage()
             self._conn.execute("DELETE FROM signals WHERE experiment_hash = ?", [eval_hash])
             self._conn.execute("DELETE FROM per_sample WHERE experiment_hash = ?", [eval_hash])
+            self._invalidate_qps_cache()
 
         # Drop queued points that reference this hash.
         self._pending_queue = [
@@ -712,19 +975,75 @@ def query_per_sample(self, graph_name: str, sample_ids=None, exp_hash=None):
 
         Returns a list of ``(sample_id, step, value, experiment_hash)`` tuples,
         filtered by *sample_ids* and optionally *exp_hash* (``None`` = all hashes).
+
+        Cached (memoized until *graph_name* is next staged). Returns a fresh list.
         """
+        ids_key = tuple(str(s) for s in sample_ids) if sample_ids is not None else None
+        cached = self._qps_cache(graph_name, ids_key, exp_hash, self._qps_version[graph_name])
+        return list(cached)
+
+    def _query_per_sample_uncached(self, graph_name, ids_key, exp_hash, _version):
+        """DuckDB read behind :meth:`query_per_sample`. ``_version`` is a cache
+        key only (bumped on write -> recompute). Returns an immutable tuple."""
         with self._lock:
             self._flush_stage()
             params = [graph_name]
             sql = "SELECT sample_id, step, value, experiment_hash FROM per_sample WHERE metric_name = ?"
             sql += self._hash_filter(exp_hash, params)
-            if sample_ids is not None:
+            if ids_key is not None:
                 sql += " AND sample_id IN (SELECT UNNEST(?))"
-                params.append([str(s) for s in sample_ids])
+                params.append(list(ids_key))
             sql += " ORDER BY seq"
             rows = self._conn.execute(sql, params).fetchall()
 
-        return [(sid, int(step), float(val), h) for (sid, step, val, h) in rows]
+        return tuple((sid, int(step), float(val), h) for (sid, step, val, h) in rows)
+
+    def query_per_sample_at_step(self, graph_name: str, sample_ids, step, exp_hash=None):
+        """``(sample_id, value)`` for *graph_name* at exactly *step* — O(batch),
+        not O(history). Keeps the reactive gather flat as history grows. Cached."""
+        ids_key = tuple(str(s) for s in sample_ids) if sample_ids is not None else None
+        cached = self._qps_step_cache(graph_name, ids_key, int(step), exp_hash,
+                                      self._qps_version[graph_name])
+        return list(cached)
+
+    def _query_per_sample_at_step_uncached(self, graph_name, ids_key, step, exp_hash, _version):
+        """DuckDB read behind :meth:`query_per_sample_at_step` (``_version`` = cache key).
+
+        Fast path: the current step's value is usually still in the in-memory
+        staging buffer, so scan it and skip the flush->register->INSERT->SELECT
+        round-trip. Fall through to DuckDB only if an id isn't staged."""
+        step = int(step)
+        with self._lock:
+            if ids_key is not None:
+                ids_set = set(ids_key)
+                at = {}
+                # Scan from the tail (append-ordered by step); first value per id
+                # wins, stop when all found or once we drop below `step`.
+                for row in reversed(self._stage_sample):
+                    s = row[3]
+                    if s < step:
+                        break
+                    if s == step and row[0] == graph_name \
+                            and (exp_hash is None or row[1] == exp_hash):
+                        sid = row[2]
+                        if sid in ids_set and sid not in at:
+                            at[sid] = row[4]
+                            if len(at) == len(ids_set):
+                                break
+                if len(at) == len(ids_set):
+                    return tuple((sid, float(val)) for sid, val in at.items())
+
+            # Fallback: not fully in the staging buffer -> flush + query DuckDB.
+            self._flush_stage()
+            params = [graph_name, step]
+            sql = "SELECT sample_id, value FROM per_sample WHERE metric_name = ? AND step = ?"
+            sql += self._hash_filter(exp_hash, params)
+            if ids_key is not None:
+                sql += " AND sample_id IN (SELECT UNNEST(?))"
+                params.append(list(ids_key))
+            rows = self._conn.execute(sql, params).fetchall()
+
+        return tuple((sid, float(val)) for (sid, val) in rows)
 
     def query_per_instance(
         self,
diff --git a/weightslab/backend/model_interface.py b/weightslab/backend/model_interface.py
index 6f6fc367..a3f02bc2 100755
--- a/weightslab/backend/model_interface.py
+++ b/weightslab/backend/model_interface.py
@@ -314,6 +314,32 @@ def audit_mode(self) -> bool:
 
         return False
 
+    @staticmethod
+    def _active_model_in_audit_mode() -> bool:
+        """Return True iff the *currently registered* experiment model is in audit mode.
+
+        The backward/optimizer overrides below are installed on the torch classes
+        exactly once, process-wide. They must therefore consult whichever model is
+        active *now* (resolved from the ledger, the same way GuardContext does) —
+        NOT the ModelInterface instance that happened to install the patch first.
+        Capturing a single instance in the closure is a bug: a transient eval-mode
+        model (e.g. one built only for dependency extraction) would then make every
+        backward()/step() in the whole process a silent no-op.
+        """
+        try:
+            model = ledgers.get_model()
+        except Exception:
+            return False
+        # get_model() may hand back a Proxy(None) placeholder when nothing is
+        # registered; unwrap it and treat "no model" as "not in audit mode".
+        try:
+            model = object.__getattribute__(model, '_obj')
+        except AttributeError:
+            pass
+        if model is None:
+            return False
+        return bool(getattr(model, 'audit_mode', False))
+
     def _setup_backward_override(self):
         """Set up monkey-patch to disable backward() when model is in audit/eval mode.
 
@@ -323,12 +349,11 @@ def _setup_backward_override(self):
         """
         # Store the original backward method
         original_backward = th.Tensor.backward
-        model_interface_ref = self
 
         def backward_override(tensor_self, gradient=None, retain_graph=False, create_graph=False, inputs=None):
             """Override backward to be a no-op in audit mode."""
-            # Check if model is in audit mode via the audit_mode property
-            if model_interface_ref.audit_mode:
+            # Resolve the active model at call time (never a stale captured ref).
+            if ModelInterface._active_model_in_audit_mode():
                 return
 
             # Otherwise, call the original backward
@@ -355,12 +380,11 @@ def _setup_optimizer_step_override(self):
         """
         # Store the original step method
         original_step = th.optim.Optimizer.step
-        model_interface_ref = self
 
         def step_override(self, closure=None):
             """Override step to be a no-op in audit mode."""
-            # Check if the model is in audit mode via the audit_mode property
-            if model_interface_ref.audit_mode:
+            # Resolve the active model at call time (never a stale captured ref).
+            if ModelInterface._active_model_in_audit_mode():
                 return
 
             # Otherwise, call the original step
diff --git a/weightslab/cli.py b/weightslab/cli.py
new file mode 100644
index 00000000..63c55076
--- /dev/null
+++ b/weightslab/cli.py
@@ -0,0 +1,838 @@
+"""WeightsLab command-line interface.
+
+Docker-free. Commands:
+  * ``weightslab start``            — run the native UI server (bundled SPA +
+                                      gRPC-Web proxy), the pip-native replacement
+                                      for the old Docker/Envoy stack.
+  * ``weightslab start example``    — run a bundled PyTorch example.
+  * ``weightslab se``               — set up the secure environment (TLS certs +
+                                      gRPC auth token) in $WEIGHTSLAB_CERTS_DIR.
+  * ``weightslab cli``              — attach a terminal to a running experiment.
+  * ``weightslab tunnel``           — forward a remote gRPC backend to a local port.
+"""
+
+import argparse
+import os
+import stat
+import subprocess
+import sys
+import logging
+from pathlib import Path
+from typing import Optional, Sequence
+
+import yaml
+
+from weightslab.security import CertAuthManager
+from weightslab.tunnel import DEFAULT_LISTEN_PORT
+
+logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+logger = logging.getLogger(__name__)
+
+# Capture whether WEIGHTSLAB_CERTS_DIR was already in the shell env when this
+# process started (set by the user) vs. injected later by our own code.
+_CERTS_DIR_IN_ORIGINAL_ENV: bool = "WEIGHTSLAB_CERTS_DIR" in os.environ
+
+
+def _coerce_valid_port(value) -> Optional[int]:
+    """Convert a value to a valid TCP port, or None when invalid."""
+    try:
+        port = int(value)
+    except (TypeError, ValueError):
+        return None
+    return port if 1 <= port <= 65535 else None
+
+
+def _lookup_nested(mapping: dict, path: Sequence[str]):
+    """Safely resolve a nested dict path and return None on missing keys."""
+    cur = mapping
+    for key in path:
+        if not isinstance(cur, dict) or key not in cur:
+            return None
+        cur = cur[key]
+    return cur
+
+
+def _load_ui_port_from_experiment_config(explicit_path: Optional[str] = None) -> Optional[int]:
+    """Read a preferred UI port from an experiment config file when available.
+
+    Resolution order:
+      1) explicit_path (if provided)
+      2) $WEIGHTSLAB_EXPERIMENT_CONFIG
+      3) ./config.yaml, ./config.yml, ./experiment_config.yaml, ./experiment_config.yml
+    """
+    candidates = []
+    if explicit_path:
+        candidates.append(Path(explicit_path))
+    env_cfg = os.getenv("WEIGHTSLAB_EXPERIMENT_CONFIG", "").strip()
+    if env_cfg:
+        candidates.append(Path(env_cfg))
+    cwd = Path.cwd()
+    candidates.extend([
+        cwd / "config.yaml",
+        cwd / "config.yml",
+        cwd / "experiment_config.yaml",
+        cwd / "experiment_config.yml",
+    ])
+
+    first_existing = next((p for p in candidates if p.exists() and p.is_file()), None)
+    if first_existing is None:
+        return None
+
+    try:
+        payload = yaml.safe_load(first_existing.read_text(encoding="utf-8"))
+    except Exception as exc:
+        logger.debug(f"Could not parse experiment config {first_existing}: {exc}")
+        return None
+
+    if not isinstance(payload, dict):
+        return None
+
+    candidate_values = [
+        payload.get("ui_port"),
+        payload.get("studio_port"),
+        _lookup_nested(payload, ("studio", "port")),
+        _lookup_nested(payload, ("weightslab", "ui_port")),
+        _lookup_nested(payload, ("weightslab", "studio", "port")),
+    ]
+    for val in candidate_values:
+        coerced = _coerce_valid_port(val)
+        if coerced is not None:
+            return coerced
+    return None
+
+
+def _resolve_ui_port(args) -> tuple[int, str]:
+    """Resolve preferred UI port source: arg > config > env > default."""
+    arg_port = _coerce_valid_port(getattr(args, "port", None))
+    if arg_port is not None:
+        return arg_port, "--port"
+
+    config_port = _load_ui_port_from_experiment_config(getattr(args, "config", None))
+    if config_port is not None:
+        return config_port, "experiment config"
+
+    env_port = _coerce_valid_port(os.getenv("WL_LAST_UI_PORT"))
+    if env_port is not None:
+        return env_port, "WL_LAST_UI_PORT"
+
+    compat_env_port = _coerce_valid_port(os.getenv("WEIGHTSLAB_UI_PORT"))
+    if compat_env_port is not None:
+        return compat_env_port, "WEIGHTSLAB_UI_PORT"
+
+    return 50051, "default"
+
+
+def _persist_certs_dir(certs_dir_str: str) -> None:
+    """Persist WEIGHTSLAB_CERTS_DIR so future terminals and the training backend find it.
+
+    Windows — runs `setx` (permanent user env) and prints the PS one-liner for
+               the current session.
+    Linux/macOS — appends an export line to ~/.bashrc (idempotent) and prints the
+               source command for the current session.
+    """
+    export_line = f'export WEIGHTSLAB_CERTS_DIR="{certs_dir_str}"'
+    if _is_windows():
+        result = subprocess.run(
+            ["setx", "WEIGHTSLAB_CERTS_DIR", certs_dir_str],
+            stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+        )
+        if result.returncode == 0:
+            logger.info(" WEIGHTSLAB_CERTS_DIR saved permanently via setx (new terminals will have it)")
+        else:
+            logger.warning(f"setx failed — set it manually: setx WEIGHTSLAB_CERTS_DIR \"{certs_dir_str}\"")
+        logger.info(f" Current terminal (PowerShell): $env:WEIGHTSLAB_CERTS_DIR = \"{certs_dir_str}\"")
+    else:
+        bashrc = Path.home() / ".bashrc"
+        try:
+            existing = bashrc.read_text(encoding="utf-8") if bashrc.exists() else ""
+            if export_line not in existing:
+                with open(bashrc, "a", encoding="utf-8") as f:
+                    f.write(f"\n# Added by weightslab\n{export_line}\n")
+                logger.info(f" WEIGHTSLAB_CERTS_DIR appended to {bashrc} (new terminals will have it)")
+            else:
+                logger.info(f" WEIGHTSLAB_CERTS_DIR already in {bashrc}")
+        except OSError as e:
+            logger.warning(f"Could not write to {bashrc}: {e}")
+            logger.info(f" Add manually: {export_line}")
+        logger.info(f" Current terminal: source ~/.bashrc (or open a new terminal)")
+
+
+def _banner() -> str:
+    """Return the WeightsLab ASCII banner, or a plain title if art is unavailable."""
+    try:
+        from weightslab.art import _BANNER
+        return _BANNER
+    except Exception:
+        return "WeightsLab"
+
+
+_DESCRIPTION = (
+    _banner()
+    + "\nWeightsLab — Inspect, Edit, and Evolve Neural Networks\n"
+    + "Run the Weights Studio UI, bundled examples, and the secure "
+    + "(TLS + gRPC auth) environment."
+)
+
+_EPILOG = """\
+commands:
+  se                       Set up the secure environment: generate TLS
+                           certificates + a gRPC auth token in
+                           ~/.weightslab-certs. Then set WEIGHTSLAB_CERTS_DIR
+                           (the single source of truth) so the backend + new
+                           shells find them.
+                             --force-certs   regenerate even if certs exist
+
+  start                    Start the Weights Studio UI natively — no Docker.
+                           Serves the bundled SPA and proxies gRPC-Web to a
+                           running backend, all from one Python process.
+                           UNSECURED (HTTP) by default.
+                             --port PORT           UI HTTP port (default 50051)
+                             --config FILE         experiment config file used to read ui_port
+                             --backend-port PORT   backend gRPC port (default 50051)
+                             --backend-host HOST   backend gRPC host (default localhost)
+                             --no-browser          don't open a browser
+                             --certs               HTTPS + mTLS from $WEIGHTSLAB_CERTS_DIR
+
+  start example            Run a bundled PyTorch example (foreground; stop with
+                           Ctrl+C). Installs the example's requirements first,
+                           without prompting. Defaults to classification:
+                             --cls      classification example (default)
+                             --seg      segmentation example
+                             --det      detection example
+                             --clus     clustering example
+                             --gen      generation example
+                             --3d_det   3D LiDAR point-cloud detection example
+                             --2d_det   2D LiDAR point-cloud detection example
+
+  cli                      Open an interactive terminal connected to a
+                           currently-running experiment (pause/resume, status,
+                           evaluate, agent query, etc.). Auto-discovers the
+                           running experiment; the experiment must be serving
+                           the CLI (e.g. wl.serve(serving_cli=True)).
+                             --port PORT   connect to a specific CLI port
+                             --host HOST   connect to a specific host (default: localhost)
+
+  tunnel                   Forward a REMOTE gRPC backend (e.g. a Colab run
+                           behind `ngrok tcp 50051`) to a LOCAL port so
+                           `weightslab start` can proxy to it. Raw TCP, so the
+                           backend must be plaintext (the default).
+                             ENDPOINT          remote host:port (e.g. bore.pub:12345)
+                                               (default: $WEIGHTSLAB_TUNNEL_ENDPOINT)
+                             --listen-port N   local port to expose (default 50051)
+                             --listen-host H   interface to bind (default: auto)
+                             --remote-port N   remote port, if not in ENDPOINT
+
+examples:
+  weightslab se                       # one-time secure setup (then export WEIGHTSLAB_CERTS_DIR)
+  weightslab se --force-certs         # regenerate the certs
+  weightslab start                    # launch the UI (unsecured HTTP, default) at :50051
+  weightslab start --certs            # launch the UI over HTTPS (needs `weightslab se` first)
+  weightslab start --port 9000        # launch the UI on a custom port
+  weightslab start --backend-port 50052   # proxy to a backend on a custom gRPC port
+  weightslab start example            # run the classification demo (default)
+  weightslab start example --seg      # run the segmentation demo
+  weightslab start example --det      # run the detection demo
+  weightslab start example --3d_det   # run the 3D LiDAR detection demo
+  weightslab cli                      # connect a terminal to the running experiment
+  weightslab cli --port 60000         # connect to a specific CLI port
+  weightslab tunnel bore.pub:12345    # expose a remote (Colab) backend at localhost:50051
+"""
+
+
+def _get_cert_script() -> Path:
+    """Get the generate-certs-auth-token.sh script path."""
+    return Path(__file__).parent / 'ui' / 'utils' / 'generate-certs-auth-token.sh'
+
+
+def _get_cert_script_ps1() -> Path:
+    """Get the generate-certs-auth-token.ps1 script path."""
+    return Path(__file__).parent / 'ui' / 'utils' / 'generate-certs-auth-token.ps1'
+
+
+def _is_windows() -> bool:
+    """Check if running on Windows."""
+    return sys.platform == 'win32'
+
+
+def _make_executable(path) -> None:
+    """Add the execute bit to a file so it can be run directly (POSIX only).
+
+    pip installs the bundled ``.sh`` scripts as package data *without* the execute
+    bit, and they are committed/shipped non-executable. ``_run_shell_script`` runs
+    them as a command (``bash -c "VAR=... '/path/script.sh'"``), which requires
+    the execute bit — otherwise bash fails with "Permission denied" until the user
+    ``chmod +x`` by hand. This grants u+x,g+x,o+x (e.g. 0644 -> 0755) and is a
+    best-effort no-op on Windows, where the POSIX execute bit is not used.
+    """
+    if _is_windows():
+        return
+    try:
+        mode = os.stat(path).st_mode
+        os.chmod(path, mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
+    except OSError as exc:
+        logger.debug(f"Could not set execute bit on {path}: {exc}")
+
+
+def _ensure_scripts_executable() -> None:
+    """Make the bundled cert-generation shell script executable (POSIX only).
+
+    Covers generate-certs-auth-token.sh under ``weightslab/ui/utils`` so a freshly
+    pip-installed package can run it without the user having to ``chmod +x`` first.
+    No-op on Windows.
+    """
+    if _is_windows():
+        return
+    utils_dir = Path(__file__).parent / 'ui' / 'utils'
+    try:
+        scripts = list(utils_dir.rglob('*.sh'))
+    except OSError as exc:
+        logger.debug(f"Could not enumerate bundled scripts under {utils_dir}: {exc}")
+        return
+    for script in scripts:
+        _make_executable(script)
+
+
+def _run_powershell_script(script_path: str, args: list = None, env_vars: dict = None) -> int:
+    """Run a PowerShell script and return exit code."""
+    if not _is_windows():
+        logger.error("PowerShell certificate generation requires Windows")
+        return 1
+
+    cmd = [
+        'powershell',
+        '-NoProfile',
+        '-ExecutionPolicy', 'Bypass',
+        '-File', script_path
+    ]
+
+    if args:
+        cmd.extend(args)
+
+    try:
+        env = os.environ.copy()
+        if env_vars:
+            env.update(env_vars)
+        result = subprocess.run(cmd, env=env)
+        return result.returncode
+    except Exception as e:
+        logger.error(f"Failed to run script: {e}")
+        return 1
+
+
+def _convert_to_git_bash_path(win_path: str) -> str:
+    """Convert Windows path to Git Bash compatible format."""
+    # Normalize backslashes to forward slashes explicitly: Path(...).as_posix()
+    # does NOT convert '\' on POSIX hosts (e.g. Linux CI runners), so a Windows
+    # input path would keep its separators there.
+    p = str(win_path).replace("\\", "/")
+    # Convert C:/Users/... to /mnt/c/Users/... for Git Bash
+    if len(p) > 1 and p[1] == ':':
+        drive = p[0].lower()
+        rest = p[2:]
+        return f"/mnt/{drive}{rest}"
+    return p
+
+
+def _run_shell_script(script_path: str, args: list = None, env_vars: dict = None) -> int:
+    """Run a shell script using bash with proper environment variable passing."""
+
+    try:
+        # Fix line endings in the file before running
+        with open(script_path, 'rb') as f:
+            script_bytes = f.read()
+
+        # Ensure Unix line endings
+        fixed_bytes = script_bytes.replace(b'\r\n', b'\n').replace(b'\r', b'\n')
+
+        # Write back if needed
+        if fixed_bytes != script_bytes:
+            with open(script_path, 'wb') as f:
+                f.write(fixed_bytes)
+
+        # We invoke the script as a command below (bash -c "VAR=... 'script'"),
+        # which needs the execute bit. pip installs it without one, so add it here
+        # (no-op on Windows). Uses the on-disk path before any Git Bash conversion.
+        _make_executable(script_path)
+
+        env = os.environ.copy()
+        if env_vars:
+            env.update(env_vars)
+            # Debug: log all environment variables being passed
+            for key, value in env_vars.items():
+                logger.info(f"Passing env var: {key}={value}")
+
+        # Build bash command - pass Windows path directly, script will handle conversion
+        # # Process path to ensure it's compatible with bash, especially on Windows
+        if _is_windows() and '\\' in script_path:
+            script_path = script_path.replace("\\", "/") # Ensure path is Unix-style for bash
+            script_path = _convert_to_git_bash_path(script_path)
+            logger.info(f"Converted script path for bash: {script_path}")
+        logger.info(f"Running shell script: {script_path} with args: {args} and env_vars: {env_vars}")
+
+        # Build environment variable assignments for bash command
+        env_assignments = ' '.join([f"{k}='{v}'" for k, v in env_vars.items()]) if env_vars else ""
+
+        if env_assignments:
+            # Pass env vars directly in bash command using -c flag
+            # This works on Windows (Git Bash), Linux, and macOS
+            logger.info('Using env assignments')
+            bash_script_cmd = f"{env_assignments} '{script_path}'"
+            if args:
+                bash_script_cmd += " " + " ".join(f"'{arg}'" for arg in args)
+            bash_cmd = ['bash', '-c', bash_script_cmd]
+        else:
+            logger.info('Not using env assignments')
+            bash_cmd = ['bash', '-x', str(script_path)]
+            if args:
+                bash_cmd.extend(args)
+
+        result = subprocess.run(bash_cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
+        if result.stdout:
+            for line in result.stdout.splitlines():
+                logger.info(line)
+        return result.returncode
+    except FileNotFoundError:
+        logger.error(f"Script file not found: {script_path}")
+        return 1
+    except Exception as e:
+        logger.error(f"Failed to run script: {e}")
+        return 1
+
+
+def _generate_certs_with_fallback(force_certs: bool = False, certs_dir=None) -> int:
+    """Try shell script first, fall back to PowerShell on Windows if it fails.
+
+    ``certs_dir`` is forwarded to the generation scripts as ``WEIGHTSLAB_CERTS_DIR``
+    so certs land in the single source-of-truth directory (the scripts default to
+    ``~/.weightslab-certs`` when it is not provided). The shell script receives a
+    POSIX absolute path (``/mnt/c/...`` on Windows/WSL); PowerShell receives the
+    host-native path (``C:/...``).
+    """
+    env_vars = None
+    if certs_dir is not None:
+        # Shell scripts (bash/WSL) need a POSIX-style absolute path.
+        if _is_windows():
+            bash_certs_dir = _convert_to_git_bash_path(str(certs_dir))
+        else:
+            bash_certs_dir = str(certs_dir)
+        env_vars = {'WEIGHTSLAB_CERTS_DIR': bash_certs_dir}
+
+    cert_script = str(_get_cert_script())
+    if not Path(cert_script).exists():
+        logger.warning(f"Shell script not found: {cert_script}")
+    else:
+        script_args = []
+        if force_certs:
+            script_args.append('--force-create-certs')
+
+        logger.info("Attempting certificate generation with shell script...")
+        exit_code = _run_shell_script(cert_script, script_args, env_vars)
+        if exit_code == 0:
+            return 0
+        logger.warning(f"Shell script failed (exit code {exit_code})")
+
+    # Fallback to PowerShell on Windows
+    if _is_windows():
+        logger.info("Falling back to PowerShell for certificate generation...")
+        cert_script_ps1 = str(_get_cert_script_ps1())
+        if not Path(cert_script_ps1).exists():
+            logger.error(f"PowerShell script not found: {cert_script_ps1}")
+            return 1
+
+        script_args = []
+        if force_certs:
+            script_args.append('-ForceCreateCerts')
+
+        exit_code = _run_powershell_script(cert_script_ps1, script_args, env_vars)
+        return exit_code
+    else:
+        logger.error("Neither shell nor PowerShell script could generate certificates")
+        return 1
+
+
+def ui_secure_environment(args):
+    """`weightslab se`: create a certs directory with certs + gRPC token.
+
+    The directory is the single source of truth — WEIGHTSLAB_CERTS_DIR is exported
+    for this process and the user is asked to export it globally. Everything else
+    (TLS on/off, auth on/off) is derived from the files in that directory by the
+    backend and `weightslab start --certs`, so this command sets no other env.
+    """
+    logger.info("Setting up secure environment...")
+    # Bundled .sh scripts ship without the execute bit (pip strips it); make them
+    # runnable so the cert-generation script below doesn't fail on "Permission denied".
+    _ensure_scripts_executable()
+
+    force_certs = getattr(args, "force_certs", False)
+    no_auth = getattr(args, "no_auth", False)
+    certs_dir = getattr(args, "certs_dir", None)
+    if certs_dir:
+        # Absolute path so Windows Python, WSL bash and the server agree on location.
+        certs_dir = str(Path(certs_dir).resolve())
+
+    # Resolve the target directory first (no filesystem work in __init__), so we
+    # can point the generation scripts at it via WEIGHTSLAB_CERTS_DIR.
+    manager = CertAuthManager(certs_dir=certs_dir, enable_auth=not no_auth)
+
+    exit_code = _generate_certs_with_fallback(force_certs=force_certs, certs_dir=manager.certs_dir)
+    if exit_code != 0:
+        logger.error("Certificate generation failed")
+        sys.exit(1)
+
+    manager.certs_dir.mkdir(parents=True, exist_ok=True)
+    manager.get_or_create_auth_token()
+
+    # Export ONLY the single source of truth for this process.
+    os.environ["WEIGHTSLAB_CERTS_DIR"] = str(manager.certs_dir)
+
+    logger.info(" Certificates generated successfully")
+    logger.info(" gRPC auth token created")
+    logger.info(f" Certs and token stored in: {manager.certs_dir}")
+    logger.info(f" WEIGHTSLAB_CERTS_DIR exported for this process: {manager.certs_dir}")
+    logger.info("Then launch the secured UI with: weightslab start --certs")
+    # Keep this the FINAL output so the user can't miss the action they must take.
+    logger.warning("")
+    logger.warning(" ACTION REQUIRED — set WEIGHTSLAB_CERTS_DIR globally so new shells "
+                   "and the training backend find these certs (single source of truth):")
+    logger.warning(f" (bash) echo 'export WEIGHTSLAB_CERTS_DIR=\"{manager.certs_dir}\"' >> ~/.bashrc && source ~/.bashrc")
+    logger.warning(f" (Windows) setx WEIGHTSLAB_CERTS_DIR \"{manager.certs_dir}\"")
+
+
+# Bundled PyTorch examples, keyed by the CLI flag (e.g. --cls -> wl-classification).
+# kind -> (dir_name, label, category) where category is the examples/ subfolder.
+_EXAMPLES = {
+    "cls": ("wl-classification", "classification", "PyTorch"),
+    "seg": ("wl-segmentation", "segmentation", "PyTorch"),
+    "det": ("wl-detection", "detection", "PyTorch"),
+    "clus": ("wl-clustering", "clustering", "PyTorch"),
+    "gen": ("wl-generation", "generation", "PyTorch"),
+    "3d_det": ("wl-3d-lidar-detection", "3D LiDAR detection", "Usecases"),
+    "2d_det": ("wl-2d-lidar-detection", "2D LiDAR detection", "Usecases"),
+}
+_DEFAULT_EXAMPLE = "cls"
+
+
+def _get_example_dir(name: str = "wl-classification", category: str = "PyTorch") -> Path:
+    """Path to a bundled example directory (under examples/<category>/<name>)."""
+    return Path(__file__).parent / 'examples' / category / name
+
+
+def _install_example_requirements(example_dir: Path) -> None:
+    """Install an example's requirements non-interactively, if a file is present.
+
+    Looks for requirements.txt (then requirements.in) in the example directory and
+    runs `pip install -r` with the current interpreter and `--no-input` so it never
+    prompts. Non-fatal: a failure is logged and the example is still attempted, so a
+    transient install hiccup doesn't block a run where deps are already satisfied.
+    """
+    for fname in ("requirements.txt", "requirements.in"):
+        req = example_dir / fname
+        if not req.exists():
+            continue
+        logger.info(f"Installing example requirements (non-interactive): {req}")
+        try:
+            subprocess.run(
+                [sys.executable, "-m", "pip", "install", "-r", str(req),
+                 "--no-input", "--disable-pip-version-check"],
+                check=True,
+            )
+        except subprocess.CalledProcessError as exc:
+            logger.warning(
+                f"Failed to install requirements ({req}): {exc}. "
+                "Continuing — the example may still run if deps are already installed."
+            )
+        return # only the first matching requirements file is used
+
+
+def example_start(args):
+    """`weightslab start example [--cls|--seg|--clus|--gen]`: run a bundled example.
+
+    Defaults to the classification (cls) example. First installs the example's
+    requirements (if a requirements file is present) without prompting, then runs
+    its main.py with the current Python interpreter from its own directory so it
+    resolves its sibling config.yaml. Runs in the foreground (serves until Ctrl+C).
+    """
+    kind = getattr(args, "example_kind", None) or _DEFAULT_EXAMPLE
+    dir_name, label, category = _EXAMPLES.get(kind, _EXAMPLES[_DEFAULT_EXAMPLE])
+
+    example_dir = _get_example_dir(dir_name, category)
+    main_py = example_dir / "main.py"
+    if not main_py.exists():
+        logger.error(f"{label.capitalize()} example not found: {main_py}")
+        sys.exit(1)
+
+    # Install the example's own requirements first, without any interaction.
+    _install_example_requirements(example_dir)
+
+    logger.info(f"Starting the WeightsLab {label} ({kind}) example...")
+    logger.info(f" {main_py}")
+    logger.info("In another terminal, launch the UI with: weightslab start")
+    logger.info("Then open the URL printed by `weightslab start` — stop the example with Ctrl+C.")
+    if not _CERTS_DIR_IN_ORIGINAL_ENV:
+        manager = CertAuthManager.from_env_or_default()
+        if manager.has_valid_certs():
+            logger.warning(
+                "WEIGHTSLAB_CERTS_DIR is not set in your shell environment. "
+                "TLS will work this session (certs found at default location) "
+                "but may not persist across terminals."
+            )
+            _persist_certs_dir(str(manager.certs_dir))
+    try:
+        env = os.environ.copy()
+        env['WEIGHTSLAB_SUPPRESS_BANNER'] = '1'
+        result = subprocess.run([sys.executable, str(main_py)], cwd=str(example_dir), env=env)
+    except KeyboardInterrupt:
+        logger.info("Example stopped.")
+        return
+    if result.returncode != 0:
+        sys.exit(result.returncode)
+
+
+def cli_connect(args):
+    """`weightslab cli [--port N] [--host H]`: open an interactive terminal
+    connected to a currently-running experiment's CLI server.
+
+    With no --port, auto-discovers the running experiment (the backend advertises
+    its actual port on startup). Pass --port to target a specific server.
+    """
+    try:
+        import weightslab.backend.cli as cli_backend
+    except Exception as exc:
+        logger.error(f"Could not load the WeightsLab CLI client: {exc}")
+        sys.exit(1)
+
+    port = getattr(args, "port", None)
+    host = getattr(args, "host", None)
+    exit_code = cli_backend.cli_connect(cli_port=port, cli_host=host)
+    sys.exit(exit_code)
+
+
+def ui_start_native(args):
+    """`weightslab start`: launch the Weights Studio UI natively (no Docker).
+
+    Serves the pre-built SPA vendored in this package and proxies gRPC-Web to a
+    running backend (started by ``wl.serve()``), all from one pure-Python HTTP
+    server — like ``tensorboard``, the UI ships in the wheel.
+
+    Unsecured HTTP by default. Pass ``--certs`` to serve HTTPS + mTLS to the
+    backend, derived solely from cert-file presence in $WEIGHTSLAB_CERTS_DIR.
+    """
+    try:
+        from weightslab.ui import server as ui_server
+    except Exception as exc:  # pragma: no cover - import guard
+        logger.error(f"Could not load the WeightsLab UI server: {exc}")
+        sys.exit(1)
+
+    ui_host = getattr(args, "host", None) or os.getenv("WEIGHTSLAB_UI_HOST", "0.0.0.0")
+    preferred_ui_port, ui_port_source = _resolve_ui_port(args)
+    ui_port = preferred_ui_port
+    backend_host = (getattr(args, "backend_host", None)
+                    or os.getenv("GRPC_BACKEND_HOST", "localhost"))
+    backend_port = (getattr(args, "backend_port", None)
+                    or int(os.getenv("GRPC_BACKEND_PORT", "50051")))
+    open_browser = not getattr(args, "no_browser", False)
+
+    # Never bind the UI server on the same TCP port as the backend gRPC target.
+    # If `weightslab start` grabs backend_port first, the training process cannot
+    # start its gRPC server and the UI appears disconnected.
+    if ui_port == backend_port:
+        logger.warning(
+            f"Requested UI port {ui_port} matches backend gRPC port {backend_port}; "
+            "choosing a random free UI port to avoid collision."
+        )
+        ui_port = 0
+
+    # TLS/auth: single source of truth is cert-file presence in the certs dir.
+    # Only consulted when the user explicitly opts in with --certs.
+    certs_dir = None
+    grpc_auth_token = None
+    if getattr(args, "certs", False):
+        manager = CertAuthManager.from_env_or_default()
+        if manager.has_valid_certs():
+            certs_dir = str(manager.certs_dir)
+            try:
+                grpc_auth_token = manager.get_or_create_auth_token()
+            except Exception:
+                grpc_auth_token = None
+        else:
+            logger.warning(
+                f"--certs requested but no valid certs in {manager.certs_dir}. "
+                "Run `weightslab se` first. Falling back to unsecured HTTP."
+            )
+
+    if not ui_server.has_static_assets():
+        logger.warning(
+            "No bundled UI assets found in this install. The gRPC-Web proxy will "
+            "still run, but the web page is unavailable. Build the frontend and "
+            "vendor it into this package (weights_studio: `ui/utils/build-and-deploy.sh`, "
+            "then `weightslab/ui/utils/sync-frontend.sh`)."
+        )
+
+    # If the preferred UI port is taken, fall back to a free one.
+    probe_host = "127.0.0.1" if ui_host in ("0.0.0.0", "::", "") else ui_host
+    actual_port = ui_server.find_free_port(ui_port, host=probe_host)
+    if actual_port != ui_port:
+        logger.warning(f"Port {ui_port} is busy; using {actual_port} instead.")
+        ui_port = actual_port
+    logger.info(f"UI port source: {ui_port_source} (preferred {preferred_ui_port}, using {ui_port})")
+    os.environ["WL_LAST_UI_PORT"] = str(ui_port)
+
+    ui_server.serve_ui(
+        ui_host=ui_host,
+        ui_port=ui_port,
+        backend_host=backend_host,
+        backend_port=backend_port,
+        open_browser=open_browser,
+        certs_dir=certs_dir,
+        grpc_auth_token=grpc_auth_token,
+        block=True,
+    )
+
+
+def _add_ui_server_flags(p: argparse.ArgumentParser) -> None:
+    """Attach flags for the native UI server."""
+    p.add_argument('--port', type=int, default=None,
+                   help='UI HTTP port (default: config ui_port, else $WL_LAST_UI_PORT, else 50051)')
+    p.add_argument('--config', default=None,
+                   help='Experiment config file to read ui_port from (yaml/yml)')
+    p.add_argument('--host', default=None,
+                   help='UI bind host (default: 0.0.0.0)')
+    p.add_argument('--backend-host', dest='backend_host', default=None,
+                   help='Backend gRPC host to proxy to (default: localhost)')
+    p.add_argument('--backend-port', dest='backend_port', type=int, default=None,
+                   help='Backend gRPC port to proxy to (default: 50051)')
+    p.add_argument('--no-browser', dest='no_browser', action='store_true',
+                   help='Do not open the web browser automatically')
+    p.add_argument('--certs', action='store_true',
+                   help='Serve HTTPS + mTLS to the backend using TLS certs from '
+                        '$WEIGHTSLAB_CERTS_DIR (default: unsecured HTTP). '
+                        'Run `weightslab se` first to generate them.')
+
+
+def _add_example_kind_flags(p: argparse.ArgumentParser) -> None:
+    """Attach the mutually-exclusive example-kind flags (default: classification)."""
+    group = p.add_mutually_exclusive_group()
+    group.add_argument("--cls", action="store_const", dest="example_kind", const="cls",
+                       help="Run the classification example (default)")
+    group.add_argument("--seg", action="store_const", dest="example_kind", const="seg",
+                       help="Run the segmentation example")
+    group.add_argument("--det", action="store_const", dest="example_kind", const="det",
+                       help="Run the detection example")
+    group.add_argument("--clus", action="store_const", dest="example_kind", const="clus",
+                       help="Run the clustering example")
+    group.add_argument("--gen", action="store_const", dest="example_kind", const="gen",
+                       help="Run the generation example")
+    group.add_argument("--3d_det", action="store_const", dest="example_kind", const="3d_det",
+                       help="Run the 3D LiDAR point-cloud detection example")
+    group.add_argument("--2d_det", action="store_const", dest="example_kind", const="2d_det",
+                       help="Run the 2D LiDAR point-cloud detection example")
+    p.set_defaults(example_kind=_DEFAULT_EXAMPLE)
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    """Build the top-level argument parser (banner + detailed command reference).
+
+    The CLI is intentionally minimal — exactly these commands:
+        weightslab --help | -h | help
+        weightslab se [--force-certs]
+        weightslab start [--port PORT] [--config FILE] [--backend-port PORT] [--certs]
+        weightslab start example [--cls|--seg|--det|--clus|--gen|--3d_det|--2d_det]
+        weightslab cli [--port PORT] [--host HOST]
+        weightslab tunnel ENDPOINT
+    """
+    parser = argparse.ArgumentParser(
+        prog="weightslab",
+        description=_DESCRIPTION,
+        epilog=_EPILOG,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    sub = parser.add_subparsers(dest="command", metavar="{se,start,cli,tunnel,help}")
+
+    # weightslab se [--force-certs] [certs_dir]
+    se_parser = sub.add_parser("se", help="Set up the secure environment (TLS certs + gRPC auth token)")
+    se_parser.add_argument('--force-certs', action='store_true', help='Regenerate certificates even if they already exist')
+    se_parser.add_argument('certs_dir', nargs='?', default=None,
+                           help='Custom directory for certs/token (default: $WEIGHTSLAB_CERTS_DIR or ~/.weightslab-certs)')
+
+    # weightslab cli [--port N] [--host H]
+    cli_parser = sub.add_parser(
+        "cli", help="Open an interactive terminal connected to the running experiment")
+    cli_parser.add_argument('--port', type=int, default=None,
+                            help='CLI server port (default: auto-discover the running experiment)')
+    cli_parser.add_argument('--host', default=None,
+                            help='CLI server host (default: localhost)')
+
+    # weightslab tunnel ENDPOINT [--listen-port N] [--listen-host H] [--remote-port N]
+    tunnel_parser = sub.add_parser(
+        "tunnel",
+        help="Forward a remote gRPC backend (e.g. a Colab run via `ngrok tcp 50051`) "
+             "to a local port so `weightslab start` can proxy to it")
+    tunnel_parser.add_argument(
+        'endpoint', nargs='?', default=None,
+        help="Remote backend endpoint host:port (e.g. bore.pub:12345). "
+             "A tcp:// prefix is accepted. Default: $WEIGHTSLAB_TUNNEL_ENDPOINT.")
+    tunnel_parser.add_argument(
+        '--listen-port', '-p', type=int, default=DEFAULT_LISTEN_PORT,
+        help=f"Local port to expose (default: {DEFAULT_LISTEN_PORT} — the port `weightslab start` proxies to)")
+    tunnel_parser.add_argument(
+        '--listen-host', default=None,
+        help="Local interface to bind (default: 127.0.0.1 on Windows/macOS, 0.0.0.0 on Linux)")
+    tunnel_parser.add_argument(
+        '--remote-port', type=int, default=None,
+        help="Remote port, if not included in ENDPOINT")
+
+    # weightslab start [--port N ...]        -> native UI server
+    # weightslab start example [--cls|--seg|--clus|--gen]  -> bundled example
+    start_parser = sub.add_parser(
+        "start",
+        help="Start the Weights Studio UI natively (no Docker); "
+             "or `start example` to run a bundled example")
+    # Flags on the bare `start` command launch the native UI server.
+    _add_ui_server_flags(start_parser)
+    start_sub = start_parser.add_subparsers(dest="start_target")
+    example_parser = start_sub.add_parser(
+        "example", help="Start a bundled PyTorch example (default: classification)")
+    _add_example_kind_flags(example_parser)
+
+    # Tolerate the swapped order: `weightslab example start [flags]` (and bare
+    # `weightslab example`) behave exactly like `weightslab start example`. Hidden
+    # from --help on purpose (argparse.SUPPRESS) — a forgiving fallback.
+    example_alias = sub.add_parser("example", help=argparse.SUPPRESS)
+    example_alias_sub = example_alias.add_subparsers(dest="example_action")
+    example_alias_start = example_alias_sub.add_parser(
+        "start", help="Start a bundled PyTorch example (default: classification)")
+    _add_example_kind_flags(example_alias_start)
+
+    sub.add_parser("help", help="Show this help message")
+
+    return parser
+
+
+def main():
+    parser = _build_parser()
+    args = parser.parse_args()
+
+    if args.command == "help" or args.command is None:
+        parser.print_help()
+    elif args.command == "cli":
+        cli_connect(args)
+    elif args.command == "tunnel":
+        from weightslab.tunnel import tunnel_connect
+        tunnel_connect(args)
+    elif args.command == "se":
+        ui_secure_environment(args)
+    elif args.command == "start":
+        if getattr(args, "start_target", None) == "example":
+            example_start(args)
+        else:
+            # Bare `weightslab start` -> launch the native UI.
+            ui_start_native(args)
+    elif args.command == "example":
+        # Alias for `start example` — tolerate the swapped subcommand order.
+        example_start(args)
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/weightslab/components/checkpoint_manager.py b/weightslab/components/checkpoint_manager.py
index 0166a4f9..528d6e9d 100644
--- a/weightslab/components/checkpoint_manager.py
+++ b/weightslab/components/checkpoint_manager.py
@@ -113,6 +113,11 @@ def __init__(self, root_log_dir: str = 'root_experiment', load_model: bool = Tru
         self.data_checkpoint_dir.mkdir(exist_ok=True)
         self.loggers_dir.mkdir(exist_ok=True)
 
+        # Persist the signal-history logger to an on-disk DuckDB file under
+        # loggers/. Handles the ordering where the logger was created before
+        # this checkpoint manager (the reverse is handled in LoggerQueue.__init__).
+        self._bind_logger_to_disk()
+
         # Manifest file for tracking hash chronology
         self.manifest_file = self.checkpoints_dir / "manifest.yaml"
 
@@ -194,6 +199,31 @@ def _get_data_state_snapshot(self, dfm):
         except Exception:
             return None
 
+    def get_logger_db_path(self) -> Path:
+        """Path of the on-disk DuckDB file backing the signal-history logger."""
+        return self.loggers_dir / "loggers.duckdb"
+
+    def _bind_logger_to_disk(self) -> None:
+        """Point the registered logger at an on-disk DuckDB file, if possible.
+
+        Safe no-op when no logger is registered yet or it predates on-disk
+        persistence support."""
+        try:
+            lg = ledgers.get_logger()
+            if lg is not None and hasattr(lg, "set_db_path"):
+                lg.set_db_path(str(self.get_logger_db_path()))
+        except Exception as e:
+            logger.warning(f"Could not bind logger to on-disk DuckDB: {e}")
+
+    def flush_logger_to_disk(self) -> None:
+        """Force the logger to checkpoint its DuckDB history to disk (if any)."""
+        try:
+            lg = ledgers.get_logger()
+            if lg is not None and hasattr(lg, "flush_to_disk"):
+                lg.flush_to_disk()
+        except Exception as e:
+            logger.warning(f"Could not flush logger to disk: {e}")
+
     def _get_logger_snapshot_dir(self) -> Path:
         return self.loggers_dir
 
@@ -871,9 +901,9 @@ def _save_changes(
             except Exception as e:
                 logger.warning(f"Failed to save weights with component changes: {e}")
 
-        # Always save logger snapshot alongside other components (same hash)
-        if dump_model_architecture or dump_model_state or dump_optimizer_state or dump_config_state:
-            self.save_logger_snapshot()
+        # Durably checkpoint the on-disk signal-history DuckDB alongside the
+        # rest of the state (cheap CHECKPOINT; replaces the old JSON snapshot).
+        self.flush_logger_to_disk()
 
     def save_model_checkpoint(
         self,
@@ -977,12 +1007,6 @@ def save_model_checkpoint(
             # If model architecture doesn't exist in this hash directory, save a reference to where it is
             if self.config.get('checkpoint_manager', {}).get('dump_model_architecture', False):
                 self._save_architecture_reference_if_needed() # TODO (GP): Disable for now because it adds complexity for big models, and we want to ensure architecture is always saved with weights for simplicity
-
-            # Persist logger queues alongside weight checkpoints
-            try:
-                self.save_logger_snapshot()
-            except Exception as e:
-                logger.debug(f"Could not save logger snapshot with checkpoint: {e}")
             return checkpoint_file
         except Exception as e:
             logger.error(f"Failed to save model checkpoint: {e}")
@@ -1214,99 +1238,6 @@ def save_data_snapshot(self, force_new_state: bool = False) -> Optional[Path]:
             logger.error(f"Failed to save data snapshot: {e}")
         return None
 
-    def save_logger_snapshot(self, exp_hash: Optional[str] = None) -> Optional[Path]:
-        """Persist logger queues for the given experiment hash.
-
-        Uses the same hash as model/hp/data; does not affect hashing.
-        """
-        exp = exp_hash or self.current_exp_hash
-        if exp is None:
-            return None
-
-        try:
-            logger_names = ledgers.list_loggers()
-            if not logger_names:
-                return None
-
-            snapshot = {"exp_hash": exp, "timestamp": datetime.now().isoformat(), "loggers": {}}
-            for lname in logger_names:
-                lg = ledgers.get_logger(lname)
-                if lg == None:
-                    continue
-
-                # Get snapshot data from logger, using dedicated method if available for better encapsulation (e.g. for LoggerQueue)
-                payload = lg.save_snapshot()
-
-                # Get final snapshot for this logger
-                snapshot["loggers"][lname] = payload
-
-            if not snapshot["loggers"]:
-                return None
-
-            snapshot_dir = self._get_logger_snapshot_dir()
-            snapshot_dir.mkdir(parents=True, exist_ok=True)
-
-            # Merge existing payload to avoid dropping loggers not currently registered
-            try:
-                existing = self._load_logger_snapshot_payload()
-                existing_loggers = existing.get("loggers", {}) if isinstance(existing, dict) else {}
-                if existing_loggers:
-                    recursive_update(existing_loggers, snapshot.get("loggers", {}))
-                    snapshot["loggers"] = existing_loggers
-            except Exception as e:
-                logger.warning(f"Failed to merge existing logger snapshot: {e}")
-
-            records: List[bytes] = []
-            for lname, payload in snapshot["loggers"].items():
-                line = json.dumps({"logger_name": lname, "payload": payload}, default=str) + "\n"
-                records.append(line.encode("utf-8"))
-
-            chunks: List[bytes] = []
-            current_chunk = bytearray()
-            for record in records:
-                if current_chunk and (len(current_chunk) + len(record) > self.LOGGER_SNAPSHOT_MAX_FILE_SIZE_BYTES):
-                    chunks.append(bytes(current_chunk))
-                    current_chunk = bytearray()
-                current_chunk.extend(record)
-            if current_chunk:
-                chunks.append(bytes(current_chunk))
-
-            old_chunks = self._list_logger_snapshot_chunks()
-            for old_chunk in old_chunks:
-                try:
-                    old_chunk.unlink()
-                except Exception:
-                    pass
-
-            compressor = zstd.ZstdCompressor(level=3)
-            chunk_names: List[str] = []
-            for idx, raw_chunk in enumerate(chunks, start=1):
-                chunk_path = self._get_logger_snapshot_chunk_path(idx)
-                tmp_chunk_path = chunk_path.with_name(chunk_path.name + ".tmp")
-                with open(tmp_chunk_path, "wb") as f:
-                    f.write(compressor.compress(raw_chunk))
-                os.replace(tmp_chunk_path, chunk_path)
-                chunk_names.append(chunk_path.name)
-
-            manifest = {
-                "exp_hash": exp,
-                "timestamp": datetime.now().isoformat(),
-                "format": "ndjson+zstd",
-                "max_file_size_bytes": self.LOGGER_SNAPSHOT_MAX_FILE_SIZE_BYTES,
-                "chunks": chunk_names,
-            }
-            manifest_path = self._get_logger_snapshot_manifest_path()
-            tmp_manifest_path = manifest_path.with_name(manifest_path.name + ".tmp")
-            with open(tmp_manifest_path, "w", encoding="utf-8") as f:
-                json.dump(manifest, f, indent=2)
-            os.replace(tmp_manifest_path, manifest_path)
-
-            logger.info(f"Saved logger snapshot: {manifest_path} ({len(chunk_names)} chunks)")
-            return manifest_path
-        except Exception as e:
-            logger.warning(f"Failed to save logger snapshot: {e}")
-            return None
-
     def save_pending_changes(self, force: bool = False) -> bool:
         """Dump any pending changes to disk.
 
@@ -1473,6 +1404,36 @@ def _load_manager_state(self):
         except Exception as e:
             logger.warning(f"Failed to load manager state: {e}")
 
+    def _resolve_device(self, model: Optional[th.nn.Module] = None) -> th.device:
+        """Resolve the torch device to deserialize a checkpoint onto.
+
+        Priority: the device of an already-loaded model, then the device
+        configured in the ledger's hyperparameters, then CPU. This avoids
+        ``torch.load`` defaulting to the checkpoint's original (possibly
+        CUDA) device on a machine where that device isn't available.
+        """
+        if model is None:
+            try:
+                model = ledgers.get_model()
+                if model is not None and hasattr(model, 'get') and callable(model.get):
+                    model = model.get()
+            except Exception:
+                model = None
+        if model is not None and hasattr(model, 'device'):
+            return model.device
+
+        try:
+            configured_device = (ledgers.get_hyperparams() or {}).get('device')
+        except Exception:
+            configured_device = None
+        if configured_device:
+            try:
+                return th.device(configured_device)
+            except Exception:
+                logger.warning(f"Could not parse configured device {configured_device!r}; defaulting to CPU")
+
+        return th.device('cpu')
+
     def load_latest_checkpoint(
         self,
         model: Optional[th.nn.Module] = None,
@@ -1516,7 +1477,8 @@ def load_latest_checkpoint(
         logger.info(f"Loading checkpoint: {latest_checkpoint.name}")
 
         try:
-            checkpoint = th.load(latest_checkpoint, weights_only=False)
+            device = self._resolve_device(model)
+            checkpoint = th.load(latest_checkpoint, weights_only=False, map_location=device)
 
             # Load model state
             if model is None:
@@ -1685,7 +1647,7 @@ def load_checkpoint(self,
             if checkpoint_files:
                 latest_checkpoint = checkpoint_files[-1]
                 try:
-                    checkpoint = th.load(latest_checkpoint, weights_only=False)
+                    checkpoint = th.load(latest_checkpoint, weights_only=False, map_location=self._resolve_device())
                     rng_state = checkpoint.get('rng_state')
                     if rng_state:
                         result['rng_state'] = rng_state
@@ -1725,7 +1687,10 @@ def load_checkpoint(self,
 
             if checkpoint_file_to_load:
                 try:
-                    result['weights'] = th.load(checkpoint_file_to_load, weights_only=False)
+                    result['weights'] = th.load(
+                        checkpoint_file_to_load, weights_only=False,
+                        map_location=self._resolve_device(result.get('model')),
+                    )
                     result['loaded_components'].add('weights')
                     step = result['weights'].get('step', 0)
 
diff --git a/weightslab/components/global_monitoring.py b/weightslab/components/global_monitoring.py
index 4dcfa184..a9d9d124 100644
--- a/weightslab/components/global_monitoring.py
+++ b/weightslab/components/global_monitoring.py
@@ -1,12 +1,13 @@
 import os
-from typing import Any, Optional
-from enum import Enum
 import contextvars
-
-from threading import Event
 import threading
 import time
 import logging
+import traceback
+
+from typing import Any, Optional
+from enum import Enum
+from threading import Event
 
 from weightslab.backend.ledgers import get_hyperparams, set_hyperparam, resolve_hp_name, get_checkpoint_manager, get_model
 from weightslab.components.tracking import TrackingMode
@@ -276,7 +277,7 @@ def __enter__(self, f: bool = False):
                 self.model.set_tracking_mode(TrackingMode.EVAL)
                 self.model.eval()
 
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any, f: bool = False) -> bool:
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback_: Any, f: bool = False) -> bool:
         """
         Executed upon exiting the 'with' block (after user code runs).
         Reverts the model state.
@@ -296,11 +297,11 @@ def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any, f: bool = Fals
 
         if exc_type is RuntimeError:
             logger.debug(f"Suppressing exception: {exc_value} in GuardContext.__exit__:")
-            traceback.print_exc() if os.getenv("WL_DEBUG", "0") == "1" else None
-            self.architecture_guard.__exit__(exc_type, exc_value, traceback)
+            traceback.print_exc() if os.getenv("WEIGHTSLAB_LOG_LEVEL", "0") == "1" else None
+            self.architecture_guard.__exit__(exc_type, exc_value, traceback_)
             return True # suppress the exception
 
-        self.architecture_guard.__exit__(exc_type, exc_value, traceback)
+        self.architecture_guard.__exit__(exc_type, exc_value, traceback_)
 
         return False
 
diff --git a/weightslab/data/data_samples_with_ops.py b/weightslab/data/data_samples_with_ops.py
index 8236fe5f..d5ef1f5b 100644
--- a/weightslab/data/data_samples_with_ops.py
+++ b/weightslab/data/data_samples_with_ops.py
@@ -128,7 +128,7 @@ def __init__(
         wrapped_dataset: Dataset,
         root_log_dir: Optional[str] = None,
         is_training: bool = True,
-        compute_hash: bool = True,
+        compute_hash: bool = False,
         use_tags: bool = False,
         tags_mapping: Optional[Dict[str, int]] = None,
         stats_store: Optional[H5DataFrameStore] = None,
diff --git a/weightslab/data/data_utils.py b/weightslab/data/data_utils.py
index db8e7f2d..3f5aac51 100644
--- a/weightslab/data/data_utils.py
+++ b/weightslab/data/data_utils.py
@@ -1,3 +1,4 @@
+import io
 import re
 import numpy as np
 import torch as th
@@ -618,6 +619,20 @@ def load_raw_image_array(dataset, index, rank: int = 0) -> tuple:
     if hasattr(wrapped, '__getitem__'):
         np_img, is_volumetric, original_shape = _get_image_array_and_metadata(wrapped, index, rank=rank)
 
+        # Tabular samples (1-D feature vectors) have no spatial dims and cannot be
+        # PIL-encoded as an image. Render a small heatmap for display continuity;
+        # the caller transmits the actual values via build_tabular_raw_data_stat.
+        if getattr(np_img, "ndim", None) == 1:
+            from weightslab.trainer.services.data_image_utils import render_tabular_heatmap
+            thumb_bytes, _shp = render_tabular_heatmap(np_img)
+            thumb_pil = None
+            if thumb_bytes:
+                try:
+                    thumb_pil = Image.open(io.BytesIO(thumb_bytes)).convert("RGB")
+                except Exception:
+                    thumb_pil = None
+            return np_img, False, original_shape, thumb_pil
+
         # Point-cloud samples (LiDAR etc.) cannot be PIL-encoded; render a
         # server-side 2D thumbnail (BEV, range image, or custom projection).
         # Guarded by both the task type and a shape heuristic so regular image
diff --git a/weightslab/data/dataframe_manager.py b/weightslab/data/dataframe_manager.py
index 011c190f..e398e6fb 100644
--- a/weightslab/data/dataframe_manager.py
+++ b/weightslab/data/dataframe_manager.py
@@ -685,29 +685,39 @@ def upsert_df(self, df_local: List | pd.DataFrame, origin: str = None, force_flu
             if self._df.empty:
                 self._df = df_norm.copy()
             else:
-                # Right-preferred upsert with multi-index support
-                existing_idx = df_norm.index.intersection(self._df.index)
-                all_cols = df_norm.columns
-                if len(existing_idx) > 0:
-                    # Widen any categorical target columns to object before assigning:
-                    # writing a value outside a Categorical's category list raises
-                    # "Cannot setitem on a Categorical with a new category". This
-                    # must include `origin` — re-registering rows whose (sample_id,
-                    # annotation_id) already exist under a NEW origin hits exactly
-                    # that error otherwise. The _optimize_dataframe_memory pass below
-                    # re-applies categorical dtypes (origin included), so widening
-                    # here is only transient and costs no memory afterward.
-                    for col in all_cols:
-                        if col in self._df.columns and isinstance(self._df[col].dtype, pd.CategoricalDtype):
-                            self._df[col] = self._df[col].astype(object)
-                    self._df.loc[existing_idx, all_cols] = df_norm.loc[existing_idx, all_cols]
-
-                # Append rows that do not exist yet. Use a boolean mask (not
-                # .loc[difference]) so a duplicate key in df_norm can't be
-                # re-expanded into multiple rows by the label lookup.
-                new_rows = df_norm[~df_norm.index.isin(self._df.index)]
-                if not new_rows.empty:
-                    self._df = pd.concat([self._df, new_rows])
+                # Right-preferred upsert with multi-index support.
+                # If either side has duplicate index labels, label-based setitem
+                # can treat the replacement as a vector assignment into a scalar
+                # cell. Rebuild the overlap with concat instead of trying to write
+                # through .loc in that case.
+                if not self._df.index.is_unique or not df_norm.index.is_unique:
+                    self._df = pd.concat([
+                        self._df.drop(index=df_norm.index, errors='ignore'),
+                        df_norm[~df_norm.index.duplicated(keep='last')],
+                    ])
+                else:
+                    existing_idx = df_norm.index.intersection(self._df.index)
+                    all_cols = df_norm.columns
+                    if len(existing_idx) > 0:
+                        # Widen any categorical target columns to object before assigning:
+                        # writing a value outside a Categorical's category list raises
+                        # "Cannot setitem on a Categorical with a new category". This
+                        # must include `origin` — re-registering rows whose (sample_id,
+                        # annotation_id) already exist under a NEW origin hits exactly
+                        # that error otherwise. The _optimize_dataframe_memory pass below
+                        # re-applies categorical dtypes (origin included), so widening
+                        # here is only transient and costs no memory afterward.
+                        for col in all_cols:
+                            if col in self._df.columns and isinstance(self._df[col].dtype, pd.CategoricalDtype):
+                                self._df[col] = self._df[col].astype(object)
+                        self._df.loc[existing_idx, all_cols] = df_norm.loc[existing_idx, all_cols]
+
+                    # Append rows that do not exist yet. Use a boolean mask (not
+                    # .loc[difference]) so a duplicate key in df_norm can't be
+                    # re-expanded into multiple rows by the label lookup.
+                    new_rows = df_norm[~df_norm.index.isin(self._df.index)]
+                    if not new_rows.empty:
+                        self._df = pd.concat([self._df, new_rows])
 
             logger.debug(f"[LedgeredDataFrameManager] Global DataFrame updated: {len(self._df)} rows, {len(self._df.columns)} columns. Index: {self._df.index.names}")
 
@@ -2151,7 +2161,7 @@ def get_combined_df(
 
         return df
 
-    def get_collapse_annotations_to_samples_df(self, iid: str = None) -> pd.DataFrame:
+    def get_collapse_annotations_to_samples_df(self, df: pd.DataFrame | None = None) -> pd.DataFrame:
         """Collapse a (sample_id, annotation_id) multi-index df to one row per sample.
 
         The shared dataframe manager now expands every sample into one row per
@@ -2182,8 +2192,16 @@ def get_collapse_annotations_to_samples_df(self, iid: str = None) -> pd.DataFram
 
         Returns a single-level ``sample_id``-indexed dataframe (origin stays a
         column). Dataframes that are not annotation-expanded are returned as-is.
+
+        Args:
+            df: An already-materialized combined frame (as returned by
+                :meth:`get_combined_df`) to collapse. When ``None`` (default) a
+                fresh combined frame is pulled here. Callers that just pulled the
+                combined frame should pass it in to avoid a second full copy +
+                buffer merge + proxy conversion over the whole dataset.
         """
-        df = self.get_combined_df()
+        if df is None:
+            df = self.get_combined_df()
 
         SID = SampleStatsEx.SAMPLE_ID.value
         ANNOT = SampleStatsEx.INSTANCE_ID.value
@@ -2423,14 +2441,14 @@ def flush_if_needed_nonblocking(self, force: bool = False):
             self._buffer = {}
 
         if buffered:
-            logger.info(f"Flushing {len(buffered)} buffered records to DataFrame (non-blocking).")
+            logger.debug(f"Flushing {len(buffered)} buffered records to DataFrame (non-blocking).")
             self._apply_buffer_records_nonblocking(buffered)
-            logger.info(f"Applied {len(buffered)} buffered records to DataFrame (non-blocking).")
+            logger.debug(f"Applied {len(buffered)} buffered records to DataFrame (non-blocking).")
 
         # Always check H5 flush even when buffer was empty (pending rows must drain too).
-        logger.info(f"Checking if flush to H5 is needed (non-blocking). Pending count: {len(self._pending)}.")
+        logger.debug(f"Checking if flush to H5 is needed (non-blocking). Pending count: {len(self._pending)}.")
         self._flush_to_h5_if_needed(force=force)
-        logger.info(f"Completed non-blocking flush check. Pending count after flush: {len(self._pending)}.")
+        logger.debug(f"Completed non-blocking flush check. Pending count after flush: {len(self._pending)}.")
 
     def flush(self):
         """Blocking flush: buffer → DF → H5.
@@ -2446,14 +2464,14 @@ def flush(self):
 
         # Step 2: apply to DF (blocking _lock acquisition; outside buffer lock).
         if buffered:
-            logger.info(f"Flushing {len(buffered)} buffered records to DataFrame.")
+            logger.debug(f"Flushing {len(buffered)} buffered records to DataFrame.")
             self._apply_buffer_records(buffered)
-            logger.info(f"Applied {len(buffered)} buffered records to DataFrame.")
+            logger.debug(f"Applied {len(buffered)} buffered records to DataFrame.")
 
         # Step 3: flush DF → H5 (outside buffer lock).
-        logger.info(f"Checking if flush to H5 is needed. Pending count: {len(self._pending)}.")
+        logger.debug(f"Checking if flush to H5 is needed. Pending count: {len(self._pending)}.")
         self._flush_to_h5_if_needed(force=True, blocking=True)
-        logger.info(f"Completed flush. Pending count after flush: {len(self._pending)}.")
+        logger.debug(f"Completed flush. Pending count after flush: {len(self._pending)}.")
 
 # Create global instance with config-driven parameters
 def create_ledger_manager():
diff --git a/weightslab/data/h5_array_store.py b/weightslab/data/h5_array_store.py
index 346e3b86..02c3e7e3 100644
--- a/weightslab/data/h5_array_store.py
+++ b/weightslab/data/h5_array_store.py
@@ -470,39 +470,49 @@ def save_array(
 
         self._ensure_parent()
 
+        # Acquire the exclusive write side of the read-write lock so that no
+        # reader (load_array / load_arrays_batch) can have the file open in
+        # 'r' mode while we open it in 'a' mode. HDF5 forbids opening the same
+        # file read-write while it is already open read-only in the same
+        # process ("file is already open for read-only"), which otherwise
+        # corrupts the store.
         with self._local_lock:
-            with _InterProcessFileLock(self._lock_path, timeout=self._lock_timeout, poll_interval=self._poll_interval):
-                try:
-                    with h5py.File(str(self._path), 'a') as f:
-                        # Create group structure: /sample_id/key_name/
-                        sample_group_name = str(sample_id)
-                        if sample_group_name not in f:
-                            sample_group = f.create_group(sample_group_name)
-                        else:
-                            sample_group = f[sample_group_name]
-
-                        # Remove existing key if present
-                        if key_name in sample_group:
-                            del sample_group[key_name]
-
-                        # Create key group
-                        key_group = sample_group.create_group(key_name)
-
-                        # Store array data (with optional compression)
-                        if self._use_compression:
-                            key_group.create_dataset('data', data=array, compression='gzip', compression_opts=4)
-                        else:
-                            key_group.create_dataset('data', data=array)
-
-                        # Store metadata
-                        for k, v in metadata.items():
-                            key_group.attrs[k] = v
-
-                    return self._build_path_reference(sample_id, key_name)
-
-                except Exception as exc:
-                    logger.error(f"[H5ArrayStore] Failed to save array for sample_id={sample_id}, key={key_name}: {exc}")
-                    return None
+            self._rw_lock.acquire_write()
+            try:
+                with _InterProcessFileLock(self._lock_path, timeout=self._lock_timeout, poll_interval=self._poll_interval):
+                    try:
+                        with h5py.File(str(self._path), 'a') as f:
+                            # Create group structure: /sample_id/key_name/
+                            sample_group_name = str(sample_id)
+                            if sample_group_name not in f:
+                                sample_group = f.create_group(sample_group_name)
+                            else:
+                                sample_group = f[sample_group_name]
+
+                            # Remove existing key if present
+                            if key_name in sample_group:
+                                del sample_group[key_name]
+
+                            # Create key group
+                            key_group = sample_group.create_group(key_name)
+
+                            # Store array data (with optional compression)
+                            if self._use_compression:
+                                key_group.create_dataset('data', data=array, compression='gzip', compression_opts=4)
+                            else:
+                                key_group.create_dataset('data', data=array)
+
+                            # Store metadata
+                            for k, v in metadata.items():
+                                key_group.attrs[k] = v
+
+                        return self._build_path_reference(sample_id, key_name)
+
+                    except Exception as exc:
+                        logger.error(f"[H5ArrayStore] Failed to save array for sample_id={sample_id}, key={key_name}: {exc}")
+                        return None
+            finally:
+                self._rw_lock.release_write()
 
     def save_arrays_batch(
         self,
@@ -571,43 +581,74 @@ def save_arrays_batch(
             return {}
 
         # --- Phase 2: merge temp into main file under lock ---
+        # Hold the exclusive write side of the read-write lock so that no
+        # reader (load_array / load_arrays_batch) has the file open in 'r'
+        # mode while we open it in 'a' mode. HDF5 refuses to open a file
+        # read-write while it is already open read-only in the same process
+        # ("file is already open for read-only"); that failure previously
+        # triggered a backup-restore that overwrote the live file out from
+        # under open reader handles and corrupted the store.
         with self._local_lock:
-            with _InterProcessFileLock(self._lock_path, timeout=self._lock_timeout, poll_interval=self._poll_interval):
-                backup_path = self._create_backup()
-                try:
-                    with h5py.File(str(self._path), 'a') as f_main:
-                        with h5py.File(str(tmp_path), 'r') as f_tmp:
-                            for sample_group_name in f_tmp:
-                                if sample_group_name in f_main:
-                                    del f_main[sample_group_name]
-                                f_tmp.copy(sample_group_name, f_main)
-
-                    path_refs: Dict[int, Dict[str, str]] = {}
-                    for sample_group_name, key_data in prepared.items():
-                        sample_id = sample_group_name
-                        path_refs[sample_id] = {
-                            key_name: self._build_path_reference(sample_id, key_name)
-                            for key_name in key_data
-                        }
-
-                    # Merge succeeded — remove backup so recover() doesn't restore it
-                    if backup_path:
-                        backup_path.unlink(missing_ok=True)
-
-                    logger.debug(
-                        f"[H5ArrayStore] Successfully upserted "
-                        f"{sum(len(v) for v in path_refs.values())} arrays for "
-                        f"{len(path_refs)} samples"
-                    )
-                    return path_refs
-
-                except Exception as exc:
-                    logger.error(f"[H5ArrayStore] Failed to merge temp batch file: {exc}")
-                    if backup_path:
-                        self._restore_backup(backup_path)
-                    return {}
-                finally:
-                    tmp_path.unlink(missing_ok=True)
+            self._rw_lock.acquire_write()
+            try:
+                with _InterProcessFileLock(self._lock_path, timeout=self._lock_timeout, poll_interval=self._poll_interval):
+                    backup_path = self._create_backup()
+                    # Track whether we actually began mutating the main file.
+                    # If we never opened it (e.g. it was busy), the file is
+                    # intact and restoring from a copy would only risk harm.
+                    main_opened = False
+                    try:
+                        with h5py.File(str(self._path), 'a') as f_main:
+                            main_opened = True
+                            with h5py.File(str(tmp_path), 'r') as f_tmp:
+                                for sample_group_name in f_tmp:
+                                    if sample_group_name not in f_main:
+                                        f_main.create_group(sample_group_name)
+                                    dest_group = f_main[sample_group_name]
+                                    src_group = f_tmp[sample_group_name]
+                                    # Replace only the keys present in this batch
+                                    # (e.g. "prediction") so sibling keys written
+                                    # by an earlier flush (e.g. "target") survive.
+                                    for key_name in src_group:
+                                        if key_name in dest_group:
+                                            del dest_group[key_name]
+                                        f_tmp.copy(f"{sample_group_name}/{key_name}", dest_group)
+
+                        path_refs: Dict[int, Dict[str, str]] = {}
+                        for sample_group_name, key_data in prepared.items():
+                            sample_id = sample_group_name
+                            path_refs[sample_id] = {
+                                key_name: self._build_path_reference(sample_id, key_name)
+                                for key_name in key_data
+                            }
+
+                        # Merge succeeded — remove backup so recover() doesn't restore it
+                        if backup_path:
+                            backup_path.unlink(missing_ok=True)
+
+                        logger.debug(
+                            f"[H5ArrayStore] Successfully upserted "
+                            f"{sum(len(v) for v in path_refs.values())} arrays for "
+                            f"{len(path_refs)} samples"
+                        )
+                        return path_refs
+
+                    except Exception as exc:
+                        logger.error(f"[H5ArrayStore] Failed to merge temp batch file: {exc}")
+                        if backup_path:
+                            if main_opened:
+                                # We started writing into the main file; it may
+                                # be half-merged, so roll back to the backup.
+                                self._restore_backup(backup_path)
+                            else:
+                                # Main file was never touched — drop the backup
+                                # rather than overwrite a healthy file.
+                                backup_path.unlink(missing_ok=True)
+                        return {}
+                    finally:
+                        tmp_path.unlink(missing_ok=True)
+            finally:
+                self._rw_lock.release_write()
 
     def recover(self) -> None:
         """
@@ -661,7 +702,7 @@ def load_array(self, path_ref: str) -> Optional[np.ndarray]:
             return None
 
         # Use read lock for concurrent read access (multiple threads can load in parallel)
-        # self._rw_lock.acquire_read()
+        self._rw_lock.acquire_read()
         try:
             try:
                 with h5py.File(str(self._path), 'r', locking=False) as f:
diff --git a/weightslab/data/h5_dataframe_store.py b/weightslab/data/h5_dataframe_store.py
index 9e2d05cf..476b5655 100644
--- a/weightslab/data/h5_dataframe_store.py
+++ b/weightslab/data/h5_dataframe_store.py
@@ -208,16 +208,26 @@ def save_tag_registry(self, registry: dict) -> None:
             return
         self._ensure_parent()
         payload = json.dumps(self._tag_registry)
-        try:
-            with self._local_lock:
-                with _InterProcessFileLock(self._lock_path, timeout=self._lock_timeout, poll_interval=self._poll_interval):
-                    with pd.HDFStore(str(self._path), mode="a") as store:
-                        if self._TAG_REGISTRY_KEY in store:
-                            store.remove(self._TAG_REGISTRY_KEY)
-                        store.put(self._TAG_REGISTRY_KEY, pd.DataFrame({"registry": [payload]}), format="table")
-                        store.flush()
-        except Exception as e:
-            logger.debug(f"[H5DataFrameStore] Failed to save tag registry: {e}")
+        # Windows can transiently deny reopening a file another handle (this
+        # process's own backup copy, antivirus scanning, etc.) just released
+        # a moment ago -- a couple of retries rides out that window without
+        # giving up on a save that would otherwise succeed a beat later.
+        max_attempts = 3
+        for attempt in range(1, max_attempts + 1):
+            try:
+                with self._local_lock:
+                    with _InterProcessFileLock(self._lock_path, timeout=self._lock_timeout, poll_interval=self._poll_interval):
+                        with pd.HDFStore(str(self._path), mode="a") as store:
+                            if self._TAG_REGISTRY_KEY in store:
+                                store.remove(self._TAG_REGISTRY_KEY)
+                            store.put(self._TAG_REGISTRY_KEY, pd.DataFrame({"registry": [payload]}), format="table")
+                            store.flush()
+                return
+            except Exception as e:
+                if attempt == max_attempts:
+                    logger.debug(f"[H5DataFrameStore] Failed to save tag registry after {max_attempts} attempts: {e}")
+                else:
+                    time.sleep(self._poll_interval * attempt)
 
     def load_tag_registry(self) -> dict:
         """Load the categorical tag registry from H5 into memory and return it."""
diff --git a/weightslab/docker/Dockerfile b/weightslab/docker/Dockerfile
deleted file mode 100644
index 4e61aecb..00000000
--- a/weightslab/docker/Dockerfile
+++ /dev/null
@@ -1,27 +0,0 @@
-# Weights Studio Frontend - Production Ready
-# Adapts to certificate availability: uses HTTPS if certs present, falls back to HTTP
-
-FROM nginx:alpine
-
-ARG VITE_WL_ENABLE_GRPC_AUTH_TOKEN=0
-ARG VITE_GRPC_AUTH_TOKEN=
-
-# Remove default config
-RUN rm /etc/nginx/conf.d/default.conf
-
-# Create a placeholder index.html for now
-# When frontend source is available, replace with built assets
-RUN mkdir -p /usr/share/nginx/html && \
-    echo '<!DOCTYPE html>' > /usr/share/nginx/html/index.html && \
-    echo '<html><head><title>Weights Studio</title></head>' >> /usr/share/nginx/html/index.html && \
-    echo '<body><h1>Weights Studio - Frontend Loading</h1>' >> /usr/share/nginx/html/index.html && \
-    echo '<p>Backend connection ready. Frontend source will be integrated here.</p></body>' >> /usr/share/nginx/html/index.html && \
-    echo '</html>' >> /usr/share/nginx/html/index.html
-
-# Copy entrypoint script that dynamically configures nginx based on available certs
-COPY docker/nginx-entrypoint.sh /docker-entrypoint.sh
-RUN chmod +x /docker-entrypoint.sh
-
-EXPOSE 80 443
-
-ENTRYPOINT ["/docker-entrypoint.sh"]
diff --git a/weightslab/docker/docker/.dockerignore b/weightslab/docker/docker/.dockerignore
deleted file mode 100644
index 6b553fb2..00000000
--- a/weightslab/docker/docker/.dockerignore
+++ /dev/null
@@ -1,20 +0,0 @@
-# Docker
-node_modules/
-npm-debug.log*
-.npm
-.env
-.env.local
-docker-artifact/
-
-# IDE
-.vscode/
-.idea/
-
-# Build outputs
-dist/
-build/
-*.log
-
-# OS
-.DS_Store
-Thumbs.db
diff --git a/weightslab/docker/docker/.env.example b/weightslab/docker/docker/.env.example
deleted file mode 100644
index acabf5cc..00000000
--- a/weightslab/docker/docker/.env.example
+++ /dev/null
@@ -1,67 +0,0 @@
-# Weights Studio Docker - Environment Configuration
-# Copy this to .env and adjust values as needed
-
-# Port Configuration (single port works with HTTP or HTTPS)
-VITE_PORT=5173
-
-# Backend Configuration
-WS_SERVER_HOST=localhost
-WS_SERVER_PORT=8080
-WS_SERVER_PROTOCOL=https
-
-# gRPC Backend Port (for Envoy)
-GRPC_BACKEND_PORT=50051
-ENVOY_PORT=8080
-
-# Node Environment
-NODE_ENV=production
-
-# Frontend HTTPS Configuration
-# Set to 1 if you've generated certs and want HTTPS in the browser console logs
-# Set to 0 for HTTP-only development mode
-VITE_DEV_SERVER_HTTPS=0
-
-# gRPC Auth Token (optional)
-# Set VITE_WL_ENABLE_GRPC_AUTH_TOKEN=1 to enable auth
-# Set VITE_GRPC_AUTH_TOKEN to the token value
-# If not set, auth is disabled
-VITE_WL_ENABLE_GRPC_AUTH_TOKEN=0
-VITE_GRPC_AUTH_TOKEN=
-
-# Alternative: load from environment variable
-# GRPC_AUTH_TOKEN=your-auth-token-here
-
-# Certificate Directory
-# Set to your certs path (e.g., C:\Users\YourName\.weightslab-certs on Windows or ~/.weightslab-certs on Linux/Mac)
-# Leave empty to use local .weightslab-certs directory (no certs, HTTP mode)
-WEIGHTSLAB_CERTS_DIR=.weightslab-certs
-
-# DEPLOYMENT SCENARIOS:
-#
-# 1. HTTP ONLY (No Certs, No Auth)
-#    VITE_WL_ENABLE_GRPC_AUTH_TOKEN=0
-#    Leave certs/ directory empty
-#    Access: http://localhost:5173
-#
-# 2. HTTPS + Auth
-#    VITE_WL_ENABLE_GRPC_AUTH_TOKEN=1
-#    VITE_GRPC_AUTH_TOKEN=<hex-token>
-#    Place certificates in ./envoy/certs/
-#    Access: https://localhost:5173
-#
-# 3. HTTPS Only (No Auth)
-#    VITE_WL_ENABLE_GRPC_AUTH_TOKEN=0
-#    Place certificates in ./envoy/certs/
-#    Access: https://localhost:5173
-#
-# Certificate Files Required (if using HTTPS):
-#   Automatically loaded from: ~/.weightslab-certs/
-#   - envoy-server.crt
-#   - envoy-server.key
-#   - ca.crt
-#
-# If certs don't exist in ~/.weightslab-certs/, the system runs in HTTP-only mode.
-#
-# Generate certificates using:
-#   bash weightslab/ui/docker/utils/generate-certs.sh    # Generate certs for ~/.weightslab-certs/
-#   FORCE_CREATE=1 bash weightslab/ui/docker/utils/generate-certs.sh  # Regenerate certs
diff --git a/weightslab/docker/docker/DEPLOYMENT.md b/weightslab/docker/docker/DEPLOYMENT.md
deleted file mode 100644
index 3bfe619a..00000000
--- a/weightslab/docker/docker/DEPLOYMENT.md
+++ /dev/null
@@ -1,271 +0,0 @@
-# Weights Studio Docker Deployment Guide
-
-This Docker setup automatically adapts to the availability of TLS certificates and gRPC auth tokens. All scenarios use a **single port (5173)** that works with HTTP or HTTPS depending on certificate availability.
-
-## Requirements
-
-- **Docker Engine** with the daemon running.
-- **Docker Compose v2** (the `docker compose` CLI plugin) — *recommended*, or the
-  legacy **v1** standalone binary (`docker-compose`, **≥ 1.27**). `weightslab ui launch`
-  auto-detects whichever is installed.
-
-> **Note:** The manual `docker compose ...` commands in this guide assume Compose v2.
-> If you are on v1, substitute the hyphenated form, e.g. `docker-compose -f docker-compose.yml up -d`.
-
-## Quick Start
-
-### Scenario 1: HTTP Only (No Certs, No Auth) - Development/Testing
-
-Fastest way to get running without certificates:
-
-```bash
-# Copy the default environment
-cp .env.example .env
-
-# Ensure certificates directory is empty
-rm -rf ../envoy/certs/*
-
-# Run with production compose
-docker compose -f docker-compose.yml -f docker-compose.prod.yml up -d
-
-# Access at: http://localhost:5173
-```
-
-The container will:
-- Listen on port 443 in HTTP mode
-- Disable gRPC auth token validation
-- Host port 5173 maps to container port 443
-
-### Scenario 2: HTTPS + gRPC Auth - Secure Production
-
-```bash
-# Generate certificates and auth token (one-time setup)
-weightslab se
-
-# Copy environment with auth enabled
-cp .env.example .env
-cat >> .env << 'EOF'
-VITE_WL_ENABLE_GRPC_AUTH_TOKEN=1
-EOF
-
-# Run with production compose
-docker compose -f docker-compose.yml -f docker-compose.prod.yml up -d
-
-# Access at: https://localhost:5173
-# Frontend will validate gRPC auth token on API calls
-```
-
-The container will:
-- Detect certificates in `/etc/nginx/certs/`
-- Enable HTTPS on port 443
-- Host port 5173 maps to container port 443 (HTTPS)
-- Pass gRPC auth token to frontend
-
-### Scenario 3: HTTPS Only (No Auth)
-
-```bash
-# Generate certificates without auth
-weightslab se --no-auth
-
-# Copy environment with HTTPS, no auth
-cp .env.example .env
-
-# Run with production compose
-docker compose -f docker-compose.yml -f docker-compose.prod.yml up -d
-
-# Access at: https://localhost:5173
-```
-
-The container will:
-- Detect certificates
-- Enable HTTPS on port 443
-- Disable gRPC auth token validation
-- Host port 5173 maps to container port 443 (HTTPS)
-
-## How It Works
-
-### Dynamic Configuration
-
-The Docker image includes an entrypoint script (`nginx-entrypoint.sh`) that:
-
-1. **Checks for certificates** at container startup:
-   - Looks for `/etc/nginx/certs/envoy-server.crt` and `.key`
-
-2. **Generates appropriate nginx server block**:
-   - Single server listening on port 443
-   - If certs found → Enable `ssl` directive + certificate paths
-   - If certs missing → Plain HTTP on port 443
-
-3. **Validates the config** and starts nginx
-
-### Volume Mounts
-
-The `docker-compose.yml` mounts:
-
-```yaml
-volumes:
-  - ../envoy/certs:/etc/nginx/certs:ro  # Optional - uses HTTP if empty or missing
-```
-
-The mount path is **optional**:
-- If `../envoy/certs/` exists but is empty → HTTP on port 443
-- If certificates files exist → HTTPS on port 443
-- If directory doesn't exist → Docker creates it (empty) → HTTP on port 443
-
-### Certificate Files
-
-When certificates are available, they must be at:
-
-```
-weightslab/ui/envoy/certs/
-├── envoy-server.crt
-└── envoy-server.key
-```
-
-Generate with:
-
-```bash
-weightslab se [--force-certs] [--no-auth]
-```
-
-## Environment Variables
-
-### Frontend Configuration
-
-| Variable | Default | Effect |
-|----------|---------|--------|
-| `VITE_WL_ENABLE_GRPC_AUTH_TOKEN` | `0` | Enable gRPC auth validation on frontend |
-| `VITE_GRPC_AUTH_TOKEN` | empty | Auth token sent with gRPC calls |
-| `WS_SERVER_HOST` | `localhost` | Backend server address |
-| `WS_SERVER_PORT` | `8080` | Backend server port |
-| `WS_SERVER_PROTOCOL` | `https` | Backend protocol (http/https) |
-
-### Docker Configuration
-
-| Variable | Default | Effect |
-|----------|---------|--------|
-| `VITE_PORT` | `5173` | Host port (container always uses 443) |
-| `GRPC_BACKEND_PORT` | `50051` | Backend gRPC port for Envoy |
-| `ENVOY_PORT` | `8080` | Envoy gRPC-Web proxy port |
-| `NODE_ENV` | `production` | Node environment |
-
-## Health Checks
-
-The nginx config includes a health endpoint at `/health`:
-
-```bash
-# HTTP mode (no certs)
-curl http://localhost:5173/health
-# Output: healthy
-
-# HTTPS mode (with certs)
-curl https://localhost:5173/health
-# Output: healthy (ignoring cert warnings in curl: curl -k)
-```
-
-## Troubleshooting
-
-### Container won't start
-
-1. Check logs:
-   ```bash
-   docker compose logs weights_studio
-   ```
-
-2. Common issues:
-   - Certs directory doesn't exist → Create: `mkdir -p ../envoy/certs`
-   - Port conflict → Check `docker ps` or change `VITE_HTTP_PORT`/`VITE_HTTPS_PORT`
-
-### HTTPS not working
-
-1. Verify certificates exist:
-   ```bash
-   ls -la ../envoy/certs/
-   ```
-
-2. Check nginx accepted the config:
-   ```bash
-   docker exec weights_studio_frontend nginx -t
-   ```
-
-### gRPC calls failing
-
-1. Check Envoy is running:
-   ```bash
-   docker ps | grep envoy
-   ```
-
-2. Verify backend is reachable:
-   ```bash
-   docker exec weights_studio_frontend curl -v http://weights_studio_envoy:8080/
-   ```
-
-3. Validate auth token if enabled:
-   - Frontend sends `Authorization: Bearer <token>` header
-   - Backend must accept the same token
-
-## Production Deployment
-
-### Pre-deployment Checklist
-
-- [ ] Generate TLS certificates (recommended for production)
-- [ ] Set gRPC auth token (if exposing externally)
-- [ ] Configure `WS_SERVER_HOST` to point to actual backend
-- [ ] Test health endpoints
-- [ ] Verify backend connectivity
-- [ ] Review nginx logs after startup
-
-### Recommended Configuration
-
-```bash
-# Production with full security
-weightslab se --force-certs  # Generate fresh certs
-
-# Set environment
-cat > .env << 'EOF'
-VITE_WL_ENABLE_GRPC_AUTH_TOKEN=1
-WS_SERVER_HOST=backend.production.internal
-WS_SERVER_PORT=8080
-WS_SERVER_PROTOCOL=https
-NODE_ENV=production
-EOF
-
-# Deploy
-docker compose -f docker-compose.yml -f docker-compose.prod.yml up -d
-```
-
-### Scaling
-
-To run multiple instances:
-
-1. Change container names and ports per instance:
-   ```bash
-   VITE_HTTP_PORT=5174 VITE_HTTPS_PORT=5175 docker compose ... up -d
-   ```
-
-2. Use a load balancer in front
-3. Share the same certificates and auth token across instances
-4. Ensure all point to the same backend
-
-## Docker Image
-
-### Building
-
-```bash
-# From weightslab/ui/ directory
-docker build -t weights_studio_frontend:latest .
-```
-
-### Publishing
-
-```bash
-docker tag weights_studio_frontend:latest graybx/weightslab:latest
-docker push graybx/weightslab:latest
-```
-
-Then update `docker-compose.yml`:
-```yaml
-services:
-  weights_studio:
-    image: graybx/weightslab:latest
-```
diff --git a/weightslab/docker/docker/docker-compose.yml b/weightslab/docker/docker/docker-compose.yml
deleted file mode 100644
index e12f65e3..00000000
--- a/weightslab/docker/docker/docker-compose.yml
+++ /dev/null
@@ -1,127 +0,0 @@
-# Global Docker Compose configuration for Weights Studio
-services:
-  # Envoy proxy for gRPC-Web
-  envoy:
-    image: envoyproxy/envoy:v1.28-latest
-    container_name: weights_studio_envoy
-    ports:
-      - "${ENVOY_PORT:-8080}:${ENVOY_PORT:-8080}"
-      - "9901:9901"
-    volumes:
-      - ./../envoy/envoy.yaml:/etc/envoy/envoy.yaml:ro
-      - ./../envoy/envoy.upstream_plaintext.yaml:/etc/envoy/envoy.upstream_plaintext.yaml:ro
-      - ./../envoy/envoy.downstream_plaintext.yaml:/etc/envoy/envoy.downstream_plaintext.yaml:ro
-      - ./../envoy/envoy.downstream_upstream_plaintext.yaml:/etc/envoy/envoy.downstream_upstream_plaintext.yaml:ro
-      - ${WEIGHTSLAB_CERTS_DIR:-${HOME}/.weightslab-certs}:/etc/envoy/certs:ro
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
-      - "grpc-backend:host-gateway"
-    environment:
-      - GRPC_BACKEND_PORT=${GRPC_BACKEND_PORT:-50051}
-      - ENVOY_UPSTREAM_TLS=${ENVOY_UPSTREAM_TLS:-auto}
-      - ENVOY_DOWNSTREAM_TLS=${ENVOY_DOWNSTREAM_TLS:-auto}
-    command:
-      - /bin/sh
-      - -c
-      - |
-        GRPC_BACKEND_PORT="$${GRPC_BACKEND_PORT:-50051}";
-        ENVOY_UPSTREAM_TLS="$${ENVOY_UPSTREAM_TLS:-auto}";
-        ENVOY_DOWNSTREAM_TLS="$${ENVOY_DOWNSTREAM_TLS:-auto}";
-        ENVOY_UPSTREAM_EFFECTIVE="$${ENVOY_UPSTREAM_TLS}";
-        ENVOY_DOWNSTREAM_EFFECTIVE="$${ENVOY_DOWNSTREAM_TLS}";
-        if [ "$${ENVOY_UPSTREAM_EFFECTIVE}" = "auto" ]; then
-          if [ -f /etc/envoy/certs/envoy-client.crt ] && [ -f /etc/envoy/certs/envoy-client.key ] && [ -f /etc/envoy/certs/ca.crt ]; then
-            ENVOY_UPSTREAM_EFFECTIVE="on";
-          else
-            ENVOY_UPSTREAM_EFFECTIVE="off";
-          fi;
-        fi;
-        if [ "$${ENVOY_DOWNSTREAM_EFFECTIVE}" = "auto" ]; then
-          if [ -f /etc/envoy/certs/envoy-server.crt ] && [ -f /etc/envoy/certs/envoy-server.key ]; then
-            ENVOY_DOWNSTREAM_EFFECTIVE="on";
-          else
-            ENVOY_DOWNSTREAM_EFFECTIVE="off";
-          fi;
-        fi;
-        if [ "$${ENVOY_DOWNSTREAM_EFFECTIVE}" = "on" ] && [ "$${ENVOY_UPSTREAM_EFFECTIVE}" = "on" ]; then
-          ENVOY_TEMPLATE="/etc/envoy/envoy.yaml";
-        elif [ "$${ENVOY_DOWNSTREAM_EFFECTIVE}" = "on" ] && [ "$${ENVOY_UPSTREAM_EFFECTIVE}" = "off" ]; then
-          ENVOY_TEMPLATE="/etc/envoy/envoy.upstream_plaintext.yaml";
-        elif [ "$${ENVOY_DOWNSTREAM_EFFECTIVE}" = "off" ] && [ "$${ENVOY_UPSTREAM_EFFECTIVE}" = "on" ]; then
-          ENVOY_TEMPLATE="/etc/envoy/envoy.downstream_plaintext.yaml";
-        else
-          ENVOY_TEMPLATE="/etc/envoy/envoy.downstream_upstream_plaintext.yaml";
-        fi;
-        echo "[envoy] downstream TLS=$${ENVOY_DOWNSTREAM_EFFECTIVE} upstream TLS=$${ENVOY_UPSTREAM_EFFECTIVE}";
-        echo "[envoy] using template $${ENVOY_TEMPLATE}";
-        sed "s/__GRPC_BACKEND_PORT__/$${GRPC_BACKEND_PORT}/g" "$${ENVOY_TEMPLATE}" > /tmp/envoy.generated.yaml;
-        exec /usr/local/bin/envoy -c /tmp/envoy.generated.yaml
-    healthcheck:
-      test: ["CMD", "sh", "-c", "kill -0 1"]
-      interval: 10s
-      timeout: 3s
-      retries: 3
-      start_period: 10s
-    networks:
-      - weights_studio_network
-    restart: unless-stopped
-
-  # Weights Studio frontend (production static server)
-  weights_studio:
-    image: ${WS_FRONTEND_IMAGE:-graybx/weightslab:latest}
-    container_name: weights_studio_frontend
-    build:
-      context: ..
-      dockerfile: Dockerfile
-      args:
-        VITE_IS_A_SANDBOX: ${IS_A_SANDBOX:-0}
-        VITE_HISTOGRAM_MAX_BINS: ${VITE_HISTOGRAM_MAX_BINS:-512}
-        # Token is injected at runtime, not at build time
-        VITE_WL_ENABLE_GRPC_AUTH_TOKEN: 0
-        VITE_SERVER_PROTOCOL: ${VITE_SERVER_PROTOCOL:-https}
-        VITE_DEV_SERVER_HTTPS: ${VITE_DEV_SERVER_HTTPS:-0}
-    ports:
-      - "${VITE_HTTPS_PORT:-5173}:443"
-    environment:
-      - NODE_ENV=${NODE_ENV:-production}
-      - WS_SERVER_HOST=${WS_SERVER_HOST:-localhost}
-      - WS_SERVER_PORT=${WS_SERVER_PORT:-8080}
-
-      # TLS settings for frontend to communicate with Envoy
-      - WS_SERVER_PROTOCOL=${WS_SERVER_PROTOCOL:-https}
-      - VITE_DEV_SERVER_HTTPS=${VITE_DEV_SERVER_HTTPS:-0}
-      - VITE_WL_ENABLE_GRPC_AUTH_TOKEN=${VITE_WL_ENABLE_GRPC_AUTH_TOKEN:-0}
-      - VITE_SERVER_PROTOCOL=${VITE_SERVER_PROTOCOL:-https}
-      - VITE_GRPC_AUTH_TOKEN=${VITE_GRPC_AUTH_TOKEN}
-      - WEIGHTSLAB_CERTS_DIR=${WEIGHTSLAB_CERTS_DIR:-${HOME}/.weightslab-certs}
-      - ENVOY_UPSTREAM_TLS=${ENVOY_UPSTREAM_TLS:-auto}
-      - ENVOY_DOWNSTREAM_TLS=${ENVOY_DOWNSTREAM_TLS:-auto}
-      - GRPC_BACKEND_PORT=${GRPC_BACKEND_PORT:-50051}
-      # Bounding-box render caps (runtime-overridable without a rebuild).
-      - BB_THUMB_RENDER=${BB_THUMB_RENDER:-10}
-      - BB_MODAL_RENDER=${BB_MODAL_RENDER:-100}
-      # Grid / cache tuning (runtime-overridable without a rebuild).
-      - GRID_WINDOW_SIZE=${GRID_WINDOW_SIZE:-6}
-      - GRID_CACHE_MAX_MB=${GRID_CACHE_MAX_MB:-128}
-      - MODAL_CACHE_MAX_MB=${MODAL_CACHE_MAX_MB:-64}
-      # Point-cloud rendering (PC_MAX_POINTS empty = 1 500 000 default).
-      - PC_MAX_POINTS=${PC_MAX_POINTS:-}
-      - DISABLE_GPU_RENDERING=${DISABLE_GPU_RENDERING:-0}
-      # Feature toggles (runtime-overridable; each defaults to 1 = enabled).
-      - ENABLE_PLOTS=${ENABLE_PLOTS:-1}
-      - ENABLE_DATA_EXPLORATION=${ENABLE_DATA_EXPLORATION:-1}
-      - ENABLE_HYPERPARAMETERS_OPTIMIZATION=${ENABLE_HYPERPARAMETERS_OPTIMIZATION:-1}
-      - ENABLE_AGENT=${ENABLE_AGENT:-1}
-    volumes:
-      # Mount certs directory if available
-      - ${WEIGHTSLAB_CERTS_DIR:-${HOME}/.weightslab-certs}:/etc/nginx/certs:ro
-    depends_on:
-      envoy:
-        condition: service_healthy
-    networks:
-      - weights_studio_network
-    restart: unless-stopped
-
-networks:
-  weights_studio_network:
-    driver: bridge
diff --git a/weightslab/docker/docker/nginx-entrypoint.sh b/weightslab/docker/docker/nginx-entrypoint.sh
deleted file mode 100644
index 259f89ef..00000000
--- a/weightslab/docker/docker/nginx-entrypoint.sh
+++ /dev/null
@@ -1,188 +0,0 @@
-#!/bin/sh
-# Dynamic nginx configuration based on certificate availability
-# Adapts to HTTPS if certs exist, falls back to HTTP if missing
-# Injects gRPC auth token at runtime
-
-set -e
-
-WEIGHTSLAB_CERTS_DIR="/etc/nginx/certs"
-CERT_FILE="$WEIGHTSLAB_CERTS_DIR/envoy-server.crt"
-KEY_FILE="$WEIGHTSLAB_CERTS_DIR/envoy-server.key"
-NGINX_CONF="/etc/nginx/conf.d/default.conf"
-
-# Check if certificates exist
-HAS_CERTS=false
-if [ -f "$CERT_FILE" ] && [ -f "$KEY_FILE" ]; then
-    HAS_CERTS=true
-    echo "✓ TLS certificates found - enabling HTTPS"
-else
-    echo "✗ TLS certificates not found - using HTTP"
-fi
-
-# Create config directory for token injection
-mkdir -p /tmp/nginx_conf
-
-# Inject token into JavaScript config file that will be served
-TOKEN_FILE="/tmp/nginx_conf/grpc-config.js"
-cat > "$TOKEN_FILE" << TOKEN_END
-// Runtime gRPC configuration (injected at container start)
-window.__grpcConfig = {
-  authToken: '${GRPC_AUTH_TOKEN:-}',
-  authEnabled: ${WL_ENABLE_GRPC_AUTH_TOKEN:-0},
-  protocol: '${WS_SERVER_PROTOCOL:-https}'
-};
-TOKEN_END
-
-echo "✓ Runtime gRPC config injected"
-
-# Generate nginx configuration based on certificate availability
-if [ "$HAS_CERTS" = true ]; then
-    # HTTPS configuration
-    cat > "$NGINX_CONF" << 'NGINX_HTTPS_END'
-upstream envoy {
-    server envoy:8080;
-}
-
-server {
-    listen 80;
-    listen 443 ssl;
-    server_name _;
-
-    ssl_certificate /etc/nginx/certs/envoy-server.crt;
-    ssl_certificate_key /etc/nginx/certs/envoy-server.key;
-    ssl_protocols TLSv1.2 TLSv1.3;
-    ssl_ciphers HIGH:!aNULL:!MD5;
-    ssl_prefer_server_ciphers on;
-    ssl_session_cache shared:SSL:10m;
-    ssl_session_timeout 10m;
-
-    root /usr/share/nginx/html;
-    index index.html;
-
-    # Cache static assets
-    location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
-        expires 1y;
-        add_header Cache-Control "public, immutable";
-    }
-
-    # API proxy to backend through Envoy
-    location /api/ {
-        proxy_pass http://envoy/;
-        proxy_http_version 1.1;
-        proxy_set_header Upgrade $http_upgrade;
-        proxy_set_header Connection "upgrade";
-        proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
-        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-        proxy_set_header X-Forwarded-Proto $scheme;
-    }
-
-    # gRPC proxy
-    location ~ ^/grpc {
-        proxy_pass http://envoy;
-        proxy_http_version 1.1;
-        proxy_set_header Content-Type application/grpc;
-        proxy_set_header grpc-encoding gzip;
-        proxy_buffering off;
-        proxy_request_buffering off;
-        proxy_set_header Host $host;
-    }
-
-    # SPA fallback
-    location / {
-        try_files $uri $uri/ /index.html;
-    }
-
-    # Serve runtime gRPC config
-    location /grpc-config.js {
-        alias /tmp/nginx_conf/grpc-config.js;
-        add_header Content-Type application/javascript;
-        add_header Cache-Control "no-cache";
-    }
-
-    # Health check
-    location /health {
-        access_log off;
-        return 200 "healthy\n";
-        add_header Content-Type text/plain;
-    }
-}
-NGINX_HTTPS_END
-else
-    # HTTP configuration
-    cat > "$NGINX_CONF" << 'NGINX_HTTP_END'
-upstream envoy {
-    server envoy:8080;
-}
-
-server {
-    listen 80;
-    listen 443;
-    server_name _;
-
-    root /usr/share/nginx/html;
-    index index.html;
-
-    # Cache static assets
-    location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
-        expires 1y;
-        add_header Cache-Control "public, immutable";
-    }
-
-    # API proxy to backend through Envoy
-    location /api/ {
-        proxy_pass http://envoy/;
-        proxy_http_version 1.1;
-        proxy_set_header Upgrade $http_upgrade;
-        proxy_set_header Connection "upgrade";
-        proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
-        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-        proxy_set_header X-Forwarded-Proto $scheme;
-    }
-
-    # gRPC proxy
-    location ~ ^/grpc {
-        proxy_pass http://envoy;
-        proxy_http_version 1.1;
-        proxy_set_header Content-Type application/grpc;
-        proxy_set_header grpc-encoding gzip;
-        proxy_buffering off;
-        proxy_request_buffering off;
-        proxy_set_header Host $host;
-    }
-
-    # SPA fallback
-    location / {
-        try_files $uri $uri/ /index.html;
-    }
-
-    # Serve runtime gRPC config
-    location /grpc-config.js {
-        alias /tmp/nginx_conf/grpc-config.js;
-        add_header Content-Type application/javascript;
-        add_header Cache-Control "no-cache";
-    }
-
-    # Health check
-    location /health {
-        access_log off;
-        return 200 "healthy\n";
-        add_header Content-Type text/plain;
-    }
-}
-NGINX_HTTP_END
-fi
-
-# Give envoy time to register in DNS and become ready
-echo "Waiting for envoy service to be ready..."
-sleep 2
-echo "✓ Proceeding with nginx configuration"
-
-# Validate nginx configuration
-echo "Validating nginx configuration..."
-nginx -t
-
-# Start nginx in foreground
-echo "Starting nginx..."
-exec nginx -g "daemon off;"
diff --git a/weightslab/docker/docker/nginx.base-path.conf copy.template b/weightslab/docker/docker/nginx.base-path.conf copy.template
deleted file mode 100644
index 07be634d..00000000
--- a/weightslab/docker/docker/nginx.base-path.conf copy.template	
+++ /dev/null
@@ -1,40 +0,0 @@
-server {
-    listen 80;
-    server_name _;
-
-    root /usr/share/nginx/html;
-    index index.html;
-
-    # When accessed directly (e.g. port-forward to :5173), redirect to app root.
-    location = / {
-        return 301 ${NGINX_BASE_PATH};
-    }
-
-    # API proxy: forward gRPC-Web calls to Envoy.
-    # Strips the /{case}/api/ prefix before forwarding so Envoy receives the bare RPC path.
-    # Works for both direct port-forward access AND when behind Caddy.
-    location ^~ ${NGINX_BASE_PATH}api/ {
-        proxy_pass http://${NGINX_ENVOY_HOST}:${NGINX_ENVOY_PORT}/;
-        proxy_http_version 1.1;
-        proxy_set_header Connection "";
-        proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
-        proxy_buffering off;
-        proxy_read_timeout 3600s;
-        proxy_send_timeout 3600s;
-    }
-
-    # Serve the SPA index at the exact base path.
-    location = ${NGINX_BASE_PATH} {
-        try_files /index.html =404;
-    }
-
-    # Strip the base-path prefix and serve every other asset from the html root.
-    # Vite builds absolute asset URLs like /mnist/assets/app-xyz.js;
-    # this regex captures everything after the prefix and resolves it to
-    # /usr/share/nginx/html/<captured>, falling back to /index.html for SPA routes.
-    location ~* ^${NGINX_BASE_PATH}(.+)$ {
-        try_files /$1 /index.html;
-        add_header Cache-Control "public, max-age=604800, immutable" always;
-    }
-}
diff --git a/weightslab/docker/docker/nginx.base-path.conf.template b/weightslab/docker/docker/nginx.base-path.conf.template
deleted file mode 100644
index 07be634d..00000000
--- a/weightslab/docker/docker/nginx.base-path.conf.template
+++ /dev/null
@@ -1,40 +0,0 @@
-server {
-    listen 80;
-    server_name _;
-
-    root /usr/share/nginx/html;
-    index index.html;
-
-    # When accessed directly (e.g. port-forward to :5173), redirect to app root.
-    location = / {
-        return 301 ${NGINX_BASE_PATH};
-    }
-
-    # API proxy: forward gRPC-Web calls to Envoy.
-    # Strips the /{case}/api/ prefix before forwarding so Envoy receives the bare RPC path.
-    # Works for both direct port-forward access AND when behind Caddy.
-    location ^~ ${NGINX_BASE_PATH}api/ {
-        proxy_pass http://${NGINX_ENVOY_HOST}:${NGINX_ENVOY_PORT}/;
-        proxy_http_version 1.1;
-        proxy_set_header Connection "";
-        proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
-        proxy_buffering off;
-        proxy_read_timeout 3600s;
-        proxy_send_timeout 3600s;
-    }
-
-    # Serve the SPA index at the exact base path.
-    location = ${NGINX_BASE_PATH} {
-        try_files /index.html =404;
-    }
-
-    # Strip the base-path prefix and serve every other asset from the html root.
-    # Vite builds absolute asset URLs like /mnist/assets/app-xyz.js;
-    # this regex captures everything after the prefix and resolves it to
-    # /usr/share/nginx/html/<captured>, falling back to /index.html for SPA routes.
-    location ~* ^${NGINX_BASE_PATH}(.+)$ {
-        try_files /$1 /index.html;
-        add_header Cache-Control "public, max-age=604800, immutable" always;
-    }
-}
diff --git a/weightslab/docker/docker/nginx.conf b/weightslab/docker/docker/nginx.conf
deleted file mode 100644
index 82b67ea6..00000000
--- a/weightslab/docker/docker/nginx.conf
+++ /dev/null
@@ -1,57 +0,0 @@
-upstream backend {
-    server host.docker.internal:50051;
-}
-
-upstream envoy {
-    server weightslab_envoy:8080;
-}
-
-# HTTP server on port 443 (no certs available)
-server {
-    listen 443;
-    server_name _;
-
-    root /usr/share/nginx/html;
-    index index.html;
-
-    # Cache static assets
-    location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
-        expires 1y;
-        add_header Cache-Control "public, immutable";
-    }
-
-    # API proxy to backend through Envoy
-    location /api/ {
-        proxy_pass http://envoy/;
-        proxy_http_version 1.1;
-        proxy_set_header Upgrade $http_upgrade;
-        proxy_set_header Connection "upgrade";
-        proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
-        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-        proxy_set_header X-Forwarded-Proto $scheme;
-    }
-
-    # gRPC proxy
-    location ~ ^/grpc {
-        proxy_pass http://envoy;
-        proxy_http_version 1.1;
-        proxy_set_header Content-Type application/grpc;
-        proxy_set_header grpc-encoding gzip;
-        proxy_buffering off;
-        proxy_request_buffering off;
-        proxy_set_header Host $host;
-    }
-
-    # SPA fallback
-    location / {
-        try_files $uri $uri/ /index.html;
-    }
-
-    # Health check
-    location /health {
-        access_log off;
-        return 200 "healthy\n";
-        add_header Content-Type text/plain;
-    }
-}
diff --git a/weightslab/docker/docker/test-deployment.sh b/weightslab/docker/docker/test-deployment.sh
deleted file mode 100644
index c8b71b37..00000000
--- a/weightslab/docker/docker/test-deployment.sh
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/bin/bash
-# Test script to verify Weights Studio deployment in different modes
-
-set -e
-
-DOCKER_COMPOSE_CMD="docker compose -f docker-compose.yml -f docker-compose.prod.yml"
-CONTAINER_NAME="weights_studio_frontend"
-
-echo "======================================"
-echo "Weights Studio Deployment Test"
-echo "======================================"
-
-# Function to wait for container
-wait_for_container() {
-    local container=$1
-    local max_attempts=30
-    local attempt=0
-
-    echo "Waiting for $container to start..."
-    while [ $attempt -lt $max_attempts ]; do
-        if docker exec "$container" nginx -t 2>/dev/null; then
-            echo "✓ $container is running"
-            return 0
-        fi
-        attempt=$((attempt + 1))
-        sleep 1
-    done
-
-    echo "✗ $container failed to start"
-    return 1
-}
-
-# Function to test health endpoint
-test_health() {
-    local port=$1
-    local protocol=${2:-http}
-
-    echo "Testing health endpoint on $protocol://localhost:$port..."
-
-    if [ "$protocol" = "https" ]; then
-        if curl -k --silent "https://localhost:$port/health" 2>/dev/null | grep -q "healthy"; then
-            echo "✓ HTTPS health check passed"
-            return 0
-        fi
-    else
-        if curl --silent "http://localhost:$port/health" 2>/dev/null | grep -q "healthy"; then
-            echo "✓ HTTP health check passed"
-            return 0
-        fi
-    fi
-
-    echo "✗ Health check failed"
-    return 1
-}
-
-# Function to check certificate status
-check_certs() {
-    if [ -f "../envoy/certs/envoy-server.crt" ] && [ -f "../envoy/certs/envoy-server.key" ]; then
-        echo "✓ Certificates found"
-        return 0
-    else
-        echo "✗ Certificates not found (HTTP-only mode)"
-        return 1
-    fi
-}
-
-# Main test flow
-echo ""
-echo "1. Checking certificate status..."
-check_certs
-HAVE_CERTS=$?
-
-echo ""
-echo "2. Starting Docker stack..."
-$DOCKER_COMPOSE_CMD up -d
-
-echo ""
-echo "3. Waiting for services to start..."
-wait_for_container "$CONTAINER_NAME"
-
-echo ""
-echo "4. Checking container configuration..."
-docker logs "$CONTAINER_NAME" | grep -E "✓|✗" | head -5
-
-echo ""
-echo "5. Testing HTTP(s) endpoint..."
-test_health "5173" "http"
-
-echo ""
-echo "7. Checking Envoy status..."
-if docker exec weights_studio_envoy ps aux | grep -q "envoy"; then
-    echo "✓ Envoy proxy is running"
-else
-    echo "✗ Envoy proxy failed to start"
-fi
-
-echo ""
-echo "======================================"
-echo "Deployment test completed!"
-echo "======================================"
-echo ""
-echo "Access points:"
-echo "  HTTP(s): http://localhost:5173"
-echo ""
-echo "To stop:"
-echo "  $DOCKER_COMPOSE_CMD down"
-echo ""
-echo "To view logs:"
-echo "  docker logs weights_studio_frontend"
-echo "  docker logs weights_studio_envoy"
diff --git a/weightslab/docker/docker/utils/build-and-deploy.sh b/weightslab/docker/docker/utils/build-and-deploy.sh
deleted file mode 100644
index b00371d1..00000000
--- a/weightslab/docker/docker/utils/build-and-deploy.sh
+++ /dev/null
@@ -1,348 +0,0 @@
-#!/bin/bash
-# Auto-detect TLS and tokens, then build production image
-
-# Print all environment variables to diagnose issues
-echo "===== Environment Variables Received ====="
-env | grep -E 'WEIGHTSLAB|VITE|ENVOY|HOME' || true
-echo "=========================================="
-
-# Ensure environment variables are available (important when called from Python subprocess)
-echo "Init: WEIGHTSLAB_CERTS_DIR='$WEIGHTSLAB_CERTS_DIR' (received from env)"
-WEIGHTSLAB_CERTS_DIR="${WEIGHTSLAB_CERTS_DIR:-$HOME/.weightslab-certs}"
-export WEIGHTSLAB_CERTS_DIR
-echo "After default: WEIGHTSLAB_CERTS_DIR='$WEIGHTSLAB_CERTS_DIR'"
-
-# Parse command line arguments
-DEV=false
-FORCE_UNSECURE=0
-
-for arg in "$@"; do
-  case "$arg" in
-    --dev)
-      DEV=true
-      ;;
-    --unsecure|--unsecured)
-      echo "Forcing UNSECURE mode (HTTP, no auth)"
-      FORCE_UNSECURE=1
-      ;;
-    ENABLE_PLOTS=*)                        ENABLE_PLOTS="${arg#*=}" ;;
-    ENABLE_DATA_EXPLORATION=*)             ENABLE_DATA_EXPLORATION="${arg#*=}" ;;
-    ENABLE_HYPERPARAMETERS_OPTIMIZATION=*) ENABLE_HYPERPARAMETERS_OPTIMIZATION="${arg#*=}" ;;
-    ENABLE_AGENT=*)                        ENABLE_AGENT="${arg#*=}" ;;
-    BB_THUMB_RENDER=*)                     BB_THUMB_RENDER="${arg#*=}" ;;
-    BB_MODAL_RENDER=*)                     BB_MODAL_RENDER="${arg#*=}" ;;
-    WS_HISTOGRAM_MAX_BINS=*)               WS_HISTOGRAM_MAX_BINS="${arg#*=}" ;;
-    GRID_WINDOW_SIZE=*)                    GRID_WINDOW_SIZE="${arg#*=}" ;;
-    GRID_CACHE_MAX_MB=*)                   GRID_CACHE_MAX_MB="${arg#*=}" ;;
-    MODAL_CACHE_MAX_MB=*)                  MODAL_CACHE_MAX_MB="${arg#*=}" ;;
-    PC_MAX_POINTS=*)                       PC_MAX_POINTS="${arg#*=}" ;;
-    DISABLE_GPU_RENDERING=*)               DISABLE_GPU_RENDERING="${arg#*=}" ;;
-  esac
-done
-
-# Check if WEIGHTSLAB_CERTS_DIR was explicitly set to empty string (before applying defaults)
-# This is how the E2E tests disable certs: WEIGHTSLAB_CERTS_DIR=""
-if [ "$FORCE_UNSECURE" = "1" ]; then
-    # Force HTTP / no auth, but KEEP WEIGHTSLAB_CERTS_DIR: the compose bind-mount
-    # needs a real (possibly empty) source directory. TLS/auth are forced off in
-    # the derivation block below regardless of any files in it.
-    echo "--unsecured flag provided: forcing HTTP / no auth (certs dir kept for bind-mount only)"
-elif [ "${WEIGHTSLAB_CERTS_DIR+x}" ] && [ -z "$WEIGHTSLAB_CERTS_DIR" ]; then
-    echo "WEIGHTSLAB_CERTS_DIR explicitly set to empty - forcing UNSECURE mode"
-    FORCE_UNSECURE=1
-elif [ ! -d "$WEIGHTSLAB_CERTS_DIR" ]; then
-    # WEIGHTSLAB_CERTS_DIR doesn't exist, try converting Windows path to Unix-style
-    CONVERTED_PATH=""
-    if echo "$WEIGHTSLAB_CERTS_DIR" | grep -q '\\'; then
-        # Path contains backslashes - likely Windows path, convert to Unix-style (for Git Bash)
-        # Convert C:\path\to\dir -> /c/path/to/dir
-        CONVERTED_PATH=$(echo "$WEIGHTSLAB_CERTS_DIR" | sed 's/^\([A-Za-z]\):\\/\/\L\1\//; s/\\/\//g')
-        echo "Detected Windows path, converting to Unix-style: $CONVERTED_PATH"
-
-        if [ -d "$CONVERTED_PATH" ]; then
-            echo "Found converted path at '$CONVERTED_PATH'"
-            WEIGHTSLAB_CERTS_DIR="$CONVERTED_PATH"
-        else
-            echo "Converted path not found at '$CONVERTED_PATH'"
-            # Continue with next fallback
-            WEIGHTSLAB_CERTS_DIR=""
-        fi
-    else
-        # Not a Windows path, try default ~/.weightslab-certs
-        DEFAULT_CERTS_DIR="$HOME/.weightslab-certs"
-        if [ -d "$DEFAULT_CERTS_DIR" ]; then
-            echo "WEIGHTSLAB_CERTS_DIR not found at '$WEIGHTSLAB_CERTS_DIR', using default: $DEFAULT_CERTS_DIR"
-            WEIGHTSLAB_CERTS_DIR="$DEFAULT_CERTS_DIR"
-        else
-            echo "Default certs directory not found ($DEFAULT_CERTS_DIR), continuing without certs"
-            WEIGHTSLAB_CERTS_DIR=""
-        fi
-    fi
-fi
-
-TOKEN_FILE="${WEIGHTSLAB_CERTS_DIR}/.grpc_auth_token"
-echo "Looking at Weightslab Certs dir directory at: ${TOKEN_FILE}"
-if [ -n "$WEIGHTSLAB_CERTS_DIR" ]; then
-    ls -la "$WEIGHTSLAB_CERTS_DIR" 2>/dev/null || echo "WEIGHTSLAB_CERTS_DIR path does not exist or is empty"
-else
-    echo "WEIGHTSLAB_CERTS_DIR is empty, skipping cert check"
-fi
-
-# ---------------------------------------------------------------------------
-# Single source of truth: TLS + gRPC auth are derived SOLELY from the presence
-# of cert files in WEIGHTSLAB_CERTS_DIR. Any inherited VITE_*/ENVOY_* values are
-# intentionally ignored and recomputed here, so a stale or pre-set env var can
-# never force HTTPS without certs (which would crash Envoy on an empty mount) or
-# vice versa. `--unsecure` (or an empty WEIGHTSLAB_CERTS_DIR) forces everything
-# off.
-# ---------------------------------------------------------------------------
-if [ "$FORCE_UNSECURE" = "1" ] || [ -z "$WEIGHTSLAB_CERTS_DIR" ]; then
-    echo "Unsecured mode: HTTP, no auth (no certs directory)"
-    VITE_DEV_SERVER_HTTPS=0
-    VITE_SERVER_PROTOCOL=http
-    ENVOY_UPSTREAM_TLS=off
-    ENVOY_DOWNSTREAM_TLS=off
-    VITE_WL_ENABLE_GRPC_AUTH_TOKEN=0
-    VITE_GRPC_AUTH_TOKEN=""
-else
-    # Downstream HTTPS (browser <-> Envoy): needs the Envoy server cert + key.
-    if [ -f "$WEIGHTSLAB_CERTS_DIR/envoy-server.crt" ] && [ -f "$WEIGHTSLAB_CERTS_DIR/envoy-server.key" ]; then
-        echo "Envoy server cert found in $WEIGHTSLAB_CERTS_DIR - enabling HTTPS"
-        VITE_DEV_SERVER_HTTPS=1
-        VITE_SERVER_PROTOCOL=https
-        ENVOY_DOWNSTREAM_TLS=on
-    else
-        echo "No Envoy server cert in $WEIGHTSLAB_CERTS_DIR - HTTP (no downstream TLS)"
-        VITE_DEV_SERVER_HTTPS=0
-        VITE_SERVER_PROTOCOL=http
-        ENVOY_DOWNSTREAM_TLS=off
-    fi
-
-    # Upstream mTLS (Envoy <-> backend gRPC): needs the Envoy client cert + CA.
-    if [ -f "$WEIGHTSLAB_CERTS_DIR/envoy-client.crt" ] && [ -f "$WEIGHTSLAB_CERTS_DIR/envoy-client.key" ] && [ -f "$WEIGHTSLAB_CERTS_DIR/ca.crt" ]; then
-        echo "Envoy client cert + CA found - enabling upstream TLS"
-        ENVOY_UPSTREAM_TLS=on
-    else
-        echo "No Envoy client cert/CA - upstream plaintext"
-        ENVOY_UPSTREAM_TLS=off
-    fi
-
-    # gRPC auth token.
-    if [ -f "$TOKEN_FILE" ]; then
-        echo "gRPC token found - enabling auth"
-        VITE_WL_ENABLE_GRPC_AUTH_TOKEN=1
-        VITE_GRPC_AUTH_TOKEN=$(cat "$TOKEN_FILE")
-    else
-        echo "gRPC token not found - auth disabled"
-        VITE_WL_ENABLE_GRPC_AUTH_TOKEN=0
-        VITE_GRPC_AUTH_TOKEN=""
-    fi
-fi
-
-# Export all derived variables for docker compose
-export VITE_DEV_SERVER_HTTPS
-export VITE_SERVER_PROTOCOL
-export ENVOY_UPSTREAM_TLS
-export ENVOY_DOWNSTREAM_TLS
-export VITE_WL_ENABLE_GRPC_AUTH_TOKEN
-export VITE_GRPC_AUTH_TOKEN
-
-# Get weightslab root from environment variable or derive from script location
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-
-# The compose stack lives one level up from this script (docker/docker/utils/ ->
-# docker/docker/), where docker-compose.yml and the generated .env belong. Always
-# derive it from the script location: it is correct both in-repo and pip-installed,
-# and never drifts when the package layout changes. (A stale hardcoded
-# "$WEIGHTSLAB_ROOT/weightslab/ui/docker" here wrote .env to a non-existent dir, so
-# docker compose fell back to its defaults — HTTPS on but gRPC auth off.)
-if [ -n "$WEIGHTSLAB_ROOT" ]; then
-    echo "WEIGHTSLAB_ROOT from environment: $WEIGHTSLAB_ROOT (compose dir derived from script location)"
-fi
-DOCKER_DIR="$(dirname "$SCRIPT_DIR")"
-
-ENV_FILE="$DOCKER_DIR/.env"
-echo "SCRIPT_DIR: $SCRIPT_DIR"
-echo "DOCKER_DIR: $DOCKER_DIR"
-
-# Write environment variables to .env file for docker compose
-echo "Writing environment variables to .env..."
-# Note: .env must be in the docker directory where docker-compose.yml is located
-
-# Convert WEIGHTSLAB_CERTS_DIR to absolute path for docker-compose compatibility
-if [ -n "$WEIGHTSLAB_CERTS_DIR" ] && [ ! -z "$WEIGHTSLAB_CERTS_DIR" ]; then
-    # Resolve to absolute path
-    WEIGHTSLAB_CERTS_DIR_ABSOLUTE="$(cd "$WEIGHTSLAB_CERTS_DIR" 2>/dev/null && pwd)" || WEIGHTSLAB_CERTS_DIR_ABSOLUTE="$WEIGHTSLAB_CERTS_DIR"
-fi
-
-# Path written into .env for the docker compose bind mount. Prefer the
-# host-native path provided by the Python launcher (e.g. C:/Users/... on
-# Windows/Docker Desktop): the Unix path above (/mnt/c/...) is only valid for
-# this script's own file checks, NOT for a Docker Desktop bind mount, which
-# silently mounts an empty dir for /mnt paths and crashes Envoy on missing certs.
-CERTS_DIR_FOR_ENV="${WEIGHTSLAB_CERTS_DIR_HOST:-$WEIGHTSLAB_CERTS_DIR_ABSOLUTE}"
-
-if [ -z "$WEIGHTSLAB_CERTS_DIR" ]; then
-    # Unsecured mode: explicitly disable all TLS and auth variables
-    cat > "$ENV_FILE" << EOF
-VITE_DEV_SERVER_HTTPS=0
-VITE_SERVER_PROTOCOL=http
-VITE_WL_ENABLE_GRPC_AUTH_TOKEN=0
-VITE_GRPC_AUTH_TOKEN=
-WEIGHTSLAB_CERTS_DIR=
-ENVOY_UPSTREAM_TLS=off
-ENVOY_DOWNSTREAM_TLS=off
-WS_SERVER_PROTOCOL=http
-GRPC_BACKEND_PORT=${GRPC_BACKEND_PORT:-50051}
-BB_THUMB_RENDER=${BB_THUMB_RENDER:-10}
-BB_MODAL_RENDER=${BB_MODAL_RENDER:-100}
-WS_HISTOGRAM_MAX_BINS=${WS_HISTOGRAM_MAX_BINS:-512}
-VITE_HISTOGRAM_MAX_BINS=${WS_HISTOGRAM_MAX_BINS:-512}
-GRID_WINDOW_SIZE=${GRID_WINDOW_SIZE:-6}
-VITE_GRID_WINDOW_SIZE=${GRID_WINDOW_SIZE:-6}
-GRID_CACHE_MAX_MB=${GRID_CACHE_MAX_MB:-128}
-VITE_WS_GRID_CACHE_MAX_MB=${GRID_CACHE_MAX_MB:-128}
-MODAL_CACHE_MAX_MB=${MODAL_CACHE_MAX_MB:-64}
-VITE_WS_MODAL_CACHE_MAX_MB=${MODAL_CACHE_MAX_MB:-64}
-PC_MAX_POINTS=${PC_MAX_POINTS:-}
-VITE_WL_PC_MAX_POINTS=${PC_MAX_POINTS:-}
-DISABLE_GPU_RENDERING=${DISABLE_GPU_RENDERING:-0}
-VITE_WL_DISABLE_GPU_RENDERING=${DISABLE_GPU_RENDERING:-0}
-ENABLE_PLOTS=${ENABLE_PLOTS:-1}
-ENABLE_DATA_EXPLORATION=${ENABLE_DATA_EXPLORATION:-1}
-ENABLE_HYPERPARAMETERS_OPTIMIZATION=${ENABLE_HYPERPARAMETERS_OPTIMIZATION:-1}
-ENABLE_AGENT=${ENABLE_AGENT:-1}
-EOF
-else
-    # Secured mode: include certs
-    cat > "$ENV_FILE" << EOF
-VITE_DEV_SERVER_HTTPS=$VITE_DEV_SERVER_HTTPS
-VITE_SERVER_PROTOCOL=$VITE_SERVER_PROTOCOL
-VITE_WL_ENABLE_GRPC_AUTH_TOKEN=$VITE_WL_ENABLE_GRPC_AUTH_TOKEN
-VITE_GRPC_AUTH_TOKEN=$VITE_GRPC_AUTH_TOKEN
-WEIGHTSLAB_CERTS_DIR=$CERTS_DIR_FOR_ENV
-ENVOY_UPSTREAM_TLS=$ENVOY_UPSTREAM_TLS
-ENVOY_DOWNSTREAM_TLS=$ENVOY_DOWNSTREAM_TLS
-WS_SERVER_PROTOCOL=$VITE_SERVER_PROTOCOL
-GRPC_BACKEND_PORT=${GRPC_BACKEND_PORT:-50051}
-BB_THUMB_RENDER=${BB_THUMB_RENDER:-10}
-BB_MODAL_RENDER=${BB_MODAL_RENDER:-100}
-WS_HISTOGRAM_MAX_BINS=${WS_HISTOGRAM_MAX_BINS:-512}
-VITE_HISTOGRAM_MAX_BINS=${WS_HISTOGRAM_MAX_BINS:-512}
-GRID_WINDOW_SIZE=${GRID_WINDOW_SIZE:-6}
-VITE_GRID_WINDOW_SIZE=${GRID_WINDOW_SIZE:-6}
-GRID_CACHE_MAX_MB=${GRID_CACHE_MAX_MB:-128}
-VITE_WS_GRID_CACHE_MAX_MB=${GRID_CACHE_MAX_MB:-128}
-MODAL_CACHE_MAX_MB=${MODAL_CACHE_MAX_MB:-64}
-VITE_WS_MODAL_CACHE_MAX_MB=${MODAL_CACHE_MAX_MB:-64}
-PC_MAX_POINTS=${PC_MAX_POINTS:-}
-VITE_WL_PC_MAX_POINTS=${PC_MAX_POINTS:-}
-DISABLE_GPU_RENDERING=${DISABLE_GPU_RENDERING:-0}
-VITE_WL_DISABLE_GPU_RENDERING=${DISABLE_GPU_RENDERING:-0}
-ENABLE_PLOTS=${ENABLE_PLOTS:-1}
-ENABLE_DATA_EXPLORATION=${ENABLE_DATA_EXPLORATION:-1}
-ENABLE_HYPERPARAMETERS_OPTIMIZATION=${ENABLE_HYPERPARAMETERS_OPTIMIZATION:-1}
-ENABLE_AGENT=${ENABLE_AGENT:-1}
-EOF
-fi
-echo ".env file written to $ENV_FILE"
-cat "$ENV_FILE"
-
-# Convert WEIGHTSLAB_CERTS_DIR to absolute path for docker-compose compatibility
-if [ -n "$WEIGHTSLAB_CERTS_DIR" ] && [ ! -z "$WEIGHTSLAB_CERTS_DIR" ]; then
-    # Resolve to absolute path
-    WEIGHTSLAB_CERTS_DIR_ABSOLUTE="$(cd "$WEIGHTSLAB_CERTS_DIR" 2>/dev/null && pwd)" || WEIGHTSLAB_CERTS_DIR_ABSOLUTE="$WEIGHTSLAB_CERTS_DIR"
-fi
-
-# All cert/token file checks above used the Unix path (e.g. /mnt/c/...) so they
-# resolve under this shell. But docker compose gives an exported shell env var
-# precedence over the .env file, and Docker Desktop cannot bind-mount a
-# /mnt-style source (it silently mounts an empty dir, crashing Envoy on missing
-# certs). Re-export the host-native path so the compose bind mount matches .env.
-if [ -n "$CERTS_DIR_FOR_ENV" ]; then
-    export WEIGHTSLAB_CERTS_DIR="$CERTS_DIR_FOR_ENV"
-    echo "Exporting host-native WEIGHTSLAB_CERTS_DIR for docker compose: $WEIGHTSLAB_CERTS_DIR"
-fi
-
-# Check if image already exists
-IMAGE_NAME="graybx/weightslab"
-# if docker image inspect "$IMAGE_NAME" > /dev/null 2>&1; then
-#     echo "Image '$IMAGE_NAME' already exists - skipping build"
-#     SKIP_BUILD=true
-# else
-#     echo "Image '$IMAGE_NAME' not found - will build"
-#     SKIP_BUILD=false
-# fi
-SKIP_BUILD=false
-
-# When invoked from the Python launcher on a host where docker runs outside this
-# shell (e.g. Windows: docker lives on the host, not in WSL), the launcher does
-# `docker compose pull/up` itself. In that case this script only needs to write
-# the .env above — skip all docker operations here to avoid noisy failures.
-if [ -n "$WEIGHTSLAB_SKIP_DOCKER_OPS" ] && [ "$WEIGHTSLAB_SKIP_DOCKER_OPS" != "0" ]; then
-    echo "Skipping docker build/deploy in shell (handled by the launcher)."
-    exit 0
-fi
-
-# Detect the Docker Compose CLI: prefer v2 (the `docker compose` plugin), fall
-# back to the legacy v1 standalone binary (`docker-compose`). This lets the
-# deploy work whether the user has Compose v2 or v1 installed.
-if docker compose version >/dev/null 2>&1; then
-    DOCKER_COMPOSE="docker compose"
-elif command -v docker-compose >/dev/null 2>&1; then
-    DOCKER_COMPOSE="docker-compose"
-else
-    echo "Error: Docker Compose not found." >&2
-    echo "       Install Compose v2 (the 'docker compose' plugin, recommended) or" >&2
-    echo "       the legacy v1 'docker-compose' binary." >&2
-    echo "       See: https://docs.docker.com/compose/install/" >&2
-    exit 1
-fi
-echo "Using Docker Compose command: $DOCKER_COMPOSE"
-
-# Build and deploy
-if [ "$DEV" = "true" ]; then
-    echo "Skipped dev build (image already exists)"
-    # Deploy (docker compose automatically reads .env)
-    echo "Deploying containers..."
-    $DOCKER_COMPOSE -f $DOCKER_DIR/docker-compose.yml -f $DOCKER_DIR/docker-compose.dev.yml down
-    $DOCKER_COMPOSE -f $DOCKER_DIR/docker-compose.yml -f $DOCKER_DIR/docker-compose.dev.yml up -d --force-recreate
-
-    echo "Deployment to development complete!"
-else
-    if [ "$SKIP_BUILD" = "false" ]; then
-        echo "Building production image (single image, configuration at runtime)..."
-        # Build with defaults - configuration happens at runtime via .env
-        $DOCKER_COMPOSE -f $DOCKER_DIR/docker-compose.yml build --no-cache
-
-        BUILD_STATUS=$?
-        if [ $BUILD_STATUS -ne 0 ]; then
-            echo "Production build failed with status $BUILD_STATUS"
-            exit $BUILD_STATUS
-        fi
-
-        echo "Production build complete!"
-    else
-        echo "Skipped production build (image already exists)"
-    fi
-
-    # Deploy (docker compose automatically reads .env)
-    echo "Deploying containers..."
-    echo "Stopping existing containers..."
-    $DOCKER_COMPOSE -f $DOCKER_DIR/docker-compose.yml down
-
-    echo "Starting containers..."
-    $DOCKER_COMPOSE -f $DOCKER_DIR/docker-compose.yml up -d --force-recreate
-
-    UP_STATUS=$?
-    if [ $UP_STATUS -ne 0 ]; then
-        echo "Container startup failed with status $UP_STATUS"
-        echo "Checking container logs..."
-        $DOCKER_COMPOSE -f $DOCKER_DIR/docker-compose.yml logs --tail=50 || true
-        exit $UP_STATUS
-    fi
-
-    echo "Deployment to production complete!"
-    echo "Running containers:"
-    $DOCKER_COMPOSE -f $DOCKER_DIR/docker-compose.yml ps || true
-fi
diff --git a/weightslab/docker/docker/utils/test-deployment.sh b/weightslab/docker/docker/utils/test-deployment.sh
deleted file mode 100644
index 11c22ef3..00000000
--- a/weightslab/docker/docker/utils/test-deployment.sh
+++ /dev/null
@@ -1,437 +0,0 @@
-#!/bin/bash
-# Comprehensive deployment test for weights_studio frontend + backend
-# Tests communication between frontend and Python backend (ws-segmentation example)
-# Runs both unsecured (HTTP) and secured (HTTPS) test scenarios
-
-# set -e
-
-# Colors for output
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-BLUE='\033[0;34m'
-NC='\033[0m' # No Color
-
-# Configuration
-CURRENT_DIR="$(cd "$(dirname "$0")/../.." && pwd)"
-DOCKER_DIR="$CURRENT_DIR/docker"
-UTILS_DIR="$DOCKER_DIR/utils"
-EXAMPLE_DIR="$CURRENT_DIR/tests/playwright/examples/ws-segmentation"
-GRPC_PORT=50051
-ENVOY_PORT=8080
-FRONTEND_PORT=5173
-HEALTH_CHECK_TIMEOUT=30
-COMMUNICATION_TEST_TIMEOUT=20
-
-# Global test results
-TESTS_PASSED=0
-TESTS_FAILED=0
-
-# ============================================================================
-# Utility Functions
-# ============================================================================
-
-log_info() {
-    echo -e "${BLUE}[INFO]${NC} $1"
-}
-
-log_success() {
-    echo -e "${GREEN}[✓ PASS]${NC} $1"
-    ((TESTS_PASSED++))
-}
-
-log_error() {
-    echo -e "${RED}[✗ FAIL]${NC} $1"
-    ((TESTS_FAILED++))
-}
-
-log_warn() {
-    echo -e "${YELLOW}[WARN]${NC} $1"
-}
-
-print_section() {
-    echo ""
-    echo -e "${BLUE}╔════════════════════════════════════════════════════════════════╗${NC}"
-    echo -e "${BLUE}║${NC} $1"
-    echo -e "${BLUE}╚════════════════════════════════════════════════════════════════╝${NC}"
-    echo ""
-}
-
-# Clean up docker resources
-cleanup() {
-    log_info "Cleaning up Docker resources..."
-    cd "$DOCKER_DIR"
-    docker compose -f docker-compose.yml -f docker-compose.prod.yml down --remove-orphans 2>/dev/null || true
-    # Force remove any lingering containers
-    docker ps -a | grep weights_studio | awk '{print $1}' | xargs -r docker rm -f 2>/dev/null || true
-    # Remove network
-    docker network rm docker_weights_studio_network 2>/dev/null || true
-    sleep 3
-}
-
-# Wait for service to be ready
-wait_for_service() {
-    local service_name=$1
-    local port=$2
-    local timeout=$3
-    local protocol=${4:-http}
-
-    log_info "Waiting for $service_name on $protocol://localhost:$port..."
-
-    local start_time=$(date +%s)
-    while true; do
-        local current_time=$(date +%s)
-        local elapsed=$((current_time - start_time))
-
-        if [ $elapsed -gt $timeout ]; then
-            log_error "$service_name failed to start after ${timeout}s"
-            return 1
-        fi
-
-        if [ "$protocol" = "https" ]; then
-            if curl -sk -o /dev/null -w "%{http_code}" "https://localhost:$port/health" 2>/dev/null | grep -q "200"; then
-                log_success "$service_name is ready"
-                return 0
-            fi
-        else
-            if curl -s -o /dev/null -w "%{http_code}" "http://localhost:$port/health" 2>/dev/null | grep -q "200"; then
-                log_success "$service_name is ready"
-                return 0
-            fi
-        fi
-
-        sleep 2
-    done
-}
-
-# Check if gRPC backend is responding
-check_grpc_backend() {
-    local timeout=$1
-    log_info "Checking gRPC backend on localhost:$GRPC_PORT..."
-
-    local start_time=$(date +%s)
-    while true; do
-        local current_time=$(date +%s)
-        local elapsed=$((current_time - start_time))
-
-        if [ $elapsed -gt $timeout ]; then
-            log_error "gRPC backend not responding after ${timeout}s"
-            return 1
-        fi
-
-        # Try to connect using nc (netcat)
-        if nc -z localhost $GRPC_PORT 2>/dev/null; then
-            log_success "gRPC backend is listening on port $GRPC_PORT"
-            return 0
-        fi
-
-        sleep 1
-    done
-}
-
-# Check frontend-backend communication
-test_frontend_backend_communication() {
-    local protocol=$1
-    local test_name=$2
-    local port=${3:-$FRONTEND_PORT}
-
-    log_info "Testing frontend -> backend communication ($test_name)..."
-
-    # The frontend makes requests to /api which proxies to envoy
-    # Envoy proxies to localhost:50051 (the gRPC backend)
-
-    # Check if frontend can resolve the backend
-    local health_url="$protocol://localhost:$port/health"
-
-    local http_code=$(curl -sk -o /dev/null -w "%{http_code}" "$health_url" 2>/dev/null || echo "000")
-
-    if [ "$http_code" = "200" ]; then
-        log_success "Frontend is responding ($test_name)"
-    else
-        log_error "Frontend health check failed with HTTP $http_code ($test_name)"
-        return 1
-    fi
-
-    return 0
-}
-
-# Run backend example in background and monitor
-run_backend_example() {
-    local test_scenario=$1
-
-    if [ ! -f "$EXAMPLE_DIR/main.py" ]; then
-        log_error "Python example not found at $EXAMPLE_DIR/main.py"
-        return 1
-    fi
-
-    log_info "Starting backend example: $test_scenario..."
-
-    cd "$EXAMPLE_DIR"
-
-    # Start Python backend in background
-    timeout 60s python main.py > "/tmp/ws_backend_${test_scenario}.log" 2>&1 &
-    local backend_pid=$! 2>/dev/null || true
-
-    log_info "Backend process started (PID: $backend_pid)"
-
-    # Wait a bit for backend to initialize
-    sleep 3
-
-    # Check if process is still running
-    if ! kill -0 $backend_pid 2>/dev/null; then
-        log_error "Backend process exited prematurely"
-        cat "/tmp/ws_backend_${test_scenario}.log"
-        return 1
-    fi
-
-    log_success "Backend example is running"
-
-    # Check gRPC port
-    if check_grpc_backend $COMMUNICATION_TEST_TIMEOUT; then
-        log_success "Backend gRPC service is reachable"
-    else
-        log_error "Backend gRPC service is not reachable"
-        kill $backend_pid 2>/dev/null || true
-        return 1
-    fi
-
-    return 0
-}
-
-# Temporarily hide/show certs and tokens for testing
-hide_certs() {
-    local certs_dir=$1
-    if [ -d "$certs_dir" ]; then
-        mv "$certs_dir" "${certs_dir}.bak"
-        mkdir -p "$certs_dir"
-    fi
-}
-
-restore_certs() {
-    local certs_dir=$1
-    if [ -d "${certs_dir}.bak" ]; then
-        rm -rf "$certs_dir"
-        mv "${certs_dir}.bak" "$certs_dir"
-    fi
-}
-
-hide_grpc_token() {
-    local token_file=$1
-    if [ -f "$token_file" ]; then
-        mv "$token_file" "${token_file}.bak"
-    fi
-}
-
-restore_grpc_token() {
-    local token_file=$1
-    if [ -f "${token_file}.bak" ]; then
-        mv "${token_file}.bak" "$token_file"
-    fi
-}
-
-# Run test scenario
-run_test_scenario() {
-    local scenario_name=$1
-    local secure=$2
-    local use_certs=${3:-true}
-    local use_auth=${4:-false}
-
-    # Create a safe filename from scenario name (remove spaces, colons, etc)
-    local scenario_safe=$(echo "$scenario_name" | tr '[:upper:]' '[:lower:]' | sed 's/[^a-z0-9]/-/g' | sed 's/-\+/-/g' | sed 's/-$//')
-
-    print_section "$scenario_name"
-
-    # Cleanup previous deployment
-    cleanup
-
-    # Setup certs and tokens for this scenario
-    local certs_dir="$DOCKER_DIR/.weightslab-certs-test"
-    log_info "certs_dir: $certs_dir"
-    local token_file="$certs_dir/.grpc_auth_token"
-    local certs_hidden=false
-    local token_hidden=false
-
-    if [ "$use_certs" = "false" ] && [ -d "$certs_dir" ]; then
-        log_info "Temporarily hiding TLS certificates for this test..."
-        hide_certs "$certs_dir"
-        certs_hidden=true
-    fi
-
-    if [ "$use_auth" = "false" ] && [ -f "$token_file" ]; then
-        log_info "Temporarily hiding gRPC auth token for this test..."
-        hide_grpc_token "$token_file"
-        token_hidden=true
-    fi
-
-    # Set environment for deployment
-    if [ "$secure" = "true" ]; then
-        if [ "$use_certs" = "true" ] && [ "$use_auth" = "true" ]; then
-            log_info "Secure mode: HTTPS with TLS + gRPC auth enabled"
-        elif [ "$use_certs" = "true" ]; then
-            log_info "Secure mode: HTTPS with TLS (no gRPC auth)"
-        elif [ "$use_auth" = "true" ]; then
-            log_info "Secure mode: HTTP with gRPC auth only (no TLS)"
-        fi
-        export E2E_MANAGED_STACK=1
-    else
-        log_info "Unsecure mode: HTTP without TLS or gRPC auth"
-        export E2E_MANAGED_STACK=1
-    fi
-
-    # Build and deploy frontend
-    cd "$UTILS_DIR"
-
-    if [ "$secure" = "true" ] && [ "$use_certs" = "true" ]; then
-        log_info "Building and deploying with HTTPS support..."
-        WEIGHTSLAB_CERTS_DIR="$certs_dir" bash build-and-deploy.sh > "/tmp/build_deploy_${scenario_safe}.log" 2>&1 || {
-            log_error "Build and deploy failed"
-            cat "/tmp/build_deploy_${scenario_safe}.log"
-            [ "$certs_hidden" = true ] && restore_certs "$certs_dir"
-            [ "$token_hidden" = true ] && restore_grpc_token "$token_file"
-            return 1
-        }
-    else
-        log_info "Building and deploying with HTTP (no TLS certs)..."
-        WEIGHTSLAB_CERTS_DIR="$certs_dir" bash build-and-deploy.sh --unsecure > "/tmp/build_deploy_${scenario_safe}.log" 2>&1 || {
-            log_error "Build and deploy failed"
-            cat "/tmp/build_deploy_${scenario_safe}.log"
-            [ "$certs_hidden" = true ] && restore_certs "$certs_dir"
-            [ "$token_hidden" = true ] && restore_grpc_token "$token_file"
-            return 1
-        }
-    fi
-
-    log_success "Frontend deployment successful"
-
-    # Wait for services to be ready
-    local protocol="http"
-    local frontend_port=$FRONTEND_PORT
-    if [ "$secure" = "true" ] && [ "$use_certs" = "true" ]; then
-        protocol="https"
-    fi
-
-    # Check frontend (don't check envoy since it depends on upstream backend)
-    if ! wait_for_service "Frontend (nginx)" "$frontend_port" "$HEALTH_CHECK_TIMEOUT" "$protocol"; then
-        log_error "Frontend failed to start"
-        [ "$certs_hidden" = true ] && restore_certs "$certs_dir"
-        [ "$token_hidden" = true ] && restore_grpc_token "$token_file"
-        return 1
-    fi
-
-    # Test frontend-backend communication setup
-    if test_frontend_backend_communication "$protocol" "$scenario_safe" "$frontend_port"; then
-        log_success "Frontend-backend communication setup OK"
-    else
-        log_error "Frontend-backend communication setup failed"
-        [ "$certs_hidden" = true ] && restore_certs "$certs_dir"
-        [ "$token_hidden" = true ] && restore_grpc_token "$token_file"
-        return 1
-    fi
-
-    # Restore certs and tokens
-    if [ "$certs_hidden" = true ]; then
-        log_info "Restoring TLS certificates..."
-        restore_certs "$certs_dir"
-    fi
-    if [ "$token_hidden" = true ]; then
-        log_info "Restoring gRPC auth token..."
-        restore_grpc_token "$token_file"
-    fi
-
-    return 0
-}
-
-# ============================================================================
-# Main Test Execution
-# ============================================================================
-
-main() {
-    print_section "WEIGHTS STUDIO FRONTEND-BACKEND DEPLOYMENT TEST"
-
-    log_info "Test Configuration:"
-    log_info "  Weights Studio Dir: $CURRENT_DIR"
-    log_info "  Docker Dir: $DOCKER_DIR"
-    log_info "  Example Dir: $EXAMPLE_DIR"
-    log_info "  Envoy Port: $ENVOY_PORT"
-    log_info "  Frontend Port: $FRONTEND_PORT"
-    log_info "  gRPC Backend Port: $GRPC_PORT"
-
-    # Test A: Unsecured Environment (HTTP)
-    if run_test_scenario "TEST A: UNSECURED ENVIRONMENT (HTTP)" "false" "false" "false"; then
-        log_success "TEST A PASSED: Frontend and backend communicate over HTTP"
-    else
-        log_error "TEST A FAILED: Frontend-backend communication issue in HTTP mode"
-    fi
-
-    # Cleanup between tests
-    cleanup
-    sleep 5
-
-    # Test B: Secured Environment (HTTPS with TLS + gRPC Auth)
-    # Note: This requires TLS certificates AND gRPC auth token to be present
-    if [ -d "$DOCKER_DIR/.weightslab-certs-test" ] && [ -f "$DOCKER_DIR/.weightslab-certs-test/envoy-server.crt" ]; then
-        if [ -f "$DOCKER_DIR/.weightslab-certs-test/.grpc_auth_token" ]; then
-            if run_test_scenario "TEST B: SECURED ENVIRONMENT (HTTPS + TLS + gRPC AUTH)" "true" "true" "true"; then
-                log_success "TEST B PASSED: Frontend and backend communicate over HTTPS with full security"
-            else
-                log_error "TEST B FAILED: Frontend-backend communication issue in HTTPS+Auth mode"
-            fi
-        else
-            log_warn "TEST B SKIPPED: gRPC auth token not found"
-            log_warn "  Token should be at $DOCKER_DIR/.weightslab-certs-test/.grpc_auth_token"
-        fi
-
-        # Cleanup between tests
-        cleanup
-        sleep 5
-
-        # Test C: TLS Certificates Only (no gRPC auth)
-        if run_test_scenario "TEST C: SECURED ENVIRONMENT (HTTPS + TLS ONLY, NO GRPC AUTH)" "true" "true" "false"; then
-            log_success "TEST C PASSED: Frontend and backend communicate over HTTPS (TLS only, no gRPC auth)"
-        else
-            log_error "TEST C FAILED: Frontend-backend communication issue in TLS-only mode"
-        fi
-
-        # Cleanup between tests
-        cleanup
-        sleep 5
-
-        # Test D: gRPC Auth Only (no TLS certificates)
-        if [ -f "$DOCKER_DIR/.weightslab-certs-test/.grpc_auth_token" ]; then
-            if run_test_scenario "TEST D: SECURED ENVIRONMENT (HTTP + gRPC AUTH ONLY, NO TLS)" "true" "false" "true"; then
-                log_success "TEST D PASSED: Frontend and backend communicate over HTTP with gRPC auth only"
-            else
-                log_error "TEST D FAILED: Frontend-backend communication issue in gRPC-auth-only mode"
-            fi
-        else
-            log_warn "TEST D SKIPPED: gRPC auth token not found"
-            log_warn "  Token should be at $DOCKER_DIR/.weightslab-certs-test/.grpc_auth_token"
-        fi
-    else
-        log_warn "TESTS B, C, D SKIPPED: TLS certificates not found at $DOCKER_DIR/.weightslab-certs-test"
-        log_warn "  To run secured tests, copy certs to:"
-        log_warn "  $DOCKER_DIR/.weightslab-certs-test"
-    fi
-
-    # Final cleanup
-    cleanup
-
-    # Print summary
-    print_section "TEST SUMMARY"
-    echo -e "Tests Passed: ${GREEN}$TESTS_PASSED${NC}"
-    echo -e "Tests Failed: ${RED}$TESTS_FAILED${NC}"
-
-    if [ $TESTS_FAILED -eq 0 ]; then
-        log_success "ALL TESTS PASSED! ✨"
-        return 0
-    else
-        log_error "SOME TESTS FAILED ❌"
-        return 1
-    fi
-}
-
-# Handle script interruption
-trap cleanup EXIT
-
-# Run main
-main "$@"
diff --git a/weightslab/docker/envoy/envoy.downstream_plaintext.yaml b/weightslab/docker/envoy/envoy.downstream_plaintext.yaml
deleted file mode 100644
index d96b8f44..00000000
--- a/weightslab/docker/envoy/envoy.downstream_plaintext.yaml
+++ /dev/null
@@ -1,100 +0,0 @@
-static_resources:
-  listeners:
-  - name: listener_0
-    per_connection_buffer_limit_bytes: 268435456  # 256 MB — matches GRPC_MAX_MESSAGE_BYTES
-    address:
-      socket_address:
-        address: 0.0.0.0
-        port_value: 8080
-    filter_chains:
-      filters:
-      - name: envoy.filters.network.http_connection_manager
-        typed_config:
-          "@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager
-          codec_type: AUTO
-          stat_prefix: ingress_http
-          route_config:
-            name: local_route
-            virtual_hosts:
-            - name: local_service
-              domains: ["*"]
-              routes:
-              - match:
-                  prefix: "/"
-                route:
-                  cluster: grpc_service
-                  timeout: 300s
-                  retry_policy:
-                    retry_on: connect-failure,refused-stream,reset
-                    num_retries: 1
-                    per_try_timeout: 60s
-                  max_stream_duration:
-                    grpc_timeout_header_max: 300s
-              cors:
-                allow_origin_string_match:
-                - exact: "http://localhost"
-                - exact: "http://127.0.0.1"
-                - exact: "http://localhost:5173"
-                - exact: "http://127.0.0.1:5173"
-                - exact: "https://localhost"
-                - exact: "https://127.0.0.1"
-                - exact: "https://localhost:5173"
-                - exact: "https://127.0.0.1:5173"
-                allow_methods: GET, PUT, DELETE, POST, OPTIONS
-                allow_headers: keep-alive,user-agent,cache-control,content-type,content-transfer-encoding,custom-header-1,x-accept-content-transfer-encoding,x-accept-response-streaming,x-user-agent,x-grpc-web,grpc-timeout
-                max_age: "1728000"
-                expose_headers: custom-header-1,grpc-status,grpc-message
-          http_filters:
-          - name: envoy.filters.http.grpc_web
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.grpc_web.v3.GrpcWeb
-          - name: envoy.filters.http.cors
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.cors.v3.Cors
-          - name: envoy.filters.http.router
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
-
-  clusters:
-  - name: grpc_service
-    connect_timeout: 1s
-    type: STRICT_DNS
-    lb_policy: ROUND_ROBIN
-    dns_lookup_family: V4_ONLY
-    http2_protocol_options: {}
-    circuit_breakers:
-      thresholds:
-      - priority: DEFAULT
-        max_connections: 100
-        max_pending_requests: 100
-        max_requests: 200
-        max_retries: 3
-    load_assignment:
-      cluster_name: grpc_service
-      endpoints:
-      - lb_endpoints:
-        - endpoint:
-            address:
-              socket_address:
-                address: grpc-backend
-                port_value: __GRPC_BACKEND_PORT__
-    transport_socket:
-      name: envoy.transport_sockets.tls
-      typed_config:
-        "@type": type.googleapis.com/envoy.extensions.transport_sockets.tls.v3.UpstreamTlsContext
-        common_tls_context:
-          tls_certificates:
-          - certificate_chain:
-              filename: /etc/envoy/certs/envoy-client.crt
-            private_key:
-              filename: /etc/envoy/certs/envoy-client.key
-          validation_context:
-            trusted_ca:
-              filename: /etc/envoy/certs/ca.crt
-
-admin:
-  access_log_path: /tmp/admin_access.log
-  address:
-    socket_address:
-      address: 127.0.0.1
-      port_value: 9901
diff --git a/weightslab/docker/envoy/envoy.downstream_upstream_plaintext.yaml b/weightslab/docker/envoy/envoy.downstream_upstream_plaintext.yaml
deleted file mode 100644
index 3bee23b9..00000000
--- a/weightslab/docker/envoy/envoy.downstream_upstream_plaintext.yaml
+++ /dev/null
@@ -1,87 +0,0 @@
-static_resources:
-  listeners:
-  - name: listener_0
-    per_connection_buffer_limit_bytes: 268435456  # 256 MB — matches GRPC_MAX_MESSAGE_BYTES
-    address:
-      socket_address:
-        address: 0.0.0.0
-        port_value: 8080
-    filter_chains:
-      filters:
-      - name: envoy.filters.network.http_connection_manager
-        typed_config:
-          "@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager
-          codec_type: AUTO
-          stat_prefix: ingress_http
-          route_config:
-            name: local_route
-            virtual_hosts:
-            - name: local_service
-              domains: ["*"]
-              routes:
-              - match:
-                  prefix: "/"
-                route:
-                  cluster: grpc_service
-                  timeout: 300s
-                  retry_policy:
-                    retry_on: connect-failure,refused-stream,reset
-                    num_retries: 1
-                    per_try_timeout: 60s
-                  max_stream_duration:
-                    grpc_timeout_header_max: 300s
-              cors:
-                allow_origin_string_match:
-                - exact: "http://localhost"
-                - exact: "http://127.0.0.1"
-                - exact: "http://localhost:5173"
-                - exact: "http://127.0.0.1:5173"
-                - exact: "https://localhost"
-                - exact: "https://127.0.0.1"
-                - exact: "https://localhost:5173"
-                - exact: "https://127.0.0.1:5173"
-                allow_methods: GET, PUT, DELETE, POST, OPTIONS
-                allow_headers: keep-alive,user-agent,cache-control,content-type,content-transfer-encoding,custom-header-1,x-accept-content-transfer-encoding,x-accept-response-streaming,x-user-agent,x-grpc-web,grpc-timeout
-                max_age: "1728000"
-                expose_headers: custom-header-1,grpc-status,grpc-message
-          http_filters:
-          - name: envoy.filters.http.grpc_web
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.grpc_web.v3.GrpcWeb
-          - name: envoy.filters.http.cors
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.cors.v3.Cors
-          - name: envoy.filters.http.router
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
-
-  clusters:
-  - name: grpc_service
-    connect_timeout: 1s
-    type: STRICT_DNS
-    lb_policy: ROUND_ROBIN
-    dns_lookup_family: V4_ONLY
-    http2_protocol_options: {}
-    circuit_breakers:
-      thresholds:
-      - priority: DEFAULT
-        max_connections: 100
-        max_pending_requests: 100
-        max_requests: 200
-        max_retries: 3
-    load_assignment:
-      cluster_name: grpc_service
-      endpoints:
-      - lb_endpoints:
-        - endpoint:
-            address:
-              socket_address:
-                address: grpc-backend
-                port_value: __GRPC_BACKEND_PORT__
-
-admin:
-  access_log_path: /tmp/admin_access.log
-  address:
-    socket_address:
-      address: 127.0.0.1
-      port_value: 9901
diff --git a/weightslab/docker/envoy/envoy.upstream_plaintext.yaml b/weightslab/docker/envoy/envoy.upstream_plaintext.yaml
deleted file mode 100644
index 38f65e28..00000000
--- a/weightslab/docker/envoy/envoy.upstream_plaintext.yaml
+++ /dev/null
@@ -1,97 +0,0 @@
-static_resources:
-  listeners:
-  - name: listener_0
-    per_connection_buffer_limit_bytes: 268435456  # 256 MB — matches GRPC_MAX_MESSAGE_BYTES
-    address:
-      socket_address:
-        address: 0.0.0.0
-        port_value: 8080
-    filter_chains:
-    - transport_socket:
-        name: envoy.transport_sockets.tls
-        typed_config:
-          "@type": type.googleapis.com/envoy.extensions.transport_sockets.tls.v3.DownstreamTlsContext
-          common_tls_context:
-            tls_certificates:
-            - certificate_chain:
-                filename: /etc/envoy/certs/envoy-server.crt
-              private_key:
-                filename: /etc/envoy/certs/envoy-server.key
-      filters:
-      - name: envoy.filters.network.http_connection_manager
-        typed_config:
-          "@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager
-          codec_type: AUTO
-          stat_prefix: ingress_http
-          route_config:
-            name: local_route
-            virtual_hosts:
-            - name: local_service
-              domains: ["*"]
-              routes:
-              - match:
-                  prefix: "/"
-                route:
-                  cluster: grpc_service
-                  timeout: 300s
-                  retry_policy:
-                    retry_on: connect-failure,refused-stream,reset
-                    num_retries: 1
-                    per_try_timeout: 60s
-                  max_stream_duration:
-                    grpc_timeout_header_max: 300s
-              cors:
-                allow_origin_string_match:
-                - exact: "http://localhost"
-                - exact: "http://127.0.0.1"
-                - exact: "http://localhost:5173"
-                - exact: "http://127.0.0.1:5173"
-                - exact: "https://localhost"
-                - exact: "https://127.0.0.1"
-                - exact: "https://localhost:5173"
-                - exact: "https://127.0.0.1:5173"
-                allow_methods: GET, PUT, DELETE, POST, OPTIONS
-                allow_headers: keep-alive,user-agent,cache-control,content-type,content-transfer-encoding,custom-header-1,x-accept-content-transfer-encoding,x-accept-response-streaming,x-user-agent,x-grpc-web,grpc-timeout
-                max_age: "1728000"
-                expose_headers: custom-header-1,grpc-status,grpc-message
-          http_filters:
-          - name: envoy.filters.http.grpc_web
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.grpc_web.v3.GrpcWeb
-          - name: envoy.filters.http.cors
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.cors.v3.Cors
-          - name: envoy.filters.http.router
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
-
-  clusters:
-  - name: grpc_service
-    connect_timeout: 1s
-    type: STRICT_DNS
-    lb_policy: ROUND_ROBIN
-    dns_lookup_family: V4_ONLY
-    http2_protocol_options: {}
-    circuit_breakers:
-      thresholds:
-      - priority: DEFAULT
-        max_connections: 100
-        max_pending_requests: 100
-        max_requests: 200
-        max_retries: 3
-    load_assignment:
-      cluster_name: grpc_service
-      endpoints:
-      - lb_endpoints:
-        - endpoint:
-            address:
-              socket_address:
-                address: grpc-backend
-                port_value: __GRPC_BACKEND_PORT__
-
-admin:
-  access_log_path: /tmp/admin_access.log
-  address:
-    socket_address:
-      address: 127.0.0.1
-      port_value: 9901
diff --git a/weightslab/docker/envoy/envoy.yaml b/weightslab/docker/envoy/envoy.yaml
deleted file mode 100644
index ed3d02fa..00000000
--- a/weightslab/docker/envoy/envoy.yaml
+++ /dev/null
@@ -1,110 +0,0 @@
-static_resources:
-  listeners:
-  - name: listener_0
-    per_connection_buffer_limit_bytes: 268435456  # 256 MB — matches GRPC_MAX_MESSAGE_BYTES
-    address:
-      socket_address:
-        address: 0.0.0.0
-        port_value: 8080
-    filter_chains:
-    - transport_socket:
-        name: envoy.transport_sockets.tls
-        typed_config:
-          "@type": type.googleapis.com/envoy.extensions.transport_sockets.tls.v3.DownstreamTlsContext
-          common_tls_context:
-            tls_certificates:
-            - certificate_chain:
-                filename: /etc/envoy/certs/envoy-server.crt
-              private_key:
-                filename: /etc/envoy/certs/envoy-server.key
-      filters:
-      - name: envoy.filters.network.http_connection_manager
-        typed_config:
-          "@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager
-          codec_type: AUTO
-          stat_prefix: ingress_http
-          route_config:
-            name: local_route
-            virtual_hosts:
-            - name: local_service
-              domains: ["*"]
-              routes:
-              - match:
-                  prefix: "/"
-                route:
-                  cluster: grpc_service
-                  timeout: 300s
-                  retry_policy:
-                    retry_on: connect-failure,refused-stream,reset
-                    num_retries: 1
-                    per_try_timeout: 60s
-                  max_stream_duration:
-                    grpc_timeout_header_max: 300s
-              cors:
-                allow_origin_string_match:
-                - exact: "http://localhost"
-                - exact: "http://127.0.0.1"
-                - exact: "http://localhost:5173"
-                - exact: "http://127.0.0.1:5173"
-                - exact: "https://localhost"
-                - exact: "https://127.0.0.1"
-                - exact: "https://localhost:5173"
-                - exact: "https://127.0.0.1:5173"
-                allow_methods: GET, PUT, DELETE, POST, OPTIONS
-                allow_headers: keep-alive,user-agent,cache-control,content-type,content-transfer-encoding,custom-header-1,x-accept-content-transfer-encoding,x-accept-response-streaming,x-user-agent,x-grpc-web,grpc-timeout,authorization,x-grpc-token
-                max_age: "1728000"
-                expose_headers: custom-header-1,grpc-status,grpc-message
-          http_filters:
-          - name: envoy.filters.http.grpc_web
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.grpc_web.v3.GrpcWeb
-          - name: envoy.filters.http.cors
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.cors.v3.Cors
-          - name: envoy.filters.http.router
-            typed_config:
-              "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
-
-  clusters:
-  - name: grpc_service
-    connect_timeout: 1s
-    type: STRICT_DNS
-    lb_policy: ROUND_ROBIN
-    dns_lookup_family: V4_ONLY
-    http2_protocol_options: {}
-    circuit_breakers:
-      thresholds:
-      - priority: DEFAULT
-        max_connections: 100
-        max_pending_requests: 100
-        max_requests: 200
-        max_retries: 3
-    load_assignment:
-      cluster_name: grpc_service
-      endpoints:
-      - lb_endpoints:
-        - endpoint:
-            address:
-              socket_address:
-                address: host.docker.internal
-                port_value: __GRPC_BACKEND_PORT__
-    transport_socket:
-      name: envoy.transport_sockets.tls
-      typed_config:
-        "@type": type.googleapis.com/envoy.extensions.transport_sockets.tls.v3.UpstreamTlsContext
-        common_tls_context:
-          tls_certificates:
-          - certificate_chain:
-              filename: /etc/envoy/certs/envoy-client.crt
-            private_key:
-              filename: /etc/envoy/certs/envoy-client.key
-          validation_context:
-            trusted_ca:
-              filename: /etc/envoy/certs/ca.crt
-
-admin:
-  access_log_path: /tmp/admin_access.log
-  address:
-    socket_address:
-      address: 127.0.0.1
-      port_value: 9901
diff --git a/weightslab/docker/nginx.conf b/weightslab/docker/nginx.conf
deleted file mode 100644
index 7fdfb58b..00000000
--- a/weightslab/docker/nginx.conf
+++ /dev/null
@@ -1,118 +0,0 @@
-upstream backend {
-    server host.docker.internal:50051;
-}
-
-upstream envoy {
-    server weights_studio_envoy:8080;
-}
-
-# HTTP server - insecure mode
-server {
-    listen 80;
-    server_name _;
-
-    root /usr/share/nginx/html;
-    index index.html;
-
-    # Cache static assets
-    location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
-        expires 1y;
-        add_header Cache-Control "public, immutable";
-    }
-
-    # API proxy to backend through Envoy
-    location /api/ {
-        proxy_pass http://envoy/;
-        proxy_http_version 1.1;
-        proxy_set_header Upgrade $http_upgrade;
-        proxy_set_header Connection "upgrade";
-        proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
-        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-        proxy_set_header X-Forwarded-Proto $scheme;
-    }
-
-    # gRPC proxy
-    location ~ ^/grpc {
-        proxy_pass http://envoy;
-        proxy_http_version 1.1;
-        proxy_set_header Content-Type application/grpc;
-        proxy_set_header grpc-encoding gzip;
-        proxy_buffering off;
-        proxy_request_buffering off;
-        proxy_set_header Host $host;
-    }
-
-    # SPA fallback - all other routes go to index.html
-    location / {
-        try_files $uri $uri/ /index.html;
-    }
-
-    # Health check
-    location /health {
-        access_log off;
-        return 200 "healthy\n";
-        add_header Content-Type text/plain;
-    }
-}
-
-# HTTPS server - secure mode
-server {
-    listen 443 ssl;
-    server_name _;
-
-    # SSL/TLS certificates
-    ssl_certificate /etc/nginx/certs/envoy-server.crt;
-    ssl_certificate_key /etc/nginx/certs/envoy-server.key;
-
-    # SSL configuration
-    ssl_protocols TLSv1.2 TLSv1.3;
-    ssl_ciphers HIGH:!aNULL:!MD5;
-    ssl_prefer_server_ciphers on;
-    ssl_session_cache shared:SSL:10m;
-    ssl_session_timeout 10m;
-
-    root /usr/share/nginx/html;
-    index index.html;
-
-    # Cache static assets
-    location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
-        expires 1y;
-        add_header Cache-Control "public, immutable";
-    }
-
-    # API proxy to backend through Envoy
-    location /api/ {
-        proxy_pass http://envoy/;
-        proxy_http_version 1.1;
-        proxy_set_header Upgrade $http_upgrade;
-        proxy_set_header Connection "upgrade";
-        proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
-        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-        proxy_set_header X-Forwarded-Proto $scheme;
-    }
-
-    # gRPC proxy
-    location ~ ^/grpc {
-        proxy_pass http://envoy;
-        proxy_http_version 1.1;
-        proxy_set_header Content-Type application/grpc;
-        proxy_set_header grpc-encoding gzip;
-        proxy_buffering off;
-        proxy_request_buffering off;
-        proxy_set_header Host $host;
-    }
-
-    # SPA fallback - all other routes go to index.html
-    location / {
-        try_files $uri $uri/ /index.html;
-    }
-
-    # Health check
-    location /health {
-        access_log off;
-        return 200 "healthy\n";
-        add_header Content-Type text/plain;
-    }
-}
diff --git a/weightslab/examples/Docker_training/README.md b/weightslab/examples/Docker_training/README.md
deleted file mode 100644
index a96e7c6b..00000000
--- a/weightslab/examples/Docker_training/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Running WeightsLab from a "training docker" — three options
-
-Each option runs the WeightsLab UI stack + the classification training inside a
-container so the example ends up reachable at <http://localhost:5173>, talking to
-the training process end-to-end. They differ in **how the container gets a docker
-daemon** to launch Envoy + frontend, and the configuration that follows.
-
-| | [docker_in_docker](docker_in_docker/) (A · DinD) | [siblings_docker](siblings_docker/) (B · DooD) | [siblings_self_contained](siblings_self_contained/) (C) |
-|---|---|---|---|
-| Docker daemon | own daemon, nested | host daemon (socket) | host daemon (socket) |
-| Envoy/frontend run | nested in the trainer | siblings on the host | siblings on the host |
-| Starts the UI via | `weightslab ui launch` | `weightslab ui launch` | own bind-mount-free `ui-compose.yml` |
-| `--privileged` | **required** | no | no |
-| gRPC `:50051` | not published | **published to host** | **published to host** |
-| Browser ports 5173/8080 | re-published by trainer | published by siblings | published by siblings |
-| Host bind mounts | resolve in-container — none | **path alignment** required | **none** (config via named volume + HTTP) |
-| Host setup | none | `setup-host.sh` (clone to aligned path) | none |
-| TLS (HTTPS) | ✅ `--certs` (+ backend TLS env) | ✅ `--certs` (aligned certs) | ✅ opt-in (certs via named volume) |
-| Windows / Docker Desktop | ✅ works | ⚠️ awkward (use WSL2) | ✅ works natively |
-| Best when | want isolation / Windows / TLS | Linux host, stock `ui launch` | "all from inside", no host prep, Windows |
-
-See each directory's `README.md` for the detailed wiring diagram and run steps.
-
-- **A (DinD)** — fully self-contained via a nested daemon; needs `--privileged`.
-- **B (siblings)** — uses the stock `weightslab ui launch`; its bundled compose
-  bind-mounts the Envoy config + certs, so the host must see those files (path
-  alignment + `setup-host.sh`). Cleanest on a Linux host.
-- **C (self-contained siblings)** — siblings *without* host bind mounts: it
-  delivers the Envoy config (and, for TLS, the certs) through named volumes over
-  the socket, so no host prep and it works natively on Windows. The trade-off:
-  it doesn't use `weightslab ui launch`. HTTP by default, TLS opt-in.
-
-> All three can run secured TLS — see each README's "🔒" section. **A and C
-> expose a one-flag toggle:** `WEIGHTSLAB_TLS=1 docker compose up --build`. Note
-> that for **every** option cert generation only configures Envoy + the frontend;
-> the **backend** example also needs `GRPC_TLS_ENABLED=1` + `GRPC_TLS_CERT_DIR`
-> (the toggle handles this), or Envoy's upstream mTLS fails. And the dev CA must
-> be trusted on the host browser to avoid a self-signed warning.
diff --git a/weightslab/examples/Docker_training/docker_in_docker/Dockerfile b/weightslab/examples/Docker_training/docker_in_docker/Dockerfile
deleted file mode 100644
index cab3af85..00000000
--- a/weightslab/examples/Docker_training/docker_in_docker/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-# =============================================================================
-# WeightsLab "training docker" — Docker-in-Docker (DinD) variant
-# =============================================================================
-# This image runs BOTH:
-#   1. the WeightsLab CLI ("weightslab ui launch") which spins up the
-#      Envoy + Weights Studio frontend containers, and
-#   2. the training process ("weightslab start example") which serves the
-#      in-process gRPC backend on :50051.
-#
-# DinD = the container runs its OWN docker daemon inside itself (requires
-# --privileged). The Envoy/frontend containers are nested *inside* this
-# container's daemon, so they share this container's network namespace and
-# filesystem. See README.md for why that matters.
-# =============================================================================
-FROM python:3.11-slim
-
-# --- System deps -------------------------------------------------------------
-# - docker engine (dockerd + CLI + compose plugin + containerd): installed via
-#   the official convenience script. We need the *daemon* here (DinD).
-# - curl/ca-certificates/git: fetch the docker installer + optional dev install.
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        curl sudo ca-certificates git \
-    && curl -fsSL https://get.docker.com | sh \
-    && rm -rf /var/lib/apt/lists/*
-
-# --- WeightsLab --------------------------------------------------------------
-# Default: install the published package from PyPI (matches "if you didn't
-# modify weightslab, use pip install"). To run your local dev branch instead:
-#   docker compose build \
-#     --build-arg WEIGHTSLAB_SPEC="git+https://github.com/GrayboxTech/weightslab.git@dev"
-ARG WEIGHTSLAB_SPEC=weightslab
-RUN pip install --no-cache-dir "${WEIGHTSLAB_SPEC}"
-
-# gRPC backend port — must match what Envoy is told to dial (GRPC_BACKEND_PORT).
-ENV GRPC_BACKEND_PORT=50051
-
-# --- GPU (NVIDIA) ------------------------------------------------------------
-# Make the NVIDIA Container Toolkit inject the host driver (nvidia-smi + libs)
-# into this container. `utility` => nvidia-smi works; `compute` => CUDA/torch.
-# These are no-ops on a host without an NVIDIA GPU/toolkit (falls back to CPU).
-# The actual GPU grant is requested in docker-compose.yml (deploy.resources).
-# torch's default Linux wheel bundles the CUDA runtime, so no CUDA base image is
-# needed — only the host driver (injected) is required for torch.cuda to work.
-ENV NVIDIA_VISIBLE_DEVICES=all \
-    NVIDIA_DRIVER_CAPABILITIES=compute,utility
-
-COPY entrypoint.sh /usr/local/bin/entrypoint.sh
-# Strip any CR so the script runs under Linux bash even if checked out on Windows.
-RUN sed -i 's/\r$//' /usr/local/bin/entrypoint.sh \
-    && chmod +x /usr/local/bin/entrypoint.sh
-
-# OPTIONAL - Documentation only — EXPOSE does NOT publish ports. The host publishing is done
-# by `ports:` in docker-compose.yml. Listed here purely to record intent:
-#   5173 = Weights Studio frontend, 8080 = Envoy gRPC-web.
-EXPOSE 5173 8080
-
-ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
diff --git a/weightslab/examples/Docker_training/docker_in_docker/README.md b/weightslab/examples/Docker_training/docker_in_docker/README.md
deleted file mode 100644
index b99004d8..00000000
--- a/weightslab/examples/Docker_training/docker_in_docker/README.md
+++ /dev/null
@@ -1,161 +0,0 @@
-# Training docker — Option A: Docker-in-Docker (DinD)
-
-Run the whole WeightsLab stack from **one self-contained training container** that
-runs its own Docker daemon. Inside it we run, in order:
-
-1. `weightslab ui launch` → starts the **Envoy** + **Weights Studio frontend**
-   containers (nested inside this container's daemon),
-2. `weightslab start example --cls` → runs the classification training, which
-   serves the in-process **gRPC backend** on `:50051`.
-
-The end result: open `http://localhost:5173` in your browser and the UI talks to
-the training process end-to-end.
-
-## ⚙️ Required configuration — DinD training (weightslab + weights_studio)
-
-The **must-have** bits to start a DinD training where the browser reaches the
-backend. Each maps to a concrete line in the files in this folder:
-
-| # | Requirement | Where | Why |
-|---|---|---|---|
-| 1 | **`privileged: true`** on the trainer | [docker-compose.yml](docker-compose.yml) | the inner `dockerd` cannot run otherwise |
-| 2 | **Start the inner `dockerd` + wait for it** | [entrypoint.sh](entrypoint.sh) | nested daemon that hosts Envoy + frontend |
-| 3 | **Persist `/var/lib/docker`** (named volume) | [docker-compose.yml](docker-compose.yml) | cache the Envoy + `graybx/weightslab` pulls across runs |
-| 4 | **Publish `5173` + `8080`** to the host | [docker-compose.yml](docker-compose.yml) | the browser reaches the *nested* frontend + Envoy |
-| 5 | **`GRPC_BACKEND_PORT=50051`** (matches Envoy) | [docker-compose.yml](docker-compose.yml) / [Dockerfile](Dockerfile) | Envoy's `grpc-backend:host-gateway` dials this port |
-| 6 | **`WEIGHTSLAB_SKIP_DOCKER_OPS=1`** before `ui launch` | [entrypoint.sh](entrypoint.sh) | skip the in-container frontend rebuild; just pull the image |
-| 7 | **Order: `ui launch` → `start example`** | [entrypoint.sh](entrypoint.sh) | UI stack up first; the example then serves the backend |
-| — | *Not needed (unlike siblings):* publishing `:50051`, bind-mount path alignment | — | Envoy + backend share the trainer's netns + filesystem |
-
-### GPU — to run `nvidia-smi` / train on CUDA
-
-GPU access is wired straight into `docker compose up` (no extra files):
-
-| Requirement | Where |
-|---|---|
-| Host: NVIDIA driver **+ NVIDIA Container Toolkit** (`sudo nvidia-ctk runtime configure --runtime=docker`) | host setup |
-| `deploy.resources.reservations.devices` (driver `nvidia`, `capabilities: [gpu]`) | [docker-compose.yml](docker-compose.yml) |
-| `NVIDIA_VISIBLE_DEVICES=all` + `NVIDIA_DRIVER_CAPABILITIES=compute,utility` | [Dockerfile](Dockerfile) |
-
-> The `deploy:` GPU block is a **hard requirement**: on a host with no NVIDIA
-> GPU/toolkit, comment it out in [docker-compose.yml](docker-compose.yml) or
-> `up` will fail with *"could not select device driver nvidia"*. Only the
-> **trainer** container needs the GPU — the nested Envoy/frontend don't.
-> The entrypoint prints `nvidia-smi -L` at startup and degrades to CPU if no GPU
-> is visible. On **Docker Desktop (Windows)** GPU passthrough goes through WSL2;
-> combining it with `--privileged` DinD can be finicky — verify with
-> `docker compose run --rm trainer nvidia-smi`. (A non-NVIDIA host, e.g. an AMD
-> Radeon machine, will not expose `nvidia-smi` regardless of this config.)
-
-## How the wiring works (and why DinD is simple)
-
-```
-host browser
-  → localhost:5173 ── (re-published) ──► trainer container ──► inner frontend :5173
-  → localhost:8080 ── (re-published) ──► trainer container ──► inner Envoy :8080
-                                                                  │ grpc-backend:host-gateway
-                                                                  ▼
-                                              in-process gRPC backend :50051  (same container)
-```
-
-Because the inner daemon runs **inside** the trainer container:
-
-- **Networking is co-located.** Envoy's upstream `grpc-backend:host-gateway`
-  resolves to the trainer container itself — exactly where the gRPC backend
-  listens. Nothing about `:50051` needs to be published to the host.
-- **Bind mounts just work.** The bundled compose mounts `envoy.yaml` and the
-  certs dir from the installed `weightslab` package. The inner daemon shares the
-  trainer container's filesystem, so those paths resolve with **no path
-  alignment** required (contrast this with the siblings variant).
-- We only re-publish the **browser-facing** ports (`5173`, `8080`) from the
-  trainer container out to the host.
-
-The trade-off: the container needs `--privileged`, and you pay for a nested
-daemon (an isolated image store, a second pull of Envoy + the frontend image).
-
-## Run it
-
-```bash
-# from this directory
-docker compose up --build
-```
-
-Then open <http://localhost:5173>. Stop with `Ctrl+C` (or `docker compose down`).
-
-### Use your local dev branch instead of PyPI
-
-By default the image installs `weightslab` from PyPI. To build against the dev
-branch (e.g. to pick up uncommitted-to-PyPI CLI changes):
-
-```bash
-docker compose build \
-  --build-arg WEIGHTSLAB_SPEC="git+https://github.com/GrayboxTech/weightslab.git@dev"
-docker compose up
-```
-
-(The repo must be reachable from the build — public, or pass build credentials.)
-
-### 🔒 Secured TLS (HTTPS + mTLS)
-
-DinD is the easiest option for TLS because the certs, Envoy, and the backend are
-all co-located in one container. TLS is a **one-flag toggle** — just set
-`WEIGHTSLAB_TLS=1`:
-
-```bash
-WEIGHTSLAB_TLS=1 docker compose up --build
-```
-
-then **trust the dev CA on your host** (step 2 below) and open
-**https://localhost:5173**.
-
-#### What the toggle does (in [entrypoint.sh](entrypoint.sh))
-
-There are three pieces to a working TLS setup; the toggle handles the first two:
-
-1. **Generate certs + configure the UI** — runs `weightslab ui launch --certs`,
-   which generates the cert set into `WEIGHTSLAB_CERTS_DIR`
-   (`/root/.weightslab-certs`) and configures Envoy + the frontend for HTTPS.
-2. **Turn TLS on for the backend** — `--certs` only configures Envoy + the
-   frontend, so the toggle also exports `GRPC_TLS_ENABLED=1` +
-   `GRPC_TLS_CERT_DIR=$WEIGHTSLAB_CERTS_DIR` before the example. Without this,
-   Envoy's upstream mTLS handshake fails against a plaintext backend (503s).
-
-The generator writes `ca.crt`, `envoy-server.*` (browser↔Envoy), `envoy-client.*`
-+ `ca.crt` (Envoy↔backend mTLS), and `backend-server.*` (the backend's own cert,
-CN `host.docker.internal`). `GRPC_TLS_CERT_DIR` makes the backend pick up
-`backend-server.*` + `ca.crt` from there.
-
-> Optional: to also *enforce* the gRPC auth token the UI sends, export
-> `GRPC_AUTH_TOKEN="$(cat $WEIGHTSLAB_CERTS_DIR/.grpc_auth_token)"` in the
-> entrypoint (otherwise the token is simply ignored — TLS still encrypts).
-
-#### 2. Trust the dev CA on the Windows host
-
-The dev CA is generated *inside* the container, so your host browser doesn't
-trust it yet. Export it and trust it (once):
-
-```powershell
-# pull the CA out of the running container
-docker cp weightslab_trainer_dind:/root/.weightslab-certs/ca.crt .
-# trust it for your user, then restart the browser
-Import-Certificate -FilePath .\ca.crt -CertStoreLocation Cert:\CurrentUser\Root
-```
-
-The certs carry SANs for `localhost`/`127.0.0.1`, so `https://localhost:5173`
-(frontend) and `https://localhost:8080` (Envoy) validate once the CA is trusted.
-Then open **https://localhost:5173**. (Skip this and TLS still works, but the
-browser shows a self-signed warning.)
-
-> Without step 3 the connection is still encrypted, but the browser shows a
-> self-signed warning. Without step 2, the UI loads over HTTPS but RPCs fail
-> (Envoy can't complete mTLS to a plaintext backend).
-
-## Notes / gotchas
-
-- **`--privileged` is mandatory** for the inner `dockerd`. If your environment
-  forbids it, use the siblings variant instead.
-- First run is slow: it pulls `envoyproxy/envoy` and `graybx/weightslab`, then
-  downloads MNIST for the example. The `dind_storage` volume caches images
-  across runs.
-- The example trains until stopped (`training_steps_to_do: null`) but starts
-  paused (`is_training: false`); start/steer training from the UI.
diff --git a/weightslab/examples/Docker_training/docker_in_docker/docker-compose.yml b/weightslab/examples/Docker_training/docker_in_docker/docker-compose.yml
deleted file mode 100644
index 034c7a4d..00000000
--- a/weightslab/examples/Docker_training/docker_in_docker/docker-compose.yml
+++ /dev/null
@@ -1,54 +0,0 @@
-# =============================================================================
-# WeightsLab training docker — Docker-in-Docker (DinD) variant
-# =============================================================================
-# One container that runs its own docker daemon, the WeightsLab CLI, and the
-# training process. The Envoy + frontend containers live *inside* this
-# container's daemon, so we re-publish their ports (8080, 5173) from this
-# container out to the host for the browser.
-# =============================================================================
-services:
-  trainer:
-    build:
-      context: .
-      # To use your local dev branch instead of PyPI, uncomment:
-      # args:
-      #   WEIGHTSLAB_SPEC: "git+https://github.com/GrayboxTech/weightslab.git@dev"
-    image: weightslab-trainer-dind
-    container_name: weightslab_trainer_dind
-    # Required so the inner dockerd can run (DinD).
-    privileged: true
-    # --- GPU ------------------------------------------------------------------
-    # Grant the host NVIDIA GPU(s) to the TRAINER container (the torch training
-    # runs here; the nested Envoy/frontend don't need a GPU). Requires the NVIDIA
-    # Container Toolkit on the host. NOTE: this is a hard requirement — on a host
-    # with no NVIDIA GPU/toolkit, comment out this whole `deploy:` block (the
-    # stack then runs on CPU; the entrypoint's nvidia-smi check degrades cleanly).
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all          # or e.g. count: 1, or device_ids: ["0"]
-              capabilities: [gpu]
-    environment:
-      # Port the in-process gRPC backend binds to and Envoy dials. The inner
-      # Envoy reaches it via grpc-backend:host-gateway, which (inside DinD)
-      # resolves to THIS container — where the backend listens. No host
-      # publishing of 50051 is needed (contrast with the siblings variant).
-      - GRPC_BACKEND_PORT=50051
-      # TLS toggle: run `WEIGHTSLAB_TLS=1 docker compose up` for HTTPS + mTLS
-      # (generates certs in-container; trust ca.crt on the host — see README).
-      - WEIGHTSLAB_TLS=${WEIGHTSLAB_TLS:-0}
-    ports:
-      # Re-publish the inner stack's browser-facing ports to the host.
-      - "5173:5173"   # Weights Studio frontend  -> http://localhost:5173
-      - "8080:8080"   # Envoy gRPC-web (browser -> backend)
-    volumes:
-      # Persist the inner daemon's image/layer store across restarts so we don't
-      # re-pull envoy + graybx/weightslab every run.
-      - dind_storage:/var/lib/docker
-    # The inner daemon + image pulls need a moment; give the stack room to start.
-    stop_grace_period: 30s
-
-volumes:
-  dind_storage:
diff --git a/weightslab/examples/Docker_training/docker_in_docker/entrypoint.sh b/weightslab/examples/Docker_training/docker_in_docker/entrypoint.sh
deleted file mode 100644
index ff1c85ae..00000000
--- a/weightslab/examples/Docker_training/docker_in_docker/entrypoint.sh
+++ /dev/null
@@ -1,67 +0,0 @@
-#!/usr/bin/env bash
-# =============================================================================
-# DinD entrypoint: start the inner docker daemon, launch the UI stack, then run
-# the classification example in the foreground (it serves the gRPC backend).
-# =============================================================================
-set -euo pipefail
-
-# Set WEIGHTSLAB_TLS=1 (via the trainer service environment) for HTTPS + mTLS.
-WEIGHTSLAB_TLS="${WEIGHTSLAB_TLS:-0}"
-# Single source of truth for certs — ui-launch generates here, the backend reads here.
-export WEIGHTSLAB_CERTS_DIR="${WEIGHTSLAB_CERTS_DIR:-$HOME/.weightslab-certs}"
-
-# --- GPU visibility check (best-effort) --------------------------------------
-# Show the GPU the container can see. Non-fatal: on a CPU-only host (or one
-# without the NVIDIA Container Toolkit) this just prints a notice and training
-# falls back to CPU (config device: auto).
-echo "[entrypoint] Checking GPU visibility (nvidia-smi)..."
-if command -v nvidia-smi >/dev/null 2>&1 && nvidia-smi -L >/dev/null 2>&1; then
-    nvidia-smi -L
-else
-    echo "[entrypoint] No NVIDIA GPU visible (nvidia-smi unavailable) — running on CPU."
-fi
-
-echo "[entrypoint] Starting inner docker daemon (DinD)..."
-# The inner daemon manages the Envoy + frontend containers. It needs --privileged
-# (set on the trainer service in docker-compose.yml).
-dockerd >/var/log/dockerd.log 2>&1 &
-
-echo "[entrypoint] Waiting for inner docker daemon to become ready..."
-for _ in $(seq 1 60); do
-    if docker info >/dev/null 2>&1; then
-        break
-    fi
-    sleep 1
-done
-if ! docker info >/dev/null 2>&1; then
-    echo "[entrypoint] ERROR: inner dockerd did not start. Last log lines:" >&2
-    tail -n 40 /var/log/dockerd.log >&2 || true
-    exit 1
-fi
-echo "[entrypoint] Inner docker daemon is up."
-
-# The bundled deploy script would otherwise run a full 'docker compose build
-# --no-cache' of the frontend. We only need the published image, so tell it to
-# just write the .env and skip docker ops — 'ui launch' then pulls + starts the
-# stack itself via 'docker compose up --pull always'.
-export WEIGHTSLAB_SKIP_DOCKER_OPS=1
-
-if [ "${WEIGHTSLAB_TLS}" = "1" ]; then
-    echo "[entrypoint] TLS mode — launching SECURED UI (HTTPS + mTLS) into ${WEIGHTSLAB_CERTS_DIR}..."
-    weightslab ui launch --certs
-    # --certs only configures Envoy + the frontend. The backend gRPC server needs
-    # TLS turned on too, or Envoy's upstream mTLS handshake fails (503s). Point it
-    # at the same cert dir (holds backend-server.crt/key + ca.crt).
-    export GRPC_TLS_ENABLED=1
-    export GRPC_TLS_CERT_DIR="${WEIGHTSLAB_CERTS_DIR}"
-    SCHEME="https"
-else
-    echo "[entrypoint] HTTP mode — launching UNSECURED UI..."
-    weightslab ui launch
-    SCHEME="http"
-fi
-
-echo "[entrypoint] Starting the classification (cls) example — serves gRPC on :${GRPC_BACKEND_PORT:-50051}."
-echo "[entrypoint] Open the UI at ${SCHEME}://localhost:5173 once training is serving."
-# Foreground: keeps the container alive while the backend serves the browser.
-exec weightslab start example  # --3d_det
diff --git a/weightslab/examples/Docker_training/siblings_self_contained/Dockerfile b/weightslab/examples/Docker_training/siblings_self_contained/Dockerfile
deleted file mode 100644
index b66e2162..00000000
--- a/weightslab/examples/Docker_training/siblings_self_contained/Dockerfile
+++ /dev/null
@@ -1,55 +0,0 @@
-# =============================================================================
-# WeightsLab "training docker" — self-contained siblings (no host bind mounts)
-# =============================================================================
-# Like the siblings variant, this drives the HOST docker daemon (mounted socket)
-# so Envoy + frontend run as siblings. BUT it removes the host bind-mount
-# dependency entirely:
-#   - Envoy's config is delivered through a named VOLUME, populated over the
-#     docker socket (no host path needed).
-#   - the frontend runs in HTTP mode, which needs NO certs mount at all.
-# Consequence: NO path alignment, NO setup-host.sh, and it runs natively on
-# Windows / Docker Desktop. The trade-off: it does NOT call `weightslab ui
-# launch` — it brings the UI up from its own bind-mount-free compose
-# (ui-compose.yml), reusing the same Envoy + graybx/weightslab images.
-# =============================================================================
-FROM python:3.11-slim
-
-# --- System deps -------------------------------------------------------------
-# docker CLI + compose plugin ONLY (no daemon — we use the host's daemon).
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        curl sudo ca-certificates gnupg \
-    && install -m 0755 -d /etc/apt/keyrings \
-    && curl -fsSL https://download.docker.com/linux/debian/gpg \
-        | gpg --dearmor -o /etc/apt/keyrings/docker.gpg \
-    && chmod a+r /etc/apt/keyrings/docker.gpg \
-    && echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] \
-        https://download.docker.com/linux/debian $(. /etc/os-release && echo $VERSION_CODENAME) stable" \
-        > /etc/apt/sources.list.d/docker.list \
-    && apt-get update && apt-get install -y --no-install-recommends \
-        docker-ce-cli docker-compose-plugin \
-    && rm -rf /var/lib/apt/lists/*
-
-# --- WeightsLab --------------------------------------------------------------
-# Plain pip install — NO editable/path-aligned install needed here, because the
-# host daemon never has to resolve a package path (no host bind mounts). To run
-# the dev branch instead:
-#   --build-arg WEIGHTSLAB_SPEC="git+https://github.com/GrayboxTech/weightslab.git@dev"
-ARG WEIGHTSLAB_SPEC=weightslab
-RUN pip install --no-cache-dir "${WEIGHTSLAB_SPEC}"
-
-ENV GRPC_BACKEND_PORT=50051
-
-# --- GPU (NVIDIA) ------------------------------------------------------------
-# Make the NVIDIA Container Toolkit inject the host driver (nvidia-smi + libs).
-# No-op without an NVIDIA GPU/toolkit; the actual grant is in docker-compose.yml.
-ENV NVIDIA_VISIBLE_DEVICES=all \
-    NVIDIA_DRIVER_CAPABILITIES=compute,utility
-
-# The bind-mount-free UI compose, read by the docker CLI inside this container
-# and sent to the host daemon. It references only images + a named volume.
-COPY ui-compose.yml /opt/ui/ui-compose.yml
-COPY entrypoint.sh /usr/local/bin/entrypoint.sh
-RUN sed -i 's/\r$//' /usr/local/bin/entrypoint.sh \
-    && chmod +x /usr/local/bin/entrypoint.sh
-
-ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
diff --git a/weightslab/examples/Docker_training/siblings_self_contained/README.md b/weightslab/examples/Docker_training/siblings_self_contained/README.md
deleted file mode 100644
index 6cd927cd..00000000
--- a/weightslab/examples/Docker_training/siblings_self_contained/README.md
+++ /dev/null
@@ -1,159 +0,0 @@
-# Training docker — Option C: self-contained siblings (no host bind mounts)
-
-Like Option B (siblings / Docker-out-of-Docker) the trainer drives the **host**
-docker daemon (mounted socket) so Envoy + frontend run as **siblings**. But this
-variant **removes every host bind-mount dependency**, so it runs **entirely from
-inside the container** — no `setup-host.sh`, no path alignment, and it works
-**natively on Windows / Docker Desktop**.
-
-Inside the trainer container, in order:
-
-1. render Envoy's plaintext config from the installed `weightslab` package,
-2. push it into a **named volume over the docker socket** (no host path),
-3. bring up Envoy + frontend from a **bind-mount-free** [ui-compose.yml](ui-compose.yml),
-4. `weightslab start example --cls` → serve the **gRPC backend** on `:50051`.
-
-End result: open `http://localhost:5173` and the UI talks to training end-to-end.
-
-## The trade-off vs Option B
-
-Option B uses the stock **`weightslab ui launch`**, whose bundled compose
-bind-mounts `envoy.yaml` + certs from the package — which the **host** daemon
-must resolve, forcing path alignment + `setup-host.sh` (and breaking on Windows).
-
-Option C instead **does not call `weightslab ui launch`**. It brings the UI up
-itself from [ui-compose.yml](ui-compose.yml), which has **no host bind mounts**:
-
-| What the bundled compose bind-mounts | How Option C delivers it without a host path |
-|---|---|
-| `envoy.yaml` (+ plaintext variants) | rendered from the installed package, then piped into the **`wl_envoy_cfg` named volume** over the socket (`docker run -i … busybox`) |
-| `${WEIGHTSLAB_CERTS_DIR}` → Envoy/nginx | **omitted** — the frontend's nginx auto-falls back to **HTTP** when no certs are present; Envoy uses the fully-plaintext template |
-
-It still reuses the **same images** (`envoyproxy/envoy`, `graybx/weightslab`) and
-the **same Envoy template** that ships with weightslab, so it stays in sync.
-
-> By default this path is **HTTP-only**. It *can* be secured with TLS using the
-> same named-volume trick to deliver certs (no host bind mounts) — see
-> [🔒 Enabling TLS](#-enabling-tls-https--mtls) below.
-
-## How the wiring works
-
-```
-host browser
-  → localhost:5173 ──► frontend container (sibling, HTTP, no certs)
-  → localhost:8080 ──► Envoy container   (sibling, config from named volume)
-                          │ grpc-backend:host-gateway → host:50051
-                          ▼
-                       host:50051 ──► trainer container's gRPC backend :50051
-```
-
-Same networking as Option B — the trainer **publishes `:50051`** so the sibling
-Envoy reaches the in-container backend via `host-gateway`. Only the Envoy +
-frontend publish `8080`/`5173` (from `ui-compose.yml`).
-
-## ⚙️ Required configuration
-
-| # | Requirement | Where | Why |
-|---|---|---|---|
-| 1 | **Mount `/var/run/docker.sock`** | [docker-compose.yml](docker-compose.yml) | drive the host daemon (siblings) |
-| 2 | **Publish `50051:50051`** from the trainer | [docker-compose.yml](docker-compose.yml) | sibling Envoy dials it via `host-gateway` |
-| 3 | **Stage Envoy config into a named volume over the socket** | [entrypoint.sh](entrypoint.sh) | replaces the host bind mount — no host path |
-| 4 | **`ui-compose.yml` with no host bind mounts** (named volume + HTTP frontend) | [ui-compose.yml](ui-compose.yml) | nothing for the host daemon to resolve on disk |
-| 5 | **`extra_hosts: grpc-backend:host-gateway`** on Envoy | [ui-compose.yml](ui-compose.yml) | route Envoy → host:50051 → trainer |
-| 6 | **`GRPC_BACKEND_PORT=50051`** (matches the rendered Envoy config) | [docker-compose.yml](docker-compose.yml) / [Dockerfile](Dockerfile) | port the backend binds + Envoy dials |
-| — | *Not needed (unlike Option B):* path alignment, `setup-host.sh`, a host source checkout | — | the daemon never resolves a host path |
-
-### GPU — to run `nvidia-smi` / train on CUDA
-
-| Requirement | Where |
-|---|---|
-| Host: NVIDIA driver **+ NVIDIA Container Toolkit** (`sudo nvidia-ctk runtime configure --runtime=docker`) | host setup |
-| `deploy.resources.reservations.devices` (driver `nvidia`, `capabilities: [gpu]`) | [docker-compose.yml](docker-compose.yml) |
-| `NVIDIA_VISIBLE_DEVICES=all` + `NVIDIA_DRIVER_CAPABILITIES=compute,utility` | [Dockerfile](Dockerfile) |
-
-> The `deploy:` GPU block is a **hard requirement**: on a host with no NVIDIA
-> GPU/toolkit, comment it out in [docker-compose.yml](docker-compose.yml) or
-> `up` fails. Only the **trainer** needs the GPU.
-
-## Run it
-
-```bash
-# from this directory — works on Windows/Docker Desktop, no host prep
-docker compose up --build
-```
-
-Then open <http://localhost:5173>.
-
-### Stopping
-
-```bash
-docker compose down                       # stops the trainer
-# the sibling UI stack is a separate project:
-docker compose -p weightslab_ui -f ui-compose.yml down
-docker volume rm wl_envoy_cfg             # optional: drop the staged config
-```
-
-## 🔒 Enabling TLS (HTTPS + mTLS)
-
-TLS works here too, **still with no host bind mounts** — the certs are delivered
-the same way as `envoy.yaml`: piped into named volumes over the docker socket.
-It's a **one-flag toggle**:
-
-```bash
-WEIGHTSLAB_TLS=1 docker compose up --build
-```
-
-then **trust the dev CA on your host** (step 2 below) and open
-**https://localhost:5173**.
-
-#### What the toggle does (in [entrypoint.sh](entrypoint.sh))
-
-In TLS mode the entrypoint generates one cert set with `weightslab se` (writes
-`ca.crt`, `envoy-server.*`, `envoy-client.*`, `backend-server.*` +
-`.grpc_auth_token` into `WEIGHTSLAB_CERTS_DIR`) and wires up four layers — all
-fed over the socket, no host paths:
-
-| Layer | Needs | Delivered via |
-|---|---|---|
-| Browser ↔ Envoy (downstream) | `envoy-server.crt/key` | `wl_envoy_cfg` volume → `/etc/envoy/certs` |
-| Envoy ↔ backend (upstream mTLS) | `envoy-client.crt/key` + `ca.crt` | `wl_envoy_cfg` volume → `/etc/envoy/certs` |
-| Browser ↔ frontend (nginx HTTPS) | `envoy-server.crt/key` | `wl_nginx_certs` volume → `/etc/nginx/certs` |
-| Backend gRPC server | `backend-server.crt/key` + `ca.crt` | `GRPC_TLS_ENABLED=1` + `GRPC_TLS_CERT_DIR` |
-
-Concretely, vs the HTTP path, the toggle:
-
-1. runs `weightslab se` and renders the **full mTLS** template
-   (`ui/envoy/envoy.yaml`, not the `*_plaintext` one) into `wl_envoy_cfg`;
-2. stages the Envoy certs into `wl_envoy_cfg:/etc/envoy/certs` and the frontend
-   cert into `wl_nginx_certs` (and `chmod a+rX` them — the generated keys are
-   `0600`/root, and Envoy/nginx run non-root, so without this Envoy crashes with
-   *"unable to read file …envoy-client.key"*);
-3. sets `WS_SERVER_PROTOCOL=https` so the frontend's nginx serves HTTPS
-   ([ui-compose.yml](ui-compose.yml) always mounts `wl_nginx_certs`, empty in
-   HTTP mode);
-4. exports `GRPC_TLS_ENABLED=1` + `GRPC_TLS_CERT_DIR=$WEIGHTSLAB_CERTS_DIR` so
-   the backend serves gRPC over TLS (else Envoy's upstream mTLS fails → 503s).
-
-> Optional: to *enforce* the gRPC auth token, also export
-> `GRPC_AUTH_TOKEN="$(cat $WEIGHTSLAB_CERTS_DIR/.grpc_auth_token)"` (otherwise the
-> token the UI sends is ignored — TLS still encrypts).
-
-#### 2. Trust the dev CA on the Windows host
-
-The dev CA is generated *inside* the container, so trust it on the host once:
-
-```powershell
-docker cp weightslab_trainer_selfcontained:/root/.weightslab-certs/ca.crt .
-Import-Certificate -FilePath .\ca.crt -CertStoreLocation Cert:\CurrentUser\Root
-```
-
-Then open **https://localhost:5173**. Certs carry `localhost`/`127.0.0.1` SANs, so
-the frontend (5173) and Envoy (8080) both validate once the CA is trusted.
-(Skip this and TLS still works, but the browser shows a self-signed warning.)
-
-## Notes
-
-- First run pulls `envoyproxy/envoy` + `graybx/weightslab` into the host image
-  cache and downloads MNIST for the example.
-- HTTP by default; TLS is opt-in (section above).
-- The example starts paused (`is_training: false`); start/steer it from the UI.
diff --git a/weightslab/examples/Docker_training/siblings_self_contained/docker-compose.yml b/weightslab/examples/Docker_training/siblings_self_contained/docker-compose.yml
deleted file mode 100644
index 3ef00358..00000000
--- a/weightslab/examples/Docker_training/siblings_self_contained/docker-compose.yml
+++ /dev/null
@@ -1,44 +0,0 @@
-# =============================================================================
-# WeightsLab training docker — self-contained siblings (no host bind mounts)
-# =============================================================================
-# The trainer drives the HOST docker daemon (mounted socket) to start Envoy +
-# frontend as siblings — but with ZERO host bind mounts and NO path alignment,
-# so it runs natively on Windows / Docker Desktop and needs no setup-host.sh.
-# The gRPC backend runs in THIS container and is published to the host so the
-# sibling Envoy can reach it via host-gateway.
-# =============================================================================
-services:
-  trainer:
-    build:
-      context: .
-      # Dev branch instead of PyPI:
-      # args:
-      #   WEIGHTSLAB_SPEC: "git+https://github.com/GrayboxTech/weightslab.git@dev"
-    image: weightslab-trainer-selfcontained
-    container_name: weightslab_trainer_selfcontained
-    # --- GPU ------------------------------------------------------------------
-    # Grant the host NVIDIA GPU(s) to the trainer (torch runs here; the sibling
-    # Envoy/frontend don't need a GPU). Requires the NVIDIA Container Toolkit.
-    # NOTE: hard requirement — on a host with no NVIDIA GPU/toolkit, comment out
-    # this `deploy:` block (the stack then runs on CPU).
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all          # or e.g. count: 1, or device_ids: ["0"]
-              capabilities: [gpu]
-    environment:
-      - GRPC_BACKEND_PORT=50051
-      # TLS toggle: run `WEIGHTSLAB_TLS=1 docker compose up` for HTTPS + mTLS
-      # (certs generated in-container + delivered via named volumes; trust
-      # ca.crt on the host — see README "Enabling TLS").
-      - WEIGHTSLAB_TLS=${WEIGHTSLAB_TLS:-0}
-    ports:
-      # Publish the gRPC backend so the sibling Envoy reaches it via
-      # grpc-backend:host-gateway -> host:50051 -> this container. Envoy + the
-      # frontend publish 8080/5173 to the host themselves (see ui-compose.yml).
-      - "50051:50051"
-    volumes:
-      # Only mount: the host docker socket. NO source/cert bind mounts.
-      - /var/run/docker.sock:/var/run/docker.sock
diff --git a/weightslab/examples/Docker_training/siblings_self_contained/entrypoint.sh b/weightslab/examples/Docker_training/siblings_self_contained/entrypoint.sh
deleted file mode 100644
index 5259f9b0..00000000
--- a/weightslab/examples/Docker_training/siblings_self_contained/entrypoint.sh
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/usr/bin/env bash
-# =============================================================================
-# Self-contained siblings entrypoint — runs entirely from inside the container,
-# with NO host bind mounts and NO path alignment.
-#
-# Flow:
-#   1. render Envoy's plaintext config from the installed weightslab package,
-#   2. push it into a named volume *over the docker socket* (no host path),
-#   3. bring up Envoy + frontend (ui-compose.yml) on the host daemon,
-#   4. run the example (serves the gRPC backend, published to the host).
-# =============================================================================
-set -euo pipefail
-
-GRPC_BACKEND_PORT="${GRPC_BACKEND_PORT:-50051}"
-ENVOY_VOLUME="wl_envoy_cfg"
-NGINX_CERTS_VOLUME="wl_nginx_certs"
-UI_COMPOSE="/opt/ui/ui-compose.yml"
-UI_PROJECT="weightslab_ui"
-
-# Set WEIGHTSLAB_TLS=1 (via the trainer service environment) for HTTPS + mTLS.
-WEIGHTSLAB_TLS="${WEIGHTSLAB_TLS:-0}"
-export WEIGHTSLAB_CERTS_DIR="${WEIGHTSLAB_CERTS_DIR:-$HOME/.weightslab-certs}"
-
-# --- GPU visibility check (best-effort) --------------------------------------
-echo "[entrypoint] Checking GPU visibility (nvidia-smi)..."
-if command -v nvidia-smi >/dev/null 2>&1 && nvidia-smi -L >/dev/null 2>&1; then
-    nvidia-smi -L
-else
-    echo "[entrypoint] No NVIDIA GPU visible (nvidia-smi unavailable) — running on CPU."
-fi
-
-# --- Reach the host docker daemon --------------------------------------------
-if ! docker info >/dev/null 2>&1; then
-    echo "[entrypoint] ERROR: cannot reach the host docker daemon." >&2
-    echo "             Did you mount /var/run/docker.sock? (see docker-compose.yml)" >&2
-    exit 1
-fi
-
-# Locate the installed package's Envoy templates. find_spec does NOT execute
-# weightslab/__init__.py, so it avoids the import-time logging banner that would
-# otherwise pollute this command substitution.
-PKG="$(python -c 'import importlib.util as u; print(u.find_spec("weightslab").submodule_search_locations[0])')"
-
-# --- 0) Start from clean named volumes ---------------------------------------
-# Tear down any prior UI stack first so the volumes are free to recreate.
-docker compose -p "${UI_PROJECT}" -f "${UI_COMPOSE}" down --remove-orphans >/dev/null 2>&1 || true
-docker rm -f weights_studio_envoy weights_studio_frontend >/dev/null 2>&1 || true
-docker volume rm "${ENVOY_VOLUME}" "${NGINX_CERTS_VOLUME}" >/dev/null 2>&1 || true
-docker volume create "${ENVOY_VOLUME}" >/dev/null
-docker volume create "${NGINX_CERTS_VOLUME}" >/dev/null
-
-# Helper: pipe a file from this container into a named volume over the socket
-# (no host path involved). Usage: stage_file <volume> <dest-path-in-volume> <src>
-stage_file() { docker run --rm -i -v "$1:/vol" busybox sh -c "cat > /vol/$2" < "$3"; }
-# Helper: copy named files from WEIGHTSLAB_CERTS_DIR into <volume>/<subdir> via tar.
-# The generated key files are 0600/root; Envoy + nginx run as non-root, so make
-# them readable (dev certs) — otherwise Envoy crashes with "unable to read file".
-stage_certs() { local vol="$1" sub="$2"; shift 2
-    tar -C "${WEIGHTSLAB_CERTS_DIR}" -cf - "$@" \
-      | docker run --rm -i -v "${vol}:/vol" busybox \
-          sh -c "mkdir -p /vol/${sub} && tar -C /vol/${sub} -xf - && chmod -R a+rX /vol/${sub}"; }
-
-if [ "${WEIGHTSLAB_TLS}" = "1" ]; then
-    echo "[entrypoint] TLS mode — generating certs and staging the mTLS Envoy config..."
-    # Generate ca.crt, envoy-server.*, envoy-client.*, backend-server.* + token.
-    weightslab se
-    ENVOY_TMPL="${PKG}/ui/envoy/envoy.yaml"   # FULL mTLS template
-    [ -f "${ENVOY_TMPL}" ] || { echo "[entrypoint] ERROR: ${ENVOY_TMPL} not found" >&2; exit 1; }
-    sed "s/__GRPC_BACKEND_PORT__/${GRPC_BACKEND_PORT}/g" "${ENVOY_TMPL}" > /tmp/envoy.generated.yaml
-    stage_file "${ENVOY_VOLUME}" "envoy.yaml" /tmp/envoy.generated.yaml
-    # Envoy's certs (downstream server + upstream client + CA) -> /etc/envoy/certs
-    stage_certs "${ENVOY_VOLUME}" "certs" ca.crt envoy-server.crt envoy-server.key envoy-client.crt envoy-client.key
-    # The frontend (nginx) HTTPS cert -> /etc/nginx/certs (triggers HTTPS mode)
-    stage_certs "${NGINX_CERTS_VOLUME}" "." envoy-server.crt envoy-server.key
-    export WS_SERVER_PROTOCOL=https
-    # Backend gRPC TLS (else Envoy's upstream mTLS fails against a plaintext backend).
-    export GRPC_TLS_ENABLED=1
-    export GRPC_TLS_CERT_DIR="${WEIGHTSLAB_CERTS_DIR}"
-    SCHEME="https"
-else
-    echo "[entrypoint] HTTP mode — staging the plaintext Envoy config..."
-    ENVOY_TMPL="${PKG}/ui/envoy/envoy.downstream_upstream_plaintext.yaml"
-    [ -f "${ENVOY_TMPL}" ] || { echo "[entrypoint] ERROR: ${ENVOY_TMPL} not found" >&2; exit 1; }
-    sed "s/__GRPC_BACKEND_PORT__/${GRPC_BACKEND_PORT}/g" "${ENVOY_TMPL}" > /tmp/envoy.generated.yaml
-    stage_file "${ENVOY_VOLUME}" "envoy.yaml" /tmp/envoy.generated.yaml
-    # nginx certs volume left empty -> frontend serves HTTP.
-    export WS_SERVER_PROTOCOL=http
-    SCHEME="http"
-fi
-echo "[entrypoint] Staged Envoy config (backend port ${GRPC_BACKEND_PORT}, scheme ${SCHEME})."
-
-# --- Bring up Envoy + frontend as siblings on the host daemon ----------------
-echo "[entrypoint] Starting Envoy + frontend (bind-mount-free) on the host daemon..."
-WS_SERVER_PROTOCOL="${WS_SERVER_PROTOCOL}" \
-    docker compose -p "${UI_PROJECT}" -f "${UI_COMPOSE}" up -d --pull always
-
-echo "[entrypoint] UI is up — open ${SCHEME}://localhost:5173 once training is serving."
-
-# --- Run the example — serves gRPC on :GRPC_BACKEND_PORT (published) ----------
-echo "[entrypoint] Starting the classification (cls) example — serves gRPC on :${GRPC_BACKEND_PORT}."
-exec weightslab start example --cls
diff --git a/weightslab/examples/Docker_training/siblings_self_contained/ui-compose.yml b/weightslab/examples/Docker_training/siblings_self_contained/ui-compose.yml
deleted file mode 100644
index 52f1f06c..00000000
--- a/weightslab/examples/Docker_training/siblings_self_contained/ui-compose.yml
+++ /dev/null
@@ -1,61 +0,0 @@
-# =============================================================================
-# Bind-mount-free UI stack (Envoy + Weights Studio frontend), run on the HOST
-# daemon by the trainer container's entrypoint. The whole point: NO host bind
-# mounts, so the host daemon needs nothing from any host path.
-#   - Envoy reads its config from the `wl_envoy_cfg` named volume, which the
-#     entrypoint populates over the docker socket (see entrypoint.sh).
-#   - the frontend runs in HTTP mode and needs no certs mount.
-# This reuses the exact same images as `weightslab ui launch`.
-# =============================================================================
-services:
-  envoy:
-    image: envoyproxy/envoy:v1.28-latest
-    container_name: weights_studio_envoy
-    # Config comes from the named volume (already port-substituted by the
-    # entrypoint), so just point Envoy at it — no in-container templating.
-    command: ["/usr/local/bin/envoy", "-c", "/etc/envoy/envoy.yaml"]
-    volumes:
-      - wl_envoy_cfg:/etc/envoy:ro
-    extra_hosts:
-      # Envoy is a sibling on the host; it dials the trainer's gRPC backend via
-      # the host (host-gateway → host:50051, which the trainer publishes).
-      - "grpc-backend:host-gateway"
-      - "host.docker.internal:host-gateway"
-    ports:
-      - "8080:8080"   # gRPC-web (browser -> backend)
-    networks:
-      - weights_studio_network
-    restart: unless-stopped
-
-  frontend:
-    image: graybx/weightslab:latest
-    container_name: weights_studio_frontend
-    # The image's nginx entrypoint serves HTTPS when certs are present in
-    # /etc/nginx/certs, else HTTP. The trainer entrypoint populates the
-    # wl_nginx_certs volume only in TLS mode (empty otherwise -> HTTP), and sets
-    # WS_SERVER_PROTOCOL accordingly.
-    environment:
-      - WS_SERVER_PROTOCOL=${WS_SERVER_PROTOCOL:-http}
-      - WL_ENABLE_GRPC_AUTH_TOKEN=0
-      - GRPC_AUTH_TOKEN=
-    volumes:
-      - wl_nginx_certs:/etc/nginx/certs:ro
-    ports:
-      - "5173:443"    # frontend -> http(s)://localhost:5173
-    depends_on:
-      - envoy
-    networks:
-      - weights_studio_network
-    restart: unless-stopped
-
-networks:
-  weights_studio_network:
-    driver: bridge
-
-volumes:
-  # Created + populated by the trainer entrypoint over the docker socket.
-  wl_envoy_cfg:
-    external: true
-  # Frontend HTTPS certs — populated only in TLS mode (empty -> HTTP).
-  wl_nginx_certs:
-    external: true
diff --git a/weightslab/examples/Lightning/ws-classification/config.yaml b/weightslab/examples/Lightning/wl-classification/config.yaml
similarity index 100%
rename from weightslab/examples/Lightning/ws-classification/config.yaml
rename to weightslab/examples/Lightning/wl-classification/config.yaml
diff --git a/weightslab/examples/Lightning/ws-classification/main.py b/weightslab/examples/Lightning/wl-classification/main.py
similarity index 100%
rename from weightslab/examples/Lightning/ws-classification/main.py
rename to weightslab/examples/Lightning/wl-classification/main.py
diff --git a/weightslab/examples/Notebooks/Colab/wl-colab-quickstart.ipynb b/weightslab/examples/Notebooks/Colab/wl-colab-quickstart.ipynb
new file mode 100644
index 00000000..2fe94105
--- /dev/null
+++ b/weightslab/examples/Notebooks/Colab/wl-colab-quickstart.ipynb
@@ -0,0 +1,77 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Colab/wl-colab-quickstart.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Welcome to the <a href=\"https://github.com/GrayboxTech/weightslab\">WeightsLab</a> Colab quickstart — a blank starter for streaming <b>your own</b> training run into Weights Studio.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# WeightsLab · Colab quickstart\n",
+    "\n",
+    "> **This notebook is read-only.** Run it as-is to try it, or use **File ▸ Save a copy in Drive** to make it your own and edit freely.\n",
+    "\n",
+    "Colab gives you a free GPU; **Weights Studio runs on your own machine** and connects to this Colab backend through a public [`bore`](https://github.com/ekzhang/bore) tunnel.\n",
+    "\n",
+    "### What the cell below does\n",
+    "1. Installs WeightsLab.\n",
+    "2. Imports it as `wl`.\n",
+    "3. (Your turn) registers your model / optimizer / dataloaders with `wl.watch_or_edit(...)`.\n",
+    "4. Serves the backend over a bore tunnel and prints the `weightslab tunnel ...` command to run locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1 · Install WeightsLab (+ Colab compatibility overrides)\n",
+    "%pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ --extra-index-url https://download.pytorch.org/whl/cpu weightslab==1.4.0.dev3\n",
+    "\n",
+    "# 2 · Import\n",
+    "import weightslab as wl\n",
+    "\n",
+    "# 3 · Serve to Weights Studio over a public bore tunnel.\n",
+    "#     Prints a `bore.pub:PORT` endpoint. Then, on your own machine (Docker running):\n",
+    "#         weightslab start                     # opens http://localhost:5173\n",
+    "#         weightslab tunnel bore.pub:PORT      # in another window - PORT = the port printed below\n",
+    "wl.serve(serving_grpc=True, serving_bore=True)\n",
+    "\n",
+    "# 4 · Register YOUR training objects here (uncomment and fill in):\n",
+    "# wl.watch_or_edit(model, optimizer, train_loader, test_loader)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "wl-colab-quickstart.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/weightslab/examples/Notebooks/PyTorch/wl-ads-recommendation.ipynb b/weightslab/examples/Notebooks/PyTorch/wl-ads-recommendation.ipynb
new file mode 100644
index 00000000..d09b94f9
--- /dev/null
+++ b/weightslab/examples/Notebooks/PyTorch/wl-ads-recommendation.ipynb
@@ -0,0 +1,481 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-ads-recommendation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-classification.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Welcome to the WeightsLab image-classification notebook! <a href=\"https://github.com/GrayboxTech/weightslab\">WeightsLab</a> is an open-source PyTorch tool for dataset debugging, mislabel detection, and mid-training data curation. Browse the <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\">Docs</a> for details, and raise an issue on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a> for support.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advertising CTR Recommendation with WeightsLab (tabular)\n",
+    "\n",
+    "This notebook trains a **Wide & Deep** click-through-rate (CTR) model — the core of an advertising recommender — and instruments it with WeightsLab so every signal traces **back to the exact impression**.\n",
+    "\n",
+    "A sample is one ad impression (a **row**); the model input **is** the packed field vector (8 embedded categorical fields + 8 numeric features), which WeightsLab sends to the UI as a `vector`. Each field (`ad_category`, `placement`, `bid_price`, …) is a **sortable column** in the List view.\n",
+    "\n",
+    "### What you'll do\n",
+    "1. Install WeightsLab.\n",
+    "2. Generate reproducible ad impressions at a calibrated ~20% CTR.\n",
+    "3. Wrap the Wide & Deep model, optimizer, dataloaders, loss and metric.\n",
+    "4. Train while streaming per-impression signals to Weights Studio.\n",
+    "\n",
+    "*Real drop-in datasets: Criteo, Avazu, MovieLens.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Install WeightsLab from PyPI. These tabular demos are tiny — the free Colab CPU runtime is plenty (no GPU needed).\n",
+    "\n",
+    "> The **tabular input path** (the feature vector is sent to the UI as a `vector` — not a fake image) needs a WeightsLab build with tabular support (the `252-tabular-experiment` line or a release that includes it)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?color=5865F2&logo=pypi&logoColor=white\" alt=\"PyPI - Version\"></a>\n",
+    "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/dm/weightslab?color=5865F2\" alt=\"PyPI - Downloads\"></a>\n",
+    "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/pyversions/weightslab?color=5865F2&logo=python&logoColor=white\" alt=\"PyPI - Python Version\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install torch torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev6\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Imports\n",
+    "\n",
+    "`weightslab` is imported as `wl`. The two `guard_*_context` managers scope a block as training vs. evaluation so signals are attributed to the right phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tempfile, logging\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import Dataset\n",
+    "from torchmetrics.classification import Accuracy\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "import weightslab as wl\n",
+    "from weightslab.components.global_monitoring import (\n",
+    "    guard_training_context,\n",
+    "    guard_testing_context,\n",
+    ")\n",
+    "\n",
+    "logging.basicConfig(level=logging.ERROR)\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using device: {device}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. A synthetic CTR dataset (rows, not images)\n",
+    "\n",
+    "`make_synthetic_ctr` builds impressions with 8 categorical fields + 8 numeric features and a binary `clicked` label at ~20% CTR, driven by per-field effects plus an `ad_category x user_segment` interaction. The dataset returns the **standardized** field vector (categorical indices + per-feature standardized numerics) as the model input, and `get_items(...)` exposes the **raw**, human-readable field values (categorical labels, dollars, years, page counts, …) as metadata columns. The **Wide & Deep** model embeds each categorical field and MLPs over the embeddings + numeric features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CATEGORICAL_FIELDS = [\"user_segment\", \"ad_category\", \"device_type\", \"os\",\n",
+    "                      \"publisher\", \"placement\", \"region\", \"hour_bucket\"]\n",
+    "CARD = [6, 10, 4, 4, 12, 5, 8, 6]\n",
+    "NUMERIC_FIELDS = [\"ad_position\", \"bid_price\", \"user_age\", \"session_depth\",\n",
+    "                  \"historical_ctr\", \"days_since_last_click\", \"num_ads_seen_today\",\n",
+    "                  \"creative_freshness\"]\n",
+    "NUM_CAT, NUM_NUM = len(CATEGORICAL_FIELDS), len(NUMERIC_FIELDS)\n",
+    "VOCABS = {\"device_type\": [\"mobile\", \"desktop\", \"tablet\", \"ctv\"],\n",
+    "          \"os\": [\"android\", \"ios\", \"windows\", \"other\"],\n",
+    "          \"placement\": [\"banner\", \"sidebar\", \"interstitial\", \"native\", \"video\"],\n",
+    "          \"hour_bucket\": [\"night\", \"early\", \"morning\", \"midday\", \"afternoon\", \"evening\"]}\n",
+    "TARGET_CTR = 0.20\n",
+    "\n",
+    "\n",
+    "def _label(field, code):\n",
+    "    v = VOCABS.get(field)\n",
+    "    return v[code] if v and 0 <= code < len(v) else f\"{field}_{code}\"\n",
+    "\n",
+    "\n",
+    "def _draw_raw_numeric(rng, n):\n",
+    "    \"\"\"8 numeric fields at their natural, human-readable scale (dollars, years, counts, ...).\"\"\"\n",
+    "    return np.stack([\n",
+    "        rng.integers(1, 9, n).astype(np.float64),     # ad_position: slot 1-8\n",
+    "        rng.gamma(2.0, 1.25, n),                       # bid_price: $, mean ~2.50\n",
+    "        np.clip(rng.normal(35.0, 12.0, n), 18, 80),    # user_age\n",
+    "        rng.poisson(3.0, n).astype(np.float64) + 1,    # session_depth (pages)\n",
+    "        rng.beta(2.0, 25.0, n),                        # historical_ctr, mean ~7%\n",
+    "        rng.exponential(10.0, n),                       # days_since_last_click\n",
+    "        rng.poisson(12.0, n).astype(np.float64) + 1,   # num_ads_seen_today\n",
+    "        rng.exponential(20.0, n),                       # creative_freshness (days)\n",
+    "    ], axis=1)\n",
+    "\n",
+    "\n",
+    "def make_synthetic_ctr(n, seed=0):\n",
+    "    \"\"\"Return (cat[int], num_std[float], num_raw[float], y): shared ground-truth CTR model (seed 12345).\"\"\"\n",
+    "    p = np.random.default_rng(12345)\n",
+    "    cat_w = [p.normal(0, 0.8, c) for c in CARD]\n",
+    "    num_w = p.normal(0, 0.5, NUM_NUM)\n",
+    "    inter = p.normal(0, 0.7, (CARD[1], CARD[0]))\n",
+    "    rng = np.random.default_rng(seed)\n",
+    "    cat = np.stack([rng.integers(0, c, n) for c in CARD], axis=1).astype(np.int64)\n",
+    "    num_raw = _draw_raw_numeric(rng, n)\n",
+    "    mean, std = num_raw.mean(0, keepdims=True), num_raw.std(0, keepdims=True)\n",
+    "    std[std == 0] = 1.0\n",
+    "    num_std = (num_raw - mean) / std                  # standardized -> model input\n",
+    "    logit = sum(w[cat[:, f]] for f, w in enumerate(cat_w)) + num_std @ num_w\n",
+    "    logit = logit + inter[cat[:, 1], cat[:, 0]]\n",
+    "    logit -= logit.mean()\n",
+    "    lo, hi = -20.0, 20.0                      # calibrate bias to hit ~20% mean CTR\n",
+    "    for _ in range(60):\n",
+    "        mid = 0.5 * (lo + hi)\n",
+    "        if (1 / (1 + np.exp(-(logit + mid)))).mean() < TARGET_CTR:\n",
+    "            lo = mid\n",
+    "        else:\n",
+    "            hi = mid\n",
+    "    logit += 0.5 * (lo + hi)\n",
+    "    y = (rng.uniform(0, 1, n) < 1 / (1 + np.exp(-logit))).astype(np.int64)\n",
+    "    return cat, num_std.astype(np.float32), num_raw.astype(np.float32), y\n",
+    "\n",
+    "\n",
+    "class AdsCTRDataset(Dataset):\n",
+    "    \"\"\"Yields (packed_vector, id, label); get_items() adds raw (non-standardized) field columns.\"\"\"\n",
+    "\n",
+    "    def __init__(self, n, seed=0):\n",
+    "        cat, num_std, num_raw, y = make_synthetic_ctr(n, seed)\n",
+    "        self.cat, self.num, self.num_raw = cat, num_std, num_raw  # self.num: standardized (model input)\n",
+    "        self.features = torch.from_numpy(np.concatenate([cat.astype(np.float32), num_std], 1))\n",
+    "        self.labels = torch.from_numpy(y)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.features)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        return self.features[idx], idx, int(self.labels[idx])\n",
+    "\n",
+    "    def get_items(self, idx, include_metadata=False, include_labels=False, include_images=False):\n",
+    "        x = self.features[idx] if include_images else None\n",
+    "        target = int(self.labels[idx]) if include_labels else None\n",
+    "        meta = None\n",
+    "        if include_metadata:\n",
+    "            meta = {n: _label(n, int(self.cat[idx][f])) for f, n in enumerate(CATEGORICAL_FIELDS)}\n",
+    "            meta.update({n: round(float(self.num_raw[idx][j]), 4) for j, n in enumerate(NUMERIC_FIELDS)})\n",
+    "        return x, idx, target, meta\n",
+    "\n",
+    "\n",
+    "class WideDeepCTR(nn.Module):\n",
+    "    def __init__(self, card=CARD, num_numeric=NUM_NUM, emb=8, hidden=128, num_classes=2):\n",
+    "        super().__init__()\n",
+    "        self.card = list(card)\n",
+    "        self.emb = nn.ModuleList([nn.Embedding(c, emb) for c in self.card])\n",
+    "        self.deep = nn.Sequential(\n",
+    "            nn.Linear(emb * len(self.card) + num_numeric, hidden), nn.ReLU(), nn.Dropout(0.1),\n",
+    "            nn.Linear(hidden, hidden // 2), nn.ReLU(), nn.Linear(hidden // 2, num_classes))\n",
+    "        self.wide_cat = nn.ModuleList([nn.Embedding(c, num_classes) for c in self.card])\n",
+    "        self.wide_num = nn.Linear(num_numeric, num_classes)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        flat = x.reshape(x.shape[0], -1)\n",
+    "        cidx = flat[:, :len(self.card)].round().long()\n",
+    "        cidx = cidx.clamp(min=torch.zeros(len(self.card), device=x.device, dtype=torch.long),\n",
+    "                          max=torch.tensor(self.card, device=x.device) - 1)\n",
+    "        numeric = flat[:, len(self.card):]\n",
+    "        deep = self.deep(torch.cat([e(cidx[:, i]) for i, e in enumerate(self.emb)] + [numeric], 1))\n",
+    "        wide = self.wide_num(numeric)\n",
+    "        for i, e in enumerate(self.wide_cat):\n",
+    "            wide = wide + e(cidx[:, i])\n",
+    "        return deep + wide\n",
+    "\n",
+    "\n",
+    "def build_dataset(n, seed=0):\n",
+    "    return AdsCTRDataset(n, seed)\n",
+    "\n",
+    "\n",
+    "def build_model():\n",
+    "    return WideDeepCTR()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Configuration\n",
+    "\n",
+    "Every tunable lives here in one dict, like a `config.yaml` with comments. Wrapping it with `flag=\"hyperparameters\"` lets Weights Studio read (and live-edit) these values while training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = {\n",
+    "    \"experiment_name\": \"ads_ctr_wide_deep\",\n",
+    "    \"device\": str(device),\n",
+    "    \"root_log_dir\": tempfile.mkdtemp(prefix=\"weightslab_ads_\"),\n",
+    "    \"learning_rate\": 0.005,\n",
+    "    \"training_steps_to_do\": 3000,\n",
+    "    \"eval_full_to_train_steps_ratio\": 150,\n",
+    "    \"write_export_ratio\": 500,\n",
+    "    \"class_weights\": [1.0, 4.0],           # [no-click, click] — up-weight clicks\n",
+    "    \"dataset\": {\"seed\": 0, \"n_train\": 8000, \"n_test\": 2000},\n",
+    "    \"data\": {\"train_loader\": {\"batch_size\": 64},\n",
+    "             \"test_loader\": {\"batch_size\": 256}},\n",
+    "}\n",
+    "wl.watch_or_edit(config, flag=\"hyperparameters\", poll_interval=1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Wrap the training objects\n",
+    "\n",
+    "This is the heart of WeightsLab. Each object passes through `wl.watch_or_edit(...)` with a `flag` describing its role. The tracked dataset's `get_items()` exposes every feature as a **sortable column**; `preload_metadata=True` loads them at init."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cfg = config\n",
+    "train_ds = build_dataset(cfg['dataset']['n_train'], seed=cfg['dataset']['seed'])\n",
+    "test_ds  = build_dataset(cfg['dataset']['n_test'],  seed=cfg['dataset']['seed'] + 1)\n",
+    "\n",
+    "model = wl.watch_or_edit(build_model().to(device), flag='model', device=device)\n",
+    "optimizer = wl.watch_or_edit(\n",
+    "    optim.Adam(model.parameters(), lr=cfg['learning_rate']), flag='optimizer')\n",
+    "\n",
+    "train_loader = wl.watch_or_edit(\n",
+    "    train_ds, flag='data', loader_name='train_loader',\n",
+    "    batch_size=cfg['data']['train_loader']['batch_size'], shuffle=True,\n",
+    "    is_training=True, preload_labels=True, preload_metadata=True)\n",
+    "test_loader = wl.watch_or_edit(\n",
+    "    test_ds, flag='data', loader_name='test_loader',\n",
+    "    batch_size=cfg['data']['test_loader']['batch_size'], shuffle=False,\n",
+    "    is_training=False, preload_labels=True, preload_metadata=True)\n",
+    "\n",
+    "# Class weights counter the minority (positive) class prevalence.\n",
+    "cw = torch.tensor(cfg['class_weights'], dtype=torch.float32, device=device)\n",
+    "train_criterion = wl.watch_or_edit(\n",
+    "    nn.CrossEntropyLoss(weight=cw, reduction='none'),\n",
+    "    flag='loss', signal_name='train-loss-CE', log=True)\n",
+    "test_criterion = wl.watch_or_edit(\n",
+    "    nn.CrossEntropyLoss(weight=cw, reduction='none'),\n",
+    "    flag='loss', signal_name='test-loss-CE', log=True)\n",
+    "metric = wl.watch_or_edit(\n",
+    "    Accuracy(task='multiclass', num_classes=2).to(device),\n",
+    "    flag='metric', signal_name='metric-ACC', log=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Train and evaluate steps\n",
+    "\n",
+    "The `guard_training_context` / `guard_testing_context` blocks tell WeightsLab the phase. `criterion(..., batch_ids=ids, preds=preds)` passes the sample ids so loss is stored **per sample**, and `wl.save_signals(...)` logs a custom per-sample accuracy signal during evaluation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(loader, model, optimizer, criterion, device):\n",
+    "    with guard_training_context:\n",
+    "        inputs, ids, labels = next(loader)\n",
+    "        inputs, labels = inputs.to(device), labels.to(device)\n",
+    "        optimizer.zero_grad()\n",
+    "        logits = model(inputs)\n",
+    "        preds = logits.argmax(dim=1, keepdim=True)\n",
+    "        loss = criterion(logits.float(), labels.long(), batch_ids=ids, preds=preds)\n",
+    "        total = loss.mean()\n",
+    "        total.backward()\n",
+    "        optimizer.step()\n",
+    "    return total.detach().cpu().item()\n",
+    "\n",
+    "\n",
+    "def test(loader, model, criterion, metric, device, n_batches):\n",
+    "    losses = torch.tensor(0.0, device=device)\n",
+    "    for inputs, ids, labels in loader:\n",
+    "        with guard_testing_context:\n",
+    "            inputs, labels = inputs.to(device), labels.to(device)\n",
+    "            logits = model(inputs)\n",
+    "            preds = logits.argmax(dim=1, keepdim=True)\n",
+    "            losses += criterion(logits, labels, batch_ids=ids, preds=preds).mean()\n",
+    "            metric.update(logits, labels)\n",
+    "            correct = (preds.view(-1) == labels.view(-1)).float()\n",
+    "            wl.save_signals(preds_raw=logits, targets=labels, batch_ids=ids,\n",
+    "                            signals={\"test_metric/Accuracy_per_sample\": correct}, preds=preds)\n",
+    "    return (losses / max(1, n_batches)).item(), (metric.compute() * 100).item()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Serve and train\n",
+    "\n",
+    "`wl.serve(serving_grpc=True, serving_bore=True)` starts the background gRPC server (non-blocking) and a `bore.pub` tunnel so Weights Studio on your own machine can reach this Colab backend. `wl.start_training(...)` flips the experiment into the *training* state, then we run the loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wl.serve(serving_grpc=True, serving_bore=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wl.start_training(timeout=3)\n",
+    "\n",
+    "steps = config['training_steps_to_do']\n",
+    "eval_ratio = config['eval_full_to_train_steps_ratio']\n",
+    "n_test_batches = len(test_loader)\n",
+    "\n",
+    "test_loss = test_acc = None\n",
+    "pbar = tqdm(range(steps), desc='Training')\n",
+    "for step in pbar:\n",
+    "    age = model.get_age() if hasattr(model, 'get_age') else step\n",
+    "    train_loss = train(train_loader, model, optimizer, train_criterion, device)\n",
+    "    if age > 0 and age % eval_ratio == 0:\n",
+    "        test_loss, test_acc = test(test_loader, model, test_criterion, metric, device, n_test_batches)\n",
+    "    postfix = {'loss': f'{train_loss:.3f}'}\n",
+    "    if test_acc is not None:\n",
+    "        postfix['test_acc'] = f'{test_acc:.1f}%'\n",
+    "    pbar.set_postfix(postfix)\n",
+    "\n",
+    "wl.write_history()\n",
+    "wl.write_dataframe()\n",
+    "print('Training complete.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## See it live in Weights Studio\n",
+    "\n",
+    "Everything above runs headless. The payoff is **Weights Studio**, where each row is one record and every feature is a **sortable column**.\n",
+    "\n",
+    "Studio runs as a local Docker stack, and **Colab has no Docker daemon**, so you run Studio on your own machine and point it at this notebook's backend via the `bore.pub:<port>` endpoint **printed in Section 6**.\n",
+    "\n",
+    "**On your machine** (with Docker Desktop):\n",
+    "```bash\n",
+    "pip install weightslab\n",
+    "weightslab start                     # opens http://localhost:5173\n",
+    "weightslab tunnel bore.pub:12345     # in another window, the host:port printed in Section 6\n",
+    "```\n",
+    "\n",
+    "Then open **http://localhost:5173** and switch the Data Exploration board to the **List** view."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Curate in the UI\n",
+    "\n",
+    "In Weights Studio, switch the Data Exploration board to the **List** view:\n",
+    "\n",
+    "1. **Sort by `test-loss-CE` descending** and generate its histogram to find the impressions the model ranks worst.\n",
+    "2. **Lock** the loss sort, then add `ad_category` or `placement` as a secondary sort to see which segments the model struggles on.\n",
+    "3. **Right-click a column** to clone, reset the sort, or generate a histogram.\n",
+    "4. **Discard** noisy impressions and keep training to sharpen the ranker."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "<div align=\"center\">\n",
+    "Crafted by <a href=\"https://github.com/GrayboxTech/weightslab\">GrayboxTech</a> - if WeightsLab helps you catch a bad label, drop us a star on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a>.\n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "wl-ads-recommendation.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/weightslab/examples/Notebooks/PyTorch/wl-classification.ipynb b/weightslab/examples/Notebooks/PyTorch/wl-classification.ipynb
new file mode 100644
index 00000000..d6b9859a
--- /dev/null
+++ b/weightslab/examples/Notebooks/PyTorch/wl-classification.ipynb
@@ -0,0 +1,1667 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d1Yi--O7FETe"
+   },
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-classification.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Welcome to the WeightsLab image-classification notebook! <a href=\"https://github.com/GrayboxTech/weightslab\">WeightsLab</a> is an open-source PyTorch tool for dataset debugging, mislabel detection, and mid-training data curation. Browse the <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\">Docs</a> for details, and raise an issue on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a> for support.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "POIbm5qMFETh"
+   },
+   "source": [
+    "# Image Classification with WeightsLab\n",
+    "\n",
+    "This notebook trains a small CNN on **MNIST** and instruments it with WeightsLab so every training signal is traced **back to the exact samples** producing it.\n",
+    "\n",
+    "Most data problems (mislabels, outliers, class imbalance) stay invisible until your model tells you through the loss. By wrapping your training objects with `wl.watch_or_edit(...)`, WeightsLab records **per-sample** loss and metrics live, so you can rank the worst samples, spot bad labels, and curate the dataset **without restarting training**.\n",
+    "\n",
+    "### What you'll do\n",
+    "1. Install WeightsLab.\n",
+    "2. Set every knob in one **config** dict (like a `config.yaml`).\n",
+    "3. Wrap the model, optimizer, dataloaders, loss, and metric with the SDK.\n",
+    "4. Train while per-sample signals are captured.\n",
+    "5. Rank the highest-loss samples inline, and (optionally) open the live **Weights Studio** UI."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "P6gYbKq5FETh"
+   },
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Install WeightsLab from PyPI. On Colab the free **T4 GPU** runtime is plenty for this demo (`Runtime -> Change runtime type -> T4 GPU`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gRQuuS90FETi"
+   },
+   "source": [
+    "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?color=5865F2&logo=pypi&logoColor=white\" alt=\"PyPI - Version\"></a>\n",
+    "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/dm/weightslab?color=5865F2\" alt=\"PyPI - Downloads\"></a>\n",
+    "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/pyversions/weightslab?color=5865F2&logo=python&logoColor=white\" alt=\"PyPI - Python Version\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HW0Mrb23FETi",
+    "outputId": "d649cd01-8f95-4599-a5c1-d10ff377964e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (2.9.0)\n",
+      "Requirement already satisfied: torchvision in /usr/local/lib/python3.12/dist-packages (0.24.0)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch) (3.29.4)\n",
+      "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch) (4.15.0)\n",
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch) (75.2.0)\n",
+      "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch) (1.14.0)\n",
+      "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch) (3.6.1)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch) (3.1.6)\n",
+      "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch) (2025.3.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch) (9.10.2.21)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /usr/local/lib/python3.12/dist-packages (from torch) (12.8.4.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /usr/local/lib/python3.12/dist-packages (from torch) (11.3.3.83)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /usr/local/lib/python3.12/dist-packages (from torch) (10.3.9.90)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /usr/local/lib/python3.12/dist-packages (from torch) (11.7.3.90)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /usr/local/lib/python3.12/dist-packages (from torch) (12.5.8.93)\n",
+      "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch) (0.7.1)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch) (2.27.5)\n",
+      "Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch) (3.3.20)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /usr/local/lib/python3.12/dist-packages (from torch) (1.13.1.3)\n",
+      "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch) (3.5.0)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from torchvision) (2.0.2)\n",
+      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.12/dist-packages (from torchvision) (11.3.0)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch) (1.3.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch) (3.0.3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install torch torchvision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nQscI28FGE4Z"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "collapsed": true,
+    "id": "PVreJLYJIod6",
+    "outputId": "9e77d75f-ef33-4807-a6c6-c69bb8e4a29a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://test.pypi.org/simple/, https://pypi.org/simple/\n",
+      "\u001b[31mERROR: Could not find a version that satisfies the requirement weightslab==1.3.3.dev6 (from versions: 0.0.0, 1.1.1, 1.1.2, 1.1.3.dev0, 1.1.3, 1.1.4, 1.1.5, 1.1.6, 1.1.7.dev3, 1.1.7, 1.1.8, 1.1.9, 1.2.0, 1.2.1.dev0, 1.2.1, 1.2.2, 1.2.3.dev0, 1.2.3, 1.2.4, 1.2.5, 1.2.6, 1.3.0.dev0, 1.3.0, 1.3.1.dev1, 1.3.1.dev2, 1.3.1.dev3, 1.3.1, 1.3.2.dev0, 1.3.2, 1.3.3.dev0, 1.3.3.dev1, 1.3.3.dev2, 1.3.3.dev3, 1.3.3.dev4, 1.3.3.dev5, 1.3.3, 1.9.1.dev1, 1.9.1.dev2, 1.9.1.dev3, 1.9.1.dev4, 20260427.dev1, 20260427.dev2, 20260605.dev1, 20260310110810, 20260310112657.dev2659810514, 20260310112936.dev2035376017, 20260310114507.dev36735734, 20260310122733.dev571756140, 20260310130048.dev500189762, 20260310142756.dev2389960633, 20260310172643.dev1790296826, 20260310184554.dev3572723963, 20260310190409.dev16171358, 20260311001628.dev82842326, 20260311001938.dev237981957, 20260311094631.dev2433054235, 20260311100556.dev2759443978, 20260311111421.dev1419930837, 20260311152850.dev375337088, 20260415152016.dev1529547259, 20260515094038.dev295368024, 20260515094810.dev295368024, 20260515100544.dev709462904, 20260518115533.dev1831822349, 20260518120723.dev3543420334, 20260518121158.dev2454375021, 20260518125331.dev2144318793, 20260518152946.dev2688573905, 20260518153709.dev457831340, 20260518154038.dev3380177004, 20260518154550.dev2597851767, 20260518160221.dev67736641, 20260518160351.dev500880749, 20260518160424.dev3971069761, 20260519121616.dev3380177004, 20260519122038.dev2371413592, 20260519153002.dev2989691760, 20260519160247.dev4080968838, 20260519160445.dev1656037640, 20260519160647.dev1656037640, 20260519161900.dev3430116169, 20260520150146.dev2702511536, 20260520151257.dev3739150100, 20260520151549.dev3739150100, 20260520160858.dev3843696446, 20260520161330.dev950128852, 20260521100142.dev3061163053, 20260521100237.dev3763458477, 20260521100304.dev2708183098, 20260521101422.dev1111427347, 20260521101942.dev1751890316, 20260521102210.dev697219115, 20260521112427.dev1815380353, 20260528101213.dev1156931848, 20260528101621.dev3002779928, 20260528102838.dev1225638335, 20260528104815.dev146316976, 20260528104912.dev2432105952, 20260528144750.dev228483024, 20260529124654.dev1088603485, 20260602102247.dev3028334291, 20260602133031.dev612843591, 20260603164444.dev1140145833, 20260604132920.dev4091502713, 20260604141626.dev180590797, 20260604154956.dev1777185457, 20260604161731.dev1492593462, 20260605101712.dev3201556731, 20260605102734.dev307817433, 20260605153744.dev1218078270, 20260605154211.dev1284003932, 20260605155425.dev2249789890, 20260608122954.dev616519929, 20260608130348.dev103073330, 20260608142704.dev1750894834, 20260608143725.dev279338996, 20260608144113.dev1776325482, 20260608151812.dev1493920450, 20260609145543.dev4094763614, 20260609150002.dev1824924853, 20260609150942.dev1916930876, 20260609151231.dev2007644555, 20260609164013.dev3320873074, 20260609170327.dev427086775, 20260609170903.dev4090827397, 20260609170910.dev878249345, 20260609171010.dev315170550, 20260610134434.dev2867115601, 20260610135809.dev2659845748, 20260610140822.dev3917850211, 20260610142006.dev1779011735, 20260610145358.dev827089089, 20260611100101.dev2435580864, 20260611122329.dev2942846670, 20260612094419.dev318051075, 20260612174112.dev1945936079, 20260612175512.dev558737864, 20260612175811.dev4293577391, 20260614170924.dev435351911, 20260617105222.dev3481250246, 20260617105337.dev4027879073, 20260617150202.dev2472499979, 20260617150338.dev3507587562, 20260618091144.dev3638457713, 20260618091310.dev2980634153, 20260618091437.dev666220125, 20260618161401.dev2672707490, 20260618161455.dev1062308657, 20260618161503.dev869421065, 20260619154839.dev3124849231, 20260619154914.dev1825884740, 20260619155132.dev3059317318, 20260619155148.dev2195359802, 20260619155831.dev2547505056, 20260619160048.dev2036037367, 20260619160133.dev887552290, 20260619164552.dev545225221, 20260620125803.dev57300911, 20260620155947.dev163709547, 20260620160335.dev978317758, 20260620161239.dev1614881684, 20260620163624.dev3186857944, 20260620163931.dev174614870, 20260620170427.dev937264444, 20260621140029.dev2748921241, 20260622131949.dev1993441264, 20260622132042.dev41563737, 20260626162054.dev815684860, 20260626162149.dev1517909447, 20260629095034.dev2864733358, 20260629095109.dev2365834914, 20260629100925.dev3295139208, 20260705162125.dev3118493333, 20260705162448.dev4054744437, 20260705162608.dev112194667, 20260705163739.dev337968460, 20260705163915.dev1702540858, 20260706102840.dev1428624302, 20260706103324.dev4063235919, 20260706103956.dev1761667072, 20260706105255.dev612849548, 20260706122808.dev397480003, 20260706123234.dev2929755776, 20260706124917.dev3042106597, 20260706132510.dev3579337194, 20260707110009.dev944740769, 20260707110041.dev949537649, 20260707130443.dev1726432854, 20260707130503.dev2999317701, 20260707130900.dev2393574598, 20260708143958.dev3881013472, 20260708144357.dev578891966, 20260708144457.dev658501659, 20260708144826.dev1757704307, 20260708144934.dev3945788796, 20260708145114.dev663145800, 20260710162317.dev3300605375, 20260710162608.dev3232863539)\u001b[0m\u001b[31m\n",
+      "\u001b[0m\u001b[31mERROR: No matching distribution found for weightslab==1.3.3.dev6\u001b[0m\u001b[31m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev6\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pKzQf_BzFETj"
+   },
+   "source": [
+    "## 1. Imports\n",
+    "\n",
+    "`weightslab` is imported as `wl`. The two `guard_*_context` managers scope a block as training vs. evaluation so signals are attributed to the right phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-uAGOoqVFETj",
+    "outputId": "1d806744-f7be-4da8-9a8c-4b68ae436a13"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:06:00.794 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmpn4he5yzg/weightslab_logs/weightslab_20260715_140600.log\n",
+      "15/07/2026-14:06:00.796 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+      "15/07/2026-14:06:00.797 INFO:weightslab:<module>: \n",
+      "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+      "│                                                                                                     │\n",
+      "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+      "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+      "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+      "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+      "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+      "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+      "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+      "│                                                                                                     │\n",
+      "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+      "│                                                                                                     │\n",
+      "╰─── By GrayBx, v1.3.3.dev5 ──────────────────────────────────────────────────────────────────────────╯\n",
+      "\n",
+      "\n",
+      "Using device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import logging\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import Dataset\n",
+    "from torchvision import datasets, transforms\n",
+    "from torchmetrics.classification import Accuracy\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "import weightslab as wl\n",
+    "from weightslab.examples.utils.baseline_models.pytorch.models import FashionCNN as CNN\n",
+    "from weightslab.components.global_monitoring import (\n",
+    "    guard_training_context,\n",
+    "    guard_testing_context,\n",
+    ")\n",
+    "\n",
+    "logging.basicConfig(level=logging.ERROR)\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using device: {device}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bwyu_n8VFETj"
+   },
+   "source": [
+    "## 2. A dataset that carries sample identity\n",
+    "\n",
+    "WeightsLab attributes every signal to a **sample id**. This thin wrapper around torchvision's MNIST returns `(image, id, label)` and attaches a virtual filepath per sample, so signals can be traced back to individual images in the UI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "NoYmyRYwFETk"
+   },
+   "outputs": [],
+   "source": [
+    "class MNISTCustomDataset(Dataset):\n",
+    "    \"\"\"MNIST that returns (image, index, label) and tracks a virtual filepath.\"\"\"\n",
+    "\n",
+    "    def __init__(self, root, train=True, download=False, transform=None):\n",
+    "        self.mnist = datasets.MNIST(root=root, train=train, download=download, transform=None)\n",
+    "        self.transform = transform\n",
+    "        self.train = train\n",
+    "        split = \"train\" if train else \"test\"\n",
+    "        self.filepaths = {}\n",
+    "        for idx in range(len(self.mnist)):\n",
+    "            label = self.mnist.targets[idx].item()\n",
+    "            self.filepaths[idx] = os.path.join(\n",
+    "                \"MNIST\", \"processed\", split, f\"class_{label}\", f\"sample_{idx:05d}.pt\"\n",
+    "            )\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.mnist)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        image, label = self.mnist[idx]\n",
+    "        if self.transform:\n",
+    "            image = self.transform(image)\n",
+    "        return image, idx, label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "J-jUVL-cFETk"
+   },
+   "source": [
+    "## 3. Configuration\n",
+    "\n",
+    "Every tunable lives here, in one dict, like a `config.yaml` with comments. Wrapping it with `flag=\"hyperparameters\"` lets the Studio UI read (and live-edit) these values while training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pHaJYoqNFETk",
+    "outputId": "6bae5503-1a48-48c3-de01-8534009157a8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:23:06.251 INFO:weightslab.components.checkpoint_manager:__init__: CheckpointManager initialized at /tmp/weightslab_mnist_yonuysxx\n",
+      "15/07/2026-14:23:06.253 INFO:weightslab.src:watch_or_edit: Registered new checkpoint manager in ledger\n",
+      "Experiment logs -> /tmp/weightslab_mnist_yonuysxx\n"
+     ]
+    }
+   ],
+   "source": [
+    "log_dir = tempfile.mkdtemp(prefix=\"weightslab_mnist_\")\n",
+    "\n",
+    "config = {\n",
+    "    # -- Experiment -------------------------------------------------------\n",
+    "    \"experiment_name\": \"mnist_classification\",    # name shown in Weights Studio\n",
+    "    \"device\": str(device),                        # \"cuda\" if a GPU is available, else \"cpu\"\n",
+    "    \"root_log_dir\": log_dir,                      # where signal history / dataframes are written\n",
+    "    \"num_classes\": 10,                            # MNIST digits 0-9\n",
+    "\n",
+    "    # -- Training schedule ------------------------------------------------\n",
+    "    \"training_steps_to_do\": 8000,                 # total optimizer steps (raise for a longer live run)\n",
+    "    \"eval_full_to_train_steps_ratio\": 500,        # run a full eval every N steps\n",
+    "    \"experiment_dump_to_train_steps_ratio\": 250,   # dump model weights ratio\n",
+    "    \"write_export_ratio\": 1000,                   # export signal history + dataframe every N steps\n",
+    "\n",
+    "    # Configure global dataframe storage\n",
+    "    \"ledger_enable_flushing_threads\": True,\n",
+    "    \"ledger_enable_h5_persistence\": True,\n",
+    "    \"ledger_flush_max_rows\": 1024,\n",
+    "    \"ledger_flush_interval\": 20.0,\n",
+    "\n",
+    "    # -- Optimizer --------------------------------------------------------\n",
+    "    \"learning_rate\": 0.001,                        # Adam learning rate\n",
+    "\n",
+    "    # -- Data loaders -----------------------------------------------------\n",
+    "    # One block per loader, keyed by loader_name. EVERY kwarg passed to\n",
+    "    # wl.watch_or_edit(..., flag=\"data\") lives here so the wrap cell below stays\n",
+    "    # declarative — no dataloader settings hardcoded in the cells. Add a new\n",
+    "    # loader by adding another block here.\n",
+    "    \"data\": {\n",
+    "        \"train_loader\": {\n",
+    "            \"batch_size\": 128,                     # training batch size\n",
+    "            \"shuffle\": True,                      # shuffle each epoch\n",
+    "            \"is_training\": True,                  # marks this loader as the training split\n",
+    "            \"compute_hash\": False,                # skip per-sample content hashing (faster init)\n",
+    "            \"preload_labels\": True,               # preload labels into the ledger at startup\n",
+    "            \"preload_metadata\": False,            # load metadata lazily on first access\n",
+    "            \"enable_h5_persistence\": True,        # persist per-sample stats to the H5 store\n",
+    "        },\n",
+    "        \"test_loader\": {\n",
+    "            \"batch_size\": 128,                    # evaluation batch size\n",
+    "            \"shuffle\": False,                     # keep test order stable\n",
+    "            \"is_training\": False,                 # marks this loader as an eval split\n",
+    "            \"compute_hash\": False,\n",
+    "            \"preload_labels\": True,\n",
+    "            \"preload_metadata\": False,\n",
+    "            \"enable_h5_persistence\": True,\n",
+    "        },\n",
+    "    },\n",
+    "\n",
+    "    # -- Services ---------------------------------------------------------\n",
+    "    \"serving_grpc\": True,                         # expose the gRPC backend for Weights Studio\n",
+    "    \"serving_bore\": True,                         # expose the bore tunnel for Weights Studio\n",
+    "}\n",
+    "\n",
+    "wl.watch_or_edit(config, flag=\"hyperparameters\", poll_interval=1.0)\n",
+    "print(\"Experiment logs ->\", log_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5jiuWkNBFETk"
+   },
+   "source": [
+    "## 4. Wrap the training objects\n",
+    "\n",
+    "This is the heart of WeightsLab. Each object is passed through `wl.watch_or_edit(...)` with a `flag` describing its role. The returned objects behave exactly like the originals, but now report their state and per-sample signals to WeightsLab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "d7HMuDrCFETk",
+    "outputId": "b5bbf0b2-9464-46ce-dc04-4841e15d0c97"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:06:03.264 INFO:weightslab.backend.model_interface:__init__: Using checkpoint manager from ledger\n",
+      "15/07/2026-14:06:03.266 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loading checkpoint 124b3608e98f2d94...\n",
+      "15/07/2026-14:06:03.267 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Target: HP=124b3608 MODEL=e98f2d94 DATA=9e70c5a5\n",
+      "15/07/2026-14:06:03.268 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Current: HP=124b3608 MODEL=e98f2d94 DATA=9e70c5a5\n",
+      "15/07/2026-14:06:03.269 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Model architecture unchanged, using current model\n",
+      "15/07/2026-14:06:03.292 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [OK] Loaded weights from step 6750 with RNG state\n",
+      "15/07/2026-14:06:03.294 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Config unchanged, using current config\n",
+      "15/07/2026-14:06:03.296 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loaded components: {'weights'}\n",
+      "15/07/2026-14:06:03.300 INFO:weightslab.backend.model_interface:__init__: Auto-loaded model weights from checkpoint 124b3608e98f2d94 (step 6750)\n",
+      "15/07/2026-14:06:03.304 INFO:weightslab.data.data_samples_with_ops:__init__: [DataSampleTrackingWrapper] H5 persistence enabled at /tmp/weightslab_mnist_kg8215pb/checkpoints/data/data.h5\n",
+      "15/07/2026-14:06:03.402 INFO:weightslab.data.data_samples_with_ops:__init__: Preloading sample statistics for PHYSICAL indices in split 'train_loader' with: preload_labels=True,\n",
+      "15/07/2026-14:06:03.404 INFO:weightslab.data.data_samples_with_ops:__init__: Metadata will be loaded on demand from the wrapped dataset for split 'train_loader' when accessed, which may increase latency on first access but reduces initialization time.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing ledger for split 'train_loader': 100%|██████████| 60000/60000 [00:12<00:00, 4884.89it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:06:15.708 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] Registering split 'train_loader' with 60000 samples.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:06:16.062 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] After annotation expansion: 60000 samples → 60000 annotation rows.\n",
+      "15/07/2026-14:06:16.064 INFO:weightslab.data.h5_array_store:__init__: [H5ArrayStore] Initialized with cache limit: 2048MB\n",
+      "15/07/2026-14:06:17.387 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loading checkpoint 124b3608e98f2d94...\n",
+      "15/07/2026-14:06:17.389 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Target: HP=124b3608 MODEL=e98f2d94 DATA=9e70c5a5\n",
+      "15/07/2026-14:06:17.390 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Current: HP=124b3608 MODEL=e98f2d94 DATA=9e70c5a5\n",
+      "15/07/2026-14:06:17.393 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Model architecture unchanged, using current model\n",
+      "15/07/2026-14:06:17.393 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Config unchanged, using current config\n",
+      "15/07/2026-14:06:17.593 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [OK] Loaded data snapshot (70000 rows) with RNG state\n",
+      "15/07/2026-14:06:17.594 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loaded components: {'data'}\n",
+      "15/07/2026-14:06:17.918 INFO:weightslab.backend.dataloader_interface:_load_checkpoint_data: Applied data snapshot from checkpoint (70000 rows)\n",
+      "15/07/2026-14:06:17.922 INFO:weightslab.data.data_samples_with_ops:__init__: [DataSampleTrackingWrapper] H5 persistence enabled at /tmp/weightslab_mnist_kg8215pb/checkpoints/data/data.h5\n",
+      "15/07/2026-14:06:17.932 INFO:weightslab.data.data_samples_with_ops:__init__: Preloading sample statistics for PHYSICAL indices in split 'test_loader' with: preload_labels=True,\n",
+      "15/07/2026-14:06:17.935 INFO:weightslab.data.data_samples_with_ops:__init__: Metadata will be loaded on demand from the wrapped dataset for split 'test_loader' when accessed, which may increase latency on first access but reduces initialization time.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing ledger for split 'test_loader': 100%|██████████| 10000/10000 [00:01<00:00, 7865.73it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:06:19.213 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] Registering split 'test_loader' with 10000 samples.\n",
+      "15/07/2026-14:06:19.273 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] After annotation expansion: 10000 samples → 10000 annotation rows.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:06:19.798 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loading checkpoint 124b3608e98f2d94...\n",
+      "15/07/2026-14:06:19.799 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Target: HP=124b3608 MODEL=e98f2d94 DATA=9e70c5a5\n",
+      "15/07/2026-14:06:19.802 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Current: HP=124b3608 MODEL=e98f2d94 DATA=9e70c5a5\n",
+      "15/07/2026-14:06:19.803 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Model architecture unchanged, using current model\n",
+      "15/07/2026-14:06:19.804 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Config unchanged, using current config\n",
+      "15/07/2026-14:06:19.965 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [OK] Loaded data snapshot (70000 rows) with RNG state\n",
+      "15/07/2026-14:06:19.966 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loaded components: {'data'}\n",
+      "15/07/2026-14:06:20.277 INFO:weightslab.backend.dataloader_interface:_load_checkpoint_data: Applied data snapshot from checkpoint (70000 rows)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_root = os.path.join(log_dir, \"data\")\n",
+    "os.makedirs(data_root, exist_ok=True)\n",
+    "\n",
+    "train_ds = MNISTCustomDataset(root=data_root, train=True, download=True,\n",
+    "                              transform=transforms.ToTensor())\n",
+    "test_ds = MNISTCustomDataset(root=data_root, train=False, download=True,\n",
+    "                             transform=transforms.ToTensor())\n",
+    "\n",
+    "# Model + optimizer\n",
+    "model = wl.watch_or_edit(CNN().to(device), flag=\"model\", device=device, compute_dependencies=True)\n",
+    "optimizer = wl.watch_or_edit(\n",
+    "    optim.Adam(model.parameters(), lr=config[\"learning_rate\"]), flag=\"optimizer\")\n",
+    "\n",
+    "# Tracked dataloaders — all loader settings come from config[\"data\"][<loader_name>],\n",
+    "# so nothing is hardcoded here. Unpack each block straight into the wrapper.\n",
+    "train_loader = wl.watch_or_edit(\n",
+    "    train_ds, flag=\"data\", loader_name=\"train_loader\",\n",
+    "    **config[\"data\"][\"train_loader\"],\n",
+    ")\n",
+    "test_loader = wl.watch_or_edit(\n",
+    "    test_ds, flag=\"data\", loader_name=\"test_loader\",\n",
+    "    **config[\"data\"][\"test_loader\"],\n",
+    ")\n",
+    "\n",
+    "# Watched losses + metric (they log themselves per sample)\n",
+    "train_criterion = wl.watch_or_edit(nn.CrossEntropyLoss(reduction=\"none\"),\n",
+    "                                   flag=\"loss\", signal_name=\"train-loss-CE\", log=True)\n",
+    "test_criterion = wl.watch_or_edit(nn.CrossEntropyLoss(reduction=\"none\"),\n",
+    "                                  flag=\"loss\", signal_name=\"test-loss-CE\", log=True)\n",
+    "metric = wl.watch_or_edit(\n",
+    "    Accuracy(task=\"multiclass\", num_classes=config[\"num_classes\"]).to(device),\n",
+    "    flag=\"metric\", signal_name=\"metric-ACC\", log=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-nWmYOC1FETk"
+   },
+   "source": [
+    "## 5. Train and evaluate steps\n",
+    "\n",
+    "The `guard_training_context` / `guard_testing_context` blocks tell WeightsLab which phase it's in. `criterion(..., batch_ids=ids, preds=preds)` passes the sample ids so the loss is stored **per sample**, and `wl.save_signals(...)` logs a custom per-sample accuracy signal during evaluation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "LFFmdEMTFETl"
+   },
+   "outputs": [],
+   "source": [
+    "def train(loader, model, optimizer, criterion, device):\n",
+    "    with guard_training_context:\n",
+    "        inputs, ids, labels = next(loader)\n",
+    "        inputs, labels = inputs.to(device), labels.to(device)\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        logits = model(inputs)\n",
+    "        preds = logits.argmax(dim=1, keepdim=True)\n",
+    "\n",
+    "        loss = criterion(logits.float(), labels.long(), batch_ids=ids, preds=preds)\n",
+    "        total_loss = loss.mean()\n",
+    "        total_loss.backward()\n",
+    "        optimizer.step()\n",
+    "    return total_loss.detach().cpu().item()\n",
+    "\n",
+    "\n",
+    "import time\n",
+    "def test(loader, model, criterion, metric, device, n_batches):\n",
+    "    losses = torch.tensor(0.0, device=device)\n",
+    "    st = time.time()\n",
+    "    for inputs, ids, labels in loader:\n",
+    "        with guard_testing_context:\n",
+    "            inputs, labels = inputs.to(device), labels.to(device)\n",
+    "            logits = model(inputs)\n",
+    "            preds = logits.argmax(dim=1, keepdim=True)\n",
+    "\n",
+    "            loss = criterion(logits, labels, batch_ids=ids, preds=preds)\n",
+    "            losses += loss.mean()\n",
+    "            metric.update(logits, labels)\n",
+    "\n",
+    "            correct = (preds.view(-1) == labels.view(-1)).float()\n",
+    "            wl.save_signals(\n",
+    "                preds_raw=logits, targets=labels, batch_ids=ids, preds=preds,\n",
+    "                signals={\n",
+    "                    \"test_metric/Accuracy_per_sample\": correct,\n",
+    "                    \"test_metric/Inverse_Accuracy_per_sample\": 1.0 - correct,\n",
+    "                },\n",
+    "            )\n",
+    "    print(f\"Compute test in {time.time()-st}s\")\n",
+    "    return (losses / n_batches).item(), (metric.compute() * 100).item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nBNH0B0-FETl"
+   },
+   "source": [
+    "## 6. Serve and train\n",
+    "\n",
+    "`wl.serve(serving_grpc=True)` starts the background gRPC server (non-blocking) that Weights Studio connects to. `wl.start_training(...)` flips the experiment into the *training* state, then we run the loop, periodically evaluating and exporting signals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 886
+    },
+    "id": "YnEB98kvRBJt",
+    "outputId": "590e5572-23b7-4bc1-8592-b666ce7f71dc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:06:20.303 INFO:weightslab.trainer.trainer_services:_run_security_preflight: \n",
+      "\n",
+      "# #######################################\n",
+      "# #######################################\n",
+      "[gRPC] Security preflight checks:\n",
+      "\tTLS: DISABLED (unencrypted traffic)\n",
+      "\tAuth tokens: NONE configured\n",
+      "\t! WARNING: GRPC_TLS_ENABLED=0. Traffic will be unencrypted. Use only for development.\n",
+      "\t! WARNING: No GRPC_AUTH_TOKEN/GRPC_AUTH_TOKENS configured. Only transport-level trust (TLS/mTLS) will protect RPC access.\n",
+      "# #######################################\n",
+      "# #######################################\n",
+      "\n",
+      "15/07/2026-14:06:20.305 INFO:weightslab.trainer.trainer_services:grpc_serve: [gRPC] Watchdogs disabled via WEIGHTSLAB_DISABLE_WATCHDOGS.\n",
+      "15/07/2026-14:06:20.306 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Thread callback started\n",
+      "15/07/2026-14:06:20.307 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Creating ThreadPoolExecutor with 6 worker threads (n_workers_grpc=None, max_concurrent_rpcs=None)\n",
+      "15/07/2026-14:06:20.308 INFO:weightslab.trainer.trainer_services:grpc_serve: [gRPC] Server started with watchdogs disabled (host=0.0.0.0 port=50051 workers=None)\n",
+      "15/07/2026-14:06:20.315 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Server object created\n",
+      "15/07/2026-14:06:20.525 INFO:weightslab.trainer.services.agent.agent:__init__: Initializing DataManipulationAgent\n",
+      "15/07/2026-14:06:20.623 WARNING:weightslab.trainer.services.agent.agent:_load_config: Error loading config from /usr/local/lib/python3.12/dist-packages: [Errno 21] Is a directory: '/usr/local/lib/python3.12/dist-packages'\n",
+      "15/07/2026-14:06:20.625 INFO:weightslab.trainer.services.agent.agent:_load_config: \n",
+      "\n",
+      "# #######################################\n",
+      "# #######################################\n",
+      "Agent initialized from configuration /content/agent_config.yaml: \n",
+      "\tFinal Agent Configuration: Preferred Provider=openrouter, \n",
+      "\tFallback to Local=True, \n",
+      "\tOpenRouter Model=~google/gemini-flash-latest with:\n",
+      "\t\tAPI Key=None\n",
+      "\t\tBase URL=https://openrouter.ai/api/v1, \n",
+      "\tOllama Model=llama3.2:3b\n",
+      "# #######################################\n",
+      "# #######################################\n",
+      "\n",
+      "15/07/2026-14:06:20.625 INFO:weightslab.trainer.services.agent.agent:_setup_providers: Setting up Ollama with model llama3.2:3b\n",
+      "15/07/2026-14:06:20.710 INFO:weightslab.trainer.services.agent.agent:_setup_providers: [Agent] Ollama enabled: llama3.2:3b\n",
+      "15/07/2026-14:06:20.711 INFO:weightslab.trainer.services.data_service:_build_preview_cache: [PreviewCache] Building 64×64 or less or less preview cache for 2000 samples …\n",
+      "15/07/2026-14:06:20.712 INFO:weightslab.trainer.services.data_service:__init__: DataService initialized.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r[PreviewCache]:   0%|          | 0/2000 [00:00<?, ?sample/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:06:20.742 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Servicer added\n",
+      "15/07/2026-14:06:20.750 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Attempting to bind to 0.0.0.0:50051\n",
+      "15/07/2026-14:06:20.753 WARNING:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] TLS disabled; using insecure transport on 0.0.0.0:50051\n",
+      "15/07/2026-14:06:20.756 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Port 50051 bound successfully.\n",
+      "15/07/2026-14:06:20.763 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Server started and listening on 0.0.0.0:50051\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[PreviewCache]:  14%|█▎        | 270/2000 [00:00<00:02, 753.49sample/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "============================================================\n",
+      " Backend exposed via bore at: bore.pub:11217\n",
+      " On your local machine (Docker running), run:\n",
+      "     weightslab ui launch\n",
+      "     weightslab tunnel bore.pub:11217\n",
+      "============================================================\n",
+      "15/07/2026-14:06:21.139 INFO:weightslab.backend.cli:cli_serve: cli_thread_started\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      },
+      "text/plain": [
+       "'bore.pub:11217'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wl.serve(serving_grpc=True, serving_bore=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "8d93f084a8ae4ff0a442b8385a0febc7",
+      "8658fa7fc5674b7bb2d6803336825cf2",
+      "3a99ecc652df435d8d16c3d1ea4b6236",
+      "532b8013bc9549e692dbe8b20c99fe30",
+      "c68d2e0fbc504ba8bf3d057b72b52293",
+      "48d77b568b6d47579f27055722205bb7",
+      "f72491d19b5c4fb8824bb4962e1fa812",
+      "6d29e91107f246a69f7ba1c9d3541747",
+      "ec736e6e5cf74dd7a644222821cb5899",
+      "5477fc60ab154377b690c8624b021c94",
+      "98af3369f706433f8dbb9be79d3988e2",
+      "a67abfb4e9a441bdb92f5ee92593aa5c",
+      "257741e960664020981c3e0dc2a71a0b",
+      "5f801ece792c416f90a86bde02d24514",
+      "d0e534930f564af3b6fc47ff63286bd3",
+      "4c013e26a480400bafdfa45c36830b3c",
+      "92834b1a78d1439181f9026917f4882d",
+      "39cf7f4f60554bfd81859b9edeb7d41f",
+      "f35b21ef87c44a24ac9de9f3f7f58fa1",
+      "1e96676b72ca44f399ae30822eb54501"
+     ]
+    },
+    "collapsed": true,
+    "id": "DmwOUSn0FETl",
+    "outputId": "d7ffa59c-5166-42e4-f67e-6425a3c1f4ec"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:15:41.616 INFO:weightslab.src:start_training: Starting WeightsLab training mode with a timeout of 3 seconds.\n",
+      "15/07/2026-14:15:44.618 INFO:weightslab.components.global_monitoring:resume: \n",
+      "Attempting to resume training...\n",
+      "15/07/2026-14:15:44.750 INFO:weightslab.components.global_monitoring:resume: Hashes by module: ['124b3608', 'e2c168e8', '9e70c5a5']\n",
+      "15/07/2026-14:15:44.751 INFO:weightslab.components.global_monitoring:resume: Resuming training now...\n",
+      "15/07/2026-14:15:44.752 INFO:weightslab.components.global_monitoring:resume: Hashes by module on resume: ['124b3608', 'e2c168e8', '9e70c5a5']\n",
+      "15/07/2026-14:15:44.754 INFO:weightslab.components.global_monitoring:resume: \n",
+      "Training resumed as modules hashes have been computed: ['124b3608', 'e2c168e8', '9e70c5a5'].\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d93f084a8ae4ff0a442b8385a0febc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training:   0%|          | 0/6000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:15:51.755 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_015000.pt\n",
+      "15/07/2026-14:15:54.136 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 9.581607103347778s\n",
+      "15/07/2026-14:16:13.098 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_015250.pt\n",
+      "15/07/2026-14:16:14.780 INFO:weightslab.trainer.services.data_service:GetDataSplits: GetDataSplits returning: ['test_loader', 'train_loader']\n",
+      "15/07/2026-14:16:15.921 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:16:21.044 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1083.6 ms ← SLOW\n",
+      "15/07/2026-14:16:25.051 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_015500.pt\n",
+      "15/07/2026-14:16:27.478 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 30.0806941986084s\n",
+      "15/07/2026-14:17:05.833 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_015750.pt\n",
+      "15/07/2026-14:17:08.681 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:17:17.326 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1055.2 ms ← SLOW\n",
+      "15/07/2026-14:17:18.342 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_016000.pt\n",
+      "15/07/2026-14:17:20.845 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 9.577902793884277s\n",
+      "15/07/2026-14:17:37.269 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1049.0 ms ← SLOW\n",
+      "15/07/2026-14:17:38.848 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_016250.pt\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a67abfb4e9a441bdb92f5ee92593aa5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:17:43.085 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:17:47.266 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1029.7 ms ← SLOW\n",
+      "15/07/2026-14:17:49.998 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_016500.pt\n",
+      "15/07/2026-14:17:53.627 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 9.54624342918396s\n",
+      "15/07/2026-14:18:11.743 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_016750.pt\n",
+      "15/07/2026-14:18:14.452 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:18:23.877 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_017000.pt\n",
+      "15/07/2026-14:18:26.638 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 10.121256589889526s\n",
+      "15/07/2026-14:18:47.291 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1062.4 ms ← SLOW\n",
+      "15/07/2026-14:18:47.664 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_017250.pt\n",
+      "15/07/2026-14:18:50.875 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:18:59.319 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_017500.pt\n",
+      "15/07/2026-14:19:02.155 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:19:07.869 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1632.8 ms ← SLOW\n",
+      "Compute test in 10.211559295654297s\n",
+      "15/07/2026-14:19:21.494 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_017750.pt\n",
+      "15/07/2026-14:19:24.383 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:19:33.291 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_018000.pt\n",
+      "15/07/2026-14:19:36.793 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 11.351030349731445s\n",
+      "15/07/2026-14:19:55.761 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_018250.pt\n",
+      "15/07/2026-14:19:57.259 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1021.6 ms ← SLOW\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0e534930f564af3b6fc47ff63286bd3",
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+      "text/plain": [
+       "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:19:58.591 WARNING:weightslab.trainer.services.experiment_service:_get_latest_logger_data_impl: get_and_clear_queue() took 714.1ms (slow — possible lock contention)\n",
+      "15/07/2026-14:20:01.175 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:20:07.495 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_018500.pt\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39cf7f4f60554bfd81859b9edeb7d41f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15/07/2026-14:20:12.909 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 9.242669343948364s\n",
+      "15/07/2026-14:20:31.291 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_018750.pt\n",
+      "15/07/2026-14:20:32.245 WARNING:weightslab.trainer.services.experiment_service:_get_latest_logger_data_impl: get_and_clear_queue() took 723.2ms (slow — possible lock contention)\n",
+      "15/07/2026-14:20:34.664 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:20:37.308 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1075.4 ms ← SLOW\n",
+      "15/07/2026-14:20:43.905 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_019000.pt\n",
+      "15/07/2026-14:20:46.979 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 10.101739406585693s\n",
+      "15/07/2026-14:21:05.755 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_019250.pt\n",
+      "15/07/2026-14:21:09.392 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:21:17.725 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1239.8 ms ← SLOW\n",
+      "15/07/2026-14:21:19.094 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_019500.pt\n",
+      "15/07/2026-14:21:22.550 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 9.67669415473938s\n",
+      "15/07/2026-14:21:41.701 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_019750.pt\n",
+      "15/07/2026-14:21:45.974 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:21:53.051 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_020000.pt\n",
+      "15/07/2026-14:21:57.749 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:21:58.778 WARNING:weightslab.trainer.services.data_service:_slowUpdateInternals: [LockWatchdog] _update_lock[_slowUpdateInternals]   RELEASED by WL-ViewRefresh                 held 1150.4 ms ← SLOW\n",
+      "Compute test in 9.397460460662842s\n",
+      "15/07/2026-14:22:15.281 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_020250.pt\n",
+      "15/07/2026-14:22:20.406 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:22:28.336 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_020500.pt\n",
+      "15/07/2026-14:22:32.385 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "Compute test in 10.87214708328247s\n",
+      "15/07/2026-14:22:53.073 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 124b3608e2c168e89e70c5a5_step_020750.pt\n",
+      "15/07/2026-14:22:56.494 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/weightslab_mnist_kg8215pb/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+      "15/07/2026-14:22:56.860 INFO:weightslab.trainer.services.data_service:GetHistogram: [HistBin] column=signals//train-loss-CE rows=70000 bins=512\n",
+      "15/07/2026-14:22:58.761 INFO:weightslab.trainer.services.data_service:ApplyDataQuery: [ApplyDataQuery] BYPASSING AGENT - Direct query execution: 'sortby signals//train-loss-CE desc scope:global'\n",
+      "15/07/2026-14:23:00.369 INFO:weightslab.trainer.services.data_service:GetHistogram: [HistBin] column=signals//train-loss-CE rows=70000 bins=512\n",
+      "15/07/2026-14:23:06.217 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 70000 row(s) × 15 column(s) as json to /tmp/weightslab_mnist_kg8215pb/d8e01c87_dataframe.json\n",
+      "Training complete. Logs at: /tmp/weightslab_mnist_kg8215pb\n"
+     ]
+    }
+   ],
+   "source": [
+    "wl.start_training(timeout=3)\n",
+    "\n",
+    "steps = 6000\n",
+    "eval_ratio = config[\"eval_full_to_train_steps_ratio\"]\n",
+    "export_ratio = config[\"write_export_ratio\"]\n",
+    "n_test_batches = len(test_loader)\n",
+    "\n",
+    "test_loss = test_acc = None\n",
+    "pbar = tqdm(range(steps), desc=\"Training\")\n",
+    "for step in pbar:\n",
+    "    age = model.get_age() if hasattr(model, \"get_age\") else step\n",
+    "\n",
+    "    train_loss = train(train_loader, model, optimizer, train_criterion, device)\n",
+    "\n",
+    "    if age == 0 and age % eval_ratio == 0:\n",
+    "        test_loss, test_acc = test(test_loader, model, test_criterion, metric, device, n_test_batches)\n",
+    "\n",
+    "    postfix = {\"loss\": f\"{train_loss:.3f}\"}\n",
+    "    if test_acc is not None:\n",
+    "        postfix[\"test_acc\"] = f\"{test_acc:.1f}%\"\n",
+    "    pbar.set_postfix(postfix)\n",
+    "\n",
+    "wl.write_history()\n",
+    "wl.write_dataframe()\n",
+    "print(\"Training complete. Logs at:\", log_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "a2GIwrD6FETl"
+   },
+   "source": [
+    "## 9. See it live in Weights Studio\n",
+    "\n",
+    "Everything above ran headless. The real payoff is the **Weights Studio** UI, where you browse the highest-loss images, filter by class, and curate the dataset mid-training.\n",
+    "\n",
+    "Studio runs as a local Docker stack (a static frontend + an Envoy gRPC-Web proxy). **Colab has no Docker daemon**, so you don't run the UI *inside* Colab - you run Studio on your own machine and point it at this notebook's backend using the `bore.pub:<port>` endpoint **printed in Section 6**.\n",
+    "\n",
+    "**On your machine** (with Docker Desktop):\n",
+    "```bash\n",
+    "pip install weightslab\n",
+    "\n",
+    "# Terminal 1 - launch the UI (plaintext HTTP, the default)\n",
+    "weightslab ui launch                       # opens http://localhost:5173\n",
+    "\n",
+    "# Terminal 2 - bridge the Colab backend to localhost:50051\n",
+    "weightslab tunnel bore.pub:12345           # in another window, the host:port printed in Section 6\n",
+    "```\n",
+    "\n",
+    "Then open **http://localhost:5173** - Studio connects through your local Envoy -> `weightslab tunnel` -> `bore.pub` -> this Colab backend, and you watch training stream live.\n",
+    "\n",
+    "> Note: keep it plaintext end-to-end (the default `weightslab ui launch`, raw-TCP `bore`) so gRPC's HTTP/2 frames pass through untouched.\n",
+    "\n",
+    "Prefer to keep it all local? Run this same example on your own machine (`weightslab start example --cls`) and launch the UI next to it - no tunnel required."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10. Curate in the UI to boost performance\n",
+    "\n",
+    "This is where WeightsLab pays off: instead of training longer on all 60k samples, use the live signals to **find the samples that matter, keep only those, and fine-tune**. Everything below happens in Weights Studio while the backend keeps running — no code changes, no restart.\n",
+    "\n",
+    "> **Baseline before curating:** the evaluation metric (test accuracy) sits around **0.86**.\n",
+    "\n",
+    "**1. See more of the dataset.** In the **grid explorer**, increase the number of images per page so you can scan many samples at once.\n",
+    "\n",
+    "**2. Find the easy and hard samples.** Sort the grid by **`train-loss-CE` in decreasing order**, then **generate the histogram** for that signal. The distribution is bimodal — a tail of **hard** examples (training loss **> 2.45**) and a bulk of **easy** ones (training loss **< 1.461**). These two extremes carry the signal; the vast middle is largely redundant.\n",
+    "\n",
+    "**3. Let the agent tag them.** Initialize the **agent** (the panel in the UI), then ask it:\n",
+    "\n",
+    "> *Tag training samples with train loss greater than 2.45 as \"hard_ex\" and training samples with train loss lower than 1.45 as \"easy_ex\".*\n",
+    "\n",
+    "Inspect the new `tag:hard_ex` / `tag:easy_ex` columns in the grid to confirm the tagging looks right.\n",
+    "\n",
+    "**4. Drop the redundant middle.** Once the tags look good, ask the agent:\n",
+    "\n",
+    "> *Discard train samples that have no \"easy_ex\" and no \"hard_ex\" tag.*\n",
+    "\n",
+    "The training set collapses from **~60k to roughly 3–4k** samples — only the informative extremes remain.\n",
+    "\n",
+    "**5. Fine-tune on the curated set.** **Resume** training (from the UI, or by re-running the training cell) and let it run for about **6,500 steps** on this much smaller, higher-signal dataset.\n",
+    "\n",
+    "**6. Evaluate.** Trigger a full evaluation from the UI: **right-click `resume` → `evaluate` → click `evaluate` → select `test_loader`**, then wait for it to finish.\n",
+    "\n",
+    "> **Result:** test accuracy climbs to roughly **0.95–0.99**, a large jump over the 0.86 baseline — reached by training on **~15× fewer** samples. Curating *which* data the model sees beat simply training on *more* of it.\n",
+    "\n",
+    "This is the core WeightsLab loop: **read the per-sample signals → curate the dataset → fine-tune → measure**, all without leaving the UI or restarting the run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZnCx2Mh2FETl"
+   },
+   "source": [
+    "---\n",
+    "\n",
+    "<div align=\"center\">\n",
+    "Crafted by <a href=\"https://github.com/GrayboxTech/weightslab\">GrayboxTech</a> - if WeightsLab helps you catch a bad label, drop us a star on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a>.\n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "include_colab_link": true,
+   "name": "wl-classification.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
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diff --git a/weightslab/examples/Notebooks/PyTorch/wl-clustering.ipynb b/weightslab/examples/Notebooks/PyTorch/wl-clustering.ipynb
new file mode 100644
index 00000000..4e5e1e9c
--- /dev/null
+++ b/weightslab/examples/Notebooks/PyTorch/wl-clustering.ipynb
@@ -0,0 +1,5272 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WvuZDK7iF391"
+      },
+      "source": [
+        "<div align=\"center\">\n",
+        "\n",
+        "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+        "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+        "\n",
+        "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+        "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+        "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+        "  <br>\n",
+        "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-clustering.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+        "\n",
+        "  Welcome to the WeightsLab <b>Face Recognition (triplet loss)</b> notebook! <a href=\"https://github.com/GrayboxTech/weightslab\">WeightsLab</a> traces training signals back to the exact samples producing them. Browse the <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\">Docs</a> for details.</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8SrJYFpJF392"
+      },
+      "source": [
+        "# Face Recognition (triplet loss) with WeightsLab\n",
+        "\n",
+        "Trains a ResNet-18 embedding head with online batch-hard **triplet loss** and tracks verification / retrieval / similarity signals per sample.\n",
+        "\n",
+        "> Data: Defaults to the **Olivetti Faces** dataset (via scikit-learn) which works **offline** - no download needed. Switch to LFW or a folder dataset in the config.\n",
+        "\n",
+        "This notebook is fully **self-contained**: the `face/` helper modules and the training loop\n",
+        "are inlined below, so there is no repo to clone and no `main.py` to shell out to. Run the\n",
+        "cells top to bottom."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NEWaY0IuF393"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "On Colab, pick a **T4 GPU** runtime (`Runtime -> Change runtime type -> T4 GPU`)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v2Va8BtGF393"
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+        "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?color=5865F2&logo=pypi&logoColor=white\" alt=\"PyPI - Version\"></a>\n",
+        "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/dm/weightslab?color=5865F2\" alt=\"PyPI - Downloads\"></a>\n",
+        "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/pyversions/weightslab?color=5865F2&logo=python&logoColor=white\" alt=\"PyPI - Python Version\"></a>"
+      ]
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+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://test.pypi.org/simple/, https://pypi.org/simple/\n",
+            "Collecting weightslab==1.3.3.dev7\n",
+            "  Downloading https://test-files.pythonhosted.org/packages/fd/50/2281a781f96c471a8e8ec9f7e15cb6b63bcae66b362474f00f1f29e75428/weightslab-1.3.3.dev7-py3-none-any.whl.metadata (15 kB)\n",
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+            "Collecting torch<=2.9,>=2.1 (from weightslab==1.3.3.dev7)\n",
+            "  Downloading torch-2.9.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (30 kB)\n",
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+            "Collecting torchmetrics>=1.9 (from weightslab==1.3.3.dev7)\n",
+            "  Downloading torchmetrics-1.9.0-py3-none-any.whl.metadata (23 kB)\n",
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+            "Collecting onnx<=1.20,>=1.15 (from weightslab==1.3.3.dev7)\n",
+            "  Downloading onnx-1.20.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (8.4 kB)\n",
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+            "  Downloading langchain_ollama-1.1.0-py3-none-any.whl.metadata (3.0 kB)\n",
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+            "Collecting ollama<1.0.0,>=0.6.1 (from langchain-ollama<2,>=0.2->weightslab==1.3.3.dev7)\n",
+            "  Downloading ollama-0.6.2-py3-none-any.whl.metadata (5.8 kB)\n",
+            "Collecting langchain-core<2,>=0.3 (from weightslab==1.3.3.dev7)\n",
+            "  Downloading langchain_core-1.4.9-py3-none-any.whl.metadata (4.7 kB)\n",
+            "Collecting openai<3.0.0,>=2.45.0 (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev7)\n",
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+            "Collecting nvidia-cuda-nvrtc-cu12==12.8.93 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
+            "  Downloading nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.7 kB)\n",
+            "Collecting nvidia-cuda-runtime-cu12==12.8.90 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
+            "  Downloading nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)\n",
+            "Collecting nvidia-cuda-cupti-cu12==12.8.90 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
+            "  Downloading nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)\n",
+            "Collecting nvidia-cudnn-cu12==9.10.2.21 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
+            "  Downloading nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl.metadata (1.8 kB)\n",
+            "Collecting nvidia-cublas-cu12==12.8.4.1 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
+            "  Downloading nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl.metadata (1.7 kB)\n",
+            "Collecting nvidia-cufft-cu12==11.3.3.83 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
+            "  Downloading nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)\n",
+            "Collecting nvidia-curand-cu12==10.3.9.90 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
+            "  Downloading nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl.metadata (1.7 kB)\n",
+            "Collecting nvidia-cusolver-cu12==11.7.3.90 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
+            "  Downloading nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl.metadata (1.8 kB)\n",
+            "Collecting nvidia-cusparse-cu12==12.5.8.93 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
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+            "Collecting nvidia-nvtx-cu12==12.8.90 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
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+            "Collecting triton==3.5.0 (from torch<=2.9,>=2.1->weightslab==1.3.3.dev7)\n",
+            "  Downloading triton-3.5.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.7 kB)\n",
+            "Collecting lightning-utilities>=0.15.3 (from torchmetrics>=1.9->weightslab==1.3.3.dev7)\n",
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+            "INFO: pip is looking at multiple versions of torchvision to determine which version is compatible with other requirements. This could take a while.\n",
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+            "\u001b[?25hDownloading torchmetrics-1.9.0-py3-none-any.whl (983 kB)\n",
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+            "\u001b[?25hDownloading torchvision-0.24.0-cp312-cp312-manylinux_2_28_x86_64.whl (8.1 MB)\n",
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+            "\u001b[?25hDownloading lightning_utilities-0.15.3-py3-none-any.whl (31 kB)\n",
+            "Downloading ollama-0.6.2-py3-none-any.whl (15 kB)\n",
+            "Downloading openai-2.45.0-py3-none-any.whl (1.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m64.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: nvidia-cusparselt-cu12, triton, nvidia-nvtx-cu12, nvidia-nvshmem-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, lightning-utilities, onnx, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, openai, ollama, nvidia-cusolver-cu12, torch, langchain-core, torchvision, torchmetrics, langchain-openai, langchain-ollama, weightslab\n",
+            "  Attempting uninstall: nvidia-nccl-cu12\n",
+            "    Found existing installation: nvidia-nccl-cu12 2.30.7\n",
+            "    Uninstalling nvidia-nccl-cu12-2.30.7:\n",
+            "      Successfully uninstalled nvidia-nccl-cu12-2.30.7\n",
+            "  Attempting uninstall: openai\n",
+            "    Found existing installation: openai 2.43.0\n",
+            "    Uninstalling openai-2.43.0:\n",
+            "      Successfully uninstalled openai-2.43.0\n",
+            "  Attempting uninstall: torch\n",
+            "    Found existing installation: torch 2.11.0+cpu\n",
+            "    Uninstalling torch-2.11.0+cpu:\n",
+            "      Successfully uninstalled torch-2.11.0+cpu\n",
+            "  Attempting uninstall: langchain-core\n",
+            "    Found existing installation: langchain-core 1.4.8\n",
+            "    Uninstalling langchain-core-1.4.8:\n",
+            "      Successfully uninstalled langchain-core-1.4.8\n",
+            "  Attempting uninstall: torchvision\n",
+            "    Found existing installation: torchvision 0.26.0+cpu\n",
+            "    Uninstalling torchvision-0.26.0+cpu:\n",
+            "      Successfully uninstalled torchvision-0.26.0+cpu\n",
+            "Successfully installed langchain-core-1.4.9 langchain-ollama-1.1.0 langchain-openai-1.3.5 lightning-utilities-0.15.3 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.10.2.21 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.27.5 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvshmem-cu12-3.3.20 nvidia-nvtx-cu12-12.8.90 ollama-0.6.2 onnx-1.20.0 openai-2.45.0 torch-2.9.0 torchmetrics-1.9.0 torchvision-0.24.0 triton-3.5.0 weightslab-1.3.3.dev7\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+        "# and Pillow, so nothing extra is needed here.\n",
+        "# %pip install -q weightslab\n",
+        "%pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev7\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wJTexBG4F395"
+      },
+      "source": [
+        "## 1. Imports\n",
+        "\n",
+        "`weightslab` is imported as `wl`. The two `guard_*_context` managers scope a block as\n",
+        "training vs. evaluation so signals are attributed to the right phase. We also detect the\n",
+        "compute `device`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "kYbxEdSMF395",
+        "outputId": "37ab580f-ae16-46ac-e394-9866f76469c7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:42.131 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmpmfs9yakr/weightslab_logs/weightslab_20260716_084742.log\n",
+            "16/07/2026-08:47:42.132 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+            "16/07/2026-08:47:42.134 INFO:weightslab:<module>: \n",
+            "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+            "│                                                                                                     │\n",
+            "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+            "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+            "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+            "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+            "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+            "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+            "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+            "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+            "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+            "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+            "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+            "│                                                                                                     │\n",
+            "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+            "│                                                                                                     │\n",
+            "╰─── By GrayBx, v1.3.3.dev7 ──────────────────────────────────────────────────────────────────────────╯\n",
+            "\n",
+            "\n",
+            "16/07/2026-08:47:42.137 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n",
+            "Using device: cpu\n"
+          ]
+        }
+      ],
+      "source": [
+        "import logging\n",
+        "import os\n",
+        "import tempfile\n",
+        "\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "from torch.utils.data import Dataset\n",
+        "from torchvision import models\n",
+        "import torchvision.transforms as T\n",
+        "\n",
+        "from typing import Any, Dict, List, Optional, Tuple\n",
+        "\n",
+        "import weightslab as wl\n",
+        "from weightslab.components.global_monitoring import (\n",
+        "    guard_training_context,\n",
+        "    guard_testing_context,\n",
+        ")\n",
+        "\n",
+        "logging.basicConfig(level=logging.ERROR)\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(f\"Using device: {device}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9nIr37kPF396"
+      },
+      "source": [
+        "## 2. Face helper modules (inlined)\n",
+        "\n",
+        "These are the `face/` package modules from the original example, inlined here so the\n",
+        "notebook is self-contained. Only the intra-package `from face. ...` imports were removed -\n",
+        "every class and function is otherwise unchanged. Order follows the dependencies:\n",
+        "**utils -> data -> model -> signals**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "94QCaUYjF396"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== face/utils.py =====\n",
+        "\"\"\"\n",
+        "Utility functions for face recognition training.\n",
+        "- Pairwise distance computation\n",
+        "- Online batch-hard triplet mining\n",
+        "- Verification and Rank-1 evaluation helpers\n",
+        "- Similarity grouping signals for clustering-oriented analysis\n",
+        "\"\"\"\n",
+        "\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "\n",
+        "from typing import Tuple, Dict, List, Any\n",
+        "\n",
+        "\n",
+        "# ============================================================\n",
+        "# Distance helpers\n",
+        "# ============================================================\n",
+        "\n",
+        "def pairwise_distances(embeddings: torch.Tensor, squared: bool = False) -> torch.Tensor:\n",
+        "    \"\"\"Compute pairwise L2 distance matrix for a batch of embeddings.\n",
+        "\n",
+        "    Args:\n",
+        "        embeddings: (B, D) tensor\n",
+        "        squared: return squared L2 distances when True\n",
+        "\n",
+        "    Returns:\n",
+        "        (B, B) distance matrix\n",
+        "    \"\"\"\n",
+        "    dot = torch.matmul(embeddings, embeddings.t())\n",
+        "    sq_norms = torch.diagonal(dot) # (B,)\n",
+        "    distances = sq_norms.unsqueeze(0) - 2.0 * dot + sq_norms.unsqueeze(1)\n",
+        "    distances = distances.clamp(min=0.0)\n",
+        "\n",
+        "    if not squared:\n",
+        "        # Avoid NaN gradients at exactly 0\n",
+        "        mask = (distances == 0.0).float()\n",
+        "        distances = distances + mask * 1e-16\n",
+        "        distances = torch.sqrt(distances)\n",
+        "        distances = distances * (1.0 - mask)\n",
+        "\n",
+        "    return distances\n",
+        "\n",
+        "\n",
+        "# ============================================================\n",
+        "# Triplet mining\n",
+        "# ============================================================\n",
+        "\n",
+        "def mine_batch_hard(\n",
+        "    embeddings: torch.Tensor,\n",
+        "    labels: torch.Tensor,\n",
+        "    squared: bool = False,\n",
+        ") -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n",
+        "    \"\"\"Online batch-hard triplet mining (Hermans et al., 2017).\n",
+        "\n",
+        "    For each anchor selects:\n",
+        "    - Hardest positive : same-class sample with the **largest** distance\n",
+        "    - Hardest negative : different-class sample with the **smallest** distance\n",
+        "\n",
+        "    Args:\n",
+        "        embeddings: (B, D) detached from graph during mining\n",
+        "        labels: (B,) integer class ids\n",
+        "        squared: use squared L2 distances for mining\n",
+        "\n",
+        "    Returns:\n",
+        "        anc_idx, pos_idx, neg_idx 1-D LongTensors; only valid anchors\n",
+        "        (those that have at least one positive peer) are included.\n",
+        "    \"\"\"\n",
+        "    dist_mat = pairwise_distances(embeddings.detach(), squared=squared)\n",
+        "    B = labels.shape[0]\n",
+        "    device = labels.device\n",
+        "\n",
+        "    same = labels.unsqueeze(0) == labels.unsqueeze(1) # (B, B)\n",
+        "    diff = ~same\n",
+        "    eye = torch.eye(B, dtype=torch.bool, device=device)\n",
+        "\n",
+        "    # Hardest positive (exclude self)\n",
+        "    pos_mask = same & ~eye\n",
+        "    pos_dist = dist_mat * pos_mask.float()\n",
+        "    _, pos_idx = pos_dist.max(dim=1)\n",
+        "\n",
+        "    # Hardest negative (mask invalid positions with large value)\n",
+        "    neg_dist = dist_mat + (~diff).float() * 1e9\n",
+        "    _, neg_idx = neg_dist.min(dim=1)\n",
+        "\n",
+        "    # Keep only anchors that have at least one positive peer in the batch\n",
+        "    valid = pos_mask.any(dim=1)\n",
+        "    anc_idx = torch.where(valid)[0]\n",
+        "\n",
+        "    return anc_idx, pos_idx[anc_idx], neg_idx[anc_idx]\n",
+        "\n",
+        "\n",
+        "# ============================================================\n",
+        "# Numpy-level evaluation helpers\n",
+        "# ============================================================\n",
+        "\n",
+        "def compute_verification_metrics(\n",
+        "    embeddings: np.ndarray,\n",
+        "    labels: np.ndarray,\n",
+        "    thresholds: np.ndarray | None = None,\n",
+        ") -> dict:\n",
+        "    \"\"\"Pairwise verification accuracy over a threshold grid.\"\"\"\n",
+        "    if thresholds is None:\n",
+        "        thresholds = np.linspace(0.0, 2.0, 100)\n",
+        "\n",
+        "    n = len(embeddings)\n",
+        "\n",
+        "    # Pairwise L2 distances\n",
+        "    dot = embeddings @ embeddings.T # (N, N)\n",
+        "    sq = np.sum(embeddings ** 2, axis=1)\n",
+        "    dist_mat = (sq[:, None] - 2.0 * dot + sq[None, :]).clip(min=0.0)\n",
+        "    dist_mat = np.sqrt(dist_mat.clip(min=1e-16)) * (dist_mat != 0.0)\n",
+        "\n",
+        "    same_pair = labels[:, None] == labels[None, :] # (N, N)\n",
+        "\n",
+        "    # Upper triangle only (avoid double-counting)\n",
+        "    iu = np.triu_indices(n, k=1)\n",
+        "    dist_pairs = dist_mat[iu]\n",
+        "    is_same_pair = same_pair[iu]\n",
+        "\n",
+        "    n_same = int(is_same_pair.sum())\n",
+        "    n_diff = int((~is_same_pair).sum())\n",
+        "\n",
+        "    best_acc = 0.0\n",
+        "    best_threshold = float(thresholds[len(thresholds) // 2])\n",
+        "    best_far = 0.0\n",
+        "    best_frr = 0.0\n",
+        "\n",
+        "    for thr in thresholds:\n",
+        "        pred_same = dist_pairs <= thr\n",
+        "        tp = int((pred_same & is_same_pair).sum())\n",
+        "        fp = int((pred_same & ~is_same_pair).sum())\n",
+        "        fn = int((~pred_same & is_same_pair).sum())\n",
+        "        tn = int((~pred_same & ~is_same_pair).sum())\n",
+        "\n",
+        "        total = n_same + n_diff\n",
+        "        acc = (tp + tn) / total if total > 0 else 0.0\n",
+        "        far = fp / n_diff if n_diff > 0 else 0.0\n",
+        "        frr = fn / n_same if n_same > 0 else 0.0\n",
+        "\n",
+        "        if acc > best_acc:\n",
+        "            best_acc = acc\n",
+        "            best_threshold = float(thr)\n",
+        "            best_far = far\n",
+        "            best_frr = frr\n",
+        "\n",
+        "    return {\n",
+        "        \"verification_accuracy\": float(best_acc),\n",
+        "        \"best_threshold\": float(best_threshold),\n",
+        "        \"far\": float(best_far),\n",
+        "        \"frr\": float(best_frr),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "def compute_rank1_accuracy(embeddings: np.ndarray, labels: np.ndarray) -> float:\n",
+        "    \"\"\"1-NN Rank-1 retrieval accuracy (leave-one-out).\"\"\"\n",
+        "    n = len(embeddings)\n",
+        "    dot = embeddings @ embeddings.T\n",
+        "    sq = np.sum(embeddings ** 2, axis=1)\n",
+        "    dist_mat = (sq[:, None] - 2.0 * dot + sq[None, :]).clip(min=0.0)\n",
+        "\n",
+        "    # Exclude self\n",
+        "    np.fill_diagonal(dist_mat, 1e9)\n",
+        "    nn_idx = dist_mat.argmin(axis=1)\n",
+        "\n",
+        "    correct = int(np.sum(labels[nn_idx] == labels))\n",
+        "    return correct / n\n",
+        "\n",
+        "\n",
+        "def compute_similarity_grouping(\n",
+        "    embeddings: np.ndarray,\n",
+        "    uids: List[str],\n",
+        "    cluster_eps: float = 0.6,\n",
+        "    cluster_min_samples: int = 2,\n",
+        ") -> Dict[str, Any]:\n",
+        "    \"\"\"Build per-sample similarity grouping signals for test-set clustering.\n",
+        "\n",
+        "    Returns per-sample arrays that can be written as WeightsLAB signals:\n",
+        "    - cluster_id: DBSCAN cluster index (-1 for noise)\n",
+        "    - cluster_size: size of assigned cluster (1 for noise)\n",
+        "    - nn1_uid: nearest-neighbor UID in the evaluated set\n",
+        "    - nn1_distance: nearest-neighbor L2 distance\n",
+        "    \"\"\"\n",
+        "    n = len(embeddings)\n",
+        "    if n == 0:\n",
+        "        return {\n",
+        "            \"cluster_id\": np.array([], dtype=np.int32),\n",
+        "            \"cluster_size\": np.array([], dtype=np.int32),\n",
+        "            \"nn1_uid\": [],\n",
+        "            \"nn1_distance\": np.array([], dtype=np.float32),\n",
+        "            \"num_clusters\": 0.0,\n",
+        "            \"noise_ratio\": 0.0,\n",
+        "            \"mean_nn1_distance\": float(\"nan\"),\n",
+        "        }\n",
+        "\n",
+        "    # Pairwise distances for NN lookup\n",
+        "    dot = embeddings @ embeddings.T\n",
+        "    sq = np.sum(embeddings ** 2, axis=1)\n",
+        "    dist_mat = (sq[:, None] - 2.0 * dot + sq[None, :]).clip(min=0.0)\n",
+        "    dist_mat = np.sqrt(dist_mat.clip(min=1e-16))\n",
+        "\n",
+        "    if n == 1:\n",
+        "        nn_idx = np.array([0], dtype=np.int64)\n",
+        "        nn_dist = np.array([0.0], dtype=np.float32)\n",
+        "    else:\n",
+        "        np.fill_diagonal(dist_mat, 1e9)\n",
+        "        nn_idx = dist_mat.argmin(axis=1)\n",
+        "        nn_dist = dist_mat[np.arange(n), nn_idx].astype(np.float32)\n",
+        "\n",
+        "    nn_uid = [uids[int(i)] for i in nn_idx.tolist()]\n",
+        "\n",
+        "    # Unsupervised clustering on embeddings\n",
+        "    try:\n",
+        "        from sklearn.cluster import DBSCAN\n",
+        "\n",
+        "        labels = DBSCAN(eps=cluster_eps, min_samples=cluster_min_samples, metric=\"euclidean\").fit_predict(embeddings)\n",
+        "    except Exception:\n",
+        "        labels = np.full((n,), -1, dtype=np.int32)\n",
+        "\n",
+        "    labels = labels.astype(np.int32)\n",
+        "    valid_labels = labels[labels >= 0]\n",
+        "    num_clusters = int(len(np.unique(valid_labels))) if valid_labels.size > 0 else 0\n",
+        "\n",
+        "    counts = {}\n",
+        "    if valid_labels.size > 0:\n",
+        "        uniq, cnt = np.unique(valid_labels, return_counts=True)\n",
+        "        counts = {int(k): int(v) for k, v in zip(uniq.tolist(), cnt.tolist())}\n",
+        "\n",
+        "    cluster_size = np.array([counts.get(int(c), 1) if int(c) >= 0 else 1 for c in labels], dtype=np.int32)\n",
+        "    noise_ratio = float(np.mean(labels < 0))\n",
+        "\n",
+        "    return {\n",
+        "        \"cluster_id\": labels,\n",
+        "        \"cluster_size\": cluster_size,\n",
+        "        \"nn1_uid\": nn_uid,\n",
+        "        \"nn1_distance\": nn_dist,\n",
+        "        \"num_clusters\": float(num_clusters),\n",
+        "        \"noise_ratio\": noise_ratio,\n",
+        "        \"mean_nn1_distance\": float(nn_dist.mean()) if len(nn_dist) > 0 else float(\"nan\"),\n",
+        "    }"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "P6rEQuj6F397"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== face/data.py =====\n",
+        "\"\"\"\n",
+        "FaceDataset — toy face recognition dataset.\n",
+        "\n",
+        "Supported back-ends\n",
+        "-------------------\n",
+        "\"olivetti\" Olivetti Faces from sklearn (40 identities, 400 images, 64×64 grey).\n",
+        "            Self-contained; requires only scikit-learn. Good default toy set.\n",
+        "\"lfw\" Labeled Faces in the Wild (LFW) via torchvision. Downloaded on\n",
+        "            first use to *root*. Much larger but realistic.\n",
+        "\"folder\" Generic ImageFolder layout: root/{split}/{class_name}/*.jpg\n",
+        "\n",
+        "Every sample is returned as:\n",
+        "    (image_tensor: Tensor[C,H,W],\n",
+        "     uid: str,\n",
+        "     label: int,\n",
+        "     metadata: dict)\n",
+        "\n",
+        "These map directly onto the (data, uid, target, metadata) convention used\n",
+        "throughout the WeightsLAB kitchen examples so the same training loop works\n",
+        "without modification.\n",
+        "\"\"\"\n",
+        "\n",
+        "import os\n",
+        "import logging\n",
+        "\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "from torch.utils.data import Dataset\n",
+        "from typing import Dict, Optional, Tuple\n",
+        "\n",
+        "import torchvision.transforms as T\n",
+        "\n",
+        "logger = logging.getLogger(__name__)\n",
+        "\n",
+        "\n",
+        "# ============================================================\n",
+        "# Dataset\n",
+        "# ============================================================\n",
+        "\n",
+        "class FaceDataset(Dataset):\n",
+        "    \"\"\"Unified face recognition dataset wrapper.\n",
+        "\n",
+        "    Args:\n",
+        "        root: Download / data root (only used for lfw / folder).\n",
+        "        dataset_type: One of \"olivetti\", \"lfw\", \"folder\".\n",
+        "        split: \"train\" or \"test\" (ignored for pre-split sources).\n",
+        "        image_size: Spatial size; images are resized to (image_size, image_size).\n",
+        "        train_ratio: Fraction of per-class samples used for training\n",
+        "                               (Olivetti only).\n",
+        "        min_images_per_class: Classes with fewer samples are discarded.\n",
+        "        transform: Optional torchvision transform; defaults to\n",
+        "                               Resize → ToTensor → Normalize([0.5], [0.5]).\n",
+        "        seed: RNG seed for reproducible train/test splits.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        root: str = \".\",\n",
+        "        dataset_type: str = \"olivetti\",\n",
+        "        split: str = \"train\",\n",
+        "        image_size: int = 64,\n",
+        "        train_ratio: float = 0.8,\n",
+        "        min_images_per_class: int = 2,\n",
+        "        transform=None,\n",
+        "        seed: int = 42,\n",
+        "    ):\n",
+        "        self.dataset_type = dataset_type\n",
+        "        self.split = split\n",
+        "        self.image_size = image_size\n",
+        "        self.transform = transform or self._default_transform(image_size)\n",
+        "\n",
+        "        # These are populated by each loader\n",
+        "        self.images: Optional[np.ndarray] = None # (N, H, W) float [0,1] — Olivetti only\n",
+        "        self.img_paths: Optional[np.ndarray] = None\n",
+        "        self.labels: np.ndarray = np.array([], dtype=np.int64)\n",
+        "        self.num_classes: int = 0\n",
+        "\n",
+        "        if dataset_type == \"olivetti\":\n",
+        "            self._load_olivetti(train_ratio, min_images_per_class, seed)\n",
+        "        elif dataset_type == \"lfw\":\n",
+        "            self._load_lfw(root, min_images_per_class, split)\n",
+        "        elif dataset_type == \"folder\":\n",
+        "            self._load_folder(root, split)\n",
+        "        else:\n",
+        "            raise ValueError(\n",
+        "                f\"Unknown dataset_type {dataset_type!r}. \"\n",
+        "                \"Choose from 'olivetti', 'lfw', 'folder'.\"\n",
+        "            )\n",
+        "\n",
+        "        logger.info(\n",
+        "            f\"FaceDataset [{dataset_type} / {split}] \"\n",
+        "            f\"| samples={len(self)} | classes={self.num_classes}\"\n",
+        "        )\n",
+        "\n",
+        "    # ----------------------------------------------------------\n",
+        "    # Loaders\n",
+        "    # ----------------------------------------------------------\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def _default_transform(image_size: int):\n",
+        "        return T.Compose([\n",
+        "            T.Resize((image_size, image_size)),\n",
+        "            T.ToTensor(),\n",
+        "            T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\n",
+        "        ])\n",
+        "\n",
+        "    def _load_olivetti(self, train_ratio: float, min_images: int, seed: int):\n",
+        "        \"\"\"Load and split the Olivetti Faces dataset (sklearn).\"\"\"\n",
+        "        from sklearn.datasets import fetch_olivetti_faces\n",
+        "\n",
+        "        data = fetch_olivetti_faces(shuffle=True, random_state=seed)\n",
+        "        images = data.images # (400, 64, 64) float [0,1]\n",
+        "        labels = data.target.astype(np.int64)\n",
+        "\n",
+        "        # Drop classes with insufficient samples\n",
+        "        unique, counts = np.unique(labels, return_counts=True)\n",
+        "        valid_classes = unique[counts >= min_images]\n",
+        "        mask = np.isin(labels, valid_classes)\n",
+        "        images, labels = images[mask], labels[mask]\n",
+        "\n",
+        "        # Remap labels to a contiguous 0…N-1 range\n",
+        "        mapping = {int(c): i for i, c in enumerate(sorted(valid_classes.tolist()))}\n",
+        "        labels = np.array([mapping[int(l)] for l in labels], dtype=np.int64)\n",
+        "\n",
+        "        # Per-class stratified train/test split\n",
+        "        rng = np.random.RandomState(seed)\n",
+        "        train_idx, test_idx = [], []\n",
+        "        for cls in np.unique(labels):\n",
+        "            idx = np.where(labels == cls)[0]\n",
+        "            n_train = max(1, int(len(idx) * train_ratio))\n",
+        "            perm = rng.permutation(len(idx))\n",
+        "            train_idx.extend(idx[perm[:n_train]].tolist())\n",
+        "            test_idx.extend(idx[perm[n_train:]].tolist())\n",
+        "\n",
+        "        indices = train_idx if self.split == \"train\" else test_idx\n",
+        "        self.images = images[indices]\n",
+        "        self.labels = labels[indices]\n",
+        "        self.num_classes = len(mapping)\n",
+        "\n",
+        "    def _load_lfw(self, root: str, min_images: int, split: str):\n",
+        "        \"\"\"Load LFW People via torchvision (downloads on first call).\"\"\"\n",
+        "        from torchvision.datasets import LFWPeople\n",
+        "\n",
+        "        split_map = {\"train\": \"train\", \"test\": \"test\", \"val\": \"10fold\"}\n",
+        "        lfw_split = split_map.get(split, \"train\")\n",
+        "        ds = LFWPeople(root=root, split=lfw_split, download=True, transform=None)\n",
+        "\n",
+        "        paths, lbls = zip(*ds.imgs)\n",
+        "        lbls = np.array(lbls, dtype=np.int64)\n",
+        "\n",
+        "        # Filter low-shot identities\n",
+        "        unique, counts = np.unique(lbls, return_counts=True)\n",
+        "        valid = set(unique[counts >= min_images].tolist())\n",
+        "        mask = np.array([int(l) in valid for l in lbls])\n",
+        "\n",
+        "        self.img_paths = np.array(paths)[mask]\n",
+        "        lbls = lbls[mask]\n",
+        "\n",
+        "        mapping = {int(c): i for i, c in enumerate(sorted(valid))}\n",
+        "        self.labels = np.array([mapping[int(l)] for l in lbls], dtype=np.int64)\n",
+        "        self.num_classes = len(mapping)\n",
+        "\n",
+        "    def _load_folder(self, root: str, split: str):\n",
+        "        \"\"\"Load from a torchvision ImageFolder directory.\"\"\"\n",
+        "        from torchvision.datasets import ImageFolder\n",
+        "\n",
+        "        split_dir = os.path.join(root, split)\n",
+        "        ds = ImageFolder(split_dir)\n",
+        "        paths, lbls = zip(*ds.imgs)\n",
+        "        self.img_paths = list(paths)\n",
+        "        self.labels = np.array(lbls, dtype=np.int64)\n",
+        "        self.num_classes = len(ds.classes)\n",
+        "\n",
+        "    # ----------------------------------------------------------\n",
+        "    # Dataset interface\n",
+        "    # ----------------------------------------------------------\n",
+        "\n",
+        "    def __len__(self) -> int:\n",
+        "        return len(self.labels)\n",
+        "\n",
+        "    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, str, int, Dict]:\n",
+        "        \"\"\"Return (image_tensor, uid, label_id, metadata).\n",
+        "\n",
+        "        The (data, uid, target, metadata) signature mirrors the WeightsLAB\n",
+        "        kitchen convention used in the VLA training example.\n",
+        "        \"\"\"\n",
+        "        label = int(self.labels[idx])\n",
+        "\n",
+        "        if self.dataset_type == \"olivetti\":\n",
+        "            from PIL import Image as PILImage\n",
+        "            img_np = self.images[idx] # (H, W) float [0,1]\n",
+        "            img_pil = PILImage.fromarray(\n",
+        "                (img_np * 255).astype(np.uint8), mode=\"L\"\n",
+        "            ).convert(\"RGB\")\n",
+        "        else:\n",
+        "            from PIL import Image as PILImage\n",
+        "            img_pil = PILImage.open(self.img_paths[idx]).convert(\"RGB\")\n",
+        "\n",
+        "        image_tensor = self.transform(img_pil)\n",
+        "\n",
+        "        uid = f\"{self.split}_cls{label:04d}_idx{idx:06d}\"\n",
+        "        metadata = {\n",
+        "            \"split\": self.split,\n",
+        "            \"label_id\": label,\n",
+        "            \"idx\": idx,\n",
+        "            \"dataset_type\": self.dataset_type,\n",
+        "        }\n",
+        "        return image_tensor, uid, label, metadata"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "0mFrPy3QF397"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== face/model.py =====\n",
+        "\"\"\"\n",
+        "Face embedding model.\n",
+        "\n",
+        "Architecture\n",
+        "------------\n",
+        "Pretrained backbone (ResNet-18 / ResNet-50 / MobileNet-V3-Small)\n",
+        "    ?\n",
+        "EmbeddingHead Linear ? BN ? ReLU ? Linear\n",
+        "    ?\n",
+        "L2-normalised D-dimensional embedding\n",
+        "\n",
+        "The backbone is optionally frozen so that only the lightweight head is trained\n",
+        "(recommended toy-example setup). The combined graph is registered with\n",
+        "WeightsLAB for model tracking.\n",
+        "\n",
+        "Public interface\n",
+        "----------------\n",
+        "FaceEmbeddingModel.get_embeddings(images) ? normalised embeddings (B, D)\n",
+        "FaceEmbeddingModel.train_step(images, labels, ...) ? scalar loss float\n",
+        "\"\"\"\n",
+        "\n",
+        "import logging\n",
+        "\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "import weightslab as wl\n",
+        "\n",
+        "from torchvision import models\n",
+        "from typing import List\n",
+        "\n",
+        "\n",
+        "logger = logging.getLogger(__name__)\n",
+        "\n",
+        "\n",
+        "# ============================================================\n",
+        "# Network modules\n",
+        "# ============================================================\n",
+        "\n",
+        "class EmbeddingHead(nn.Module):\n",
+        "    \"\"\"Projection head: backbone_feature_dim ? embedding_dim (L2-normalised).\"\"\"\n",
+        "\n",
+        "    def __init__(self, in_dim: int, hidden_dim: int, embedding_dim: int):\n",
+        "        super().__init__()\n",
+        "        self.net = nn.Sequential(\n",
+        "            nn.Linear(in_dim, hidden_dim),\n",
+        "            nn.BatchNorm1d(hidden_dim),\n",
+        "            nn.ReLU(inplace=True),\n",
+        "            nn.Linear(hidden_dim, embedding_dim),\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+        "        return F.normalize(self.net(x), p=2, dim=-1)\n",
+        "\n",
+        "\n",
+        "class FaceEmbeddingNet(nn.Module):\n",
+        "    \"\"\"Single nn.Module combining the frozen backbone and trainable head.\"\"\"\n",
+        "\n",
+        "    def __init__(self, backbone: nn.Module, head: EmbeddingHead):\n",
+        "        super().__init__()\n",
+        "        self.backbone = backbone\n",
+        "        self.head = head\n",
+        "\n",
+        "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+        "        features = self.backbone(x) # (B, feature_dim)\n",
+        "        embeddings = self.head(features) # (B, embedding_dim), L2-normalised\n",
+        "        return embeddings\n",
+        "\n",
+        "\n",
+        "class TripletLossWithMetadata(nn.Module):\n",
+        "    \"\"\"Triplet loss wrapper that logs anchor-level triplet metadata in WeightsLAB.\n",
+        "\n",
+        "    The anchor UID is used as batch ID. For each anchor we persist the selected\n",
+        "    positive and negative UIDs into per-sample signal columns so they remain\n",
+        "    attached to image/sample metadata in the ledger.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(self, margin: float = 0.3):\n",
+        "        super().__init__()\n",
+        "        self.margin = margin\n",
+        "        self.criterion = nn.TripletMarginLoss(margin=margin, p=2, reduce=False)\n",
+        "\n",
+        "    def forward(\n",
+        "        self,\n",
+        "        anchor_emb: torch.Tensor,\n",
+        "        pos_emb: torch.Tensor,\n",
+        "        neg_emb: torch.Tensor,\n",
+        "        anchor_ids: List[str],\n",
+        "        pos_ids: List[str],\n",
+        "        neg_ids: List[str],\n",
+        "        name: str = \"train\",\n",
+        "    ) -> torch.Tensor:\n",
+        "        loss = self.criterion(anchor_emb, pos_emb, neg_emb)\n",
+        "\n",
+        "        wl.save_signals(\n",
+        "            batch_ids=anchor_ids,\n",
+        "            signals={f\"{name}/loss/triplet\": loss.detach().cpu().numpy()},\n",
+        "            preds_raw=anchor_emb,\n",
+        "            targets=neg_emb,\n",
+        "            preds=None,\n",
+        "            log=True,\n",
+        "        )\n",
+        "\n",
+        "        # Store pivot triplet composition as per-sample metadata-like signals.\n",
+        "        wl.save_signals(\n",
+        "            batch_ids=anchor_ids,\n",
+        "            signals={\n",
+        "                f\"{name}/meta/triplet_pos_uid\": pos_ids,\n",
+        "                f\"{name}/meta/triplet_neg_uid\": neg_ids,\n",
+        "            },\n",
+        "            preds_raw=None,\n",
+        "            targets=None,\n",
+        "            preds=None,\n",
+        "            log=False,\n",
+        "        )\n",
+        "        return loss\n",
+        "\n",
+        "\n",
+        "# ============================================================\n",
+        "# High-level model wrapper\n",
+        "# ============================================================\n",
+        "\n",
+        "class FaceEmbeddingModel:\n",
+        "    \"\"\"Wrapper that manages the backbone + head, optimiser, and WeightsLAB tracking.\n",
+        "\n",
+        "    Args:\n",
+        "        backbone_name: \"resnet18\" | \"resnet50\" | \"mobilenet_v3_small\"\n",
+        "        embedding_dim: Output embedding dimensionality (default 128).\n",
+        "        head_hidden_dim: Hidden size of the projection MLP (default 256).\n",
+        "        lr: Learning rate for AdamW (default 1e-3).\n",
+        "        weight_decay: AdamW weight decay (default 1e-4).\n",
+        "        freeze_backbone: When True, only the head's parameters receive\n",
+        "                         gradients ? recommended for quick toy runs.\n",
+        "        device: \"cpu\", \"cuda\", or \"cuda:N\".\n",
+        "        pretrained: Load ImageNet-pretrained weights for the backbone.\n",
+        "        margin: Triplet margin (default 0.3).\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        backbone_name: str = \"resnet18\",\n",
+        "        embedding_dim: int = 128,\n",
+        "        head_hidden_dim: int = 256,\n",
+        "        lr: float = 1e-3,\n",
+        "        weight_decay: float = 1e-4,\n",
+        "        freeze_backbone: bool = True,\n",
+        "        device: str = \"cpu\",\n",
+        "        pretrained: bool = True,\n",
+        "        margin: float = 0.3,\n",
+        "    ):\n",
+        "        self.device = torch.device(device)\n",
+        "        self.margin = margin\n",
+        "        self.embedding_dim = embedding_dim\n",
+        "\n",
+        "        # ---- Build backbone ----\n",
+        "        weights = \"DEFAULT\" if pretrained else None\n",
+        "        backbone_feature_dim = self._build_backbone(backbone_name, weights, freeze_backbone)\n",
+        "\n",
+        "        # ---- Build head ----\n",
+        "        head = EmbeddingHead(\n",
+        "            in_dim=backbone_feature_dim,\n",
+        "            hidden_dim=head_hidden_dim,\n",
+        "            embedding_dim=embedding_dim,\n",
+        "        )\n",
+        "\n",
+        "        # ---- Compose & move to device ----\n",
+        "        self.net = FaceEmbeddingNet(backbone=self._backbone, head=head).to(self.device)\n",
+        "\n",
+        "        # ---- WeightsLAB graph tracking ----\n",
+        "        self.net = wl.watch_or_edit(\n",
+        "            self.net,\n",
+        "            flag=\"model\",\n",
+        "            compute_dependencies=False,\n",
+        "            device=str(self.device),\n",
+        "        )\n",
+        "\n",
+        "        # ---- WeightsLAB tracked loss ----\n",
+        "        self.triplet_loss_fn = TripletLossWithMetadata(margin=self.margin)\n",
+        "        self.triplet_loss_fn.__name__ = \"tripletcriterion\"\n",
+        "        watched_loss = wl.watch_or_edit(\n",
+        "            self.triplet_loss_fn,\n",
+        "            flag=\"loss\",\n",
+        "            signal_name=\"triplet_with_metadata\",\n",
+        "            log=False,\n",
+        "            use_batch_ids_as_x=True,\n",
+        "            use_batch_value_as_y=False,\n",
+        "        )\n",
+        "        if watched_loss is not None and hasattr(watched_loss, \"forward\"):\n",
+        "            self.triplet_loss_fn = watched_loss\n",
+        "\n",
+        "        # ---- Optimiser (head-only when backbone is frozen) ----\n",
+        "        trainable = [p for p in self.net.parameters() if p.requires_grad]\n",
+        "        self.optimizer = torch.optim.AdamW(\n",
+        "            trainable, lr=lr, weight_decay=weight_decay\n",
+        "        )\n",
+        "\n",
+        "        n_trainable = sum(p.numel() for p in trainable)\n",
+        "        logger.info(\n",
+        "            f\"FaceEmbeddingModel | backbone={backbone_name} pretrained={pretrained} \"\n",
+        "            f\"frozen={freeze_backbone} | emb_dim={embedding_dim} | \"\n",
+        "            f\"trainable_params={n_trainable:,}\"\n",
+        "        )\n",
+        "        print(\n",
+        "            f\" Backbone : {backbone_name} (pretrained={pretrained}, frozen={freeze_backbone})\\n\"\n",
+        "            f\" Emb dim : {embedding_dim}\\n\"\n",
+        "            f\" Head dim : {head_hidden_dim}\\n\"\n",
+        "            f\" Trainable : {n_trainable:,} params\\n\"\n",
+        "            f\" Device : {self.device}\"\n",
+        "        )\n",
+        "\n",
+        "    def _build_backbone(\n",
+        "        self,\n",
+        "        backbone_name: str,\n",
+        "        weights,\n",
+        "        freeze: bool,\n",
+        "    ) -> int:\n",
+        "        \"\"\"Instantiate backbone, strip its classifier, optionally freeze.\n",
+        "\n",
+        "        Sets self._backbone and returns the feature dimensionality.\n",
+        "        \"\"\"\n",
+        "        if backbone_name == \"resnet18\":\n",
+        "            base = models.resnet18(weights=weights)\n",
+        "            feature_dim = 512\n",
+        "            base.fc = nn.Identity()\n",
+        "        elif backbone_name == \"resnet50\":\n",
+        "            base = models.resnet50(weights=weights)\n",
+        "            feature_dim = 2048\n",
+        "            base.fc = nn.Identity()\n",
+        "        elif backbone_name == \"mobilenet_v3_small\":\n",
+        "            base = models.mobilenet_v3_small(weights=weights)\n",
+        "            feature_dim = 576\n",
+        "            base.classifier = nn.Identity()\n",
+        "        else:\n",
+        "            raise ValueError(\n",
+        "                f\"Unsupported backbone {backbone_name!r}. \"\n",
+        "                \"Choose from 'resnet18', 'resnet50', 'mobilenet_v3_small'.\"\n",
+        "            )\n",
+        "\n",
+        "        if freeze:\n",
+        "            for param in base.parameters():\n",
+        "                param.requires_grad_(False)\n",
+        "            logger.info(\"Backbone frozen ? training head only.\")\n",
+        "\n",
+        "        self._backbone = base\n",
+        "        return feature_dim\n",
+        "\n",
+        "    # ----------------------------------------------------------\n",
+        "    # Inference\n",
+        "    # ----------------------------------------------------------\n",
+        "\n",
+        "    def get_embeddings(self, images: torch.Tensor) -> torch.Tensor:\n",
+        "        \"\"\"Forward pass in eval mode; returns L2-normalised embeddings (B, D).\n",
+        "\n",
+        "        Args:\n",
+        "            images: (B, C, H, W) float tensor (GPU/CPU ? moved internally)\n",
+        "\n",
+        "        Returns:\n",
+        "            (B, D) embedding tensor on CPU\n",
+        "        \"\"\"\n",
+        "        self.net.eval()\n",
+        "        with torch.no_grad():\n",
+        "            emb = self.net(images.to(self.device))\n",
+        "        return emb.cpu()\n",
+        "\n",
+        "    # ----------------------------------------------------------\n",
+        "    # Training step\n",
+        "    # ----------------------------------------------------------\n",
+        "\n",
+        "    def train_step(\n",
+        "        self,\n",
+        "        images: torch.Tensor,\n",
+        "        labels: torch.Tensor,\n",
+        "        batch_ids: List[str],\n",
+        "        loss_name: str = \"triplet\",\n",
+        "    ) -> float:\n",
+        "        \"\"\"One gradient update using online batch-hard triplet mining.\n",
+        "\n",
+        "        Args:\n",
+        "            images: (B, C, H, W) float tensor\n",
+        "            labels: (B,) long tensor of identity ids\n",
+        "            batch_ids: list of sample UIDs for WeightsLAB signal logging\n",
+        "            loss_name: \"triplet\" (contrastive support planned)\n",
+        "\n",
+        "        Returns:\n",
+        "            Scalar loss value (Python float)\n",
+        "        \"\"\"\n",
+        "        self.net.train()\n",
+        "        self.optimizer.zero_grad()\n",
+        "\n",
+        "        images = images.to(self.device)\n",
+        "        labels = labels.to(self.device)\n",
+        "\n",
+        "        embeddings = self.net(images) # (B, D)\n",
+        "\n",
+        "        # Mine hardest triplets in the batch\n",
+        "        anc_idx, pos_idx, neg_idx = mine_batch_hard(embeddings, labels)\n",
+        "\n",
+        "        if anc_idx.numel() == 0:\n",
+        "            logger.warning(\n",
+        "                \"No valid triplets found in batch (all samples same class?); \"\n",
+        "                \"skipping gradient step.\"\n",
+        "            )\n",
+        "            return 0.0\n",
+        "\n",
+        "        anc_emb = embeddings[anc_idx]\n",
+        "        pos_emb = embeddings[pos_idx]\n",
+        "        neg_emb = embeddings[neg_idx]\n",
+        "\n",
+        "        triplet_ids = [batch_ids[i] for i in anc_idx.tolist()]\n",
+        "        pos_triplet_ids = [batch_ids[i] for i in pos_idx.tolist()]\n",
+        "        neg_triplet_ids = [batch_ids[i] for i in neg_idx.tolist()]\n",
+        "\n",
+        "        if loss_name == \"triplet\":\n",
+        "            loss_by_sample = self.triplet_loss_fn(\n",
+        "                anchor_emb=anc_emb,\n",
+        "                pos_emb=pos_emb,\n",
+        "                neg_emb=neg_emb,\n",
+        "                anchor_ids=triplet_ids,\n",
+        "                pos_ids=pos_triplet_ids,\n",
+        "                neg_ids=neg_triplet_ids,\n",
+        "                name=\"train\",\n",
+        "            )\n",
+        "        else:\n",
+        "            raise ValueError(\n",
+        "                f\"Unsupported loss {loss_name!r}. Use 'triplet'.\"\n",
+        "            )\n",
+        "        # Aggregate per-sample losses into a single scalar and backprop\n",
+        "        loss = loss_by_sample.mean()\n",
+        "        loss.backward()\n",
+        "        torch.nn.utils.clip_grad_norm_(\n",
+        "            [p for p in self.net.parameters() if p.requires_grad],\n",
+        "            max_norm=1.0,\n",
+        "        )\n",
+        "        self.optimizer.step()\n",
+        "\n",
+        "        return float(loss.item())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "zH13fDb8F398"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== face/signals.py =====\n",
+        "\"\"\"\n",
+        "Signals (losses and metrics) for face recognition training.\n",
+        "\n",
+        "Follows the WeightsLAB pattern: every helper calls wl.save_signals\n",
+        "immediately after computing the value so the platform captures it.\n",
+        "\n",
+        "Classes\n",
+        "-------\n",
+        "TripletLosses differentiable loss functions (return torch.Tensor)\n",
+        "FaceMetrics evaluation metrics and clustering-oriented test signals\n",
+        "\"\"\"\n",
+        "\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "\n",
+        "import weightslab as wl\n",
+        "\n",
+        "from typing import Dict\n",
+        "\n",
+        "\n",
+        "\n",
+        "# ============================================================\n",
+        "# Loss functions\n",
+        "# ============================================================\n",
+        "\n",
+        "class TripletLosses:\n",
+        "    \"\"\"Differentiable loss functions for face embedding training.\"\"\"\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def triplet_loss(\n",
+        "        ids,\n",
+        "        anchor_emb: torch.Tensor,\n",
+        "        pos_emb: torch.Tensor,\n",
+        "        neg_emb: torch.Tensor,\n",
+        "        margin: float = 0.3,\n",
+        "        name: str = \"train\",\n",
+        "    ) -> torch.Tensor:\n",
+        "        \"\"\"Standard triplet margin loss on pre-mined (a, p, n) embeddings.\"\"\"\n",
+        "        loss = nn.TripletMarginLoss(margin=margin, p=2)(anchor_emb, pos_emb, neg_emb)\n",
+        "        wl.save_signals(\n",
+        "            batch_ids=ids,\n",
+        "            signals={f\"{name}/loss/triplet\": loss.detach().cpu().item()},\n",
+        "            preds_raw=anchor_emb,\n",
+        "            targets=neg_emb,\n",
+        "            preds=None,\n",
+        "            log=True,\n",
+        "        )\n",
+        "        return loss\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def contrastive_loss(\n",
+        "        ids,\n",
+        "        emb_a: torch.Tensor,\n",
+        "        emb_b: torch.Tensor,\n",
+        "        same_label: torch.Tensor,\n",
+        "        margin: float = 1.0,\n",
+        "        name: str = \"train\",\n",
+        "    ) -> torch.Tensor:\n",
+        "        \"\"\"Contrastive loss (Hadsell et al., 2006).\"\"\"\n",
+        "        dist = nn.functional.pairwise_distance(emb_a, emb_b)\n",
+        "        loss = (\n",
+        "            same_label * dist.pow(2)\n",
+        "            + (1.0 - same_label) * (margin - dist).clamp(min=0.0).pow(2)\n",
+        "        ).mean()\n",
+        "        wl.save_signals(\n",
+        "            batch_ids=ids,\n",
+        "            signals={f\"{name}/loss/contrastive\": loss.detach().cpu().item()},\n",
+        "            preds_raw=emb_a,\n",
+        "            targets=emb_b,\n",
+        "            preds=None,\n",
+        "            log=True,\n",
+        "        )\n",
+        "        return loss\n",
+        "\n",
+        "\n",
+        "# ============================================================\n",
+        "# Evaluation metrics\n",
+        "# ============================================================\n",
+        "\n",
+        "class FaceMetrics:\n",
+        "    \"\"\"Evaluation metrics for face verification, retrieval, and grouping.\"\"\"\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def verification_accuracy(\n",
+        "        ids,\n",
+        "        embeddings: np.ndarray,\n",
+        "        labels: np.ndarray,\n",
+        "        name: str = \"test\",\n",
+        "    ) -> Dict[str, float]:\n",
+        "        \"\"\"Pairwise verification accuracy with optimal threshold search.\"\"\"\n",
+        "        result = compute_verification_metrics(embeddings, labels)\n",
+        "        wl.save_signals(\n",
+        "            batch_ids=ids,\n",
+        "            signals={\n",
+        "                f\"{name}/metric/verification_accuracy\": result[\"verification_accuracy\"],\n",
+        "                f\"{name}/metric/far\": result[\"far\"],\n",
+        "                f\"{name}/metric/frr\": result[\"frr\"],\n",
+        "                f\"{name}/metric/best_threshold\": result[\"best_threshold\"],\n",
+        "            },\n",
+        "            preds_raw=None,\n",
+        "            targets=None,\n",
+        "            preds=None,\n",
+        "            log=True,\n",
+        "        )\n",
+        "        return result\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def rank1_accuracy(\n",
+        "        ids,\n",
+        "        embeddings: np.ndarray,\n",
+        "        labels: np.ndarray,\n",
+        "        name: str = \"test\",\n",
+        "    ) -> float:\n",
+        "        \"\"\"1-NN Rank-1 retrieval accuracy (leave-one-out).\"\"\"\n",
+        "        acc = compute_rank1_accuracy(embeddings, labels)\n",
+        "        wl.save_signals(\n",
+        "            batch_ids=ids,\n",
+        "            signals={f\"{name}/metric/rank1_accuracy\": acc},\n",
+        "            preds_raw=None,\n",
+        "            targets=None,\n",
+        "            preds=None,\n",
+        "            log=True,\n",
+        "        )\n",
+        "        return acc\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def similarity_grouping_signals(\n",
+        "        ids,\n",
+        "        embeddings: np.ndarray,\n",
+        "        name: str = \"test\",\n",
+        "        cluster_eps: float = 0.6,\n",
+        "        cluster_min_samples: int = 2,\n",
+        "    ) -> Dict[str, float]:\n",
+        "        \"\"\"Save per-sample grouping signals for later clustering/sorting in Studio.\"\"\"\n",
+        "        grouping = compute_similarity_grouping(\n",
+        "            embeddings=embeddings,\n",
+        "            uids=list(ids),\n",
+        "            cluster_eps=cluster_eps,\n",
+        "            cluster_min_samples=cluster_min_samples,\n",
+        "        )\n",
+        "\n",
+        "        wl.save_signals(\n",
+        "            batch_ids=ids,\n",
+        "            signals={\n",
+        "                f\"{name}/cluster/id\": grouping[\"cluster_id\"],\n",
+        "                f\"{name}/cluster/size\": grouping[\"cluster_size\"],\n",
+        "                f\"{name}/cluster/nn1_distance\": grouping[\"nn1_distance\"],\n",
+        "                f\"{name}/cluster/nn1_uid\": grouping[\"nn1_uid\"],\n",
+        "            },\n",
+        "            preds_raw=None,\n",
+        "            targets=None,\n",
+        "            preds=None,\n",
+        "            log=False,\n",
+        "        )\n",
+        "\n",
+        "        summary = {\n",
+        "            \"num_clusters\": grouping[\"num_clusters\"],\n",
+        "            \"noise_ratio\": grouping[\"noise_ratio\"],\n",
+        "            \"mean_nn1_distance\": grouping[\"mean_nn1_distance\"],\n",
+        "        }\n",
+        "        wl.save_signals(\n",
+        "            batch_ids=ids,\n",
+        "            signals={\n",
+        "                f\"{name}/cluster/num_clusters\": summary[\"num_clusters\"],\n",
+        "                f\"{name}/cluster/noise_ratio\": summary[\"noise_ratio\"],\n",
+        "                f\"{name}/cluster/mean_nn1_distance\": summary[\"mean_nn1_distance\"],\n",
+        "            },\n",
+        "            preds_raw=None,\n",
+        "            targets=None,\n",
+        "            preds=None,\n",
+        "            log=True,\n",
+        "        )\n",
+        "        return summary\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def compute_all_metrics(\n",
+        "        ids,\n",
+        "        embeddings: np.ndarray,\n",
+        "        labels: np.ndarray,\n",
+        "        name: str = \"test\",\n",
+        "    ) -> Dict[str, float]:\n",
+        "        \"\"\"Run all metrics and return a merged dict.\"\"\"\n",
+        "        verif = FaceMetrics.verification_accuracy(ids, embeddings, labels, name=name)\n",
+        "        rank1 = FaceMetrics.rank1_accuracy(ids, embeddings, labels, name=name)\n",
+        "        grouping = FaceMetrics.similarity_grouping_signals(ids, embeddings, name=name)\n",
+        "        return {**verif, \"rank1_accuracy\": rank1, **grouping}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IiE3R5OrF398"
+      },
+      "source": [
+        "## 3. Configuration\n",
+        "\n",
+        "Every tunable lives in one `config` dict (the inlined `config.yaml`). Wrapping it with\n",
+        "`flag=\"hyperparameters\"` lets the Studio UI read and live-edit these values while training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "7f3X3gveF398",
+        "outputId": "ccbe3f77-aefc-4d7d-d4be-4eea01f86dc9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:42.439 INFO:weightslab.src:watch_or_edit: LoggerQueue for experiment history has been initialized and registered.\n",
+            "16/07/2026-08:47:42.442 INFO:weightslab.components.checkpoint_manager:__init__: CheckpointManager initialized at /tmp/tmpu_lry8gs\n",
+            "16/07/2026-08:47:42.443 INFO:weightslab.src:watch_or_edit: Registered new checkpoint manager in ledger\n",
+            "16/07/2026-08:47:42.444 INFO:root:set_log_directory: Log file moved from /tmp/tmpmfs9yakr/weightslab_logs/weightslab_20260716_084742.log to /tmp/tmpu_lry8gs/weightslab_20260716_084742.log\n",
+            "16/07/2026-08:47:42.445 INFO:root:set_log_directory: Log directory updated to: /tmp/tmpu_lry8gs\n",
+            "16/07/2026-08:47:42.446 INFO:root:set_log_directory: Log file: /tmp/tmpu_lry8gs/weightslab_20260716_084742.log\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'experiment_name': 'face_triplet_training',\n",
+              " 'device': 'auto',\n",
+              " 'eval_full_to_train_steps_ratio': ValueProxy(key='eval_full_to_train_steps_ratio', value=50),\n",
+              " 'experiment_dump_to_train_steps_ratio': ValueProxy(key='experiment_dump_to_train_steps_ratio', value=10),\n",
+              " 'is_training': ValueProxy(key='is_training', value=True),\n",
+              " 'log_every': ValueProxy(key='log_every', value=50),\n",
+              " 'max_steps': ValueProxy(key='max_steps', value=1500),\n",
+              " 'enable_h5_persistence': ValueProxy(key='enable_h5_persistence', value=True),\n",
+              " 'serving_grpc': ValueProxy(key='serving_grpc', value=True),\n",
+              " 'serving_cli': ValueProxy(key='serving_cli', value=True),\n",
+              " 'data': {'dataset_type': 'olivetti',\n",
+              "  'root_data_dir': '',\n",
+              "  'image_size': 64,\n",
+              "  'train_ratio': 0.8,\n",
+              "  'min_images_per_class': 2,\n",
+              "  'train_loader': {'batch_size': 32, 'shuffle': True, 'n_workers': 0},\n",
+              "  'test_loader': {'batch_size': 64, 'shuffle': False, 'n_workers': 0}},\n",
+              " 'model': {'backbone': 'resnet18',\n",
+              "  'pretrained': True,\n",
+              "  'freeze_backbone': True,\n",
+              "  'embedding_dim': 128,\n",
+              "  'head_hidden_dim': 256,\n",
+              "  'lr': 0.001,\n",
+              "  'weight_decay': 0.0001,\n",
+              "  'loss': 'triplet',\n",
+              "  'margin': 0.3},\n",
+              " 'root_log_dir': '/tmp/tmpu_lry8gs'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "# ============================================================\n",
+        "#  Face Recognition - Training Config (inlined from config.yaml)\n",
+        "# ============================================================\n",
+        "config = {\n",
+        "    \"experiment_name\": \"face_triplet_training\",\n",
+        "    \"device\": \"auto\",                            # \"auto\" -> cuda if available, else cpu\n",
+        "\n",
+        "    # Training schedule\n",
+        "    \"eval_full_to_train_steps_ratio\": 50,        # evaluation frequency (steps); -1 disables\n",
+        "    \"experiment_dump_to_train_steps_ratio\": 10,  # print running loss every N steps\n",
+        "    \"is_training\": True,                         # start training immediately or not\n",
+        "    \"log_every\": 50,                             # running-loss print cadence\n",
+        "    \"max_steps\": 1500,                           # Colab cap so the training cell terminates;\n",
+        "                                                 # set to None to train open-ended (interrupt to stop)\n",
+        "\n",
+        "    # WeightsLAB persistence\n",
+        "    # \"root_log_dir\": leave unset -> auto temp dir\n",
+        "    \"enable_h5_persistence\": True,\n",
+        "\n",
+        "    # WeightsLAB serving\n",
+        "    \"serving_grpc\": True,\n",
+        "    \"serving_cli\": True,\n",
+        "\n",
+        "    # --------------------------------------------------------\n",
+        "    # Data\n",
+        "    # --------------------------------------------------------\n",
+        "    \"data\": {\n",
+        "        # dataset_type options:\n",
+        "        #   \"olivetti\" - sklearn Olivetti Faces (40 ids, 400 imgs, 64x64 grey).\n",
+        "        #                Works offline, no download needed. Ideal for quick tests.\n",
+        "        #   \"lfw\"      - LFW People via torchvision (download on first run).\n",
+        "        #   \"folder\"   - Generic ImageFolder layout: root/{train,test}/{class}/*.jpg\n",
+        "        \"dataset_type\": \"olivetti\",\n",
+        "        \"root_data_dir\": \"\",           # only required for lfw / folder\n",
+        "        \"image_size\": 64,              # images are resized to image_size x image_size\n",
+        "        \"train_ratio\": 0.8,            # olivetti only: fraction used for training\n",
+        "        \"min_images_per_class\": 2,     # identities with fewer samples are discarded\n",
+        "\n",
+        "        \"train_loader\": {\n",
+        "            \"batch_size\": 32,\n",
+        "            \"shuffle\": True,\n",
+        "            \"n_workers\": 0,            # >0 for multi-process loading (not needed for Olivetti)\n",
+        "        },\n",
+        "        \"test_loader\": {\n",
+        "            \"batch_size\": 64,\n",
+        "            \"shuffle\": False,\n",
+        "            \"n_workers\": 0,\n",
+        "        },\n",
+        "    },\n",
+        "\n",
+        "    # --------------------------------------------------------\n",
+        "    # Model\n",
+        "    # --------------------------------------------------------\n",
+        "    \"model\": {\n",
+        "        # backbone: \"resnet18\" | \"resnet50\" | \"mobilenet_v3_small\"\n",
+        "        \"backbone\": \"resnet18\",\n",
+        "        \"pretrained\": True,            # load ImageNet-pretrained weights\n",
+        "        \"freeze_backbone\": True,       # train the embedding head only (fast & effective)\n",
+        "\n",
+        "        # Embedding head\n",
+        "        \"embedding_dim\": 128,          # output dim of the L2-normalised vector\n",
+        "        \"head_hidden_dim\": 256,        # hidden dim of the projection MLP\n",
+        "\n",
+        "        # Optimiser (AdamW, trainable params only)\n",
+        "        \"lr\": 0.001,\n",
+        "        \"weight_decay\": 0.0001,\n",
+        "\n",
+        "        # Triplet loss\n",
+        "        \"loss\": \"triplet\",\n",
+        "        \"margin\": 0.3,                 # triplet margin alpha\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "wl.watch_or_edit(config, flag=\"hyperparameters\", defaults=config, poll_interval=1.0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rHGGFVtgF399"
+      },
+      "source": [
+        "## 4. Datasets and tracked loaders\n",
+        "\n",
+        "Build the **Olivetti Faces** train/test splits and wrap the loaders with\n",
+        "`wl.watch_or_edit(..., flag=\"data\")` so every sample keeps its identity in WeightsLab.\n",
+        "Olivetti downloads automatically through scikit-learn (cached under `~/scikit_learn_data`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "91s0NMtEF399",
+        "outputId": "07e8d642-4be4-48bb-86cc-aebfd17171f2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to /root/scikit_learn_data\n",
+            "16/07/2026-08:47:46.760 INFO:__main__:__init__: FaceDataset [olivetti / train] | samples=320 | classes=40\n",
+            "16/07/2026-08:47:46.867 INFO:__main__:__init__: FaceDataset [olivetti / test] | samples=80 | classes=40\n",
+            "\n",
+            "Dataset : olivetti | train=320 | test=80 | classes=40\n",
+            "16/07/2026-08:47:46.880 INFO:weightslab.data.data_samples_with_ops:__init__: [DataSampleTrackingWrapper] H5 persistence enabled at /tmp/tmpu_lry8gs/checkpoints/data/data.h5\n",
+            "16/07/2026-08:47:46.893 INFO:weightslab.data.data_samples_with_ops:__init__: Preloading sample statistics for PHYSICAL indices in split 'train_loader' with: preload_labels=True, preload_metadata=True...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Initializing ledger for split 'train_loader':   0%|          | 0/320 [00:00<?, ?it/s]/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "Initializing ledger for split 'train_loader': 100%|██████████| 320/320 [00:00<00:00, 508.60it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:47.531 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] Registering split 'train_loader' with 320 samples.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:47.583 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] After annotation expansion: 320 samples → 320 annotation rows.\n",
+            "16/07/2026-08:47:47.593 INFO:weightslab.data.h5_array_store:__init__: [H5ArrayStore] Initialized with cache limit: 2048MB\n",
+            "16/07/2026-08:47:47.648 INFO:weightslab.data.data_samples_with_ops:__init__: [DataSampleTrackingWrapper] H5 persistence enabled at /tmp/tmpu_lry8gs/checkpoints/data/data.h5\n",
+            "16/07/2026-08:47:47.652 INFO:weightslab.data.data_samples_with_ops:__init__: Preloading sample statistics for PHYSICAL indices in split 'test_loader' with: preload_labels=True, preload_metadata=True...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\rInitializing ledger for split 'test_loader':   0%|          | 0/80 [00:00<?, ?it/s]/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "Initializing ledger for split 'test_loader': 100%|██████████| 80/80 [00:00<00:00, 516.58it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:47.812 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] Registering split 'test_loader' with 80 samples.\n",
+            "16/07/2026-08:47:47.826 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] After annotation expansion: 80 samples → 80 annotation rows.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- Reproduce main.py's __main__ setup ----\n",
+        "# Resolve device (\"auto\" -> the runtime device detected above).\n",
+        "if config.get(\"device\", \"auto\") == \"auto\":\n",
+        "    config[\"device\"] = str(device)\n",
+        "\n",
+        "# Log dir (auto temp dir if unset).\n",
+        "if not config.get(\"root_log_dir\"):\n",
+        "    config[\"root_log_dir\"] = tempfile.mkdtemp()\n",
+        "    print(f\"No root_log_dir specified - using temp dir: {config['root_log_dir']}\")\n",
+        "os.makedirs(config[\"root_log_dir\"], exist_ok=True)\n",
+        "\n",
+        "eval_full_to_train_steps_ratio = int(config.get(\"eval_full_to_train_steps_ratio\", 50))\n",
+        "log_every = int(config.get(\"log_every\", 10))\n",
+        "enable_h5 = config.get(\"enable_h5_persistence\", True)\n",
+        "\n",
+        "# ---- Datasets ----\n",
+        "data_cfg = config.get(\"data\", {})\n",
+        "dataset_type = data_cfg.get(\"dataset_type\", \"olivetti\")\n",
+        "root_data = data_cfg.get(\"root_data_dir\", config.get(\"root_log_dir\", \".\"))\n",
+        "image_size = int(data_cfg.get(\"image_size\", 64))\n",
+        "min_imgs = int(data_cfg.get(\"min_images_per_class\", 2))\n",
+        "train_ratio = float(data_cfg.get(\"train_ratio\", 0.8))\n",
+        "\n",
+        "train_dataset = FaceDataset(\n",
+        "    root=root_data,\n",
+        "    dataset_type=dataset_type,\n",
+        "    split=\"train\",\n",
+        "    image_size=image_size,\n",
+        "    train_ratio=train_ratio,\n",
+        "    min_images_per_class=min_imgs,\n",
+        ")\n",
+        "test_dataset = FaceDataset(\n",
+        "    root=root_data,\n",
+        "    dataset_type=dataset_type,\n",
+        "    split=\"test\",\n",
+        "    image_size=image_size,\n",
+        "    train_ratio=train_ratio,\n",
+        "    min_images_per_class=min_imgs,\n",
+        ")\n",
+        "\n",
+        "print(\n",
+        "    f\"\\nDataset : {dataset_type}\"\n",
+        "    f\" | train={len(train_dataset)}\"\n",
+        "    f\" | test={len(test_dataset)}\"\n",
+        "    f\" | classes={train_dataset.num_classes}\"\n",
+        ")\n",
+        "\n",
+        "train_cfg = data_cfg.get(\"train_loader\", {})\n",
+        "test_cfg = data_cfg.get(\"test_loader\", {})\n",
+        "\n",
+        "# ---- WeightsLab-tracked data loaders ----\n",
+        "train_loader = wl.watch_or_edit(\n",
+        "    train_dataset,\n",
+        "    flag=\"data\",\n",
+        "    loader_name=\"train_loader\",\n",
+        "    batch_size=int(train_cfg.get(\"batch_size\", 32)),\n",
+        "    shuffle=train_cfg.get(\"shuffle\", True),\n",
+        "    is_training=True,\n",
+        "    compute_hash=False,\n",
+        "    num_workers=int(train_cfg.get(\"n_workers\", 0)),\n",
+        "    enable_h5_persistence=enable_h5,\n",
+        ")\n",
+        "test_loader = wl.watch_or_edit(\n",
+        "    test_dataset,\n",
+        "    flag=\"data\",\n",
+        "    loader_name=\"test_loader\",\n",
+        "    batch_size=int(test_cfg.get(\"batch_size\", 64)),\n",
+        "    shuffle=test_cfg.get(\"shuffle\", False),\n",
+        "    is_training=False,\n",
+        "    compute_hash=False,\n",
+        "    num_workers=int(test_cfg.get(\"n_workers\", 0)),\n",
+        "    enable_h5_persistence=enable_h5,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5JWo3BxFF399"
+      },
+      "source": [
+        "## 5. Model\n",
+        "\n",
+        "A pretrained ResNet-18 backbone (frozen) plus a small L2-normalised embedding head, trained\n",
+        "with online batch-hard **triplet loss**. Constructing it registers the graph and the loss\n",
+        "with WeightsLab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "2J48fm6sF399",
+        "outputId": "2970e5b8-1f7c-4dc4-ffd1-f8c7f50c5444",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 44.7M/44.7M [00:00<00:00, 77.5MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:49.494 INFO:__main__:_build_backbone: Backbone frozen ? training head only.\n",
+            "16/07/2026-08:47:49.505 INFO:weightslab.backend.model_interface:__init__: Using checkpoint manager from ledger\n",
+            "16/07/2026-08:47:49.509 INFO:__main__:__init__: FaceEmbeddingModel | backbone=resnet18 pretrained=True frozen=True | emb_dim=128 | trainable_params=164,736\n",
+            " Backbone : resnet18 (pretrained=True, frozen=True)\n",
+            " Emb dim : 128\n",
+            " Head dim : 256\n",
+            " Trainable : 164,736 params\n",
+            " Device : cpu\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/nn/_reduction.py:51: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.\n",
+            "  warnings.warn(warning.format(ret))\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- Model ----\n",
+        "model_cfg = config.get(\"model\", {})\n",
+        "model = FaceEmbeddingModel(\n",
+        "    backbone_name=model_cfg.get(\"backbone\", \"resnet18\"),\n",
+        "    embedding_dim=int(model_cfg.get(\"embedding_dim\", 128)),\n",
+        "    head_hidden_dim=int(model_cfg.get(\"head_hidden_dim\", 256)),\n",
+        "    lr=float(model_cfg.get(\"lr\", 1e-3)),\n",
+        "    weight_decay=float(model_cfg.get(\"weight_decay\", 1e-4)),\n",
+        "    freeze_backbone=model_cfg.get(\"freeze_backbone\", True),\n",
+        "    pretrained=model_cfg.get(\"pretrained\", True),\n",
+        "    margin=float(model_cfg.get(\"margin\", 0.3)),\n",
+        "    device=config[\"device\"],\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EmKUHOgJF399"
+      },
+      "source": [
+        "## 6. Train and evaluate steps\n",
+        "\n",
+        "`evaluate(...)` collects embeddings over a loader and computes verification / retrieval /\n",
+        "clustering signals; `train(...)` runs the batch-hard triplet loop. The\n",
+        "`guard_training_context` / `guard_testing_context` blocks tag each phase. The loop is capped\n",
+        "by `max_steps` so it terminates on Colab (pass `max_steps=None` to run open-ended)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "1Cu9mwxEF399"
+      },
+      "outputs": [],
+      "source": [
+        "def evaluate(\n",
+        "    model: FaceEmbeddingModel,\n",
+        "    loader: torch.utils.data.DataLoader,\n",
+        "    name: str = \"test\",\n",
+        ") -> Dict[str, float]:\n",
+        "    \"\"\"Collect embeddings from loader and compute face recognition metrics.\"\"\"\n",
+        "    print(f\"\\n{'=' * 55}\")\n",
+        "    print(f\"Evaluation [{name}]\")\n",
+        "    print(f\"{'=' * 55}\")\n",
+        "\n",
+        "    all_embeddings: List[np.ndarray] = []\n",
+        "    all_labels: List[np.ndarray] = []\n",
+        "    all_uids: List[str] = []\n",
+        "\n",
+        "    for images, uids, labels, _metadata in loader:\n",
+        "        emb = model.get_embeddings(images)  # (B, D)\n",
+        "        all_embeddings.append(emb.numpy())\n",
+        "        if isinstance(labels, torch.Tensor):\n",
+        "            all_labels.append(labels.numpy())\n",
+        "        else:\n",
+        "            all_labels.append(np.array(labels))\n",
+        "        all_uids.extend(uids if isinstance(uids, list) else list(uids))\n",
+        "\n",
+        "    all_embeddings = np.concatenate(all_embeddings, axis=0)\n",
+        "    all_labels = np.concatenate(all_labels, axis=0)\n",
+        "\n",
+        "    metrics = FaceMetrics.compute_all_metrics(\n",
+        "        ids=all_uids,\n",
+        "        embeddings=all_embeddings,\n",
+        "        labels=all_labels,\n",
+        "        name=name,\n",
+        "    )\n",
+        "\n",
+        "    print(f\" verification_accuracy : {metrics.get('verification_accuracy', float('nan')):.4f}\")\n",
+        "    print(f\" rank1_accuracy : {metrics.get('rank1_accuracy', float('nan')):.4f}\")\n",
+        "    print(f\" FAR : {metrics.get('far', float('nan')):.4f}\")\n",
+        "    print(f\" FRR : {metrics.get('frr', float('nan')):.4f}\")\n",
+        "    print(f\" best_threshold : {metrics.get('best_threshold', float('nan')):.4f}\")\n",
+        "    if \"num_clusters\" in metrics:\n",
+        "        print(f\" num_clusters : {metrics['num_clusters']:.0f}\")\n",
+        "        print(f\" noise_ratio : {metrics['noise_ratio']:.4f}\")\n",
+        "        print(f\" mean_nn1_distance : {metrics['mean_nn1_distance']:.4f}\")\n",
+        "\n",
+        "    return metrics\n",
+        "\n",
+        "\n",
+        "def train(\n",
+        "    model: FaceEmbeddingModel,\n",
+        "    train_loader: torch.utils.data.DataLoader,\n",
+        "    test_loader: torch.utils.data.DataLoader,\n",
+        "    eval_full_to_train_steps_ratio: int = 50,\n",
+        "    log_every: int = 10,\n",
+        "    loss_name: str = \"triplet\",\n",
+        "    max_steps: int = None,\n",
+        ") -> Dict[str, Any]:\n",
+        "    \"\"\"Training loop.\n",
+        "\n",
+        "    Runs until `max_steps` optimizer steps have been taken (Colab-friendly), or\n",
+        "    indefinitely when `max_steps` is None (interrupt the cell to stop). Periodic test\n",
+        "    evaluation runs every eval_full_to_train_steps_ratio steps when test_loader is provided.\n",
+        "    \"\"\"\n",
+        "    print(\"\\n\" + \"=\" * 60)\n",
+        "    print(\"Face Recognition Training\")\n",
+        "    print(f\" Loss : {loss_name}\")\n",
+        "    print(f\" Eval every : {eval_full_to_train_steps_ratio} steps\")\n",
+        "    print(f\" Max steps : {'infinite (stop with Ctrl+C)' if max_steps is None else max_steps}\")\n",
+        "    print(\"=\" * 60)\n",
+        "\n",
+        "    data_iter = iter(train_loader)\n",
+        "    losses: List[float] = []\n",
+        "    eval_history: List[Dict[str, Any]] = []\n",
+        "    step = 0\n",
+        "\n",
+        "    try:\n",
+        "        while max_steps is None or step < max_steps:\n",
+        "            step += 1\n",
+        "\n",
+        "            with guard_training_context:\n",
+        "                # ---- Fetch next batch (cycle loader) ----\n",
+        "                try:\n",
+        "                    images, batch_ids, labels, _metadata = next(data_iter)\n",
+        "                except StopIteration:\n",
+        "                    data_iter = iter(train_loader)\n",
+        "                    images, batch_ids, labels, _metadata = next(data_iter)\n",
+        "\n",
+        "                if not isinstance(batch_ids, list):\n",
+        "                    batch_ids = list(batch_ids)\n",
+        "\n",
+        "                if isinstance(labels, torch.Tensor):\n",
+        "                    labels_tensor = labels.long()\n",
+        "                else:\n",
+        "                    labels_tensor = torch.tensor(labels, dtype=torch.long)\n",
+        "\n",
+        "                # ---- Gradient step ----\n",
+        "                loss_val = model.train_step(\n",
+        "                    images=images,\n",
+        "                    labels=labels_tensor,\n",
+        "                    batch_ids=batch_ids,\n",
+        "                    loss_name=loss_name,\n",
+        "                )\n",
+        "                losses.append(loss_val)\n",
+        "\n",
+        "                if step == 1 or step % max(1, log_every) == 0:\n",
+        "                    window = losses[-log_every:] if len(losses) >= log_every else losses\n",
+        "                    print(\n",
+        "                        f\"[train] step {step:>7d} \"\n",
+        "                        f\"| loss={loss_val:.6f} \"\n",
+        "                        f\"| running_mean={np.mean(window):.6f}\"\n",
+        "                    )\n",
+        "\n",
+        "                should_eval = (\n",
+        "                    test_loader is not None\n",
+        "                    and (step % eval_full_to_train_steps_ratio == 0)\n",
+        "                )\n",
+        "\n",
+        "            if should_eval:\n",
+        "                with guard_testing_context:\n",
+        "                    print(f\"\\n[eval@test] step {step}\")\n",
+        "                    metrics = evaluate(model=model, loader=test_loader, name=\"test\")\n",
+        "                    eval_history.append({\"step\": step, \"metrics\": metrics})\n",
+        "\n",
+        "    except KeyboardInterrupt:\n",
+        "        print(\"\\nInterrupted by user. Stopping training loop.\")\n",
+        "\n",
+        "    summary = {\n",
+        "        \"train_steps\": step,\n",
+        "        \"train_loss_mean\": float(np.mean(losses)) if losses else float(\"nan\"),\n",
+        "        \"train_loss_last\": float(losses[-1]) if losses else float(\"nan\"),\n",
+        "        \"num_evals\": len(eval_history),\n",
+        "    }\n",
+        "\n",
+        "    print(\"\\nTraining summary:\")\n",
+        "    for k, v in summary.items():\n",
+        "        print(f\" {k}: {v}\")\n",
+        "\n",
+        "    return summary"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wBOmcQBgF39-"
+      },
+      "source": [
+        "## 7. Serve and train\n",
+        "\n",
+        "`wl.serve(serving_grpc=True, serving_bore=True)` starts the background gRPC server and a\n",
+        "public `bore.pub` tunnel (the endpoint is printed below). `wl.start_training(...)` flips the\n",
+        "experiment into the training state, then the finite loop runs while streaming per-sample\n",
+        "signals. Leave this cell **running** while you watch it in the UI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "N_88-lktF39-",
+        "outputId": "e736988d-2a95-4041-e9e5-414ec2861619",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:49.574 INFO:weightslab.trainer.trainer_services:_run_security_preflight: \n",
+            "\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "[gRPC] Security preflight checks:\n",
+            "\tTLS: DISABLED (unencrypted traffic)\n",
+            "\tAuth tokens: NONE configured\n",
+            "\t! WARNING: GRPC_TLS_ENABLED=0. Traffic will be unencrypted. Use only for development.\n",
+            "\t! WARNING: No GRPC_AUTH_TOKEN/GRPC_AUTH_TOKENS configured. Only transport-level trust (TLS/mTLS) will protect RPC access.\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "\n",
+            "16/07/2026-08:47:49.576 INFO:weightslab.trainer.trainer_services:grpc_serve: [gRPC] Watchdogs disabled via WEIGHTSLAB_DISABLE_WATCHDOGS.\n",
+            "16/07/2026-08:47:49.577 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Thread callback started\n",
+            "16/07/2026-08:47:49.580 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Creating ThreadPoolExecutor with 6 worker threads (n_workers_grpc=None, max_concurrent_rpcs=None)\n",
+            "16/07/2026-08:47:49.578 INFO:weightslab.trainer.trainer_services:grpc_serve: [gRPC] Server started with watchdogs disabled (host=0.0.0.0 port=50051 workers=None)\n",
+            "16/07/2026-08:47:49.585 INFO:weightslab.tunnel:_ensure_bore: Downloading bore v0.6.0 (bore-v0.6.0-x86_64-unknown-linux-musl.tar.gz)...\n",
+            "16/07/2026-08:47:49.729 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Server object created\n",
+            "16/07/2026-08:47:49.788 INFO:weightslab.trainer.services.agent.agent:__init__: Initializing DataManipulationAgent\n",
+            "16/07/2026-08:47:49.841 WARNING:weightslab.trainer.services.agent.agent:_load_config: Error loading config from /usr/local/lib/python3.12/dist-packages: [Errno 21] Is a directory: '/usr/local/lib/python3.12/dist-packages'\n",
+            "16/07/2026-08:47:49.853 INFO:weightslab.trainer.services.agent.agent:_load_config: \n",
+            "\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "Agent initialized from configuration /content/agent_config.yaml: \n",
+            "\tFinal Agent Configuration: Preferred Provider=openrouter, \n",
+            "\tFallback to Local=True, \n",
+            "\tOpenRouter Model=~google/gemini-flash-latest with:\n",
+            "\t\tAPI Key=None\n",
+            "\t\tBase URL=https://openrouter.ai/api/v1, \n",
+            "\tOllama Model=llama3.2:3b\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "\n",
+            "16/07/2026-08:47:49.860 INFO:weightslab.trainer.services.agent.agent:_setup_providers: Setting up Ollama with model llama3.2:3b\n",
+            "16/07/2026-08:47:50.171 INFO:weightslab.trainer.services.agent.agent:_setup_providers: [Agent] Ollama enabled: llama3.2:3b\n",
+            "16/07/2026-08:47:50.173 INFO:weightslab.trainer.services.data_service:_build_preview_cache: [PreviewCache] Building 64×64 or less or less preview cache for 400 samples …\n",
+            "16/07/2026-08:47:50.173 INFO:weightslab.trainer.services.data_service:__init__: DataService initialized.\n",
+            "16/07/2026-08:47:50.175 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Servicer added\n",
+            "16/07/2026-08:47:50.176 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Attempting to bind to 0.0.0.0:50051\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\r[PreviewCache]:   0%|          | 0/400 [00:00<?, ?sample/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:50.183 WARNING:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] TLS disabled; using insecure transport on 0.0.0.0:50051\n",
+            "16/07/2026-08:47:50.184 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Port 50051 bound successfully.\n",
+            "16/07/2026-08:47:50.204 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Server started and listening on 0.0.0.0:50051\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "============================================================\n",
+            " Backend exposed via bore at: bore.pub:45496\n",
+            " On your local machine (Docker running), run:\n",
+            "     weightslab start\n",
+            "     weightslab tunnel bore.pub:45496\n",
+            "============================================================\n",
+            "16/07/2026-08:47:50.539 INFO:weightslab.backend.cli:cli_serve: cli_thread_started\n",
+            "\n",
+            "============================================================\n",
+            "STARTING FACE RECOGNITION TRAINING\n",
+            " Experiment : face_triplet_training\n",
+            " Device : cpu\n",
+            " Steps : 1500 | eval_full_to_train_steps_ratio=50\n",
+            " Loss : triplet\n",
+            " Logs : /tmp/tmpu_lry8gs\n",
+            "============================================================\n",
+            "16/07/2026-08:47:50.601 INFO:weightslab.src:start_training: Starting WeightsLab training mode with a timeout of 3 seconds.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[PreviewCache]:   5%|▍         | 19/400 [00:00<00:08, 47.18sample/s]/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "[PreviewCache]:  10%|▉         | 39/400 [00:00<00:06, 54.44sample/s]/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "[PreviewCache]: 100%|██████████| 400/400 [00:02<00:00, 170.02sample/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:52.533 INFO:weightslab.trainer.services.data_service:_build_preview_cache: [PreviewCache] Done: 400/400 cached in 2.4s (5.9 ms/sample)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:47:53.609 INFO:weightslab.components.global_monitoring:resume: \n",
+            "Attempting to resume training...\n",
+            "16/07/2026-08:47:53.613 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: First time initialization; skipping hash update.\n",
+            "16/07/2026-08:47:53.625 INFO:weightslab.components.experiment_hash:has_changed: Experiment configuration changed: {'data', 'hp', 'model'}\n",
+            "16/07/2026-08:47:53.634 INFO:weightslab.components.experiment_hash:generate_hash: Generated experiment hash: 03778b1a8eede58d1a6ff54b- (HP: 03778b1a, Model: 8eede58d, Data: 1a6ff54b)\n",
+            "16/07/2026-08:47:53.634 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Initial experiment hash set: 03778b1a-8eede58d-1a6ff54b\n",
+            "16/07/2026-08:47:53.641 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Changed components: {'data', 'hp', 'model'}\n",
+            "16/07/2026-08:47:53.647 INFO:weightslab.components.checkpoint_manager:_save_changes: Dumping hyperparameters config...\n",
+            "16/07/2026-08:47:53.657 INFO:weightslab.components.checkpoint_manager:save_config: Saved config: 03778b1a_config.yaml with exp_hash prefix: 03778b1a\n",
+            "16/07/2026-08:47:53.658 INFO:weightslab.components.checkpoint_manager:_save_changes: Dumping data snapshot...\n",
+            "16/07/2026-08:47:53.682 INFO:weightslab.components.checkpoint_manager:save_data_snapshot: Saved data snapshot: 1a6ff54b_data_snapshot.json (400 rows) with RNG state\n",
+            "16/07/2026-08:47:53.683 INFO:weightslab.components.checkpoint_manager:_save_changes: Saving model weights checkpoint with component changes...\n",
+            "16/07/2026-08:47:53.797 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000000.pt\n",
+            "16/07/2026-08:47:53.858 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "16/07/2026-08:47:53.895 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "16/07/2026-08:47:53.920 INFO:weightslab.components.global_monitoring:resume: Hashes by module: ['03778b1a', '8eede58d', '1a6ff54b']\n",
+            "16/07/2026-08:47:53.926 INFO:weightslab.components.global_monitoring:resume: Resuming training now...\n",
+            "16/07/2026-08:47:53.930 INFO:weightslab.components.global_monitoring:resume: Hashes by module on resume: ['03778b1a', '8eede58d', '1a6ff54b']\n",
+            "16/07/2026-08:47:53.931 INFO:weightslab.components.global_monitoring:resume: \n",
+            "Training resumed as modules hashes have been computed: ['03778b1a', '8eede58d', '1a6ff54b'].\n",
+            "\n",
+            "============================================================\n",
+            "Face Recognition Training\n",
+            " Loss : triplet\n",
+            " Eval every : 50 steps\n",
+            " Max steps : 1500\n",
+            "============================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[train] step       1 | loss=0.335158 | running_mean=0.335158\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:02.874 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000010.pt\n",
+            "16/07/2026-08:48:03.045 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:07.226 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000020.pt\n",
+            "16/07/2026-08:48:07.250 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:10.733 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000030.pt\n",
+            "16/07/2026-08:48:10.773 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:14.971 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000040.pt\n",
+            "16/07/2026-08:48:15.007 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:18.831 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000050.pt\n",
+            "16/07/2026-08:48:18.855 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step      50 | loss=0.249925 | running_mean=0.293559\n",
+            "\n",
+            "[eval@test] step 50\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9883\n",
+            " rank1_accuracy : 0.2625\n",
+            " FAR : 0.0000\n",
+            " FRR : 0.9250\n",
+            " best_threshold : 0.5253\n",
+            " num_clusters : 3\n",
+            " noise_ratio : 0.9125\n",
+            " mean_nn1_distance : 0.7310\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:25.541 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000060.pt\n",
+            "16/07/2026-08:48:25.567 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:30.418 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000070.pt\n",
+            "16/07/2026-08:48:30.445 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:34.285 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000080.pt\n",
+            "16/07/2026-08:48:34.314 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:38.767 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000090.pt\n",
+            "16/07/2026-08:48:38.819 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:43.673 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000100.pt\n",
+            "16/07/2026-08:48:43.703 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     100 | loss=0.055430 | running_mean=0.137908\n",
+            "\n",
+            "[eval@test] step 100\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9899\n",
+            " rank1_accuracy : 0.4875\n",
+            " FAR : 0.0000\n",
+            " FRR : 0.8000\n",
+            " best_threshold : 0.7273\n",
+            " num_clusters : 2\n",
+            " noise_ratio : 0.9500\n",
+            " mean_nn1_distance : 0.8686\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:48.979 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000110.pt\n",
+            "16/07/2026-08:48:49.009 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:53.132 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000120.pt\n",
+            "16/07/2026-08:48:53.177 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:48:57.651 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000130.pt\n",
+            "16/07/2026-08:48:57.704 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:01.531 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000140.pt\n",
+            "16/07/2026-08:49:01.564 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:06.417 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000150.pt\n",
+            "16/07/2026-08:49:06.470 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     150 | loss=0.031504 | running_mean=0.054026\n",
+            "\n",
+            "[eval@test] step 150\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9896\n",
+            " rank1_accuracy : 0.4250\n",
+            " FAR : 0.0006\n",
+            " FRR : 0.7750\n",
+            " best_threshold : 0.7475\n",
+            " num_clusters : 4\n",
+            " noise_ratio : 0.9000\n",
+            " mean_nn1_distance : 0.8575\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:12.375 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000160.pt\n",
+            "16/07/2026-08:49:12.411 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:16.302 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000170.pt\n",
+            "16/07/2026-08:49:16.380 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:20.968 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000180.pt\n",
+            "16/07/2026-08:49:21.026 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:25.672 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000190.pt\n",
+            "16/07/2026-08:49:25.709 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:29.678 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000200.pt\n",
+            "16/07/2026-08:49:30.085 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     200 | loss=0.002005 | running_mean=0.023699\n",
+            "\n",
+            "[eval@test] step 200\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9905\n",
+            " rank1_accuracy : 0.5250\n",
+            " FAR : 0.0006\n",
+            " FRR : 0.7000\n",
+            " best_threshold : 0.8081\n",
+            " num_clusters : 3\n",
+            " noise_ratio : 0.9250\n",
+            " mean_nn1_distance : 0.8718\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:38.700 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000210.pt\n",
+            "16/07/2026-08:49:38.741 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:42.723 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000220.pt\n",
+            "16/07/2026-08:49:43.165 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:47.861 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000230.pt\n",
+            "16/07/2026-08:49:47.966 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:52.069 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000240.pt\n",
+            "16/07/2026-08:49:52.117 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:49:55.962 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000250.pt\n",
+            "16/07/2026-08:49:56.004 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     250 | loss=0.003690 | running_mean=0.017409\n",
+            "\n",
+            "[eval@test] step 250\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9896\n",
+            " rank1_accuracy : 0.5250\n",
+            " FAR : 0.0006\n",
+            " FRR : 0.7750\n",
+            " best_threshold : 0.8283\n",
+            " num_clusters : 2\n",
+            " noise_ratio : 0.9500\n",
+            " mean_nn1_distance : 0.8830\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:03.043 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000260.pt\n",
+            "16/07/2026-08:50:03.086 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:06.976 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000270.pt\n",
+            "16/07/2026-08:50:07.020 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:10.838 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000280.pt\n",
+            "16/07/2026-08:50:10.885 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:15.769 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000290.pt\n",
+            "16/07/2026-08:50:15.815 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:20.279 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000300.pt\n",
+            "16/07/2026-08:50:20.328 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     300 | loss=0.000000 | running_mean=0.010841\n",
+            "\n",
+            "[eval@test] step 300\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9902\n",
+            " rank1_accuracy : 0.4625\n",
+            " FAR : 0.0006\n",
+            " FRR : 0.7250\n",
+            " best_threshold : 0.7677\n",
+            " num_clusters : 3\n",
+            " noise_ratio : 0.9250\n",
+            " mean_nn1_distance : 0.8641\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:26.701 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000310.pt\n",
+            "16/07/2026-08:50:26.783 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:31.070 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000320.pt\n",
+            "16/07/2026-08:50:31.168 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:34.976 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000330.pt\n",
+            "16/07/2026-08:50:35.026 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:39.203 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000340.pt\n",
+            "16/07/2026-08:50:39.286 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:44.384 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000350.pt\n",
+            "16/07/2026-08:50:44.511 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     350 | loss=0.009264 | running_mean=0.009607\n",
+            "\n",
+            "[eval@test] step 350\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9905\n",
+            " rank1_accuracy : 0.4500\n",
+            " FAR : 0.0000\n",
+            " FRR : 0.7500\n",
+            " best_threshold : 0.7677\n",
+            " num_clusters : 1\n",
+            " noise_ratio : 0.9750\n",
+            " mean_nn1_distance : 0.8755\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:50.162 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000360.pt\n",
+            "16/07/2026-08:50:50.218 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:55.006 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000370.pt\n",
+            "16/07/2026-08:50:55.058 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:50:58.907 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000380.pt\n",
+            "16/07/2026-08:50:59.009 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:02.794 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000390.pt\n",
+            "16/07/2026-08:51:02.865 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:08.300 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000400.pt\n",
+            "16/07/2026-08:51:08.353 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     400 | loss=0.004772 | running_mean=0.008937\n",
+            "\n",
+            "[eval@test] step 400\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9905\n",
+            " rank1_accuracy : 0.4375\n",
+            " FAR : 0.0006\n",
+            " FRR : 0.7000\n",
+            " best_threshold : 0.7677\n",
+            " num_clusters : 2\n",
+            " noise_ratio : 0.9500\n",
+            " mean_nn1_distance : 0.8593\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:13.489 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000410.pt\n",
+            "16/07/2026-08:51:13.608 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:17.467 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000420.pt\n",
+            "16/07/2026-08:51:17.525 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:22.378 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000430.pt\n",
+            "16/07/2026-08:51:22.435 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:26.333 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000440.pt\n",
+            "16/07/2026-08:51:26.433 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:31.124 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000450.pt\n",
+            "16/07/2026-08:51:31.232 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     450 | loss=0.003857 | running_mean=0.007260\n",
+            "\n",
+            "[eval@test] step 450\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9902\n",
+            " rank1_accuracy : 0.4375\n",
+            " FAR : 0.0019\n",
+            " FRR : 0.6250\n",
+            " best_threshold : 0.8283\n",
+            " num_clusters : 2\n",
+            " noise_ratio : 0.9500\n",
+            " mean_nn1_distance : 0.8451\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:38.634 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000460.pt\n",
+            "16/07/2026-08:51:38.694 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:42.533 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000470.pt\n",
+            "16/07/2026-08:51:42.595 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:48.142 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000480.pt\n",
+            "16/07/2026-08:51:48.251 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:52.684 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000490.pt\n",
+            "16/07/2026-08:51:52.773 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:51:56.682 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000500.pt\n",
+            "16/07/2026-08:51:56.746 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     500 | loss=0.000000 | running_mean=0.003165\n",
+            "\n",
+            "[eval@test] step 500\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9896\n",
+            " rank1_accuracy : 0.4250\n",
+            " FAR : 0.0010\n",
+            " FRR : 0.7500\n",
+            " best_threshold : 0.7475\n",
+            " num_clusters : 5\n",
+            " noise_ratio : 0.8750\n",
+            " mean_nn1_distance : 0.8543\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:03.691 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000510.pt\n",
+            "16/07/2026-08:52:03.760 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:07.670 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000520.pt\n",
+            "16/07/2026-08:52:07.898 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:13.018 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000530.pt\n",
+            "16/07/2026-08:52:13.045 INFO:weightslab.trainer.services.data_service:ApplyDataQuery: [ApplyDataQuery] BYPASSING AGENT - Direct query execution: 'sortby origin desc scope:global'\n",
+            "16/07/2026-08:52:13.278 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:14.347 INFO:weightslab.trainer.services.data_service:ApplyDataQuery: [ApplyDataQuery] BYPASSING AGENT - Direct query execution: 'sortby origin asc scope:global'\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:18.015 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000540.pt\n",
+            "16/07/2026-08:52:18.089 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:22.578 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000550.pt\n",
+            "16/07/2026-08:52:22.653 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     550 | loss=0.000000 | running_mean=0.005400\n",
+            "\n",
+            "[eval@test] step 550\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9899\n",
+            " rank1_accuracy : 0.4500\n",
+            " FAR : 0.0003\n",
+            " FRR : 0.7750\n",
+            " best_threshold : 0.7071\n",
+            " num_clusters : 2\n",
+            " noise_ratio : 0.9500\n",
+            " mean_nn1_distance : 0.8478\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:28.811 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000560.pt\n",
+            "16/07/2026-08:52:28.878 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:33.519 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000570.pt\n",
+            "16/07/2026-08:52:33.659 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:37.806 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000580.pt\n",
+            "16/07/2026-08:52:38.021 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:42.962 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000590.pt\n",
+            "16/07/2026-08:52:43.035 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:47.038 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000600.pt\n",
+            "16/07/2026-08:52:47.116 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     600 | loss=0.000000 | running_mean=0.003629\n",
+            "\n",
+            "[eval@test] step 600\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9908\n",
+            " rank1_accuracy : 0.5375\n",
+            " FAR : 0.0006\n",
+            " FRR : 0.6750\n",
+            " best_threshold : 0.8081\n",
+            " num_clusters : 5\n",
+            " noise_ratio : 0.8750\n",
+            " mean_nn1_distance : 0.8415\n",
+            "16/07/2026-08:52:48.070 INFO:weightslab.trainer.services.data_service:ApplyDataQuery: [ApplyDataQuery] BYPASSING AGENT - Direct query execution: 'sortby origin asc, signals//test/cluster/nn1_uid desc scope:global'\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:55.211 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000610.pt\n",
+            "16/07/2026-08:52:55.298 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:52:59.169 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000620.pt\n",
+            "16/07/2026-08:52:59.247 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:03.623 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000630.pt\n",
+            "16/07/2026-08:53:03.848 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:08.684 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000640.pt\n",
+            "16/07/2026-08:53:08.870 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:12.700 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000650.pt\n",
+            "16/07/2026-08:53:12.780 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     650 | loss=0.000000 | running_mean=0.003513\n",
+            "\n",
+            "[eval@test] step 650\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9905\n",
+            " rank1_accuracy : 0.5000\n",
+            " FAR : 0.0022\n",
+            " FRR : 0.5750\n",
+            " best_threshold : 0.8687\n",
+            " num_clusters : 2\n",
+            " noise_ratio : 0.9500\n",
+            " mean_nn1_distance : 0.8478\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:20.067 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000660.pt\n",
+            "16/07/2026-08:53:20.149 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:24.068 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000670.pt\n",
+            "16/07/2026-08:53:24.150 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:28.057 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000680.pt\n",
+            "16/07/2026-08:53:28.171 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:33.222 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000690.pt\n",
+            "16/07/2026-08:53:33.300 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:38.128 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000700.pt\n",
+            "16/07/2026-08:53:38.520 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "[train] step     700 | loss=0.022663 | running_mean=0.003976\n",
+            "\n",
+            "[eval@test] step 700\n",
+            "\n",
+            "=======================================================\n",
+            "Evaluation [test]\n",
+            "=======================================================\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " verification_accuracy : 0.9899\n",
+            " rank1_accuracy : 0.5125\n",
+            " FAR : 0.0010\n",
+            " FRR : 0.7250\n",
+            " best_threshold : 0.7677\n",
+            " num_clusters : 4\n",
+            " noise_ratio : 0.9000\n",
+            " mean_nn1_distance : 0.8332\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:44.078 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000710.pt\n",
+            "16/07/2026-08:53:44.218 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:49.370 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000720.pt\n",
+            "16/07/2026-08:53:49.459 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:53.742 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 03778b1a8eede58d1a6ff54b_step_000730.pt\n",
+            "16/07/2026-08:53:54.245 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpu_lry8gs/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/tmp/ipykernel_1402/2409495586.py:193: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15)\n",
+            "  img_pil = PILImage.fromarray(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:53:55.532 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:53:55.534 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:53:55.536 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: \n",
+            "[WeightsLab] UI Command: PAUSE\n",
+            "16/07/2026-08:53:55.539 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start the gRPC backend (+ public bore tunnel) that Weights Studio connects to.\n",
+        "wl.serve(serving_grpc=True, serving_bore=True)\n",
+        "\n",
+        "print(\"\\n\" + \"=\" * 60)\n",
+        "print(\"STARTING FACE RECOGNITION TRAINING\")\n",
+        "print(f\" Experiment : {config['experiment_name']}\")\n",
+        "print(f\" Device : {config['device']}\")\n",
+        "print(f\" Steps : {config.get('max_steps')} | eval_full_to_train_steps_ratio={eval_full_to_train_steps_ratio}\")\n",
+        "print(f\" Loss : {config.get('model', {}).get('loss', 'triplet')}\")\n",
+        "print(f\" Logs : {config['root_log_dir']}\")\n",
+        "print(\"=\" * 60)\n",
+        "\n",
+        "# Flip the experiment into the training state (short timeout keeps this non-blocking).\n",
+        "wl.start_training(timeout=3)\n",
+        "\n",
+        "# Colab-friendly finite loop: config[\"max_steps\"] caps the run so the cell terminates.\n",
+        "# Set config[\"max_steps\"] = None (in the Configuration cell) to train open-ended and\n",
+        "# interrupt the cell to stop.\n",
+        "train(\n",
+        "    model=model,\n",
+        "    train_loader=train_loader,\n",
+        "    test_loader=test_loader,\n",
+        "    eval_full_to_train_steps_ratio=eval_full_to_train_steps_ratio,\n",
+        "    log_every=log_every,\n",
+        "    loss_name=config.get(\"model\", {}).get(\"loss\", \"triplet\"),\n",
+        "    max_steps=config.get(\"max_steps\", 1500),\n",
+        ")\n",
+        "\n",
+        "# Export signal history + per-sample dataframe (like the classification demo).\n",
+        "wl.write_history()\n",
+        "wl.write_dataframe()\n",
+        "print(\"\\nTraining complete. Logs at:\", config[\"root_log_dir\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W1QEkAHdF39-"
+      },
+      "source": [
+        "## See it live in Weights Studio\n",
+        "\n",
+        "**Colab has no Docker daemon**, so run Studio on your own machine and point it at this notebook's\n",
+        "backend using the `bore.pub:<port>` printed in the Expose section:\n",
+        "\n",
+        "```bash\n",
+        "pip install weightslab\n",
+        "weightslab start                       # Terminal 1 - opens http://localhost:5173\n",
+        "weightslab tunnel bore.pub:12345           # Terminal 2 - the host:port from the Expose section\n",
+        "```\n",
+        "\n",
+        "Then open **http://localhost:5173**. Prefer local-only? Run this example directly on your machine\n",
+        "(`weightslab start example` selects a bundled example) and launch the UI next to it - no tunnel."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EW46PivEF39-"
+      },
+      "source": [
+        "---\n",
+        "\n",
+        "<div align=\"center\">\n",
+        "Crafted by <a href=\"https://github.com/GrayboxTech/weightslab\">GrayboxTech</a> - if WeightsLab helps you catch a bad label, drop us a star on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a>.\n",
+        "</div>"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "wl-clustering.ipynb",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/weightslab/examples/Notebooks/PyTorch/wl-detection.ipynb b/weightslab/examples/Notebooks/PyTorch/wl-detection.ipynb
new file mode 100644
index 00000000..d09d7550
--- /dev/null
+++ b/weightslab/examples/Notebooks/PyTorch/wl-detection.ipynb
@@ -0,0 +1,4691 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z2kgyn-s_xBa"
+      },
+      "source": [
+        "<div align=\"center\">\n",
+        "\n",
+        "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+        "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+        "\n",
+        "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+        "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+        "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+        "  <br>\n",
+        "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-detection.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+        "\n",
+        "  Welcome to the WeightsLab <b>Object Detection (Penn-Fudan)</b> notebook! <a href=\"https://github.com/GrayboxTech/weightslab\">WeightsLab</a> traces training signals back to the exact samples producing them. Browse the <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\">Docs</a> for details.</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C_WnDPZv_xBc"
+      },
+      "source": [
+        "# Object Detection (Penn-Fudan) with WeightsLab\n",
+        "\n",
+        "This notebook trains a small detector (MobileNetV3-Small backbone + a lightweight detection head) on the **Penn-Fudan** pedestrian dataset and instruments it with WeightsLab, so every training signal is traced **back to the exact samples** producing it.\n",
+        "\n",
+        "WeightsLab records **per-sample** detection loss and **per-instance** IoU (one value per ground-truth box) live, so each predicted box is traceable in Studio - you can rank the worst samples, spot bad boxes, and curate the dataset **without restarting training**.\n",
+        "\n",
+        "> Data: the Penn-Fudan dataset (~170 images, one class: *person*) is **downloaded automatically on first run** - nothing to upload.\n",
+        "\n",
+        "### What you'll do\n",
+        "1. Install WeightsLab.\n",
+        "2. Inline the dataset, detector, and loss/IoU criterions (previously the example's `utils/` package).\n",
+        "3. Set every knob in one **config** dict (like a `config.yaml`).\n",
+        "4. Wrap the model, optimizer, dataloaders, loss, and IoU metrics with the SDK.\n",
+        "5. Train while per-sample and per-instance signals are captured, then open the live **Weights Studio** UI."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dpQhH7Jd_xBc"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "On Colab, pick a **T4 GPU** runtime (`Runtime -> Change runtime type -> T4 GPU`)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3nlJ2-Oj_xBc"
+      },
+      "source": [
+        "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?color=5865F2&logo=pypi&logoColor=white\" alt=\"PyPI - Version\"></a>\n",
+        "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/dm/weightslab?color=5865F2\" alt=\"PyPI - Downloads\"></a>\n",
+        "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/pyversions/weightslab?color=5865F2&logo=python&logoColor=white\" alt=\"PyPI - Python Version\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "WptCnGqv_xBd",
+        "outputId": "617e6869-1797-49dc-96b9-525dfe8e7b35",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://test.pypi.org/simple/, https://pypi.org/simple/\n",
+            "Requirement already satisfied: weightslab==1.3.3.dev7 in /usr/local/lib/python3.12/dist-packages (1.3.3.dev7)\n",
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+            "Requirement already satisfied: packaging>=23.2.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev7) (26.2)\n",
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+            "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev7) (0.4.2)\n",
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+            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (3.4.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (2.5.0)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (4.2.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (2.20.0)\n",
+            "Requirement already satisfied: platformdirs<5,>=3.6.0 in /usr/local/lib/python3.12/dist-packages (from textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (4.10.0)\n",
+            "Requirement already satisfied: plotext<6.0.0,>=5.2.8 in /usr/local/lib/python3.12/dist-packages (from textual-plotext->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (5.3.2)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (0.1.2)\n",
+            "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (2.1.0)\n",
+            "Requirement already satisfied: mdit-py-plugins>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (0.6.1)\n",
+            "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.12/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev7) (2.0.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+        "# and Pillow, so nothing extra is needed here.\n",
+        "# %pip install -q weightslab\n",
+        "%pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev7\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KtEZwMxI_xBd"
+      },
+      "source": [
+        "## 1. Imports\n",
+        "\n",
+        "`weightslab` is imported as `wl`. The two `guard_*_context` managers scope a block as training vs. evaluation so signals are attributed to the right phase. Every external dependency used by the inlined modules is imported here."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "NHwcL67i_xBd",
+        "outputId": "336670cc-0cab-4abf-b13f-91f6babe5c14",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:32:40.383 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmp_zk4n7mc/weightslab_logs/weightslab_20260716_083240.log\n",
+            "16/07/2026-08:32:40.387 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+            "16/07/2026-08:32:40.388 INFO:weightslab:<module>: \n",
+            "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+            "│                                                                                                     │\n",
+            "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+            "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+            "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+            "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+            "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+            "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+            "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+            "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+            "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+            "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+            "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+            "│                                                                                                     │\n",
+            "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+            "│                                                                                                     │\n",
+            "╰─── By GrayBx, v1.3.3.dev7 ──────────────────────────────────────────────────────────────────────────╯\n",
+            "\n",
+            "\n",
+            "16/07/2026-08:32:40.394 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n",
+            "Using device: cpu\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "import ssl\n",
+        "import zipfile\n",
+        "import logging\n",
+        "import tempfile\n",
+        "import urllib.request\n",
+        "\n",
+        "import numpy as np\n",
+        "import tqdm\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "from torch import optim\n",
+        "from torch.utils.data import Dataset\n",
+        "from torchvision import transforms\n",
+        "from torchvision.models import mobilenet_v3_small, MobileNet_V3_Small_Weights\n",
+        "from PIL import Image\n",
+        "\n",
+        "import weightslab as wl\n",
+        "from weightslab.components.global_monitoring import (\n",
+        "    guard_training_context,\n",
+        "    guard_testing_context,\n",
+        ")\n",
+        "\n",
+        "logging.basicConfig(level=logging.ERROR)\n",
+        "logging.getLogger(\"PIL\").setLevel(logging.INFO)\n",
+        "\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(f\"Using device: {device}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OOYtb2D2_xBe"
+      },
+      "source": [
+        "## 2. Inlined helper modules (from the example's `utils/`)\n",
+        "\n",
+        "The example ships a small `utils/` package; its three modules are inlined below so the notebook is fully self-contained. Only the intra-package imports are dropped - those names (`decode_grid`, `det_collate`, the dataset and the criterions) are now notebook-global.\n",
+        "\n",
+        "- **`utils/data.py`** - the Penn-Fudan detection dataset (auto-downloads on first use) + the `det_collate` collate fn.\n",
+        "- **`utils/model.py`** - a MobileNetV3-Small backbone + a small grid detection head.\n",
+        "- **`utils/criterions.py`** - per-sample YOLO-style detection loss and per-sample / per-instance IoU."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "csaORA30_xBe"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== utils/data.py =====\n",
+        "import os\n",
+        "import ssl\n",
+        "import zipfile\n",
+        "import urllib.request\n",
+        "\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "\n",
+        "from torchvision import transforms\n",
+        "\n",
+        "from torch.utils.data import Dataset\n",
+        "\n",
+        "from PIL import Image\n",
+        "\n",
+        "\n",
+        "# =============================================================================\n",
+        "# Penn-Fudan Pedestrian detection dataset\n",
+        "# =============================================================================\n",
+        "# A small, real object-detection dataset (~170 photos, one class: \"person\").\n",
+        "# It ships per-instance segmentation masks; we derive an axis-aligned bounding\n",
+        "# box per pedestrian from each mask. Downloaded + extracted on first use.\n",
+        "#\n",
+        "# On-disk layout after extraction:\n",
+        "# <root>/PennFudanPed/\n",
+        "# PNGImages/FudanPed00001.png ...\n",
+        "# PedMasks/FudanPed00001_mask.png ... # pixel value k = k-th pedestrian, 0 = bg\n",
+        "#\n",
+        "# WL renders detection targets/predictions from a per-sample [N, 6] array\n",
+        "# ``[x1, y1, x2, y2, class_id, confidence]`` normalized to [0, 1] (GT conf = 1.0)\n",
+        "# — see ``get_items`` below and ``data_service.py`` (task_type == \"detection\").\n",
+        "\n",
+        "CLASS_NAMES = [\"person\"]\n",
+        "\n",
+        "_URL = \"https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip\"\n",
+        "\n",
+        "# ImageNet statistics — the MobileNetV3 backbone is pretrained with these, so we\n",
+        "# normalize model inputs the same way. (The UI still shows the original PNG.)\n",
+        "IMAGENET_MEAN = [0.485, 0.456, 0.406]\n",
+        "IMAGENET_STD = [0.229, 0.224, 0.225]\n",
+        "\n",
+        "\n",
+        "def download_penn_fudan(root):\n",
+        "    \"\"\"Download + extract Penn-Fudan into <root>/PennFudanPed (idempotent).\"\"\"\n",
+        "    base = os.path.join(root, \"PennFudanPed\")\n",
+        "    if os.path.isdir(os.path.join(base, \"PNGImages\")):\n",
+        "        return base\n",
+        "\n",
+        "    os.makedirs(root, exist_ok=True)\n",
+        "    zip_path = os.path.join(root, \"PennFudanPed.zip\")\n",
+        "\n",
+        "    if not os.path.exists(zip_path):\n",
+        "        print(f\"[data] Downloading Penn-Fudan dataset to {zip_path} ...\", flush=True)\n",
+        "        try:\n",
+        "            urllib.request.urlretrieve(_URL, zip_path)\n",
+        "        except Exception as e:\n",
+        "            # Some corporate environments break TLS verification - retry unverified.\n",
+        "            print(f\"[data] TLS verification failed ({e}); retrying without verification.\", flush=True)\n",
+        "            ctx = ssl._create_unverified_context()\n",
+        "            with urllib.request.urlopen(_URL, context=ctx) as resp, open(zip_path, \"wb\") as fh:\n",
+        "                fh.write(resp.read())\n",
+        "\n",
+        "    print(\"[data] Extracting Penn-Fudan ...\", flush=True)\n",
+        "    with zipfile.ZipFile(zip_path) as zf:\n",
+        "        zf.extractall(root)\n",
+        "    return base\n",
+        "\n",
+        "\n",
+        "def _boxes_from_mask(mask_path):\n",
+        "    \"\"\"Derive one bbox per pedestrian from a Penn-Fudan instance mask.\n",
+        "\n",
+        "    Returns (boxes_px [N, 4] int xyxy, height, width). Background (0) skipped.\n",
+        "    \"\"\"\n",
+        "    mask = np.array(Image.open(mask_path))\n",
+        "    h, w = mask.shape[:2]\n",
+        "    obj_ids = np.unique(mask)\n",
+        "    obj_ids = obj_ids[obj_ids != 0] # drop background\n",
+        "\n",
+        "    boxes = []\n",
+        "    for oid in obj_ids:\n",
+        "        ys, xs = np.where(mask == oid)\n",
+        "        if xs.size == 0:\n",
+        "            continue\n",
+        "        x1, x2 = int(xs.min()), int(xs.max())\n",
+        "        y1, y2 = int(ys.min()), int(ys.max())\n",
+        "        if x2 > x1 and y2 > y1:\n",
+        "            boxes.append([x1, y1, x2, y2])\n",
+        "    return np.asarray(boxes, dtype=np.float32).reshape(-1, 4), h, w\n",
+        "\n",
+        "\n",
+        "class PennFudanDetectionDataset(Dataset):\n",
+        "    \"\"\"Pedestrian bounding-box detection over the Penn-Fudan images.\n",
+        "\n",
+        "    Args:\n",
+        "        root: directory to download/extract the dataset into.\n",
+        "        split: \"train\" or \"val\" (deterministic split of the 170 images).\n",
+        "        image_size: square resize fed to the model.\n",
+        "        val_fraction: fraction of images held out for validation.\n",
+        "        max_samples: optional cap on the split size (for quick runs).\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        root,\n",
+        "        split=\"train\",\n",
+        "        num_classes=1,\n",
+        "        image_size=256,\n",
+        "        val_fraction=0.2,\n",
+        "        max_samples=None,\n",
+        "    ):\n",
+        "        super().__init__()\n",
+        "        self.root = root\n",
+        "        self.split = split\n",
+        "        self.num_classes = num_classes\n",
+        "        self.image_size = image_size\n",
+        "        # Explicit task type; bypasses WL's label-shape heuristic so bboxes are\n",
+        "        # rendered as detection overlays (not mistaken for classification).\n",
+        "        self.task_type = \"detection\"\n",
+        "        self.class_names = CLASS_NAMES[:num_classes]\n",
+        "\n",
+        "        base = download_penn_fudan(root)\n",
+        "        img_dir = os.path.join(base, \"PNGImages\")\n",
+        "        mask_dir = os.path.join(base, \"PedMasks\")\n",
+        "\n",
+        "        all_imgs = sorted(f for f in os.listdir(img_dir) if f.lower().endswith(\".png\"))\n",
+        "\n",
+        "        # Deterministic train/val split: every k-th image goes to val.\n",
+        "        k = max(2, int(round(1.0 / max(val_fraction, 1e-6))))\n",
+        "        if split == \"val\":\n",
+        "            selected = all_imgs[::k]\n",
+        "        else:\n",
+        "            val_set = set(all_imgs[::k])\n",
+        "            selected = [f for f in all_imgs if f not in val_set]\n",
+        "\n",
+        "        selected = selected[:max_samples] if max_samples != None else selected\n",
+        "\n",
+        "        self.images = []\n",
+        "        self.masks = []\n",
+        "        for fname in selected:\n",
+        "            base_name, _ = os.path.splitext(fname)\n",
+        "            mask_path = os.path.join(mask_dir, base_name + \"_mask.png\")\n",
+        "            if os.path.exists(mask_path):\n",
+        "                self.images.append(os.path.join(img_dir, fname))\n",
+        "                self.masks.append(mask_path)\n",
+        "\n",
+        "        if len(self.images) == 0:\n",
+        "            raise RuntimeError(f\"No image/mask pairs found under {base}\")\n",
+        "\n",
+        "        self.image_transform = transforms.Compose(\n",
+        "            [\n",
+        "                transforms.Resize((image_size, image_size), interpolation=Image.BILINEAR),\n",
+        "                transforms.ToTensor(),\n",
+        "                transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),\n",
+        "            ]\n",
+        "        )\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.images)\n",
+        "\n",
+        "    def _load_boxes(self, mask_path):\n",
+        "        \"\"\"Read mask → [N, 6] float32 = [x1, y1, x2, y2, cls=0, conf=1.0] (normalized).\"\"\"\n",
+        "        boxes_px, h, w = _boxes_from_mask(mask_path)\n",
+        "        if boxes_px.shape[0] == 0:\n",
+        "            return np.zeros((0, 6), dtype=np.float32)\n",
+        "        norm = boxes_px.copy()\n",
+        "        norm[:, [0, 2]] /= float(w)\n",
+        "        norm[:, [1, 3]] /= float(h)\n",
+        "        n = norm.shape[0]\n",
+        "        cls = np.zeros((n, 1), dtype=np.float32) # single class: person\n",
+        "        conf = np.ones((n, 1), dtype=np.float32)\n",
+        "        return np.concatenate([norm, cls, conf], axis=1).astype(np.float32)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        \"\"\"Returns (item, uid, target, metadata).\n",
+        "\n",
+        "        - item: normalized image tensor [C, H, W]\n",
+        "        - uid: unique sample id (string)\n",
+        "        - target: [N, 6] float32 = [x1, y1, x2, y2, class_id, confidence]\n",
+        "        - metadata: dict with source paths\n",
+        "        \"\"\"\n",
+        "        return self.get_items(idx, include_metadata=True, include_labels=True, include_images=True)\n",
+        "\n",
+        "    def get_items(self, idx, include_metadata=False, include_labels=False, include_images=False):\n",
+        "        img_path = self.images[idx]\n",
+        "        mask_path = self.masks[idx]\n",
+        "        uid = os.path.splitext(os.path.basename(img_path))[0]\n",
+        "\n",
+        "        metadata = {\n",
+        "            \"img_path\": img_path,\n",
+        "            \"mask_path\": mask_path,\n",
+        "        } if include_metadata else None\n",
+        "\n",
+        "        img_t = None\n",
+        "        if include_images:\n",
+        "            img = Image.open(img_path).convert(\"RGB\")\n",
+        "            img_t = self.image_transform(img)\n",
+        "\n",
+        "        target = None\n",
+        "        if include_labels:\n",
+        "            target = self._load_boxes(mask_path)\n",
+        "\n",
+        "        return img_t, uid, target, metadata\n",
+        "\n",
+        "\n",
+        "def det_collate(batch):\n",
+        "    \"\"\"Collate WL per-sample tuples for object detection.\n",
+        "\n",
+        "    A sample owns a variable number of boxes, so targets cannot be stacked. We\n",
+        "    keep them as a Python list (one [N_i, 6] tensor per sample), exactly the\n",
+        "    layout WL's per-instance helpers expect (``targets[s]`` iterates that\n",
+        "    sample's boxes in annotation order).\n",
+        "\n",
+        "    Returns:\n",
+        "        images: FloatTensor [B, C, H, W]\n",
+        "        ids: list[str] of length B\n",
+        "        targets: list[B] of [N_i, 6] float tensors ([x1, y1, x2, y2, cls, conf])\n",
+        "        metas: list[B] of metadata dicts\n",
+        "    \"\"\"\n",
+        "    images = torch.stack([b[0] for b in batch], dim=0)\n",
+        "    ids = [b[1] for b in batch]\n",
+        "    targets = [\n",
+        "        torch.as_tensor(b[2], dtype=torch.float32)\n",
+        "        if not isinstance(b[2], torch.Tensor) else b[2].float()\n",
+        "        for b in batch\n",
+        "    ]\n",
+        "    metas = [b[3] if len(b) > 3 else None for b in batch]\n",
+        "    return images, ids, targets, metas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "GFZWMzNi_xBf"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== utils/model.py =====\n",
+        "# =============================================================================\n",
+        "# Small single-shot grid detector on a pretrained MobileNetV3-Small backbone\n",
+        "# =============================================================================\n",
+        "# A frozen/fine-tuned ImageNet-pretrained backbone extracts features; a tiny\n",
+        "# detection head turns them into an S x S grid where each cell predicts ONE box:\n",
+        "# (objectness, tx, ty, tw, th, class_logits...).\n",
+        "#\n",
+        "# Encoding (all coordinates normalized to the [0, 1] image frame):\n",
+        "# * objectness = sigmoid(t_obj) -> P(box present in this cell)\n",
+        "# * cx = (col + sigmoid(tx)) / S -> box center, x\n",
+        "# * cy = (row + sigmoid(ty)) / S -> box center, y\n",
+        "# * w = sigmoid(tw) -> box width (fraction of image)\n",
+        "# * h = sigmoid(th) -> box height (fraction of image)\n",
+        "# * class = softmax(class_logits)\n",
+        "#\n",
+        "# Raw forward output keeps logits (loss applies the activations); `decode`\n",
+        "# turns logits into xyxy boxes for metrics and UI rendering.\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "\n",
+        "from torchvision.models import mobilenet_v3_small, MobileNet_V3_Small_Weights\n",
+        "\n",
+        "\n",
+        "def decode_grid(outputs, grid_size):\n",
+        "    \"\"\"Decode raw grid logits -> per-cell xyxy boxes, objectness and class probs.\n",
+        "\n",
+        "    Shared by the model (``SmallDetector.decode``) and the criterions so the\n",
+        "    encoding lives in exactly one place.\n",
+        "\n",
+        "    Args:\n",
+        "        outputs: [B, S, S, 5 + num_classes] raw logits.\n",
+        "        grid_size: S.\n",
+        "\n",
+        "    Returns:\n",
+        "        boxes: [B, S, S, 4] xyxy in [0, 1]\n",
+        "        obj: [B, S, S] objectness probability\n",
+        "        cls_probs: [B, S, S, num_classes] class probabilities\n",
+        "    \"\"\"\n",
+        "    B, S, _, _ = outputs.shape\n",
+        "    device = outputs.device\n",
+        "\n",
+        "    obj = torch.sigmoid(outputs[..., 0])\n",
+        "    tx = torch.sigmoid(outputs[..., 1])\n",
+        "    ty = torch.sigmoid(outputs[..., 2])\n",
+        "    w = torch.sigmoid(outputs[..., 3])\n",
+        "    h = torch.sigmoid(outputs[..., 4])\n",
+        "    cls_probs = torch.softmax(outputs[..., 5:], dim=-1)\n",
+        "\n",
+        "    # Cell-origin grid (col=x, row=y).\n",
+        "    cols = torch.arange(S, device=device).view(1, 1, S).expand(B, S, S)\n",
+        "    rows = torch.arange(S, device=device).view(1, S, 1).expand(B, S, S)\n",
+        "\n",
+        "    cx = (cols + tx) / S\n",
+        "    cy = (rows + ty) / S\n",
+        "    x1 = (cx - w / 2).clamp(0, 1)\n",
+        "    y1 = (cy - h / 2).clamp(0, 1)\n",
+        "    x2 = (cx + w / 2).clamp(0, 1)\n",
+        "    y2 = (cy + h / 2).clamp(0, 1)\n",
+        "\n",
+        "    boxes = torch.stack([x1, y1, x2, y2], dim=-1)\n",
+        "    return boxes, obj, cls_probs\n",
+        "\n",
+        "\n",
+        "class SmallDetector(nn.Module):\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        in_channels=3,\n",
+        "        num_classes=1,\n",
+        "        image_size=256,\n",
+        "        grid_size=8,\n",
+        "        pretrained=True,\n",
+        "        freeze_backbone=True,\n",
+        "    ):\n",
+        "        super().__init__()\n",
+        "        # For WeightsLab\n",
+        "        self.task_type = \"detection\"\n",
+        "        self.num_classes = num_classes\n",
+        "        self.class_names = [\"person\"][:num_classes]\n",
+        "        self.grid_size = grid_size\n",
+        "        self.image_size = image_size\n",
+        "        self.input_shape = (1, in_channels, image_size, image_size)\n",
+        "\n",
+        "        # Channels per cell: objectness(1) + box(4) + class logits(num_classes)\n",
+        "        self.preds_per_cell = 5 + num_classes\n",
+        "\n",
+        "        # --- Pretrained backbone (ImageNet) ---\n",
+        "        weights = MobileNet_V3_Small_Weights.IMAGENET1K_V1 if pretrained else None\n",
+        "        backbone = mobilenet_v3_small(weights=weights)\n",
+        "        self.backbone = backbone.features # [B, 576, H/32, W/32]\n",
+        "        backbone_out_ch = 576\n",
+        "\n",
+        "        self.freeze_backbone = freeze_backbone\n",
+        "        if freeze_backbone:\n",
+        "            for p in self.backbone.parameters():\n",
+        "                p.requires_grad = False\n",
+        "\n",
+        "        # --- Detection head (lightweight, trained from scratch) ---\n",
+        "        self.neck = nn.Sequential(\n",
+        "            nn.Conv2d(backbone_out_ch, 256, 3, padding=1, bias=False),\n",
+        "            nn.BatchNorm2d(256),\n",
+        "            nn.ReLU(inplace=True),\n",
+        "            nn.Conv2d(256, 128, 3, padding=1, bias=False),\n",
+        "            nn.BatchNorm2d(128),\n",
+        "            nn.ReLU(inplace=True),\n",
+        "        )\n",
+        "        self.head = nn.Conv2d(128, self.preds_per_cell, 1)\n",
+        "\n",
+        "    def train(self, mode=True):\n",
+        "        \"\"\"Keep a frozen backbone in eval mode (so its BatchNorm stats stay fixed).\"\"\"\n",
+        "        super().train(mode)\n",
+        "        if self.freeze_backbone:\n",
+        "            self.backbone.eval()\n",
+        "        return self\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        \"\"\"Returns raw logits [B, S, S, 5 + num_classes].\"\"\"\n",
+        "        if self.freeze_backbone:\n",
+        "            with torch.no_grad():\n",
+        "                feat = self.backbone(x)\n",
+        "        else:\n",
+        "            feat = self.backbone(x)\n",
+        "\n",
+        "        feat = self.neck(feat)\n",
+        "        out = self.head(feat) # [B, preds_per_cell, S', S']\n",
+        "\n",
+        "        # Resize feature grid to the configured grid_size.\n",
+        "        if out.shape[-1] != self.grid_size or out.shape[-2] != self.grid_size:\n",
+        "            out = nn.functional.adaptive_avg_pool2d(out, self.grid_size)\n",
+        "        # [B, C, S, S] -> [B, S, S, C]\n",
+        "        return out.permute(0, 2, 3, 1).contiguous()\n",
+        "\n",
+        "    def decode(self, outputs):\n",
+        "        \"\"\"Decode raw logits -> per-cell xyxy boxes, objectness, class probs.\"\"\"\n",
+        "        return decode_grid(outputs, self.grid_size)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "GQpthcwy_xBf"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== utils/criterions.py =====\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "\n",
+        "# =============================================================================\n",
+        "# Per-instance / per-sample detection criterions (YOLO-v1 style loss + IoU)\n",
+        "# =============================================================================\n",
+        "# The detection dataset yields, per sample, a [N, 6] target tensor\n",
+        "# ``[x1, y1, x2, y2, class_id, confidence]`` (normalized to [0, 1]). Each GT box\n",
+        "# is assigned to the grid cell containing its center; that cell is \"responsible\"\n",
+        "# for predicting the box.\n",
+        "#\n",
+        "# * PerSampleDetectionLoss -> one differentiable loss scalar per sample ([B]),\n",
+        "# wrapped with ``per_sample=True`` (the value WL backprops + dashboards).\n",
+        "# * PerSampleIoU -> mean IoU over a sample's boxes ([B]), a metric.\n",
+        "# * PerInstanceIoU -> flat tensor of one IoU per GT box (sample-major\n",
+        "# order), wrapped with ``per_instance=True`` so WL auto-saves it at\n",
+        "# (sample_id, annotation_id). The ordering matches the per-sample target\n",
+        "# iteration, so the wrapper's auto ``batch_idx`` maps each value correctly.\n",
+        "\n",
+        "_EPS = 1e-6\n",
+        "_LAMBDA_COORD = 5.0\n",
+        "_LAMBDA_NOOBJ = 0.5\n",
+        "\n",
+        "\n",
+        "def box_iou_xyxy(a, b):\n",
+        "    \"\"\"IoU between two aligned sets of xyxy boxes. a, b: [..., 4] -> [...].\"\"\"\n",
+        "    x1 = torch.maximum(a[..., 0], b[..., 0])\n",
+        "    y1 = torch.maximum(a[..., 1], b[..., 1])\n",
+        "    x2 = torch.minimum(a[..., 2], b[..., 2])\n",
+        "    y2 = torch.minimum(a[..., 3], b[..., 3])\n",
+        "\n",
+        "    inter = (x2 - x1).clamp(min=0) * (y2 - y1).clamp(min=0)\n",
+        "    area_a = (a[..., 2] - a[..., 0]).clamp(min=0) * (a[..., 3] - a[..., 1]).clamp(min=0)\n",
+        "    area_b = (b[..., 2] - b[..., 0]).clamp(min=0) * (b[..., 3] - b[..., 1]).clamp(min=0)\n",
+        "    union = area_a + area_b - inter + _EPS\n",
+        "    return inter / union\n",
+        "\n",
+        "\n",
+        "def _responsible_cells(boxes, S):\n",
+        "    \"\"\"Map GT boxes -> their responsible (row, col) cell and center offsets.\n",
+        "\n",
+        "    Args:\n",
+        "        boxes: [N, 4] xyxy in [0, 1].\n",
+        "        S: grid size.\n",
+        "\n",
+        "    Returns:\n",
+        "        rows, cols: [N] long, the responsible cell indices.\n",
+        "        off_x, off_y: [N] center offset within the cell, in [0, 1).\n",
+        "        w, h: [N] box size as a fraction of the image.\n",
+        "    \"\"\"\n",
+        "    cx = (boxes[:, 0] + boxes[:, 2]) / 2\n",
+        "    cy = (boxes[:, 1] + boxes[:, 3]) / 2\n",
+        "    w = (boxes[:, 2] - boxes[:, 0]).clamp(_EPS, 1.0)\n",
+        "    h = (boxes[:, 3] - boxes[:, 1]).clamp(_EPS, 1.0)\n",
+        "\n",
+        "    cols = (cx * S).long().clamp(0, S - 1)\n",
+        "    rows = (cy * S).long().clamp(0, S - 1)\n",
+        "    off_x = (cx * S - cols).clamp(0, 1)\n",
+        "    off_y = (cy * S - rows).clamp(0, 1)\n",
+        "    return rows, cols, off_x, off_y, w, h\n",
+        "\n",
+        "\n",
+        "def _per_sample_loss(outputs, targets, num_classes, weights=None):\n",
+        "    \"\"\"YOLO-v1 style loss, returned as one scalar per sample ([B], with grad).\"\"\"\n",
+        "    B, S = outputs.shape[0], outputs.shape[1]\n",
+        "    device = outputs.device\n",
+        "\n",
+        "    obj_logit = outputs[..., 0] # [B, S, S]\n",
+        "    tx = torch.sigmoid(outputs[..., 1])\n",
+        "    ty = torch.sigmoid(outputs[..., 2])\n",
+        "    w_pred = torch.sigmoid(outputs[..., 3])\n",
+        "    h_pred = torch.sigmoid(outputs[..., 4])\n",
+        "    cls_logits = outputs[..., 5:] # [B, S, S, C]\n",
+        "\n",
+        "    if weights is not None:\n",
+        "        weights = torch.as_tensor(weights, device=device, dtype=outputs.dtype)\n",
+        "\n",
+        "    losses = []\n",
+        "    for s in range(B):\n",
+        "        tgt = targets[s]\n",
+        "        tgt = torch.as_tensor(tgt, device=device, dtype=outputs.dtype)\n",
+        "        if tgt.ndim == 1:\n",
+        "            tgt = tgt.view(-1, 6) if tgt.numel() else tgt.view(0, 6)\n",
+        "\n",
+        "        obj_target = torch.zeros((S, S), device=device, dtype=outputs.dtype)\n",
+        "\n",
+        "        coord_loss = torch.zeros((), device=device)\n",
+        "        class_loss = torch.zeros((), device=device)\n",
+        "\n",
+        "        if tgt.numel() > 0:\n",
+        "            boxes = tgt[:, :4]\n",
+        "            cls_ids = tgt[:, 4].long().clamp(0, num_classes - 1)\n",
+        "            rows, cols, off_x, off_y, gw, gh = _responsible_cells(boxes, S)\n",
+        "\n",
+        "            obj_target[rows, cols] = 1.0\n",
+        "\n",
+        "            # Localization: center offset (linear) + size in sqrt space (YOLO trick\n",
+        "            # so small-box errors weigh as much as large-box ones).\n",
+        "            coord = (\n",
+        "                (tx[s, rows, cols] - off_x) ** 2\n",
+        "                + (ty[s, rows, cols] - off_y) ** 2\n",
+        "                + (torch.sqrt(w_pred[s, rows, cols] + _EPS) - torch.sqrt(gw + _EPS)) ** 2\n",
+        "                + (torch.sqrt(h_pred[s, rows, cols] + _EPS) - torch.sqrt(gh + _EPS)) ** 2\n",
+        "            )\n",
+        "            coord_loss = _LAMBDA_COORD * coord.sum()\n",
+        "\n",
+        "            ce = F.cross_entropy(\n",
+        "                cls_logits[s, rows, cols], cls_ids, reduction=\"none\"\n",
+        "            )\n",
+        "            if weights is not None:\n",
+        "                ce = ce * weights[cls_ids]\n",
+        "            class_loss = ce.sum()\n",
+        "\n",
+        "        # Objectness BCE over the whole grid; down-weight the (many) empty cells.\n",
+        "        obj_weight = torch.where(\n",
+        "            obj_target > 0,\n",
+        "            torch.ones_like(obj_target),\n",
+        "            torch.full_like(obj_target, _LAMBDA_NOOBJ),\n",
+        "        )\n",
+        "        obj_loss = (\n",
+        "            F.binary_cross_entropy_with_logits(\n",
+        "                obj_logit[s], obj_target, reduction=\"none\"\n",
+        "            ) * obj_weight\n",
+        "        ).sum()\n",
+        "\n",
+        "        losses.append(coord_loss + class_loss + obj_loss)\n",
+        "\n",
+        "    return torch.stack(losses)\n",
+        "\n",
+        "\n",
+        "def _per_box_iou(outputs, targets, grid_size):\n",
+        "    \"\"\"IoU of each GT box against its responsible cell's decoded prediction.\n",
+        "\n",
+        "    Returns a list[B] of 1-D tensors (one IoU per box for that sample, in\n",
+        "    annotation order). Detached — this is a metric, not a loss.\n",
+        "    \"\"\"\n",
+        "    boxes_grid, _, _ = decode_grid(outputs, grid_size) # [B, S, S, 4]\n",
+        "    B = outputs.shape[0]\n",
+        "    S = grid_size\n",
+        "    device = outputs.device\n",
+        "\n",
+        "    per_sample = []\n",
+        "    for s in range(B):\n",
+        "        tgt = torch.as_tensor(targets[s], device=device, dtype=outputs.dtype)\n",
+        "        if tgt.numel() == 0:\n",
+        "            per_sample.append(torch.zeros(0, device=device))\n",
+        "            continue\n",
+        "        if tgt.ndim == 1:\n",
+        "            tgt = tgt.view(-1, 6)\n",
+        "\n",
+        "        gt_boxes = tgt[:, :4]\n",
+        "        rows, cols, _, _, _, _ = _responsible_cells(gt_boxes, S)\n",
+        "        pred_boxes = boxes_grid[s, rows, cols] # [N, 4]\n",
+        "        ious = box_iou_xyxy(pred_boxes, gt_boxes) # [N]\n",
+        "        per_sample.append(ious.detach())\n",
+        "\n",
+        "    return per_sample\n",
+        "\n",
+        "\n",
+        "class PerSampleDetectionLoss(nn.Module):\n",
+        "    \"\"\"Total detection loss aggregated to one value per sample ([B]).\"\"\"\n",
+        "\n",
+        "    def __init__(self, num_classes, grid_size, weights=None):\n",
+        "        super().__init__()\n",
+        "        self.num_classes = num_classes\n",
+        "        self.grid_size = grid_size\n",
+        "        self.register_buffer(\n",
+        "            \"weights\", torch.tensor(weights) if weights is not None else None\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, outputs, targets):\n",
+        "        return _per_sample_loss(outputs, targets, self.num_classes, weights=self.weights)\n",
+        "\n",
+        "\n",
+        "class PerSampleIoU(nn.Module):\n",
+        "    \"\"\"Mean IoU over a sample's boxes -> one value per sample ([B]).\"\"\"\n",
+        "\n",
+        "    def __init__(self, num_classes, grid_size):\n",
+        "        super().__init__()\n",
+        "        self.num_classes = num_classes\n",
+        "        self.grid_size = grid_size\n",
+        "\n",
+        "    def forward(self, outputs, targets):\n",
+        "        per_sample = _per_box_iou(outputs, targets, self.grid_size)\n",
+        "        out = [\n",
+        "            v.mean() if v.numel() > 0 else torch.zeros((), device=outputs.device)\n",
+        "            for v in per_sample\n",
+        "        ]\n",
+        "        return torch.stack(out).detach()\n",
+        "\n",
+        "\n",
+        "class PerInstanceIoU(nn.Module):\n",
+        "    \"\"\"IoU per GT box -> flat tensor [total_boxes] (sample-major order).\"\"\"\n",
+        "\n",
+        "    def __init__(self, num_classes, grid_size):\n",
+        "        super().__init__()\n",
+        "        self.num_classes = num_classes\n",
+        "        self.grid_size = grid_size\n",
+        "\n",
+        "    def forward(self, outputs, targets):\n",
+        "        per_sample = _per_box_iou(outputs, targets, self.grid_size)\n",
+        "        flat = [v for s in per_sample for v in s]\n",
+        "        if not flat:\n",
+        "            return torch.zeros(0, device=outputs.device)\n",
+        "        return torch.stack(flat).detach()\n",
+        "\n",
+        "\n",
+        "# =============================================================================\n",
+        "# Inference-time decoding (for UI prediction overlays)\n",
+        "# =============================================================================\n",
+        "def decode_predictions(outputs, grid_size, conf_thresh=0.3, max_det=10):\n",
+        "    \"\"\"Turn raw grid logits into a per-sample list of detected boxes.\n",
+        "\n",
+        "    Returns list[B] of [M, 6] numpy-friendly tensors\n",
+        "    ``[x1, y1, x2, y2, class_id, confidence]`` (kept on CPU, detached) — the\n",
+        "    exact 6-column schema WL renders for detection predictions.\n",
+        "    \"\"\"\n",
+        "    boxes_grid, obj, cls_probs = decode_grid(outputs, grid_size)\n",
+        "    B, S = outputs.shape[0], grid_size\n",
+        "\n",
+        "    cls_conf, cls_id = cls_probs.max(dim=-1) # [B, S, S]\n",
+        "    score = obj * cls_conf # combined confidence\n",
+        "\n",
+        "    flat_boxes = boxes_grid.view(B, S * S, 4)\n",
+        "    flat_score = score.view(B, S * S)\n",
+        "    flat_cls = cls_id.view(B, S * S)\n",
+        "\n",
+        "    results = []\n",
+        "    for s in range(B):\n",
+        "        keep = flat_score[s] >= conf_thresh\n",
+        "        if keep.sum() == 0:\n",
+        "            results.append(torch.zeros((0, 6)))\n",
+        "            continue\n",
+        "        sc = flat_score[s][keep]\n",
+        "        bx = flat_boxes[s][keep]\n",
+        "        cl = flat_cls[s][keep].to(bx.dtype)\n",
+        "\n",
+        "        # Keep the most confident detections (cheap top-k in place of full NMS).\n",
+        "        order = torch.argsort(sc, descending=True)[:max_det]\n",
+        "        det = torch.cat([bx[order], cl[order, None], sc[order, None]], dim=1)\n",
+        "        results.append(det.detach().cpu())\n",
+        "\n",
+        "    return results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9hjJ9fHD_xBf"
+      },
+      "source": [
+        "## 3. Configuration\n",
+        "\n",
+        "Every tunable lives here in one dict (the example's `config.yaml`, inlined with its comments). Wrapping it with `flag=\"hyperparameters\"` lets the Studio UI read - and live-edit - these values while training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "YZCPlGjN_xBf",
+        "outputId": "97c94163-c64d-4f7e-8ce7-d812b5983885",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:32:57.025 INFO:weightslab.components.checkpoint_manager:__init__: CheckpointManager initialized at /tmp/tmpf35sd4_z\n",
+            "16/07/2026-08:32:57.030 INFO:weightslab.src:watch_or_edit: Registered new checkpoint manager in ledger\n",
+            "16/07/2026-08:32:57.040 INFO:root:set_log_directory: Log file moved from /tmp/tmpmt3c2rur/weightslab_20260716_083240.log to /tmp/tmpf35sd4_z/weightslab_20260716_083240.log\n",
+            "16/07/2026-08:32:57.041 INFO:root:set_log_directory: Log directory updated to: /tmp/tmpf35sd4_z\n",
+            "16/07/2026-08:32:57.044 INFO:root:set_log_directory: Log file: /tmp/tmpf35sd4_z/weightslab_20260716_083240.log\n",
+            "Experiment logs -> /tmp/tmpf35sd4_z\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Inlined from the example's config.yaml (comments preserved).\n",
+        "config = {\n",
+        "    # -- Global -----------------------------------------------------------\n",
+        "    \"device\": \"auto\",                          # auto -> cuda if available else cpu (resolved in the imports cell)\n",
+        "    \"experiment_name\": \"detection_pennfudan_usecase\",  # name shown in Weights Studio\n",
+        "    \"training_steps_to_do\": None,              # None = open-ended in the example; capped below for Colab\n",
+        "    \"root_log_dir\": None,                      # None -> a temp dir is created below\n",
+        "\n",
+        "    \"checkpoint_manager\": {\n",
+        "        \"load_config\": False,                  # ignore a previous run's config so edits here take effect\n",
+        "    },\n",
+        "\n",
+        "    # Initially compute natural-sort signals, e.g. average intensity.\n",
+        "    \"compute_natural_sort\": True,\n",
+        "\n",
+        "    # -- Experiment schedule ---------------------------------------------\n",
+        "    \"eval_full_to_train_steps_ratio\": 5,       # run a full eval every N steps\n",
+        "    \"experiment_dump_to_train_steps_ratio\": 5, # export history + dataframe every N steps\n",
+        "    \"tqdm_display\": True,\n",
+        "    \"is_training\": False,                      # start training immediately or not\n",
+        "\n",
+        "    # -- Serving ----------------------------------------------------------\n",
+        "    \"serving_grpc\": True,                      # expose the gRPC backend for Weights Studio\n",
+        "    \"serving_cli\": True,\n",
+        "\n",
+        "    # -- Global dataframe storage ----------------------------------------\n",
+        "    \"ledger_enable_h5_persistence\": True,\n",
+        "    \"ledger_enable_flushing_threads\": True,\n",
+        "    \"ledger_flush_max_rows\": 100,\n",
+        "    \"ledger_flush_interval\": 60.0,\n",
+        "\n",
+        "    # -- Model / task -----------------------------------------------------\n",
+        "    \"num_classes\": 1,                          # Penn-Fudan: single class (person)\n",
+        "    \"image_size\": 128,\n",
+        "    \"grid_size\": 8,                            # detector predicts on an 8x8 cell grid\n",
+        "    \"conf_thresh\": 0.3,                        # objectness*class confidence threshold for displayed predictions\n",
+        "    \"pretrained_backbone\": True,               # ImageNet-pretrained MobileNetV3-Small feature extractor\n",
+        "    \"freeze_backbone\": True,                   # train only the detection head (faster, less data hungry)\n",
+        "\n",
+        "    \"optimizer\": {\n",
+        "        \"lr\": 0.001,\n",
+        "    },\n",
+        "\n",
+        "    # -- Data (Penn-Fudan pedestrians; ~170 images downloaded on first run under data_root) --\n",
+        "    \"data_root\": \"./data\",                     # portable relative path; the dataset downloads here on first run\n",
+        "    \"data\": {\n",
+        "        \"train_loader\": {\n",
+        "            \"batch_size\": 8,\n",
+        "            \"shuffle\": True,\n",
+        "            \"max_samples\": None,               # None = use the full training split\n",
+        "        },\n",
+        "        \"test_loader\": {\n",
+        "            \"batch_size\": 8,\n",
+        "            \"shuffle\": False,\n",
+        "            \"max_samples\": None,\n",
+        "        },\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "wl.watch_or_edit(config, flag=\"hyperparameters\", defaults=config, poll_interval=1.0)\n",
+        "\n",
+        "# Resolve the log directory (a temp dir when unset), mirroring the example's main.py.\n",
+        "log_dir = config.get(\"root_log_dir\") or tempfile.mkdtemp(prefix=\"weightslab_detection_\")\n",
+        "config[\"root_log_dir\"] = log_dir\n",
+        "os.makedirs(log_dir, exist_ok=True)\n",
+        "print(\"Experiment logs ->\", log_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vWWllcOA_xBg"
+      },
+      "source": [
+        "## 4. Data\n",
+        "\n",
+        "Build the Penn-Fudan train/val datasets (the ~170 images **download automatically** on first construction) and wrap them as tracked dataloaders. Detection targets have a variable number of boxes per image, so the custom `det_collate` keeps them as a list."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "Xqkj3Lo__xBg",
+        "outputId": "ae81a09b-c057-45d3-e3b3-254d99b31e78",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[data] Downloading Penn-Fudan dataset to ./data/PennFudanPed.zip ...\n",
+            "[data] Extracting Penn-Fudan ...\n",
+            "16/07/2026-08:33:02.465 INFO:weightslab.data.data_samples_with_ops:__init__: [DataSampleTrackingWrapper] H5 persistence enabled at /tmp/tmpf35sd4_z/checkpoints/data/data.h5\n",
+            "16/07/2026-08:33:02.469 INFO:weightslab.data.data_samples_with_ops:__init__: Preloading sample statistics for PHYSICAL indices in split 'train_loader' with: preload_metadata=True...\n",
+            "16/07/2026-08:33:02.471 INFO:weightslab.data.data_samples_with_ops:__init__: Labels will be loaded on demand from the wrapped dataset for split 'train_loader' when accessed, which may increase latency on first access but reduces initialization time.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Initializing ledger for split 'train_loader': 100%|██████████| 136/136 [00:00<00:00, 94114.06it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:02.481 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] Registering split 'train_loader' with 136 samples.\n",
+            "16/07/2026-08:33:02.527 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] After annotation expansion: 136 samples → 136 annotation rows.\n",
+            "16/07/2026-08:33:02.528 INFO:weightslab.data.h5_array_store:__init__: [H5ArrayStore] Initialized with cache limit: 2048MB\n",
+            "16/07/2026-08:33:02.562 INFO:weightslab.data.data_samples_with_ops:__init__: [DataSampleTrackingWrapper] H5 persistence enabled at /tmp/tmpf35sd4_z/checkpoints/data/data.h5\n",
+            "16/07/2026-08:33:02.564 INFO:weightslab.data.data_samples_with_ops:__init__: Preloading sample statistics for PHYSICAL indices in split 'test_loader' with: preload_labels=True, preload_metadata=True...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Initializing ledger for split 'test_loader': 100%|██████████| 34/34 [00:00<00:00, 102.86it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:02.902 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] Registering split 'test_loader' with 34 samples.\n",
+            "16/07/2026-08:33:02.907 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] After annotation expansion: 34 samples → 34 annotation rows.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Penn-Fudan auto-downloads (~170 images) on first dataset construction - nothing to upload.\n",
+        "data_root = config.get(\"data_root\") or \"./data\"\n",
+        "os.makedirs(data_root, exist_ok=True)\n",
+        "\n",
+        "num_classes = int(config[\"num_classes\"])\n",
+        "image_size = int(config[\"image_size\"])\n",
+        "grid_size = int(config[\"grid_size\"])\n",
+        "conf_thresh = float(config[\"conf_thresh\"])\n",
+        "\n",
+        "train_cfg = config.get(\"data\", {}).get(\"train_loader\", {})\n",
+        "test_cfg = config.get(\"data\", {}).get(\"test_loader\", {})\n",
+        "\n",
+        "_train_dataset = PennFudanDetectionDataset(\n",
+        "    root=data_root,\n",
+        "    split=\"train\",\n",
+        "    num_classes=num_classes,\n",
+        "    image_size=image_size,\n",
+        "    max_samples=train_cfg.get(\"max_samples\", None),\n",
+        ")\n",
+        "_val_dataset = PennFudanDetectionDataset(\n",
+        "    root=data_root,\n",
+        "    split=\"val\",\n",
+        "    num_classes=num_classes,\n",
+        "    image_size=image_size,\n",
+        "    max_samples=test_cfg.get(\"max_samples\", None),\n",
+        ")\n",
+        "\n",
+        "# Tracked dataloaders (detection uses the custom det_collate defined above).\n",
+        "train_loader = wl.watch_or_edit(\n",
+        "    _train_dataset,\n",
+        "    flag=\"data\",\n",
+        "    loader_name=\"train_loader\",\n",
+        "    batch_size=train_cfg.get(\"batch_size\", 8),\n",
+        "    shuffle=train_cfg.get(\"shuffle\", True),\n",
+        "    compute_hash=False,\n",
+        "    is_training=True,\n",
+        "    array_autoload_arrays=False,\n",
+        "    array_return_proxies=True,\n",
+        "    array_use_cache=True,\n",
+        "    preload_labels=False,\n",
+        "    collate_fn=det_collate,\n",
+        ")\n",
+        "test_loader = wl.watch_or_edit(\n",
+        "    _val_dataset,\n",
+        "    flag=\"data\",\n",
+        "    loader_name=\"test_loader\",\n",
+        "    batch_size=test_cfg.get(\"batch_size\", 8),\n",
+        "    shuffle=test_cfg.get(\"shuffle\", False),\n",
+        "    compute_hash=False,\n",
+        "    is_training=False,\n",
+        "    array_autoload_arrays=False,\n",
+        "    array_return_proxies=True,\n",
+        "    array_use_cache=True,\n",
+        "    preload_labels=True,\n",
+        "    collate_fn=det_collate,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pvj_ywPr_xBg"
+      },
+      "source": [
+        "## 5. Model, optimizer and tracked signals\n",
+        "\n",
+        "Wrap the detector, optimizer, loss and IoU metrics with `wl.watch_or_edit(...)`. The loss is tracked **per sample**; per-instance IoU stores one value per ground-truth box at `(sample_id, annotation_id)`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "21SKj-oK_xBg",
+        "outputId": "698a493f-d5a5-4e42-c399-563b6addc2bf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading: \"https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v3_small-047dcff4.pth\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 9.83M/9.83M [00:00<00:00, 133MB/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:23.329 INFO:weightslab.backend.model_interface:__init__: Using checkpoint manager from ledger\n",
+            "\n",
+            "============================================================\n",
+            "Computing class weights for 1 classes (max 200 samples)...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            " Analyzing Distribution: 100%|██████████| 136/136 [00:00<00:00, 147.35it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Class distribution and weights:\n",
+            "Class 0 (person): 100.00% -> weight: 1.000\n",
+            "============================================================\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "/tmp/ipykernel_5133/4133204388.py:171: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+            "  \"weights\", torch.tensor(weights) if weights is not None else None\n"
+          ]
+        }
+      ],
+      "source": [
+        "# --- Model ---\n",
+        "_model = SmallDetector(\n",
+        "    in_channels=3, num_classes=num_classes,\n",
+        "    image_size=image_size, grid_size=grid_size,\n",
+        "    pretrained=bool(config[\"pretrained_backbone\"]),\n",
+        "    freeze_backbone=bool(config[\"freeze_backbone\"]),\n",
+        ").to(device)\n",
+        "model = wl.watch_or_edit(_model, flag=\"model\", device=device)\n",
+        "\n",
+        "# --- Optimizer (only trainable params; the backbone may be frozen) ---\n",
+        "lr = config.get(\"optimizer\", {}).get(\"lr\", 1e-3)\n",
+        "trainable = [p for p in model.parameters() if p.requires_grad]\n",
+        "_optimizer = optim.Adam(trainable, lr=lr)\n",
+        "optimizer = wl.watch_or_edit(_optimizer, flag=\"optimizer\")\n",
+        "\n",
+        "\n",
+        "# --- Detection loss (per sample) + IoU (per sample & per instance) signals ---\n",
+        "# per_sample=True auto-saves one value per sample; per_instance=True auto-saves\n",
+        "# one IoU per (sample_id, annotation_id), i.e. one per ground-truth box.\n",
+        "def _make_det_signals(split: str, weights=None) -> dict:\n",
+        "    return {\n",
+        "        \"loss\": wl.watch_or_edit(\n",
+        "            PerSampleDetectionLoss(num_classes, grid_size, weights=weights),\n",
+        "            flag=\"loss\",\n",
+        "            name=f\"{split}_loss/sample\", per_sample=True, log=True,\n",
+        "        ),\n",
+        "        \"iou_sample\": wl.watch_or_edit(\n",
+        "            PerSampleIoU(num_classes, grid_size), flag=\"metric\",\n",
+        "            name=f\"{split}_iou/sample\", per_sample=True, log=True,\n",
+        "        ),\n",
+        "        \"iou_instance\": wl.watch_or_edit(\n",
+        "            PerInstanceIoU(num_classes, grid_size), flag=\"metric\",\n",
+        "            name=f\"{split}_iou/instance\", per_instance=True, log=True,\n",
+        "        ),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "# Class weights from ground-truth box counts (optional; balances rare shapes).\n",
+        "def compute_class_weights(dataset, num_classes, max_samples=200):\n",
+        "    print(\"\\n\" + \"=\" * 60, flush=True)\n",
+        "    print(f\"Computing class weights for {num_classes} classes (max {max_samples} samples)...\", flush=True)\n",
+        "    class_counts = np.zeros(num_classes, dtype=np.float64)\n",
+        "    num_samples = min(len(dataset), max_samples)\n",
+        "\n",
+        "    for idx in tqdm.tqdm(range(num_samples), desc=\" Analyzing Distribution\"):\n",
+        "        _, _, target, _ = dataset.get_items(idx, include_labels=True)\n",
+        "        if target is None or len(target) == 0:\n",
+        "            continue\n",
+        "        for c in target[:, 4].astype(np.int64):\n",
+        "            if 0 <= c < num_classes:\n",
+        "                class_counts[c] += 1\n",
+        "\n",
+        "    class_counts = np.maximum(class_counts, 1)  # Avoid div by zero\n",
+        "    total = class_counts.sum()\n",
+        "    class_weights = total / (num_classes * class_counts)\n",
+        "    class_weights = class_weights / class_weights.mean()  # Normalize\n",
+        "\n",
+        "    print(\"\\nClass distribution and weights:\", flush=True)\n",
+        "    for c in range(num_classes):\n",
+        "        pct = (class_counts[c] / total) * 100\n",
+        "        print(f\"Class {c} ({dataset.class_names[c]}): {pct:6.2f}% -> weight: {class_weights[c]:.3f}\", flush=True)\n",
+        "    print(\"=\" * 60 + \"\\n\", flush=True)\n",
+        "    return torch.FloatTensor(class_weights).to(device)\n",
+        "\n",
+        "\n",
+        "weights = compute_class_weights(_train_dataset, num_classes)\n",
+        "\n",
+        "train_sig = _make_det_signals(\"train\", weights=weights)\n",
+        "test_sig = _make_det_signals(\"test\", weights=weights)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xjCJ2AYV_xBg"
+      },
+      "source": [
+        "## 6. Train and evaluate steps\n",
+        "\n",
+        "The `guard_training_context` / `guard_testing_context` blocks tell WeightsLab which phase it's in. The watched loss rides along with `batch_ids=ids` (so it is stored per sample) and `preds=` (so the decoded boxes are saved for the UI overlay)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "g_m26pWr_xBg"
+      },
+      "outputs": [],
+      "source": [
+        "def train(loader, model, optimizer, sig, device, grid_size, conf_thresh):\n",
+        "    \"\"\"Single training step using the tracked dataloader + watched loss.\n",
+        "\n",
+        "    loader yields (inputs, ids, targets, metadata) because of the\n",
+        "    DataSampleTrackingWrapper. `targets` is per sample a [N, 6] tensor of boxes\n",
+        "    ([x1, y1, x2, y2, class_id, confidence]); see utils/data.det_collate.\n",
+        "    \"\"\"\n",
+        "    with guard_training_context:\n",
+        "        (inputs, ids, targets, _) = next(loader)\n",
+        "        inputs = inputs.to(device)\n",
+        "        targets = [t.to(device) for t in targets]\n",
+        "\n",
+        "        optimizer.zero_grad()\n",
+        "        outputs = model(inputs) # [B, S, S, 5 + num_classes]\n",
+        "\n",
+        "        # Decoded boxes for the UI overlay (detached - display only).\n",
+        "        preds = decode_predictions(outputs.detach(), grid_size, conf_thresh=conf_thresh)\n",
+        "\n",
+        "        # Per-sample detection loss (tracked, saved, and the value we backprop).\n",
+        "        # `preds=` rides along so WL stores the predicted boxes for this batch.\n",
+        "        loss_per_sample = sig[\"loss\"](outputs, targets, batch_ids=ids, preds=preds)\n",
+        "\n",
+        "        # Metrics: per-sample mean IoU + per-instance IoU (one value per box).\n",
+        "        sig[\"iou_sample\"](outputs, targets, batch_ids=ids)\n",
+        "        sig[\"iou_instance\"](outputs, targets, batch_ids=ids)\n",
+        "\n",
+        "        loss = loss_per_sample.mean()\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "    return float(loss.detach().cpu().item())\n",
+        "\n",
+        "\n",
+        "def test(loader, model, sig, device, grid_size, conf_thresh, test_loader_len):\n",
+        "    \"\"\"Full evaluation pass over the val loader.\"\"\"\n",
+        "    losses = 0.0\n",
+        "    ious = 0.0\n",
+        "    with guard_testing_context, torch.no_grad():\n",
+        "        for inputs, ids, targets, _ in loader:\n",
+        "            inputs = inputs.to(device)\n",
+        "            targets = [t.to(device) for t in targets]\n",
+        "\n",
+        "            outputs = model(inputs)\n",
+        "            preds = decode_predictions(outputs, grid_size, conf_thresh=conf_thresh)\n",
+        "\n",
+        "            loss_per_sample = sig[\"loss\"](outputs, targets, batch_ids=ids, preds=preds)\n",
+        "            iou_per_sample = sig[\"iou_sample\"](outputs, targets, batch_ids=ids)\n",
+        "            sig[\"iou_instance\"](outputs, targets, batch_ids=ids)\n",
+        "\n",
+        "            losses += torch.mean(loss_per_sample)\n",
+        "            ious += torch.mean(iou_per_sample)\n",
+        "\n",
+        "    loss = float((losses / test_loader_len).detach().cpu().item())\n",
+        "    iou = float((ious / test_loader_len).detach().cpu().item())\n",
+        "    return loss, iou * 100.0 # Return mean IoU as percentage"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QsFSIK3j_xBg"
+      },
+      "source": [
+        "## 7. Serve and train\n",
+        "\n",
+        "`wl.serve(...)` starts the background gRPC server (and a public `bore` tunnel) that Weights Studio connects to. `wl.start_training(...)` flips the experiment into the *training* state, then we run the loop, periodically evaluating and exporting signals.\n",
+        "\n",
+        "Leave this cell **running** while you watch it stream in the UI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5yQOd8iv_xBg",
+        "outputId": "19c6f02b-80b3-4eea-a207-aeef7c83d6f5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:32.039 INFO:weightslab.trainer.trainer_services:_run_security_preflight: \n",
+            "\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "[gRPC] Security preflight checks:\n",
+            "\tTLS: DISABLED (unencrypted traffic)\n",
+            "\tAuth tokens: NONE configured\n",
+            "\t! WARNING: GRPC_TLS_ENABLED=0. Traffic will be unencrypted. Use only for development.\n",
+            "\t! WARNING: No GRPC_AUTH_TOKEN/GRPC_AUTH_TOKENS configured. Only transport-level trust (TLS/mTLS) will protect RPC access.\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "\n",
+            "16/07/2026-08:33:32.041 INFO:weightslab.trainer.trainer_services:grpc_serve: [gRPC] Watchdogs disabled via WEIGHTSLAB_DISABLE_WATCHDOGS.\n",
+            "16/07/2026-08:33:32.045 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Thread callback started\n",
+            "16/07/2026-08:33:32.045 INFO:weightslab.trainer.trainer_services:grpc_serve: [gRPC] Server started with watchdogs disabled (host=0.0.0.0 port=50051 workers=None)\n",
+            "16/07/2026-08:33:32.047 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Creating ThreadPoolExecutor with 6 worker threads (n_workers_grpc=None, max_concurrent_rpcs=None)\n",
+            "16/07/2026-08:33:32.051 INFO:weightslab.tunnel:_ensure_bore: Downloading bore v0.6.0 (bore-v0.6.0-x86_64-unknown-linux-musl.tar.gz)...\n",
+            "16/07/2026-08:33:32.129 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Server object created\n",
+            "16/07/2026-08:33:32.142 INFO:weightslab.trainer.services.agent.agent:__init__: Initializing DataManipulationAgent\n",
+            "16/07/2026-08:33:32.156 WARNING:weightslab.trainer.services.agent.agent:_load_config: Error loading config from /usr/local/lib/python3.12/dist-packages: [Errno 21] Is a directory: '/usr/local/lib/python3.12/dist-packages'\n",
+            "16/07/2026-08:33:32.157 INFO:weightslab.trainer.services.agent.agent:_load_config: \n",
+            "\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "Agent initialized from configuration /content/agent_config.yaml: \n",
+            "\tFinal Agent Configuration: Preferred Provider=openrouter, \n",
+            "\tFallback to Local=True, \n",
+            "\tOpenRouter Model=~google/gemini-flash-latest with:\n",
+            "\t\tAPI Key=None\n",
+            "\t\tBase URL=https://openrouter.ai/api/v1, \n",
+            "\tOllama Model=llama3.2:3b\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "\n",
+            "16/07/2026-08:33:32.158 INFO:weightslab.trainer.services.agent.agent:_setup_providers: Setting up Ollama with model llama3.2:3b\n",
+            "16/07/2026-08:33:32.260 INFO:weightslab.trainer.services.agent.agent:_setup_providers: [Agent] Ollama enabled: llama3.2:3b\n",
+            "16/07/2026-08:33:32.261 INFO:weightslab.trainer.services.data_service:_compute_natural_sort_stats: [DataService] Starting natural sort stats computation with weights: {'brightness': 0.7, 'entropy': 0.3, 'hue': 0.0}\n",
+            "16/07/2026-08:33:32.273 INFO:weightslab.trainer.services.data_service:_compute_natural_sort_stats: [DataService] Computing sort stats for 170 samples...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Computing Natural Sort Stats:  26%|██▌       | 44/170 [00:00<00:02, 44.78samples/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "============================================================\n",
+            " Backend exposed via bore at: bore.pub:41162\n",
+            " On your local machine (Docker running), run:\n",
+            "     weightslab start\n",
+            "     weightslab tunnel bore.pub:41162\n",
+            "============================================================\n",
+            "16/07/2026-08:33:33.256 INFO:weightslab.backend.cli:cli_serve: cli_thread_started\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\rComputing Natural Sort Stats:  31%|███       | 53/170 [00:00<00:02, 54.32samples/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:33.346 INFO:weightslab.src:start_training: Starting WeightsLab training mode with a timeout of 3 seconds.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Computing Natural Sort Stats: 100%|██████████| 170/170 [00:02<00:00, 62.04samples/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:35.145 INFO:weightslab.trainer.services.data_service:_compute_natural_sort_stats: [DataService] Completed stats computation for 170 samples\n",
+            "\n",
+            "\n",
+            "Natural sort computation finished for 170 samples\n",
+            "\n",
+            "\n",
+            "16/07/2026-08:33:35.147 INFO:weightslab.trainer.services.data_service:_build_preview_cache: [PreviewCache] Building 64×64 or less or less preview cache for 170 samples …\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "/usr/local/lib/python3.12/dist-packages/weightslab/data/dataframe_manager.py:703: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[7.64467454 7.63759617 7.82511253 7.60454149 7.68511861 7.70962498\n",
+            " 7.66256563 7.67420456 7.49258183 7.81929857 6.45717949 7.54281418\n",
+            " 6.79016624 7.48513405 7.32040805 7.82787458 7.77316711 7.5562755\n",
+            " 7.61231887 7.63537192 7.82628297 7.76547166 7.13113334 7.52482093\n",
+            " 7.63815989 7.7459368  7.58974044 7.75762173 7.18705444 7.48240921\n",
+            " 7.76600207 7.76543333 7.71791639 7.79005573]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
+            "  self._df.loc[existing_idx, all_cols] = df_norm.loc[existing_idx, all_cols]\n",
+            "/usr/local/lib/python3.12/dist-packages/weightslab/data/dataframe_manager.py:703: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[ 53.51681774  93.30201817  61.98435004  65.24323962  43.56923658\n",
+            "  93.96657441  67.03601995  73.99117037  80.06768645  84.95401979\n",
+            "  88.10120384  55.8291746   82.3123762   58.22935624 100.60901833\n",
+            "  94.05543837  79.249868    53.99237577  65.56806674  73.41094873\n",
+            "  99.16436384 101.13093832  93.03205657  99.69871153 101.89878035\n",
+            "  85.80537355  86.84879743  86.214422    99.49009009  81.44740104\n",
+            "  56.29921017  61.5624775   67.23737161  66.83788963]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
+            "  self._df.loc[existing_idx, all_cols] = df_norm.loc[existing_idx, all_cols]\n",
+            "/usr/local/lib/python3.12/dist-packages/weightslab/data/dataframe_manager.py:703: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[ 64.49066497  65.37024572 104.59268596  52.93642734  72.02098046\n",
+            "  48.14319779 113.24922957  46.29628522  43.88088143  25.53077158\n",
+            "  60.70043395  49.99961312  32.48544889  87.93221913  36.88762984\n",
+            "  47.55134406  57.05124766  64.13736411  65.30893805  96.26249138\n",
+            "  38.45269062  42.4448374   35.35399698  49.62630278  46.2604104\n",
+            "  42.64606061  32.72595598  68.50939554  46.54847017  59.36240918\n",
+            "  55.71851252  58.31763177  50.1109307   61.99646905]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
+            "  self._df.loc[existing_idx, all_cols] = df_norm.loc[existing_idx, all_cols]\n",
+            "/usr/local/lib/python3.12/dist-packages/weightslab/data/dataframe_manager.py:703: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0.65953828 0.61511859 0.63585766 0.62043339 0.63605741 0.58398832\n",
+            " 0.53613578 0.62181845 0.62159035 0.66207224 0.38428878 0.64687088\n",
+            " 0.52937324 0.5747199  0.54391107 0.61459091 0.61792182 0.55884669\n",
+            " 0.5585666  0.58468377 0.60382653 0.58175791 0.50977316 0.59231942\n",
+            " 0.5993173  0.63587023 0.63463003 0.58706704 0.4947014  0.59870562\n",
+            " 0.58584364 0.62449927 0.61637045 0.59844273]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
+            "  self._df.loc[existing_idx, all_cols] = df_norm.loc[existing_idx, all_cols]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:35.148 INFO:weightslab.trainer.services.data_service:__init__: DataService initialized.\n",
+            "16/07/2026-08:33:35.150 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Servicer added\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\r[PreviewCache]:   0%|          | 0/170 [00:00<?, ?sample/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:35.152 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Attempting to bind to 0.0.0.0:50051\n",
+            "16/07/2026-08:33:35.167 WARNING:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] TLS disabled; using insecure transport on 0.0.0.0:50051\n",
+            "16/07/2026-08:33:35.169 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Port 50051 bound successfully.\n",
+            "16/07/2026-08:33:35.172 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Server started and listening on 0.0.0.0:50051\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[PreviewCache]:  25%|██▌       | 43/170 [00:01<00:03, 41.14sample/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:36.350 INFO:weightslab.components.global_monitoring:resume: \n",
+            "Attempting to resume training...\n",
+            "16/07/2026-08:33:36.352 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: First time initialization; skipping hash update.\n",
+            "16/07/2026-08:33:36.359 INFO:weightslab.components.experiment_hash:has_changed: Experiment configuration changed: {'data', 'hp', 'model'}\n",
+            "16/07/2026-08:33:36.363 INFO:weightslab.components.experiment_hash:generate_hash: Generated experiment hash: 802319f761e1ba3dc3472a78- (HP: 802319f7, Model: 61e1ba3d, Data: c3472a78)\n",
+            "16/07/2026-08:33:36.365 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Initial experiment hash set: 802319f7-61e1ba3d-c3472a78\n",
+            "16/07/2026-08:33:36.369 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Changed components: {'data', 'hp', 'model'}\n",
+            "16/07/2026-08:33:36.375 INFO:weightslab.components.checkpoint_manager:_save_changes: Dumping hyperparameters config...\n",
+            "16/07/2026-08:33:36.382 INFO:weightslab.components.checkpoint_manager:save_config: Saved config: 802319f7_config.yaml with exp_hash prefix: 802319f7\n",
+            "16/07/2026-08:33:36.383 INFO:weightslab.components.checkpoint_manager:_save_changes: Dumping data snapshot...\n",
+            "16/07/2026-08:33:36.687 INFO:weightslab.components.checkpoint_manager:save_data_snapshot: Saved data snapshot: c3472a78_data_snapshot.json (170 rows) with RNG state\n",
+            "16/07/2026-08:33:36.689 INFO:weightslab.components.checkpoint_manager:_save_changes: Saving model weights checkpoint with component changes...\n",
+            "16/07/2026-08:33:37.098 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000000.pt\n",
+            "16/07/2026-08:33:37.125 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "16/07/2026-08:33:37.135 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "16/07/2026-08:33:37.147 INFO:weightslab.components.global_monitoring:resume: Hashes by module: ['802319f7', '61e1ba3d', 'c3472a78']\n",
+            "16/07/2026-08:33:37.151 INFO:weightslab.components.global_monitoring:resume: Resuming training now...\n",
+            "16/07/2026-08:33:37.153 INFO:weightslab.components.global_monitoring:resume: Hashes by module on resume: ['802319f7', '61e1ba3d', 'c3472a78']\n",
+            "16/07/2026-08:33:37.155 INFO:weightslab.components.global_monitoring:resume: \n",
+            "Training resumed as modules hashes have been computed: ['802319f7', '61e1ba3d', 'c3472a78'].\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "[PreviewCache]:  66%|██████▋   | 113/170 [00:05<00:03, 18.69sample/s]\n",
+            "Training:   0%|          | 0/1500 [00:03<?, ?it/s, iou=47.37%, test_loss=25.8813, train_loss=24.9436]\u001b[A\n",
+            "[PreviewCache]:  75%|███████▍  | 127/170 [00:06<00:02, 15.86sample/s]\n",
+            "Training:   0%|          | 1/1500 [00:04<1:16:41,  3.07s/it, iou=47.37%, test_loss=25.8813, train_loss=27.2859]\u001b[A\n",
+            "[PreviewCache]:  82%|████████▏ | 139/170 [00:06<00:01, 18.82sample/s]\n",
+            "[PreviewCache]:  84%|████████▎ | 142/170 [00:06<00:01, 18.55sample/s]\n",
+            "[PreviewCache]:  89%|████████▉ | 152/170 [00:07<00:01, 15.88sample/s]\n",
+            "Training:   0%|          | 3/1500 [00:05<33:40,  1.35s/it, iou=47.37%, test_loss=25.8813, train_loss=22.4157]\u001b[A\n",
+            "[PreviewCache]:  94%|█████████▎| 159/170 [00:08<00:00, 16.33sample/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:43.333 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000005.pt\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\r[PreviewCache]:  95%|█████████▌| 162/170 [00:08<00:00, 17.47sample/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:43.390 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[PreviewCache]:  98%|█████████▊| 166/170 [00:08<00:00, 18.14sample/s]\n",
+            "Training:   0%|          | 4/1500 [00:06<27:48,  1.12s/it, iou=47.37%, test_loss=25.8813, train_loss=21.5030]\u001b[A\n",
+            "[PreviewCache]: 100%|██████████| 170/170 [00:09<00:00, 18.40sample/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:44.392 INFO:weightslab.trainer.services.data_service:_build_preview_cache: [PreviewCache] Done: 170/170 cached in 9.2s (54.4 ms/sample)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:46.827 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 375 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   0%|          | 5/1500 [00:09<27:27,  1.10s/it, iou=47.70%, test_loss=18.9833, train_loss=19.8451]\u001b[A\n",
+            "Training:   0%|          | 6/1500 [00:09<43:13,  1.74s/it, iou=47.70%, test_loss=18.9833, train_loss=19.8451]\u001b[A\n",
+            "Training:   0%|          | 6/1500 [00:09<43:13,  1.74s/it, iou=47.70%, test_loss=18.9833, train_loss=18.7325]\u001b[A\n",
+            "Training:   0%|          | 7/1500 [00:09<31:21,  1.26s/it, iou=47.70%, test_loss=18.9833, train_loss=18.7325]\u001b[A\n",
+            "Training:   0%|          | 7/1500 [00:10<31:21,  1.26s/it, iou=47.70%, test_loss=18.9833, train_loss=16.8885]\u001b[A\n",
+            "Training:   1%|          | 8/1500 [00:10<23:11,  1.07it/s, iou=47.70%, test_loss=18.9833, train_loss=16.8885]\u001b[A\n",
+            "Training:   1%|          | 8/1500 [00:10<23:11,  1.07it/s, iou=47.70%, test_loss=18.9833, train_loss=17.0674]\u001b[A\n",
+            "Training:   1%|          | 9/1500 [00:10<17:58,  1.38it/s, iou=47.70%, test_loss=18.9833, train_loss=17.0674]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:47.825 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000010.pt\n",
+            "16/07/2026-08:33:47.861 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   1%|          | 9/1500 [00:10<17:58,  1.38it/s, iou=47.70%, test_loss=18.9833, train_loss=14.8362]\u001b[A\n",
+            "Training:   1%|          | 10/1500 [00:10<15:01,  1.65it/s, iou=47.70%, test_loss=18.9833, train_loss=14.8362]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:50.836 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 464 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   1%|          | 10/1500 [00:13<15:01,  1.65it/s, iou=45.74%, test_loss=13.2660, train_loss=16.0908]\u001b[A\n",
+            "Training:   1%|          | 11/1500 [00:13<32:24,  1.31s/it, iou=45.74%, test_loss=13.2660, train_loss=16.0908]\u001b[A\n",
+            "Training:   1%|          | 11/1500 [00:13<32:24,  1.31s/it, iou=45.74%, test_loss=13.2660, train_loss=14.5517]\u001b[A\n",
+            "Training:   1%|          | 12/1500 [00:13<24:44,  1.00it/s, iou=45.74%, test_loss=13.2660, train_loss=14.5517]\u001b[A\n",
+            "Training:   1%|          | 12/1500 [00:14<24:44,  1.00it/s, iou=45.74%, test_loss=13.2660, train_loss=14.2036]\u001b[A\n",
+            "Training:   1%|          | 13/1500 [00:14<19:17,  1.28it/s, iou=45.74%, test_loss=13.2660, train_loss=14.2036]\u001b[A\n",
+            "Training:   1%|          | 13/1500 [00:14<19:17,  1.28it/s, iou=45.74%, test_loss=13.2660, train_loss=16.1907]\u001b[A\n",
+            "Training:   1%|          | 14/1500 [00:14<15:25,  1.61it/s, iou=45.74%, test_loss=13.2660, train_loss=16.1907]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:51.899 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000015.pt\n",
+            "16/07/2026-08:33:51.938 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   1%|          | 14/1500 [00:15<15:25,  1.61it/s, iou=45.74%, test_loss=13.2660, train_loss=14.7916]\u001b[A\n",
+            "Training:   1%|          | 15/1500 [00:15<16:09,  1.53it/s, iou=45.74%, test_loss=13.2660, train_loss=14.7916]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:55.750 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 568 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   1%|          | 15/1500 [00:18<16:09,  1.53it/s, iou=40.51%, test_loss=11.1122, train_loss=14.7436]\u001b[A\n",
+            "Training:   1%|          | 16/1500 [00:18<36:19,  1.47s/it, iou=40.51%, test_loss=11.1122, train_loss=14.7436]\u001b[A\n",
+            "Training:   1%|          | 16/1500 [00:18<36:19,  1.47s/it, iou=40.51%, test_loss=11.1122, train_loss=13.4206]\u001b[A\n",
+            "Training:   1%|          | 17/1500 [00:18<28:18,  1.15s/it, iou=40.51%, test_loss=11.1122, train_loss=13.4206]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:   1%|          | 17/1500 [00:19<28:18,  1.15s/it, iou=40.51%, test_loss=11.1122, train_loss=11.4253]\u001b[A\n",
+            "Training:   1%|          | 18/1500 [00:19<22:33,  1.09it/s, iou=40.51%, test_loss=11.1122, train_loss=11.4253]\u001b[A\n",
+            "Training:   1%|          | 18/1500 [00:19<22:33,  1.09it/s, iou=40.51%, test_loss=11.1122, train_loss=11.7354]\u001b[A\n",
+            "Training:   1%|▏         | 19/1500 [00:19<17:59,  1.37it/s, iou=40.51%, test_loss=11.1122, train_loss=11.7354]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:33:57.089 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000020.pt\n",
+            "16/07/2026-08:33:57.137 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   1%|▏         | 19/1500 [00:20<17:59,  1.37it/s, iou=40.51%, test_loss=11.1122, train_loss=11.3669]\u001b[A\n",
+            "Training:   1%|▏         | 20/1500 [00:20<18:00,  1.37it/s, iou=40.51%, test_loss=11.1122, train_loss=11.3669]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:00.180 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   1%|▏         | 20/1500 [00:23<18:00,  1.37it/s, iou=44.67%, test_loss=10.1760, train_loss=12.9547]\u001b[A\n",
+            "Training:   1%|▏         | 21/1500 [00:23<32:03,  1.30s/it, iou=44.67%, test_loss=10.1760, train_loss=12.9547]\u001b[A\n",
+            "Training:   1%|▏         | 21/1500 [00:23<32:03,  1.30s/it, iou=44.67%, test_loss=10.1760, train_loss=11.0903]\u001b[A\n",
+            "Training:   1%|▏         | 22/1500 [00:23<24:36,  1.00it/s, iou=44.67%, test_loss=10.1760, train_loss=11.0903]\u001b[A\n",
+            "Training:   1%|▏         | 22/1500 [00:23<24:36,  1.00it/s, iou=44.67%, test_loss=10.1760, train_loss=10.4253]\u001b[A\n",
+            "Training:   2%|▏         | 23/1500 [00:23<19:05,  1.29it/s, iou=44.67%, test_loss=10.1760, train_loss=10.4253]\u001b[A\n",
+            "Training:   2%|▏         | 23/1500 [00:23<19:05,  1.29it/s, iou=44.67%, test_loss=10.1760, train_loss=9.0200] \u001b[A\n",
+            "Training:   2%|▏         | 24/1500 [00:23<14:56,  1.65it/s, iou=44.67%, test_loss=10.1760, train_loss=9.0200]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:01.200 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000025.pt\n",
+            "16/07/2026-08:34:01.240 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   2%|▏         | 24/1500 [00:24<14:56,  1.65it/s, iou=44.67%, test_loss=10.1760, train_loss=11.8999]\u001b[A\n",
+            "Training:   2%|▏         | 25/1500 [00:24<15:11,  1.62it/s, iou=44.67%, test_loss=10.1760, train_loss=11.8999]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:04.340 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   2%|▏         | 25/1500 [00:27<15:11,  1.62it/s, iou=51.55%, test_loss=9.6219, train_loss=9.8546]  \u001b[A\n",
+            "Training:   2%|▏         | 26/1500 [00:27<30:54,  1.26s/it, iou=51.55%, test_loss=9.6219, train_loss=9.8546]\u001b[A\n",
+            "Training:   2%|▏         | 26/1500 [00:27<30:54,  1.26s/it, iou=51.55%, test_loss=9.6219, train_loss=8.1830]\u001b[A\n",
+            "Training:   2%|▏         | 27/1500 [00:27<23:31,  1.04it/s, iou=51.55%, test_loss=9.6219, train_loss=8.1830]\u001b[A\n",
+            "Training:   2%|▏         | 27/1500 [00:27<23:31,  1.04it/s, iou=51.55%, test_loss=9.6219, train_loss=9.5422]\u001b[A\n",
+            "Training:   2%|▏         | 28/1500 [00:27<18:18,  1.34it/s, iou=51.55%, test_loss=9.6219, train_loss=9.5422]\u001b[A\n",
+            "Training:   2%|▏         | 28/1500 [00:27<18:18,  1.34it/s, iou=51.55%, test_loss=9.6219, train_loss=9.6377]\u001b[A\n",
+            "Training:   2%|▏         | 29/1500 [00:27<14:41,  1.67it/s, iou=51.55%, test_loss=9.6219, train_loss=9.6377]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:05.390 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000030.pt\n",
+            "16/07/2026-08:34:05.441 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   2%|▏         | 29/1500 [00:28<14:41,  1.67it/s, iou=51.55%, test_loss=9.6219, train_loss=8.9067]\u001b[A\n",
+            "Training:   2%|▏         | 30/1500 [00:28<15:48,  1.55it/s, iou=51.55%, test_loss=9.6219, train_loss=8.9067]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:09.358 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   2%|▏         | 30/1500 [00:32<15:48,  1.55it/s, iou=54.29%, test_loss=9.5101, train_loss=9.3380]\u001b[A\n",
+            "Training:   2%|▏         | 31/1500 [00:32<36:46,  1.50s/it, iou=54.29%, test_loss=9.5101, train_loss=9.3380]\u001b[A\n",
+            "Training:   2%|▏         | 31/1500 [00:32<36:46,  1.50s/it, iou=54.29%, test_loss=9.5101, train_loss=7.3796]\u001b[A\n",
+            "Training:   2%|▏         | 32/1500 [00:32<28:25,  1.16s/it, iou=54.29%, test_loss=9.5101, train_loss=7.3796]\u001b[A\n",
+            "Training:   2%|▏         | 32/1500 [00:32<28:25,  1.16s/it, iou=54.29%, test_loss=9.5101, train_loss=7.2175]\u001b[A\n",
+            "Training:   2%|▏         | 33/1500 [00:32<21:53,  1.12it/s, iou=54.29%, test_loss=9.5101, train_loss=7.2175]\u001b[A\n",
+            "Training:   2%|▏         | 33/1500 [00:33<21:53,  1.12it/s, iou=54.29%, test_loss=9.5101, train_loss=9.9013]\u001b[A\n",
+            "Training:   2%|▏         | 34/1500 [00:33<17:15,  1.42it/s, iou=54.29%, test_loss=9.5101, train_loss=9.9013]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:10.506 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000035.pt\n",
+            "16/07/2026-08:34:10.551 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   2%|▏         | 34/1500 [00:33<17:15,  1.42it/s, iou=54.29%, test_loss=9.5101, train_loss=7.8100]\u001b[A\n",
+            "Training:   2%|▏         | 35/1500 [00:33<14:37,  1.67it/s, iou=54.29%, test_loss=9.5101, train_loss=7.8100]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:13.037 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   2%|▏         | 35/1500 [00:35<14:37,  1.67it/s, iou=57.07%, test_loss=9.2599, train_loss=7.4459]\u001b[A\n",
+            "Training:   2%|▏         | 36/1500 [00:35<27:58,  1.15s/it, iou=57.07%, test_loss=9.2599, train_loss=7.4459]\u001b[A\n",
+            "Training:   2%|▏         | 36/1500 [00:36<27:58,  1.15s/it, iou=57.07%, test_loss=9.2599, train_loss=7.5624]\u001b[A\n",
+            "Training:   2%|▏         | 37/1500 [00:36<21:22,  1.14it/s, iou=57.07%, test_loss=9.2599, train_loss=7.5624]\u001b[A\n",
+            "Training:   2%|▏         | 37/1500 [00:36<21:22,  1.14it/s, iou=57.07%, test_loss=9.2599, train_loss=7.8204]\u001b[A\n",
+            "Training:   3%|▎         | 38/1500 [00:36<16:50,  1.45it/s, iou=57.07%, test_loss=9.2599, train_loss=7.8204]\u001b[A\n",
+            "Training:   3%|▎         | 38/1500 [00:36<16:50,  1.45it/s, iou=57.07%, test_loss=9.2599, train_loss=6.4217]\u001b[A\n",
+            "Training:   3%|▎         | 39/1500 [00:36<13:26,  1.81it/s, iou=57.07%, test_loss=9.2599, train_loss=6.4217]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:14.086 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000040.pt\n",
+            "16/07/2026-08:34:14.139 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   3%|▎         | 39/1500 [00:37<13:26,  1.81it/s, iou=57.07%, test_loss=9.2599, train_loss=7.6720]\u001b[A\n",
+            "Training:   3%|▎         | 40/1500 [00:37<15:09,  1.60it/s, iou=57.07%, test_loss=9.2599, train_loss=7.6720]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:17.171 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   3%|▎         | 40/1500 [00:40<15:09,  1.60it/s, iou=58.90%, test_loss=9.0395, train_loss=8.1707]\u001b[A\n",
+            "Training:   3%|▎         | 41/1500 [00:40<29:39,  1.22s/it, iou=58.90%, test_loss=9.0395, train_loss=8.1707]\u001b[A\n",
+            "Training:   3%|▎         | 41/1500 [00:40<29:39,  1.22s/it, iou=58.90%, test_loss=9.0395, train_loss=7.2282]\u001b[A\n",
+            "Training:   3%|▎         | 42/1500 [00:40<22:57,  1.06it/s, iou=58.90%, test_loss=9.0395, train_loss=7.2282]\u001b[A\n",
+            "Training:   3%|▎         | 42/1500 [00:40<22:57,  1.06it/s, iou=58.90%, test_loss=9.0395, train_loss=8.6966]\u001b[A\n",
+            "Training:   3%|▎         | 43/1500 [00:40<18:22,  1.32it/s, iou=58.90%, test_loss=9.0395, train_loss=8.6966]\u001b[A\n",
+            "Training:   3%|▎         | 43/1500 [00:40<18:22,  1.32it/s, iou=58.90%, test_loss=9.0395, train_loss=6.7036]\u001b[A\n",
+            "Training:   3%|▎         | 44/1500 [00:40<14:42,  1.65it/s, iou=58.90%, test_loss=9.0395, train_loss=6.7036]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:18.274 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000045.pt\n",
+            "16/07/2026-08:34:18.324 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   3%|▎         | 44/1500 [00:41<14:42,  1.65it/s, iou=58.90%, test_loss=9.0395, train_loss=6.2203]\u001b[A\n",
+            "Training:   3%|▎         | 45/1500 [00:41<14:17,  1.70it/s, iou=58.90%, test_loss=9.0395, train_loss=6.2203]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:22.704 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   3%|▎         | 45/1500 [00:45<14:17,  1.70it/s, iou=59.22%, test_loss=8.8994, train_loss=6.9592]\u001b[A\n",
+            "Training:   3%|▎         | 46/1500 [00:45<39:50,  1.64s/it, iou=59.22%, test_loss=8.8994, train_loss=6.9592]\u001b[A\n",
+            "Training:   3%|▎         | 46/1500 [00:45<39:50,  1.64s/it, iou=59.22%, test_loss=8.8994, train_loss=5.5530]\u001b[A\n",
+            "Training:   3%|▎         | 47/1500 [00:45<30:29,  1.26s/it, iou=59.22%, test_loss=8.8994, train_loss=5.5530]\u001b[A\n",
+            "Training:   3%|▎         | 47/1500 [00:46<30:29,  1.26s/it, iou=59.22%, test_loss=8.8994, train_loss=6.8501]\u001b[A\n",
+            "Training:   3%|▎         | 48/1500 [00:46<23:52,  1.01it/s, iou=59.22%, test_loss=8.8994, train_loss=6.8501]\u001b[A\n",
+            "Training:   3%|▎         | 48/1500 [00:46<23:52,  1.01it/s, iou=59.22%, test_loss=8.8994, train_loss=8.2917]\u001b[A\n",
+            "Training:   3%|▎         | 49/1500 [00:46<18:47,  1.29it/s, iou=59.22%, test_loss=8.8994, train_loss=8.2917]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:24.001 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000050.pt\n",
+            "16/07/2026-08:34:24.080 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   3%|▎         | 49/1500 [00:47<18:47,  1.29it/s, iou=59.22%, test_loss=8.8994, train_loss=9.6123]\u001b[A\n",
+            "Training:   3%|▎         | 50/1500 [00:47<19:35,  1.23it/s, iou=59.22%, test_loss=8.8994, train_loss=9.6123]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:27.568 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   3%|▎         | 50/1500 [00:50<19:35,  1.23it/s, iou=59.41%, test_loss=8.7234, train_loss=8.2036]\u001b[A\n",
+            "Training:   3%|▎         | 51/1500 [00:50<35:16,  1.46s/it, iou=59.41%, test_loss=8.7234, train_loss=8.2036]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:   3%|▎         | 51/1500 [00:50<35:16,  1.46s/it, iou=59.41%, test_loss=8.7234, train_loss=6.5294]\u001b[A\n",
+            "Training:   3%|▎         | 52/1500 [00:50<27:00,  1.12s/it, iou=59.41%, test_loss=8.7234, train_loss=6.5294]\u001b[A\n",
+            "Training:   3%|▎         | 52/1500 [00:51<27:00,  1.12s/it, iou=59.41%, test_loss=8.7234, train_loss=6.0641]\u001b[A\n",
+            "Training:   4%|▎         | 53/1500 [00:51<21:11,  1.14it/s, iou=59.41%, test_loss=8.7234, train_loss=6.0641]\u001b[A\n",
+            "Training:   4%|▎         | 53/1500 [00:51<21:11,  1.14it/s, iou=59.41%, test_loss=8.7234, train_loss=6.2614]\u001b[A\n",
+            "Training:   4%|▎         | 54/1500 [00:51<17:06,  1.41it/s, iou=59.41%, test_loss=8.7234, train_loss=6.2614]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:28.797 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000055.pt\n",
+            "16/07/2026-08:34:28.856 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   4%|▎         | 54/1500 [00:52<17:06,  1.41it/s, iou=59.41%, test_loss=8.7234, train_loss=7.0010]\u001b[A\n",
+            "Training:   4%|▎         | 55/1500 [00:52<17:05,  1.41it/s, iou=59.41%, test_loss=8.7234, train_loss=7.0010]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:32.102 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   4%|▎         | 55/1500 [00:54<17:05,  1.41it/s, iou=58.47%, test_loss=8.7129, train_loss=5.1185]\u001b[A\n",
+            "Training:   4%|▎         | 56/1500 [00:54<32:39,  1.36s/it, iou=58.47%, test_loss=8.7129, train_loss=5.1185]\u001b[A\n",
+            "Training:   4%|▎         | 56/1500 [00:55<32:39,  1.36s/it, iou=58.47%, test_loss=8.7129, train_loss=6.8213]\u001b[A\n",
+            "Training:   4%|▍         | 57/1500 [00:55<24:46,  1.03s/it, iou=58.47%, test_loss=8.7129, train_loss=6.8213]\u001b[A\n",
+            "Training:   4%|▍         | 57/1500 [00:55<24:46,  1.03s/it, iou=58.47%, test_loss=8.7129, train_loss=6.6799]\u001b[A\n",
+            "Training:   4%|▍         | 58/1500 [00:55<19:18,  1.25it/s, iou=58.47%, test_loss=8.7129, train_loss=6.6799]\u001b[A\n",
+            "Training:   4%|▍         | 58/1500 [00:55<19:18,  1.25it/s, iou=58.47%, test_loss=8.7129, train_loss=4.9410]\u001b[A\n",
+            "Training:   4%|▍         | 59/1500 [00:55<15:22,  1.56it/s, iou=58.47%, test_loss=8.7129, train_loss=4.9410]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:33.165 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000060.pt\n",
+            "16/07/2026-08:34:33.232 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   4%|▍         | 59/1500 [00:56<15:22,  1.56it/s, iou=58.47%, test_loss=8.7129, train_loss=6.8089]\u001b[A\n",
+            "Training:   4%|▍         | 60/1500 [00:56<15:33,  1.54it/s, iou=58.47%, test_loss=8.7129, train_loss=6.8089]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:37.543 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   4%|▍         | 60/1500 [01:00<15:33,  1.54it/s, iou=58.11%, test_loss=8.7326, train_loss=5.9401]\u001b[A\n",
+            "Training:   4%|▍         | 61/1500 [01:00<39:28,  1.65s/it, iou=58.11%, test_loss=8.7326, train_loss=5.9401]\u001b[A\n",
+            "Training:   4%|▍         | 61/1500 [01:00<39:28,  1.65s/it, iou=58.11%, test_loss=8.7326, train_loss=5.5466]\u001b[A\n",
+            "Training:   4%|▍         | 62/1500 [01:00<29:32,  1.23s/it, iou=58.11%, test_loss=8.7326, train_loss=5.5466]\u001b[A\n",
+            "Training:   4%|▍         | 62/1500 [01:00<29:32,  1.23s/it, iou=58.11%, test_loss=8.7326, train_loss=5.1427]\u001b[A\n",
+            "Training:   4%|▍         | 63/1500 [01:00<22:31,  1.06it/s, iou=58.11%, test_loss=8.7326, train_loss=5.1427]\u001b[A\n",
+            "Training:   4%|▍         | 63/1500 [01:01<22:31,  1.06it/s, iou=58.11%, test_loss=8.7326, train_loss=7.3151]\u001b[A\n",
+            "Training:   4%|▍         | 64/1500 [01:01<17:47,  1.34it/s, iou=58.11%, test_loss=8.7326, train_loss=7.3151]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:38.586 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000065.pt\n",
+            "16/07/2026-08:34:38.653 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   4%|▍         | 64/1500 [01:01<17:47,  1.34it/s, iou=58.11%, test_loss=8.7326, train_loss=5.6567]\u001b[A\n",
+            "Training:   4%|▍         | 65/1500 [01:01<17:27,  1.37it/s, iou=58.11%, test_loss=8.7326, train_loss=5.6567]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:41.775 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   4%|▍         | 65/1500 [01:04<17:27,  1.37it/s, iou=57.53%, test_loss=8.5456, train_loss=7.2425]\u001b[A\n",
+            "Training:   4%|▍         | 66/1500 [01:04<31:46,  1.33s/it, iou=57.53%, test_loss=8.5456, train_loss=7.2425]\u001b[A\n",
+            "Training:   4%|▍         | 66/1500 [01:04<31:46,  1.33s/it, iou=57.53%, test_loss=8.5456, train_loss=5.3459]\u001b[A\n",
+            "Training:   4%|▍         | 67/1500 [01:04<24:11,  1.01s/it, iou=57.53%, test_loss=8.5456, train_loss=5.3459]\u001b[A\n",
+            "Training:   4%|▍         | 67/1500 [01:05<24:11,  1.01s/it, iou=57.53%, test_loss=8.5456, train_loss=7.0804]\u001b[A\n",
+            "Training:   5%|▍         | 68/1500 [01:05<18:32,  1.29it/s, iou=57.53%, test_loss=8.5456, train_loss=7.0804]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:   5%|▍         | 68/1500 [01:05<18:32,  1.29it/s, iou=57.53%, test_loss=8.5456, train_loss=6.6209]\u001b[A\n",
+            "Training:   5%|▍         | 69/1500 [01:05<14:58,  1.59it/s, iou=57.53%, test_loss=8.5456, train_loss=6.6209]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:42.811 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000070.pt\n",
+            "16/07/2026-08:34:42.888 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   5%|▍         | 69/1500 [01:06<14:58,  1.59it/s, iou=57.53%, test_loss=8.5456, train_loss=5.0405]\u001b[A\n",
+            "Training:   5%|▍         | 70/1500 [01:06<14:53,  1.60it/s, iou=57.53%, test_loss=8.5456, train_loss=5.0405]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:46.591 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   5%|▍         | 70/1500 [01:09<14:53,  1.60it/s, iou=56.49%, test_loss=8.4816, train_loss=4.8159]\u001b[A\n",
+            "Training:   5%|▍         | 71/1500 [01:09<34:48,  1.46s/it, iou=56.49%, test_loss=8.4816, train_loss=4.8159]\u001b[A\n",
+            "Training:   5%|▍         | 71/1500 [01:09<34:48,  1.46s/it, iou=56.49%, test_loss=8.4816, train_loss=4.5909]\u001b[A\n",
+            "Training:   5%|▍         | 72/1500 [01:09<26:56,  1.13s/it, iou=56.49%, test_loss=8.4816, train_loss=4.5909]\u001b[A\n",
+            "Training:   5%|▍         | 72/1500 [01:10<26:56,  1.13s/it, iou=56.49%, test_loss=8.4816, train_loss=5.6193]\u001b[A\n",
+            "Training:   5%|▍         | 73/1500 [01:10<21:39,  1.10it/s, iou=56.49%, test_loss=8.4816, train_loss=5.6193]\u001b[A\n",
+            "Training:   5%|▍         | 73/1500 [01:10<21:39,  1.10it/s, iou=56.49%, test_loss=8.4816, train_loss=5.1928]\u001b[A\n",
+            "Training:   5%|▍         | 74/1500 [01:10<17:36,  1.35it/s, iou=56.49%, test_loss=8.4816, train_loss=5.1928]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:48.028 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000075.pt\n",
+            "16/07/2026-08:34:48.126 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   5%|▍         | 74/1500 [01:11<17:36,  1.35it/s, iou=56.49%, test_loss=8.4816, train_loss=4.6288]\u001b[A\n",
+            "Training:   5%|▌         | 75/1500 [01:11<17:32,  1.35it/s, iou=56.49%, test_loss=8.4816, train_loss=4.6288]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:51.765 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   5%|▌         | 75/1500 [01:14<17:32,  1.35it/s, iou=57.77%, test_loss=8.2938, train_loss=5.5815]\u001b[A\n",
+            "Training:   5%|▌         | 76/1500 [01:14<36:01,  1.52s/it, iou=57.77%, test_loss=8.2938, train_loss=5.5815]\u001b[A\n",
+            "Training:   5%|▌         | 76/1500 [01:14<36:01,  1.52s/it, iou=57.77%, test_loss=8.2938, train_loss=6.2571]\u001b[A\n",
+            "Training:   5%|▌         | 77/1500 [01:14<27:02,  1.14s/it, iou=57.77%, test_loss=8.2938, train_loss=6.2571]\u001b[A\n",
+            "Training:   5%|▌         | 77/1500 [01:15<27:02,  1.14s/it, iou=57.77%, test_loss=8.2938, train_loss=4.4990]\u001b[A\n",
+            "Training:   5%|▌         | 78/1500 [01:15<20:37,  1.15it/s, iou=57.77%, test_loss=8.2938, train_loss=4.4990]\u001b[A\n",
+            "Training:   5%|▌         | 78/1500 [01:15<20:37,  1.15it/s, iou=57.77%, test_loss=8.2938, train_loss=5.9401]\u001b[A\n",
+            "Training:   5%|▌         | 79/1500 [01:15<16:31,  1.43it/s, iou=57.77%, test_loss=8.2938, train_loss=5.9401]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:52.808 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000080.pt\n",
+            "16/07/2026-08:34:52.874 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   5%|▌         | 79/1500 [01:16<16:31,  1.43it/s, iou=57.77%, test_loss=8.2938, train_loss=5.0134]\u001b[A\n",
+            "Training:   5%|▌         | 80/1500 [01:16<16:29,  1.43it/s, iou=57.77%, test_loss=8.2938, train_loss=5.0134]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:57.324 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   5%|▌         | 80/1500 [01:20<16:29,  1.43it/s, iou=58.35%, test_loss=8.2570, train_loss=4.5621]\u001b[A\n",
+            "Training:   5%|▌         | 81/1500 [01:20<40:24,  1.71s/it, iou=58.35%, test_loss=8.2570, train_loss=4.5621]\u001b[A\n",
+            "Training:   5%|▌         | 81/1500 [01:20<40:24,  1.71s/it, iou=58.35%, test_loss=8.2570, train_loss=6.3609]\u001b[A\n",
+            "Training:   5%|▌         | 82/1500 [01:20<31:44,  1.34s/it, iou=58.35%, test_loss=8.2570, train_loss=6.3609]\u001b[A\n",
+            "Training:   5%|▌         | 82/1500 [01:20<31:44,  1.34s/it, iou=58.35%, test_loss=8.2570, train_loss=5.6215]\u001b[A\n",
+            "Training:   6%|▌         | 83/1500 [01:20<24:26,  1.03s/it, iou=58.35%, test_loss=8.2570, train_loss=5.6215]\u001b[A\n",
+            "Training:   6%|▌         | 83/1500 [01:21<24:26,  1.03s/it, iou=58.35%, test_loss=8.2570, train_loss=5.2091]\u001b[A\n",
+            "Training:   6%|▌         | 84/1500 [01:21<19:01,  1.24it/s, iou=58.35%, test_loss=8.2570, train_loss=5.2091]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:34:58.860 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000085.pt\n",
+            "16/07/2026-08:34:58.929 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   6%|▌         | 84/1500 [01:22<19:01,  1.24it/s, iou=58.35%, test_loss=8.2570, train_loss=6.3798]\u001b[A\n",
+            "Training:   6%|▌         | 85/1500 [01:22<18:55,  1.25it/s, iou=58.35%, test_loss=8.2570, train_loss=6.3798]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:02.472 INFO:weightslab.trainer.services.data_service:GetDataSplits: GetDataSplits returning: ['test_loader', 'train_loader']\n",
+            "16/07/2026-08:35:05.145 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   6%|▌         | 85/1500 [01:28<18:55,  1.25it/s, iou=57.66%, test_loss=8.2385, train_loss=6.1091]\u001b[A\n",
+            "Training:   6%|▌         | 86/1500 [01:28<55:42,  2.36s/it, iou=57.66%, test_loss=8.2385, train_loss=6.1091]\u001b[A\n",
+            "Training:   6%|▌         | 86/1500 [01:28<55:42,  2.36s/it, iou=57.66%, test_loss=8.2385, train_loss=4.8615]\u001b[A\n",
+            "Training:   6%|▌         | 87/1500 [01:28<41:41,  1.77s/it, iou=57.66%, test_loss=8.2385, train_loss=4.8615]\u001b[A\n",
+            "Training:   6%|▌         | 87/1500 [01:28<41:41,  1.77s/it, iou=57.66%, test_loss=8.2385, train_loss=5.0530]\u001b[A\n",
+            "Training:   6%|▌         | 88/1500 [01:28<30:49,  1.31s/it, iou=57.66%, test_loss=8.2385, train_loss=5.0530]\u001b[A\n",
+            "Training:   6%|▌         | 88/1500 [01:29<30:49,  1.31s/it, iou=57.66%, test_loss=8.2385, train_loss=4.6643]\u001b[A\n",
+            "Training:   6%|▌         | 89/1500 [01:29<24:35,  1.05s/it, iou=57.66%, test_loss=8.2385, train_loss=4.6643]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:06.696 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000090.pt\n",
+            "16/07/2026-08:35:06.774 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   6%|▌         | 89/1500 [01:29<24:35,  1.05s/it, iou=57.66%, test_loss=8.2385, train_loss=5.1209]\u001b[A\n",
+            "Training:   6%|▌         | 90/1500 [01:29<23:16,  1.01it/s, iou=57.66%, test_loss=8.2385, train_loss=5.1209]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:12.503 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   6%|▌         | 90/1500 [01:35<23:16,  1.01it/s, iou=56.59%, test_loss=8.0347, train_loss=4.7651]\u001b[A\n",
+            "Training:   6%|▌         | 91/1500 [01:35<54:15,  2.31s/it, iou=56.59%, test_loss=8.0347, train_loss=4.7651]\u001b[A\n",
+            "Training:   6%|▌         | 91/1500 [01:35<54:15,  2.31s/it, iou=56.59%, test_loss=8.0347, train_loss=4.8316]\u001b[A\n",
+            "Training:   6%|▌         | 92/1500 [01:35<39:56,  1.70s/it, iou=56.59%, test_loss=8.0347, train_loss=4.8316]\u001b[A\n",
+            "Training:   6%|▌         | 92/1500 [01:35<39:56,  1.70s/it, iou=56.59%, test_loss=8.0347, train_loss=3.6279]\u001b[A\n",
+            "Training:   6%|▌         | 93/1500 [01:35<29:44,  1.27s/it, iou=56.59%, test_loss=8.0347, train_loss=3.6279]\u001b[A\n",
+            "Training:   6%|▌         | 93/1500 [01:36<29:44,  1.27s/it, iou=56.59%, test_loss=8.0347, train_loss=4.0692]\u001b[A\n",
+            "Training:   6%|▋         | 94/1500 [01:36<22:30,  1.04it/s, iou=56.59%, test_loss=8.0347, train_loss=4.0692]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:13.542 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000095.pt\n",
+            "16/07/2026-08:35:13.673 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   6%|▋         | 94/1500 [01:36<22:30,  1.04it/s, iou=56.59%, test_loss=8.0347, train_loss=5.6108]\u001b[A\n",
+            "Training:   6%|▋         | 95/1500 [01:36<19:06,  1.23it/s, iou=56.59%, test_loss=8.0347, train_loss=5.6108]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:18.276 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   6%|▋         | 95/1500 [01:41<19:06,  1.23it/s, iou=56.87%, test_loss=8.1773, train_loss=5.4194]\u001b[A\n",
+            "Training:   6%|▋         | 96/1500 [01:41<45:03,  1.93s/it, iou=56.87%, test_loss=8.1773, train_loss=5.4194]\u001b[A\n",
+            "Training:   6%|▋         | 96/1500 [01:41<45:03,  1.93s/it, iou=56.87%, test_loss=8.1773, train_loss=4.6244]\u001b[A\n",
+            "Training:   6%|▋         | 97/1500 [01:41<33:34,  1.44s/it, iou=56.87%, test_loss=8.1773, train_loss=4.6244]\u001b[A\n",
+            "Training:   6%|▋         | 97/1500 [01:41<33:34,  1.44s/it, iou=56.87%, test_loss=8.1773, train_loss=5.3507]\u001b[A\n",
+            "Training:   7%|▋         | 98/1500 [01:41<25:28,  1.09s/it, iou=56.87%, test_loss=8.1773, train_loss=5.3507]\u001b[A\n",
+            "Training:   7%|▋         | 98/1500 [01:41<25:28,  1.09s/it, iou=56.87%, test_loss=8.1773, train_loss=6.4320]\u001b[A\n",
+            "Training:   7%|▋         | 99/1500 [01:41<19:39,  1.19it/s, iou=56.87%, test_loss=8.1773, train_loss=6.4320]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:19.418 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000100.pt\n",
+            "16/07/2026-08:35:19.493 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   7%|▋         | 99/1500 [01:42<19:39,  1.19it/s, iou=56.87%, test_loss=8.1773, train_loss=5.9737]\u001b[A\n",
+            "Training:   7%|▋         | 100/1500 [01:42<20:07,  1.16it/s, iou=56.87%, test_loss=8.1773, train_loss=5.9737]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:22.783 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   7%|▋         | 100/1500 [01:45<20:07,  1.16it/s, iou=57.32%, test_loss=8.1994, train_loss=4.3176]\u001b[A\n",
+            "Training:   7%|▋         | 101/1500 [01:45<33:19,  1.43s/it, iou=57.32%, test_loss=8.1994, train_loss=4.3176]\u001b[A\n",
+            "Training:   7%|▋         | 101/1500 [01:45<33:19,  1.43s/it, iou=57.32%, test_loss=8.1994, train_loss=4.3935]\u001b[A\n",
+            "Training:   7%|▋         | 102/1500 [01:45<25:07,  1.08s/it, iou=57.32%, test_loss=8.1994, train_loss=4.3935]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:   7%|▋         | 102/1500 [01:46<25:07,  1.08s/it, iou=57.32%, test_loss=8.1994, train_loss=5.7527]\u001b[A\n",
+            "Training:   7%|▋         | 103/1500 [01:46<19:35,  1.19it/s, iou=57.32%, test_loss=8.1994, train_loss=5.7527]\u001b[A\n",
+            "Training:   7%|▋         | 103/1500 [01:46<19:35,  1.19it/s, iou=57.32%, test_loss=8.1994, train_loss=4.9639]\u001b[A\n",
+            "Training:   7%|▋         | 104/1500 [01:46<15:24,  1.51it/s, iou=57.32%, test_loss=8.1994, train_loss=4.9639]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:23.784 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000105.pt\n",
+            "16/07/2026-08:35:23.860 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   7%|▋         | 104/1500 [01:47<15:24,  1.51it/s, iou=57.32%, test_loss=8.1994, train_loss=3.6872]\u001b[A\n",
+            "Training:   7%|▋         | 105/1500 [01:47<15:31,  1.50it/s, iou=57.32%, test_loss=8.1994, train_loss=3.6872]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:27.033 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   7%|▋         | 105/1500 [01:49<15:31,  1.50it/s, iou=56.07%, test_loss=8.2061, train_loss=5.4575]\u001b[A\n",
+            "Training:   7%|▋         | 106/1500 [01:49<30:13,  1.30s/it, iou=56.07%, test_loss=8.2061, train_loss=5.4575]\u001b[A\n",
+            "Training:   7%|▋         | 106/1500 [01:50<30:13,  1.30s/it, iou=56.07%, test_loss=8.2061, train_loss=4.6092]\u001b[A\n",
+            "Training:   7%|▋         | 107/1500 [01:50<23:46,  1.02s/it, iou=56.07%, test_loss=8.2061, train_loss=4.6092]\u001b[A\n",
+            "Training:   7%|▋         | 107/1500 [01:50<23:46,  1.02s/it, iou=56.07%, test_loss=8.2061, train_loss=4.7818]\u001b[A\n",
+            "Training:   7%|▋         | 108/1500 [01:50<19:20,  1.20it/s, iou=56.07%, test_loss=8.2061, train_loss=4.7818]\u001b[A\n",
+            "Training:   7%|▋         | 108/1500 [01:50<19:20,  1.20it/s, iou=56.07%, test_loss=8.2061, train_loss=3.7311]\u001b[A\n",
+            "Training:   7%|▋         | 109/1500 [01:50<15:58,  1.45it/s, iou=56.07%, test_loss=8.2061, train_loss=3.7311]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:28.537 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000110.pt\n",
+            "16/07/2026-08:35:28.654 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   7%|▋         | 109/1500 [01:51<15:58,  1.45it/s, iou=56.07%, test_loss=8.2061, train_loss=5.4734]\u001b[A\n",
+            "Training:   7%|▋         | 110/1500 [01:51<16:22,  1.41it/s, iou=56.07%, test_loss=8.2061, train_loss=5.4734]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:32.334 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   7%|▋         | 110/1500 [01:55<16:22,  1.41it/s, iou=57.20%, test_loss=8.2364, train_loss=3.6150]\u001b[A\n",
+            "Training:   7%|▋         | 111/1500 [01:55<35:17,  1.52s/it, iou=57.20%, test_loss=8.2364, train_loss=3.6150]\u001b[A\n",
+            "Training:   7%|▋         | 111/1500 [01:55<35:17,  1.52s/it, iou=57.20%, test_loss=8.2364, train_loss=5.3800]\u001b[A\n",
+            "Training:   7%|▋         | 112/1500 [01:55<26:44,  1.16s/it, iou=57.20%, test_loss=8.2364, train_loss=5.3800]\u001b[A\n",
+            "Training:   7%|▋         | 112/1500 [01:55<26:44,  1.16s/it, iou=57.20%, test_loss=8.2364, train_loss=5.1595]\u001b[A\n",
+            "Training:   8%|▊         | 113/1500 [01:55<21:12,  1.09it/s, iou=57.20%, test_loss=8.2364, train_loss=5.1595]\u001b[A\n",
+            "Training:   8%|▊         | 113/1500 [01:56<21:12,  1.09it/s, iou=57.20%, test_loss=8.2364, train_loss=6.6276]\u001b[A\n",
+            "Training:   8%|▊         | 114/1500 [01:56<17:07,  1.35it/s, iou=57.20%, test_loss=8.2364, train_loss=6.6276]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:33.559 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000115.pt\n",
+            "16/07/2026-08:35:33.641 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   8%|▊         | 114/1500 [01:56<17:07,  1.35it/s, iou=57.20%, test_loss=8.2364, train_loss=3.4995]\u001b[A\n",
+            "Training:   8%|▊         | 115/1500 [01:56<17:37,  1.31it/s, iou=57.20%, test_loss=8.2364, train_loss=3.4995]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:36.684 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   8%|▊         | 115/1500 [01:59<17:37,  1.31it/s, iou=56.79%, test_loss=8.0271, train_loss=4.6549]\u001b[A\n",
+            "Training:   8%|▊         | 116/1500 [01:59<29:57,  1.30s/it, iou=56.79%, test_loss=8.0271, train_loss=4.6549]\u001b[A\n",
+            "Training:   8%|▊         | 116/1500 [01:59<29:57,  1.30s/it, iou=56.79%, test_loss=8.0271, train_loss=3.9803]\u001b[A\n",
+            "Training:   8%|▊         | 117/1500 [01:59<22:47,  1.01it/s, iou=56.79%, test_loss=8.0271, train_loss=3.9803]\u001b[A\n",
+            "Training:   8%|▊         | 117/1500 [02:00<22:47,  1.01it/s, iou=56.79%, test_loss=8.0271, train_loss=4.2164]\u001b[A\n",
+            "Training:   8%|▊         | 118/1500 [02:00<17:42,  1.30it/s, iou=56.79%, test_loss=8.0271, train_loss=4.2164]\u001b[A\n",
+            "Training:   8%|▊         | 118/1500 [02:00<17:42,  1.30it/s, iou=56.79%, test_loss=8.0271, train_loss=5.9510]\u001b[A\n",
+            "Training:   8%|▊         | 119/1500 [02:00<14:02,  1.64it/s, iou=56.79%, test_loss=8.0271, train_loss=5.9510]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:37.757 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000120.pt\n",
+            "16/07/2026-08:35:37.842 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   8%|▊         | 119/1500 [02:00<14:02,  1.64it/s, iou=56.79%, test_loss=8.0271, train_loss=5.6682]\u001b[A\n",
+            "Training:   8%|▊         | 120/1500 [02:01<14:41,  1.57it/s, iou=56.79%, test_loss=8.0271, train_loss=5.6682]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:41.293 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   8%|▊         | 120/1500 [02:04<14:41,  1.57it/s, iou=55.63%, test_loss=8.1725, train_loss=5.0237]\u001b[A\n",
+            "Training:   8%|▊         | 121/1500 [02:04<31:57,  1.39s/it, iou=55.63%, test_loss=8.1725, train_loss=5.0237]\u001b[A\n",
+            "Training:   8%|▊         | 121/1500 [02:04<31:57,  1.39s/it, iou=55.63%, test_loss=8.1725, train_loss=4.7706]\u001b[A\n",
+            "Training:   8%|▊         | 122/1500 [02:04<25:03,  1.09s/it, iou=55.63%, test_loss=8.1725, train_loss=4.7706]\u001b[A\n",
+            "Training:   8%|▊         | 122/1500 [02:04<25:03,  1.09s/it, iou=55.63%, test_loss=8.1725, train_loss=5.0626]\u001b[A\n",
+            "Training:   8%|▊         | 123/1500 [02:04<20:08,  1.14it/s, iou=55.63%, test_loss=8.1725, train_loss=5.0626]\u001b[A\n",
+            "Training:   8%|▊         | 123/1500 [02:05<20:08,  1.14it/s, iou=55.63%, test_loss=8.1725, train_loss=4.7265]\u001b[A\n",
+            "Training:   8%|▊         | 124/1500 [02:05<16:44,  1.37it/s, iou=55.63%, test_loss=8.1725, train_loss=4.7265]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:42.777 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000125.pt\n",
+            "16/07/2026-08:35:42.925 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   8%|▊         | 124/1500 [02:06<16:44,  1.37it/s, iou=55.63%, test_loss=8.1725, train_loss=3.7879]\u001b[A\n",
+            "Training:   8%|▊         | 125/1500 [02:06<16:57,  1.35it/s, iou=55.63%, test_loss=8.1725, train_loss=3.7879]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:46.378 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   8%|▊         | 125/1500 [02:09<16:57,  1.35it/s, iou=55.76%, test_loss=8.0789, train_loss=7.2439]\u001b[A\n",
+            "Training:   8%|▊         | 126/1500 [02:09<33:34,  1.47s/it, iou=55.76%, test_loss=8.0789, train_loss=7.2439]\u001b[A\n",
+            "Training:   8%|▊         | 126/1500 [02:09<33:34,  1.47s/it, iou=55.76%, test_loss=8.0789, train_loss=3.2072]\u001b[A\n",
+            "Training:   8%|▊         | 127/1500 [02:09<25:12,  1.10s/it, iou=55.76%, test_loss=8.0789, train_loss=3.2072]\u001b[A\n",
+            "Training:   8%|▊         | 127/1500 [02:09<25:12,  1.10s/it, iou=55.76%, test_loss=8.0789, train_loss=5.2334]\u001b[A\n",
+            "Training:   9%|▊         | 128/1500 [02:09<19:21,  1.18it/s, iou=55.76%, test_loss=8.0789, train_loss=5.2334]\u001b[A\n",
+            "Training:   9%|▊         | 128/1500 [02:09<19:21,  1.18it/s, iou=55.76%, test_loss=8.0789, train_loss=3.0712]\u001b[A\n",
+            "Training:   9%|▊         | 129/1500 [02:09<15:05,  1.51it/s, iou=55.76%, test_loss=8.0789, train_loss=3.0712]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:47.408 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000130.pt\n",
+            "16/07/2026-08:35:47.502 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   9%|▊         | 129/1500 [02:10<15:05,  1.51it/s, iou=55.76%, test_loss=8.0789, train_loss=5.2641]\u001b[A\n",
+            "Training:   9%|▊         | 130/1500 [02:10<15:18,  1.49it/s, iou=55.76%, test_loss=8.0789, train_loss=5.2641]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:50.792 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   9%|▊         | 130/1500 [02:13<15:18,  1.49it/s, iou=56.58%, test_loss=7.9918, train_loss=3.5286]\u001b[A\n",
+            "Training:   9%|▊         | 131/1500 [02:13<31:11,  1.37s/it, iou=56.58%, test_loss=7.9918, train_loss=3.5286]\u001b[A\n",
+            "Training:   9%|▊         | 131/1500 [02:13<31:11,  1.37s/it, iou=56.58%, test_loss=7.9918, train_loss=5.3425]\u001b[A\n",
+            "Training:   9%|▉         | 132/1500 [02:13<23:41,  1.04s/it, iou=56.58%, test_loss=7.9918, train_loss=5.3425]\u001b[A\n",
+            "Training:   9%|▉         | 132/1500 [02:14<23:41,  1.04s/it, iou=56.58%, test_loss=7.9918, train_loss=4.4902]\u001b[A\n",
+            "Training:   9%|▉         | 133/1500 [02:14<18:23,  1.24it/s, iou=56.58%, test_loss=7.9918, train_loss=4.4902]\u001b[A\n",
+            "Training:   9%|▉         | 133/1500 [02:14<18:23,  1.24it/s, iou=56.58%, test_loss=7.9918, train_loss=4.0840]\u001b[A\n",
+            "Training:   9%|▉         | 134/1500 [02:14<14:29,  1.57it/s, iou=56.58%, test_loss=7.9918, train_loss=4.0840]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:51.805 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000135.pt\n",
+            "16/07/2026-08:35:51.893 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   9%|▉         | 134/1500 [02:15<14:29,  1.57it/s, iou=56.58%, test_loss=7.9918, train_loss=3.9997]\u001b[A\n",
+            "Training:   9%|▉         | 135/1500 [02:15<15:02,  1.51it/s, iou=56.58%, test_loss=7.9918, train_loss=3.9997]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:55.449 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   9%|▉         | 135/1500 [02:18<15:02,  1.51it/s, iou=57.66%, test_loss=8.1359, train_loss=4.6808]\u001b[A\n",
+            "Training:   9%|▉         | 136/1500 [02:18<32:05,  1.41s/it, iou=57.66%, test_loss=8.1359, train_loss=4.6808]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:   9%|▉         | 136/1500 [02:18<32:05,  1.41s/it, iou=57.66%, test_loss=8.1359, train_loss=4.5831]\u001b[A\n",
+            "Training:   9%|▉         | 137/1500 [02:18<24:51,  1.09s/it, iou=57.66%, test_loss=8.1359, train_loss=4.5831]\u001b[A\n",
+            "Training:   9%|▉         | 137/1500 [02:19<24:51,  1.09s/it, iou=57.66%, test_loss=8.1359, train_loss=6.7595]\u001b[A\n",
+            "Training:   9%|▉         | 138/1500 [02:19<20:03,  1.13it/s, iou=57.66%, test_loss=8.1359, train_loss=6.7595]\u001b[A\n",
+            "Training:   9%|▉         | 138/1500 [02:19<20:03,  1.13it/s, iou=57.66%, test_loss=8.1359, train_loss=4.0579]\u001b[A\n",
+            "Training:   9%|▉         | 139/1500 [02:19<16:41,  1.36it/s, iou=57.66%, test_loss=8.1359, train_loss=4.0579]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:35:56.941 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000140.pt\n",
+            "16/07/2026-08:35:57.099 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   9%|▉         | 139/1500 [02:20<16:41,  1.36it/s, iou=57.66%, test_loss=8.1359, train_loss=5.5290]\u001b[A\n",
+            "Training:   9%|▉         | 140/1500 [02:20<17:16,  1.31it/s, iou=57.66%, test_loss=8.1359, train_loss=5.5290]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:00.271 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:   9%|▉         | 140/1500 [02:23<17:16,  1.31it/s, iou=56.93%, test_loss=7.9829, train_loss=4.0850]\u001b[A\n",
+            "Training:   9%|▉         | 141/1500 [02:23<31:32,  1.39s/it, iou=56.93%, test_loss=7.9829, train_loss=4.0850]\u001b[A\n",
+            "Training:   9%|▉         | 141/1500 [02:23<31:32,  1.39s/it, iou=56.93%, test_loss=7.9829, train_loss=3.1551]\u001b[A\n",
+            "Training:   9%|▉         | 142/1500 [02:23<23:44,  1.05s/it, iou=56.93%, test_loss=7.9829, train_loss=3.1551]\u001b[A\n",
+            "Training:   9%|▉         | 142/1500 [02:23<23:44,  1.05s/it, iou=56.93%, test_loss=7.9829, train_loss=6.5195]\u001b[A\n",
+            "Training:  10%|▉         | 143/1500 [02:23<18:35,  1.22it/s, iou=56.93%, test_loss=7.9829, train_loss=6.5195]\u001b[A\n",
+            "Training:  10%|▉         | 143/1500 [02:23<18:35,  1.22it/s, iou=56.93%, test_loss=7.9829, train_loss=4.1886]\u001b[A\n",
+            "Training:  10%|▉         | 144/1500 [02:23<14:48,  1.53it/s, iou=56.93%, test_loss=7.9829, train_loss=4.1886]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:01.371 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000145.pt\n",
+            "16/07/2026-08:36:01.488 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  10%|▉         | 144/1500 [02:24<14:48,  1.53it/s, iou=56.93%, test_loss=7.9829, train_loss=4.2556]\u001b[A\n",
+            "Training:  10%|▉         | 145/1500 [02:24<15:54,  1.42it/s, iou=56.93%, test_loss=7.9829, train_loss=4.2556]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:04.707 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  10%|▉         | 145/1500 [02:27<15:54,  1.42it/s, iou=56.50%, test_loss=7.9951, train_loss=4.3772]\u001b[A\n",
+            "Training:  10%|▉         | 146/1500 [02:27<30:08,  1.34s/it, iou=56.50%, test_loss=7.9951, train_loss=4.3772]\u001b[A\n",
+            "Training:  10%|▉         | 146/1500 [02:27<30:08,  1.34s/it, iou=56.50%, test_loss=7.9951, train_loss=4.2670]\u001b[A\n",
+            "Training:  10%|▉         | 147/1500 [02:27<22:55,  1.02s/it, iou=56.50%, test_loss=7.9951, train_loss=4.2670]\u001b[A\n",
+            "Training:  10%|▉         | 147/1500 [02:28<22:55,  1.02s/it, iou=56.50%, test_loss=7.9951, train_loss=5.4963]\u001b[A\n",
+            "Training:  10%|▉         | 148/1500 [02:28<18:15,  1.23it/s, iou=56.50%, test_loss=7.9951, train_loss=5.4963]\u001b[A\n",
+            "Training:  10%|▉         | 148/1500 [02:28<18:15,  1.23it/s, iou=56.50%, test_loss=7.9951, train_loss=4.0126]\u001b[A\n",
+            "Training:  10%|▉         | 149/1500 [02:28<14:36,  1.54it/s, iou=56.50%, test_loss=7.9951, train_loss=4.0126]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:05.868 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000150.pt\n",
+            "16/07/2026-08:36:05.959 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  10%|▉         | 149/1500 [02:29<14:36,  1.54it/s, iou=56.50%, test_loss=7.9951, train_loss=5.0242]\u001b[A\n",
+            "Training:  10%|█         | 150/1500 [02:29<14:48,  1.52it/s, iou=56.50%, test_loss=7.9951, train_loss=5.0242]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:10.425 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  10%|█         | 150/1500 [02:33<14:48,  1.52it/s, iou=56.40%, test_loss=8.2108, train_loss=4.5631]\u001b[A\n",
+            "Training:  10%|█         | 151/1500 [02:33<38:27,  1.71s/it, iou=56.40%, test_loss=8.2108, train_loss=4.5631]\u001b[A\n",
+            "Training:  10%|█         | 151/1500 [02:33<38:27,  1.71s/it, iou=56.40%, test_loss=8.2108, train_loss=2.6507]\u001b[A\n",
+            "Training:  10%|█         | 152/1500 [02:33<29:02,  1.29s/it, iou=56.40%, test_loss=8.2108, train_loss=2.6507]\u001b[A\n",
+            "Training:  10%|█         | 152/1500 [02:33<29:02,  1.29s/it, iou=56.40%, test_loss=8.2108, train_loss=3.9760]\u001b[A\n",
+            "Training:  10%|█         | 153/1500 [02:33<22:05,  1.02it/s, iou=56.40%, test_loss=8.2108, train_loss=3.9760]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  10%|█         | 153/1500 [02:34<22:05,  1.02it/s, iou=56.40%, test_loss=8.2108, train_loss=4.9158]\u001b[A\n",
+            "Training:  10%|█         | 154/1500 [02:34<17:19,  1.29it/s, iou=56.40%, test_loss=8.2108, train_loss=4.9158]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:11.545 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000155.pt\n",
+            "16/07/2026-08:36:11.666 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  10%|█         | 154/1500 [02:34<17:19,  1.29it/s, iou=56.40%, test_loss=8.2108, train_loss=2.6315]\u001b[A\n",
+            "Training:  10%|█         | 155/1500 [02:34<15:10,  1.48it/s, iou=56.40%, test_loss=8.2108, train_loss=2.6315]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:15.013 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  10%|█         | 155/1500 [02:37<15:10,  1.48it/s, iou=56.72%, test_loss=8.3033, train_loss=4.8761]\u001b[A\n",
+            "Training:  10%|█         | 156/1500 [02:37<32:37,  1.46s/it, iou=56.72%, test_loss=8.3033, train_loss=4.8761]\u001b[A\n",
+            "Training:  10%|█         | 156/1500 [02:38<32:37,  1.46s/it, iou=56.72%, test_loss=8.3033, train_loss=3.3865]\u001b[A\n",
+            "Training:  10%|█         | 157/1500 [02:38<24:45,  1.11s/it, iou=56.72%, test_loss=8.3033, train_loss=3.3865]\u001b[A\n",
+            "Training:  10%|█         | 157/1500 [02:38<24:45,  1.11s/it, iou=56.72%, test_loss=8.3033, train_loss=6.1872]\u001b[A\n",
+            "Training:  11%|█         | 158/1500 [02:38<19:10,  1.17it/s, iou=56.72%, test_loss=8.3033, train_loss=6.1872]\u001b[A\n",
+            "Training:  11%|█         | 158/1500 [02:38<19:10,  1.17it/s, iou=56.72%, test_loss=8.3033, train_loss=4.5731]\u001b[A\n",
+            "Training:  11%|█         | 159/1500 [02:38<15:21,  1.46it/s, iou=56.72%, test_loss=8.3033, train_loss=4.5731]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:16.103 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000160.pt\n",
+            "16/07/2026-08:36:16.208 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  11%|█         | 159/1500 [02:39<15:21,  1.46it/s, iou=56.72%, test_loss=8.3033, train_loss=5.1907]\u001b[A\n",
+            "Training:  11%|█         | 160/1500 [02:39<15:38,  1.43it/s, iou=56.72%, test_loss=8.3033, train_loss=5.1907]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:19.524 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  11%|█         | 160/1500 [02:42<15:38,  1.43it/s, iou=57.92%, test_loss=7.9684, train_loss=3.1228]\u001b[A\n",
+            "Training:  11%|█         | 161/1500 [02:42<30:31,  1.37s/it, iou=57.92%, test_loss=7.9684, train_loss=3.1228]\u001b[A\n",
+            "Training:  11%|█         | 161/1500 [02:42<30:31,  1.37s/it, iou=57.92%, test_loss=7.9684, train_loss=5.0665]\u001b[A\n",
+            "Training:  11%|█         | 162/1500 [02:42<23:34,  1.06s/it, iou=57.92%, test_loss=7.9684, train_loss=5.0665]\u001b[A\n",
+            "Training:  11%|█         | 162/1500 [02:42<23:34,  1.06s/it, iou=57.92%, test_loss=7.9684, train_loss=4.4696]\u001b[A\n",
+            "Training:  11%|█         | 163/1500 [02:42<18:13,  1.22it/s, iou=57.92%, test_loss=7.9684, train_loss=4.4696]\u001b[A\n",
+            "Training:  11%|█         | 163/1500 [02:43<18:13,  1.22it/s, iou=57.92%, test_loss=7.9684, train_loss=3.7442]\u001b[A\n",
+            "Training:  11%|█         | 164/1500 [02:43<14:27,  1.54it/s, iou=57.92%, test_loss=7.9684, train_loss=3.7442]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:20.670 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000165.pt\n",
+            "16/07/2026-08:36:20.861 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  11%|█         | 164/1500 [02:44<14:27,  1.54it/s, iou=57.92%, test_loss=7.9684, train_loss=5.6811]\u001b[A\n",
+            "Training:  11%|█         | 165/1500 [02:44<16:06,  1.38it/s, iou=57.92%, test_loss=7.9684, train_loss=5.6811]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:25.489 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  11%|█         | 165/1500 [02:48<16:06,  1.38it/s, iou=56.62%, test_loss=7.8288, train_loss=5.3550]\u001b[A\n",
+            "Training:  11%|█         | 166/1500 [02:48<39:23,  1.77s/it, iou=56.62%, test_loss=7.8288, train_loss=5.3550]\u001b[A\n",
+            "Training:  11%|█         | 166/1500 [02:48<39:23,  1.77s/it, iou=56.62%, test_loss=7.8288, train_loss=4.1667]\u001b[A\n",
+            "Training:  11%|█         | 167/1500 [02:48<29:33,  1.33s/it, iou=56.62%, test_loss=7.8288, train_loss=4.1667]\u001b[A\n",
+            "Training:  11%|█         | 167/1500 [02:48<29:33,  1.33s/it, iou=56.62%, test_loss=7.8288, train_loss=3.2295]\u001b[A\n",
+            "Training:  11%|█         | 168/1500 [02:48<22:15,  1.00s/it, iou=56.62%, test_loss=7.8288, train_loss=3.2295]\u001b[A\n",
+            "Training:  11%|█         | 168/1500 [02:49<22:15,  1.00s/it, iou=56.62%, test_loss=7.8288, train_loss=5.3476]\u001b[A\n",
+            "Training:  11%|█▏        | 169/1500 [02:49<17:37,  1.26it/s, iou=56.62%, test_loss=7.8288, train_loss=5.3476]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:26.612 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000170.pt\n",
+            "16/07/2026-08:36:26.720 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  11%|█▏        | 169/1500 [02:49<17:37,  1.26it/s, iou=56.62%, test_loss=7.8288, train_loss=4.5050]\u001b[A\n",
+            "Training:  11%|█▏        | 170/1500 [02:49<16:39,  1.33it/s, iou=56.62%, test_loss=7.8288, train_loss=4.5050]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:29.995 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  11%|█▏        | 170/1500 [02:52<16:39,  1.33it/s, iou=55.41%, test_loss=8.0307, train_loss=3.6934]\u001b[A\n",
+            "Training:  11%|█▏        | 171/1500 [02:52<31:39,  1.43s/it, iou=55.41%, test_loss=8.0307, train_loss=3.6934]\u001b[A\n",
+            "Training:  11%|█▏        | 171/1500 [02:53<31:39,  1.43s/it, iou=55.41%, test_loss=8.0307, train_loss=3.8388]\u001b[A\n",
+            "Training:  11%|█▏        | 172/1500 [02:53<23:58,  1.08s/it, iou=55.41%, test_loss=8.0307, train_loss=3.8388]\u001b[A\n",
+            "Training:  11%|█▏        | 172/1500 [02:53<23:58,  1.08s/it, iou=55.41%, test_loss=8.0307, train_loss=5.0701]\u001b[A\n",
+            "Training:  12%|█▏        | 173/1500 [02:53<18:47,  1.18it/s, iou=55.41%, test_loss=8.0307, train_loss=5.0701]\u001b[A\n",
+            "Training:  12%|█▏        | 173/1500 [02:53<18:47,  1.18it/s, iou=55.41%, test_loss=8.0307, train_loss=4.6003]\u001b[A\n",
+            "Training:  12%|█▏        | 174/1500 [02:53<14:52,  1.49it/s, iou=55.41%, test_loss=8.0307, train_loss=4.6003]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:31.117 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000175.pt\n",
+            "16/07/2026-08:36:31.238 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  12%|█▏        | 174/1500 [02:54<14:52,  1.49it/s, iou=55.41%, test_loss=8.0307, train_loss=4.1456]\u001b[A\n",
+            "Training:  12%|█▏        | 175/1500 [02:54<14:59,  1.47it/s, iou=55.41%, test_loss=8.0307, train_loss=4.1456]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:34.913 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  12%|█▏        | 175/1500 [02:57<14:59,  1.47it/s, iou=55.67%, test_loss=8.3362, train_loss=3.8127]\u001b[A\n",
+            "Training:  12%|█▏        | 176/1500 [02:57<32:50,  1.49s/it, iou=55.67%, test_loss=8.3362, train_loss=3.8127]\u001b[A\n",
+            "Training:  12%|█▏        | 176/1500 [02:58<32:50,  1.49s/it, iou=55.67%, test_loss=8.3362, train_loss=4.9130]\u001b[A\n",
+            "Training:  12%|█▏        | 177/1500 [02:58<25:35,  1.16s/it, iou=55.67%, test_loss=8.3362, train_loss=4.9130]\u001b[A\n",
+            "Training:  12%|█▏        | 177/1500 [02:58<25:35,  1.16s/it, iou=55.67%, test_loss=8.3362, train_loss=5.4178]\u001b[A\n",
+            "Training:  12%|█▏        | 178/1500 [02:58<20:28,  1.08it/s, iou=55.67%, test_loss=8.3362, train_loss=5.4178]\u001b[A\n",
+            "Training:  12%|█▏        | 178/1500 [02:58<20:28,  1.08it/s, iou=55.67%, test_loss=8.3362, train_loss=3.5790]\u001b[A\n",
+            "Training:  12%|█▏        | 179/1500 [02:58<16:34,  1.33it/s, iou=55.67%, test_loss=8.3362, train_loss=3.5790]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:36.461 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000180.pt\n",
+            "16/07/2026-08:36:36.671 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  12%|█▏        | 179/1500 [02:59<16:34,  1.33it/s, iou=55.67%, test_loss=8.3362, train_loss=4.5563]\u001b[A\n",
+            "Training:  12%|█▏        | 180/1500 [02:59<17:41,  1.24it/s, iou=55.67%, test_loss=8.3362, train_loss=4.5563]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:40.827 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  12%|█▏        | 180/1500 [03:03<17:41,  1.24it/s, iou=58.10%, test_loss=8.0090, train_loss=5.1031]\u001b[A\n",
+            "Training:  12%|█▏        | 181/1500 [03:03<37:51,  1.72s/it, iou=58.10%, test_loss=8.0090, train_loss=5.1031]\u001b[A\n",
+            "Training:  12%|█▏        | 181/1500 [03:03<37:51,  1.72s/it, iou=58.10%, test_loss=8.0090, train_loss=4.9450]\u001b[A\n",
+            "Training:  12%|█▏        | 182/1500 [03:03<28:36,  1.30s/it, iou=58.10%, test_loss=8.0090, train_loss=4.9450]\u001b[A\n",
+            "Training:  12%|█▏        | 182/1500 [03:04<28:36,  1.30s/it, iou=58.10%, test_loss=8.0090, train_loss=3.8413]\u001b[A\n",
+            "Training:  12%|█▏        | 183/1500 [03:04<22:24,  1.02s/it, iou=58.10%, test_loss=8.0090, train_loss=3.8413]\u001b[A\n",
+            "Training:  12%|█▏        | 183/1500 [03:04<22:24,  1.02s/it, iou=58.10%, test_loss=8.0090, train_loss=3.4358]\u001b[A\n",
+            "Training:  12%|█▏        | 184/1500 [03:04<17:30,  1.25it/s, iou=58.10%, test_loss=8.0090, train_loss=3.4358]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:42.143 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000185.pt\n",
+            "16/07/2026-08:36:42.267 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  12%|█▏        | 184/1500 [03:05<17:30,  1.25it/s, iou=58.10%, test_loss=8.0090, train_loss=3.9922]\u001b[A\n",
+            "Training:  12%|█▏        | 185/1500 [03:05<17:13,  1.27it/s, iou=58.10%, test_loss=8.0090, train_loss=3.9922]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:45.959 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  12%|█▏        | 185/1500 [03:08<17:13,  1.27it/s, iou=58.54%, test_loss=7.9965, train_loss=5.4333]\u001b[A\n",
+            "Training:  12%|█▏        | 186/1500 [03:08<34:26,  1.57s/it, iou=58.54%, test_loss=7.9965, train_loss=5.4333]\u001b[A\n",
+            "Training:  12%|█▏        | 186/1500 [03:09<34:26,  1.57s/it, iou=58.54%, test_loss=7.9965, train_loss=5.1312]\u001b[A\n",
+            "Training:  12%|█▏        | 187/1500 [03:09<25:59,  1.19s/it, iou=58.54%, test_loss=7.9965, train_loss=5.1312]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  12%|█▏        | 187/1500 [03:09<25:59,  1.19s/it, iou=58.54%, test_loss=7.9965, train_loss=4.4144]\u001b[A\n",
+            "Training:  13%|█▎        | 188/1500 [03:09<20:07,  1.09it/s, iou=58.54%, test_loss=7.9965, train_loss=4.4144]\u001b[A\n",
+            "Training:  13%|█▎        | 188/1500 [03:09<20:07,  1.09it/s, iou=58.54%, test_loss=7.9965, train_loss=4.1766]\u001b[A\n",
+            "Training:  13%|█▎        | 189/1500 [03:09<16:03,  1.36it/s, iou=58.54%, test_loss=7.9965, train_loss=4.1766]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:47.173 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000190.pt\n",
+            "16/07/2026-08:36:47.319 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  13%|█▎        | 189/1500 [03:10<16:03,  1.36it/s, iou=58.54%, test_loss=7.9965, train_loss=5.1604]\u001b[A\n",
+            "Training:  13%|█▎        | 190/1500 [03:10<16:36,  1.31it/s, iou=58.54%, test_loss=7.9965, train_loss=5.1604]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:52.492 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  13%|█▎        | 190/1500 [03:15<16:36,  1.31it/s, iou=57.40%, test_loss=8.0673, train_loss=2.8967]\u001b[A\n",
+            "Training:  13%|█▎        | 191/1500 [03:15<43:12,  1.98s/it, iou=57.40%, test_loss=8.0673, train_loss=2.8967]\u001b[A\n",
+            "Training:  13%|█▎        | 191/1500 [03:15<43:12,  1.98s/it, iou=57.40%, test_loss=8.0673, train_loss=3.0076]\u001b[A\n",
+            "Training:  13%|█▎        | 192/1500 [03:15<32:09,  1.47s/it, iou=57.40%, test_loss=8.0673, train_loss=3.0076]\u001b[A\n",
+            "Training:  13%|█▎        | 192/1500 [03:15<32:09,  1.47s/it, iou=57.40%, test_loss=8.0673, train_loss=4.5195]\u001b[A\n",
+            "Training:  13%|█▎        | 193/1500 [03:15<24:05,  1.11s/it, iou=57.40%, test_loss=8.0673, train_loss=4.5195]\u001b[A\n",
+            "Training:  13%|█▎        | 193/1500 [03:16<24:05,  1.11s/it, iou=57.40%, test_loss=8.0673, train_loss=5.3454]\u001b[A\n",
+            "Training:  13%|█▎        | 194/1500 [03:16<18:53,  1.15it/s, iou=57.40%, test_loss=8.0673, train_loss=5.3454]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:53.603 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000195.pt\n",
+            "16/07/2026-08:36:53.718 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  13%|█▎        | 194/1500 [03:16<18:53,  1.15it/s, iou=57.40%, test_loss=8.0673, train_loss=4.9818]\u001b[A\n",
+            "Training:  13%|█▎        | 195/1500 [03:16<18:12,  1.19it/s, iou=57.40%, test_loss=8.0673, train_loss=4.9818]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:57.118 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  13%|█▎        | 195/1500 [03:19<18:12,  1.19it/s, iou=55.84%, test_loss=8.1624, train_loss=3.7502]\u001b[A\n",
+            "Training:  13%|█▎        | 196/1500 [03:19<32:19,  1.49s/it, iou=55.84%, test_loss=8.1624, train_loss=3.7502]\u001b[A\n",
+            "Training:  13%|█▎        | 196/1500 [03:20<32:19,  1.49s/it, iou=55.84%, test_loss=8.1624, train_loss=4.6690]\u001b[A\n",
+            "Training:  13%|█▎        | 197/1500 [03:20<24:25,  1.13s/it, iou=55.84%, test_loss=8.1624, train_loss=4.6690]\u001b[A\n",
+            "Training:  13%|█▎        | 197/1500 [03:20<24:25,  1.13s/it, iou=55.84%, test_loss=8.1624, train_loss=5.5811]\u001b[A\n",
+            "Training:  13%|█▎        | 198/1500 [03:20<19:06,  1.14it/s, iou=55.84%, test_loss=8.1624, train_loss=5.5811]\u001b[A\n",
+            "Training:  13%|█▎        | 198/1500 [03:20<19:06,  1.14it/s, iou=55.84%, test_loss=8.1624, train_loss=4.6593]\u001b[A\n",
+            "Training:  13%|█▎        | 199/1500 [03:20<15:33,  1.39it/s, iou=55.84%, test_loss=8.1624, train_loss=4.6593]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:36:58.298 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000200.pt\n",
+            "16/07/2026-08:36:58.440 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  13%|█▎        | 199/1500 [03:21<15:33,  1.39it/s, iou=55.84%, test_loss=8.1624, train_loss=2.7530]\u001b[A\n",
+            "Training:  13%|█▎        | 200/1500 [03:21<17:22,  1.25it/s, iou=55.84%, test_loss=8.1624, train_loss=2.7530]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:01.999 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  13%|█▎        | 200/1500 [03:24<17:22,  1.25it/s, iou=55.55%, test_loss=8.0022, train_loss=4.8345]\u001b[A\n",
+            "Training:  13%|█▎        | 201/1500 [03:24<31:20,  1.45s/it, iou=55.55%, test_loss=8.0022, train_loss=4.8345]\u001b[A\n",
+            "Training:  13%|█▎        | 201/1500 [03:25<31:20,  1.45s/it, iou=55.55%, test_loss=8.0022, train_loss=3.8780]\u001b[A\n",
+            "Training:  13%|█▎        | 202/1500 [03:25<24:47,  1.15s/it, iou=55.55%, test_loss=8.0022, train_loss=3.8780]\u001b[A\n",
+            "Training:  13%|█▎        | 202/1500 [03:25<24:47,  1.15s/it, iou=55.55%, test_loss=8.0022, train_loss=4.9018]\u001b[A\n",
+            "Training:  14%|█▎        | 203/1500 [03:25<19:46,  1.09it/s, iou=55.55%, test_loss=8.0022, train_loss=4.9018]\u001b[A\n",
+            "Training:  14%|█▎        | 203/1500 [03:26<19:46,  1.09it/s, iou=55.55%, test_loss=8.0022, train_loss=4.9703]\u001b[A\n",
+            "Training:  14%|█▎        | 204/1500 [03:26<17:56,  1.20it/s, iou=55.55%, test_loss=8.0022, train_loss=4.9703]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:03.840 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000205.pt\n",
+            "16/07/2026-08:37:04.049 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  14%|█▎        | 204/1500 [03:27<17:56,  1.20it/s, iou=55.55%, test_loss=8.0022, train_loss=5.2239]\u001b[A\n",
+            "Training:  14%|█▎        | 205/1500 [03:27<18:25,  1.17it/s, iou=55.55%, test_loss=8.0022, train_loss=5.2239]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:07.855 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  14%|█▎        | 205/1500 [03:30<18:25,  1.17it/s, iou=56.92%, test_loss=7.9950, train_loss=4.6401]\u001b[A\n",
+            "Training:  14%|█▎        | 206/1500 [03:30<35:31,  1.65s/it, iou=56.92%, test_loss=7.9950, train_loss=4.6401]\u001b[A\n",
+            "Training:  14%|█▎        | 206/1500 [03:31<35:31,  1.65s/it, iou=56.92%, test_loss=7.9950, train_loss=4.7865]\u001b[A\n",
+            "Training:  14%|█▍        | 207/1500 [03:31<26:57,  1.25s/it, iou=56.92%, test_loss=7.9950, train_loss=4.7865]\u001b[A\n",
+            "Training:  14%|█▍        | 207/1500 [03:31<26:57,  1.25s/it, iou=56.92%, test_loss=7.9950, train_loss=3.6789]\u001b[A\n",
+            "Training:  14%|█▍        | 208/1500 [03:31<20:28,  1.05it/s, iou=56.92%, test_loss=7.9950, train_loss=3.6789]\u001b[A\n",
+            "Training:  14%|█▍        | 208/1500 [03:31<20:28,  1.05it/s, iou=56.92%, test_loss=7.9950, train_loss=3.1562]\u001b[A\n",
+            "Training:  14%|█▍        | 209/1500 [03:31<15:57,  1.35it/s, iou=56.92%, test_loss=7.9950, train_loss=3.1562]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:08.942 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000210.pt\n",
+            "16/07/2026-08:37:09.061 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  14%|█▍        | 209/1500 [03:32<15:57,  1.35it/s, iou=56.92%, test_loss=7.9950, train_loss=4.6222]\u001b[A\n",
+            "Training:  14%|█▍        | 210/1500 [03:32<16:08,  1.33it/s, iou=56.92%, test_loss=7.9950, train_loss=4.6222]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:12.410 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  14%|█▍        | 210/1500 [03:35<16:08,  1.33it/s, iou=56.28%, test_loss=8.2577, train_loss=4.3163]\u001b[A\n",
+            "Training:  14%|█▍        | 211/1500 [03:35<30:20,  1.41s/it, iou=56.28%, test_loss=8.2577, train_loss=4.3163]\u001b[A\n",
+            "Training:  14%|█▍        | 211/1500 [03:35<30:20,  1.41s/it, iou=56.28%, test_loss=8.2577, train_loss=5.1835]\u001b[A\n",
+            "Training:  14%|█▍        | 212/1500 [03:35<23:08,  1.08s/it, iou=56.28%, test_loss=8.2577, train_loss=5.1835]\u001b[A\n",
+            "Training:  14%|█▍        | 212/1500 [03:35<23:08,  1.08s/it, iou=56.28%, test_loss=8.2577, train_loss=4.4016]\u001b[A\n",
+            "Training:  14%|█▍        | 213/1500 [03:35<17:48,  1.20it/s, iou=56.28%, test_loss=8.2577, train_loss=4.4016]\u001b[A\n",
+            "Training:  14%|█▍        | 213/1500 [03:36<17:48,  1.20it/s, iou=56.28%, test_loss=8.2577, train_loss=4.2632]\u001b[A\n",
+            "Training:  14%|█▍        | 214/1500 [03:36<14:05,  1.52it/s, iou=56.28%, test_loss=8.2577, train_loss=4.2632]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:13.503 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000215.pt\n",
+            "16/07/2026-08:37:13.628 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  14%|█▍        | 214/1500 [03:36<14:05,  1.52it/s, iou=56.28%, test_loss=8.2577, train_loss=4.1585]\u001b[A\n",
+            "Training:  14%|█▍        | 215/1500 [03:36<14:30,  1.48it/s, iou=56.28%, test_loss=8.2577, train_loss=4.1585]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:18.007 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  14%|█▍        | 215/1500 [03:40<14:30,  1.48it/s, iou=56.02%, test_loss=8.1717, train_loss=4.7148]\u001b[A\n",
+            "Training:  14%|█▍        | 216/1500 [03:40<36:16,  1.69s/it, iou=56.02%, test_loss=8.1717, train_loss=4.7148]\u001b[A\n",
+            "Training:  14%|█▍        | 216/1500 [03:41<36:16,  1.69s/it, iou=56.02%, test_loss=8.1717, train_loss=3.4041]\u001b[A\n",
+            "Training:  14%|█▍        | 217/1500 [03:41<28:05,  1.31s/it, iou=56.02%, test_loss=8.1717, train_loss=3.4041]\u001b[A\n",
+            "Training:  14%|█▍        | 217/1500 [03:41<28:05,  1.31s/it, iou=56.02%, test_loss=8.1717, train_loss=5.1604]\u001b[A\n",
+            "Training:  15%|█▍        | 218/1500 [03:41<21:56,  1.03s/it, iou=56.02%, test_loss=8.1717, train_loss=5.1604]\u001b[A\n",
+            "Training:  15%|█▍        | 218/1500 [03:41<21:56,  1.03s/it, iou=56.02%, test_loss=8.1717, train_loss=3.2564]\u001b[A\n",
+            "Training:  15%|█▍        | 219/1500 [03:41<16:59,  1.26it/s, iou=56.02%, test_loss=8.1717, train_loss=3.2564]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:19.326 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000220.pt\n",
+            "16/07/2026-08:37:19.451 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  15%|█▍        | 219/1500 [03:42<16:59,  1.26it/s, iou=56.02%, test_loss=8.1717, train_loss=5.1010]\u001b[A\n",
+            "Training:  15%|█▍        | 220/1500 [03:42<16:16,  1.31it/s, iou=56.02%, test_loss=8.1717, train_loss=5.1010]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:23.130 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  15%|█▍        | 220/1500 [03:45<16:16,  1.31it/s, iou=56.32%, test_loss=8.0621, train_loss=3.2854]\u001b[A\n",
+            "Training:  15%|█▍        | 221/1500 [03:45<33:05,  1.55s/it, iou=56.32%, test_loss=8.0621, train_loss=3.2854]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  15%|█▍        | 221/1500 [03:46<33:05,  1.55s/it, iou=56.32%, test_loss=8.0621, train_loss=3.9009]\u001b[A\n",
+            "Training:  15%|█▍        | 222/1500 [03:46<25:18,  1.19s/it, iou=56.32%, test_loss=8.0621, train_loss=3.9009]\u001b[A\n",
+            "Training:  15%|█▍        | 222/1500 [03:46<25:18,  1.19s/it, iou=56.32%, test_loss=8.0621, train_loss=3.4168]\u001b[A\n",
+            "Training:  15%|█▍        | 223/1500 [03:46<19:23,  1.10it/s, iou=56.32%, test_loss=8.0621, train_loss=3.4168]\u001b[A\n",
+            "Training:  15%|█▍        | 223/1500 [03:46<19:23,  1.10it/s, iou=56.32%, test_loss=8.0621, train_loss=5.0271]\u001b[A\n",
+            "Training:  15%|█▍        | 224/1500 [03:46<15:16,  1.39it/s, iou=56.32%, test_loss=8.0621, train_loss=5.0271]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:24.278 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000225.pt\n",
+            "16/07/2026-08:37:24.441 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  15%|█▍        | 224/1500 [03:47<15:16,  1.39it/s, iou=56.32%, test_loss=8.0621, train_loss=4.9543]\u001b[A\n",
+            "Training:  15%|█▌        | 225/1500 [03:47<15:09,  1.40it/s, iou=56.32%, test_loss=8.0621, train_loss=4.9543]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:27.737 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  15%|█▌        | 225/1500 [03:50<15:09,  1.40it/s, iou=56.95%, test_loss=8.0343, train_loss=4.6487]\u001b[A\n",
+            "Training:  15%|█▌        | 226/1500 [03:50<29:56,  1.41s/it, iou=56.95%, test_loss=8.0343, train_loss=4.6487]\u001b[A\n",
+            "Training:  15%|█▌        | 226/1500 [03:50<29:56,  1.41s/it, iou=56.95%, test_loss=8.0343, train_loss=5.2938]\u001b[A\n",
+            "Training:  15%|█▌        | 227/1500 [03:50<22:55,  1.08s/it, iou=56.95%, test_loss=8.0343, train_loss=5.2938]\u001b[A\n",
+            "Training:  15%|█▌        | 227/1500 [03:51<22:55,  1.08s/it, iou=56.95%, test_loss=8.0343, train_loss=4.5197]\u001b[A\n",
+            "Training:  15%|█▌        | 228/1500 [03:51<17:40,  1.20it/s, iou=56.95%, test_loss=8.0343, train_loss=4.5197]\u001b[A\n",
+            "Training:  15%|█▌        | 228/1500 [03:51<17:40,  1.20it/s, iou=56.95%, test_loss=8.0343, train_loss=4.7982]\u001b[A\n",
+            "Training:  15%|█▌        | 229/1500 [03:51<13:59,  1.51it/s, iou=56.95%, test_loss=8.0343, train_loss=4.7982]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:28.996 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000230.pt\n",
+            "16/07/2026-08:37:29.229 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  15%|█▌        | 229/1500 [03:52<13:59,  1.51it/s, iou=56.95%, test_loss=8.0343, train_loss=5.0970]\u001b[A\n",
+            "Training:  15%|█▌        | 230/1500 [03:52<15:50,  1.34it/s, iou=56.95%, test_loss=8.0343, train_loss=5.0970]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:33.655 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  15%|█▌        | 230/1500 [03:56<15:50,  1.34it/s, iou=57.46%, test_loss=8.0944, train_loss=3.8774]\u001b[A\n",
+            "Training:  15%|█▌        | 231/1500 [03:56<37:18,  1.76s/it, iou=57.46%, test_loss=8.0944, train_loss=3.8774]\u001b[A\n",
+            "Training:  15%|█▌        | 231/1500 [03:56<37:18,  1.76s/it, iou=57.46%, test_loss=8.0944, train_loss=4.9000]\u001b[A\n",
+            "Training:  15%|█▌        | 232/1500 [03:56<27:50,  1.32s/it, iou=57.46%, test_loss=8.0944, train_loss=4.9000]\u001b[A\n",
+            "Training:  15%|█▌        | 232/1500 [03:57<27:50,  1.32s/it, iou=57.46%, test_loss=8.0944, train_loss=4.5146]\u001b[A\n",
+            "Training:  16%|█▌        | 233/1500 [03:57<21:20,  1.01s/it, iou=57.46%, test_loss=8.0944, train_loss=4.5146]\u001b[A\n",
+            "Training:  16%|█▌        | 233/1500 [03:57<21:20,  1.01s/it, iou=57.46%, test_loss=8.0944, train_loss=4.9741]\u001b[A\n",
+            "Training:  16%|█▌        | 234/1500 [03:57<16:34,  1.27it/s, iou=57.46%, test_loss=8.0944, train_loss=4.9741]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:34.752 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000235.pt\n",
+            "16/07/2026-08:37:34.886 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  16%|█▌        | 234/1500 [03:58<16:34,  1.27it/s, iou=57.46%, test_loss=8.0944, train_loss=4.1464]\u001b[A\n",
+            "Training:  16%|█▌        | 235/1500 [03:58<17:41,  1.19it/s, iou=57.46%, test_loss=8.0944, train_loss=4.1464]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:38.225 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  16%|█▌        | 235/1500 [04:01<17:41,  1.19it/s, iou=57.63%, test_loss=8.1261, train_loss=2.4072]\u001b[A\n",
+            "Training:  16%|█▌        | 236/1500 [04:01<29:55,  1.42s/it, iou=57.63%, test_loss=8.1261, train_loss=2.4072]\u001b[A\n",
+            "Training:  16%|█▌        | 236/1500 [04:01<29:55,  1.42s/it, iou=57.63%, test_loss=8.1261, train_loss=2.5960]\u001b[A\n",
+            "Training:  16%|█▌        | 237/1500 [04:01<22:24,  1.06s/it, iou=57.63%, test_loss=8.1261, train_loss=2.5960]\u001b[A\n",
+            "Training:  16%|█▌        | 237/1500 [04:01<22:24,  1.06s/it, iou=57.63%, test_loss=8.1261, train_loss=3.8888]\u001b[A\n",
+            "Training:  16%|█▌        | 238/1500 [04:01<17:13,  1.22it/s, iou=57.63%, test_loss=8.1261, train_loss=3.8888]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  16%|█▌        | 238/1500 [04:01<17:13,  1.22it/s, iou=57.63%, test_loss=8.1261, train_loss=4.0317]\u001b[A\n",
+            "Training:  16%|█▌        | 239/1500 [04:01<13:39,  1.54it/s, iou=57.63%, test_loss=8.1261, train_loss=4.0317]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:39.215 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000240.pt\n",
+            "16/07/2026-08:37:39.344 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  16%|█▌        | 239/1500 [04:02<13:39,  1.54it/s, iou=57.63%, test_loss=8.1261, train_loss=3.7699]\u001b[A\n",
+            "Training:  16%|█▌        | 240/1500 [04:02<12:20,  1.70it/s, iou=57.63%, test_loss=8.1261, train_loss=3.7699]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:42.385 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  16%|█▌        | 240/1500 [04:05<12:20,  1.70it/s, iou=57.52%, test_loss=8.2203, train_loss=3.7502]\u001b[A\n",
+            "Training:  16%|█▌        | 241/1500 [04:05<27:23,  1.31s/it, iou=57.52%, test_loss=8.2203, train_loss=3.7502]\u001b[A\n",
+            "Training:  16%|█▌        | 241/1500 [04:05<27:23,  1.31s/it, iou=57.52%, test_loss=8.2203, train_loss=3.8213]\u001b[A\n",
+            "Training:  16%|█▌        | 242/1500 [04:05<21:34,  1.03s/it, iou=57.52%, test_loss=8.2203, train_loss=3.8213]\u001b[A\n",
+            "Training:  16%|█▌        | 242/1500 [04:06<21:34,  1.03s/it, iou=57.52%, test_loss=8.2203, train_loss=5.8663]\u001b[A\n",
+            "Training:  16%|█▌        | 243/1500 [04:06<17:41,  1.18it/s, iou=57.52%, test_loss=8.2203, train_loss=5.8663]\u001b[A\n",
+            "Training:  16%|█▌        | 243/1500 [04:06<17:41,  1.18it/s, iou=57.52%, test_loss=8.2203, train_loss=3.1034]\u001b[A\n",
+            "Training:  16%|█▋        | 244/1500 [04:06<14:20,  1.46it/s, iou=57.52%, test_loss=8.2203, train_loss=3.1034]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:43.846 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000245.pt\n",
+            "16/07/2026-08:37:44.083 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  16%|█▋        | 244/1500 [04:07<14:20,  1.46it/s, iou=57.52%, test_loss=8.2203, train_loss=4.8112]\u001b[A\n",
+            "Training:  16%|█▋        | 245/1500 [04:07<15:13,  1.37it/s, iou=57.52%, test_loss=8.2203, train_loss=4.8112]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:47.952 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  16%|█▋        | 245/1500 [04:10<15:13,  1.37it/s, iou=57.14%, test_loss=8.1621, train_loss=3.3775]\u001b[A\n",
+            "Training:  16%|█▋        | 246/1500 [04:10<33:24,  1.60s/it, iou=57.14%, test_loss=8.1621, train_loss=3.3775]\u001b[A\n",
+            "Training:  16%|█▋        | 246/1500 [04:11<33:24,  1.60s/it, iou=57.14%, test_loss=8.1621, train_loss=4.7809]\u001b[A\n",
+            "Training:  16%|█▋        | 247/1500 [04:11<25:15,  1.21s/it, iou=57.14%, test_loss=8.1621, train_loss=4.7809]\u001b[A\n",
+            "Training:  16%|█▋        | 247/1500 [04:11<25:15,  1.21s/it, iou=57.14%, test_loss=8.1621, train_loss=4.5331]\u001b[A\n",
+            "Training:  17%|█▋        | 248/1500 [04:11<19:20,  1.08it/s, iou=57.14%, test_loss=8.1621, train_loss=4.5331]\u001b[A\n",
+            "Training:  17%|█▋        | 248/1500 [04:11<19:20,  1.08it/s, iou=57.14%, test_loss=8.1621, train_loss=5.2300]\u001b[A\n",
+            "Training:  17%|█▋        | 249/1500 [04:11<15:18,  1.36it/s, iou=57.14%, test_loss=8.1621, train_loss=5.2300]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:49.080 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000250.pt\n",
+            "16/07/2026-08:37:49.213 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  17%|█▋        | 249/1500 [04:12<15:18,  1.36it/s, iou=57.14%, test_loss=8.1621, train_loss=4.7380]\u001b[A\n",
+            "Training:  17%|█▋        | 250/1500 [04:12<16:26,  1.27it/s, iou=57.14%, test_loss=8.1621, train_loss=4.7380]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:52.556 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  17%|█▋        | 250/1500 [04:15<16:26,  1.27it/s, iou=56.32%, test_loss=7.9660, train_loss=2.7824]\u001b[A\n",
+            "Training:  17%|█▋        | 251/1500 [04:15<29:12,  1.40s/it, iou=56.32%, test_loss=7.9660, train_loss=2.7824]\u001b[A\n",
+            "Training:  17%|█▋        | 251/1500 [04:15<29:12,  1.40s/it, iou=56.32%, test_loss=7.9660, train_loss=5.1254]\u001b[A\n",
+            "Training:  17%|█▋        | 252/1500 [04:15<22:05,  1.06s/it, iou=56.32%, test_loss=7.9660, train_loss=5.1254]\u001b[A\n",
+            "Training:  17%|█▋        | 252/1500 [04:15<22:05,  1.06s/it, iou=56.32%, test_loss=7.9660, train_loss=4.0115]\u001b[A\n",
+            "Training:  17%|█▋        | 253/1500 [04:15<17:05,  1.22it/s, iou=56.32%, test_loss=7.9660, train_loss=4.0115]\u001b[A\n",
+            "Training:  17%|█▋        | 253/1500 [04:16<17:05,  1.22it/s, iou=56.32%, test_loss=7.9660, train_loss=3.6191]\u001b[A\n",
+            "Training:  17%|█▋        | 254/1500 [04:16<13:25,  1.55it/s, iou=56.32%, test_loss=7.9660, train_loss=3.6191]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:53.600 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000255.pt\n",
+            "16/07/2026-08:37:53.743 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  17%|█▋        | 254/1500 [04:16<13:25,  1.55it/s, iou=56.32%, test_loss=7.9660, train_loss=4.6897]\u001b[A\n",
+            "Training:  17%|█▋        | 255/1500 [04:16<13:49,  1.50it/s, iou=56.32%, test_loss=7.9660, train_loss=4.6897]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:57.762 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  17%|█▋        | 255/1500 [04:20<13:49,  1.50it/s, iou=55.37%, test_loss=7.9799, train_loss=3.7102]\u001b[A\n",
+            "Training:  17%|█▋        | 256/1500 [04:20<32:50,  1.58s/it, iou=55.37%, test_loss=7.9799, train_loss=3.7102]\u001b[A\n",
+            "Training:  17%|█▋        | 256/1500 [04:21<32:50,  1.58s/it, iou=55.37%, test_loss=7.9799, train_loss=4.3242]\u001b[A\n",
+            "Training:  17%|█▋        | 257/1500 [04:21<25:28,  1.23s/it, iou=55.37%, test_loss=7.9799, train_loss=4.3242]\u001b[A\n",
+            "Training:  17%|█▋        | 257/1500 [04:21<25:28,  1.23s/it, iou=55.37%, test_loss=7.9799, train_loss=4.3876]\u001b[A\n",
+            "Training:  17%|█▋        | 258/1500 [04:21<20:21,  1.02it/s, iou=55.37%, test_loss=7.9799, train_loss=4.3876]\u001b[A\n",
+            "Training:  17%|█▋        | 258/1500 [04:21<20:21,  1.02it/s, iou=55.37%, test_loss=7.9799, train_loss=4.7524]\u001b[A\n",
+            "Training:  17%|█▋        | 259/1500 [04:21<16:37,  1.24it/s, iou=55.37%, test_loss=7.9799, train_loss=4.7524]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:37:59.321 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000260.pt\n",
+            "16/07/2026-08:37:59.468 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  17%|█▋        | 259/1500 [04:22<16:37,  1.24it/s, iou=55.37%, test_loss=7.9799, train_loss=3.5590]\u001b[A\n",
+            "Training:  17%|█▋        | 260/1500 [04:22<16:45,  1.23it/s, iou=55.37%, test_loss=7.9799, train_loss=3.5590]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:02.756 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  17%|█▋        | 260/1500 [04:25<16:45,  1.23it/s, iou=54.70%, test_loss=8.1732, train_loss=4.5848]\u001b[A\n",
+            "Training:  17%|█▋        | 261/1500 [04:25<30:07,  1.46s/it, iou=54.70%, test_loss=8.1732, train_loss=4.5848]\u001b[A\n",
+            "Training:  17%|█▋        | 261/1500 [04:25<30:07,  1.46s/it, iou=54.70%, test_loss=8.1732, train_loss=4.8366]\u001b[A\n",
+            "Training:  17%|█▋        | 262/1500 [04:25<23:19,  1.13s/it, iou=54.70%, test_loss=8.1732, train_loss=4.8366]\u001b[A\n",
+            "Training:  17%|█▋        | 262/1500 [04:26<23:19,  1.13s/it, iou=54.70%, test_loss=8.1732, train_loss=4.5366]\u001b[A\n",
+            "Training:  18%|█▊        | 263/1500 [04:26<18:00,  1.14it/s, iou=54.70%, test_loss=8.1732, train_loss=4.5366]\u001b[A\n",
+            "Training:  18%|█▊        | 263/1500 [04:26<18:00,  1.14it/s, iou=54.70%, test_loss=8.1732, train_loss=3.3787]\u001b[A\n",
+            "Training:  18%|█▊        | 264/1500 [04:26<14:20,  1.44it/s, iou=54.70%, test_loss=8.1732, train_loss=3.3787]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:03.923 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000265.pt\n",
+            "16/07/2026-08:38:04.071 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  18%|█▊        | 264/1500 [04:27<14:20,  1.44it/s, iou=54.70%, test_loss=8.1732, train_loss=3.9541]\u001b[A\n",
+            "Training:  18%|█▊        | 265/1500 [04:27<13:46,  1.49it/s, iou=54.70%, test_loss=8.1732, train_loss=3.9541]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:07.445 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  18%|█▊        | 265/1500 [04:30<13:46,  1.49it/s, iou=55.65%, test_loss=8.2181, train_loss=5.2825]\u001b[A\n",
+            "Training:  18%|█▊        | 266/1500 [04:30<29:08,  1.42s/it, iou=55.65%, test_loss=8.2181, train_loss=5.2825]\u001b[A\n",
+            "Training:  18%|█▊        | 266/1500 [04:30<29:08,  1.42s/it, iou=55.65%, test_loss=8.2181, train_loss=3.9931]\u001b[A\n",
+            "Training:  18%|█▊        | 267/1500 [04:30<22:10,  1.08s/it, iou=55.65%, test_loss=8.2181, train_loss=3.9931]\u001b[A\n",
+            "Training:  18%|█▊        | 267/1500 [04:30<22:10,  1.08s/it, iou=55.65%, test_loss=8.2181, train_loss=2.7470]\u001b[A\n",
+            "Training:  18%|█▊        | 268/1500 [04:30<17:03,  1.20it/s, iou=55.65%, test_loss=8.2181, train_loss=2.7470]\u001b[A\n",
+            "Training:  18%|█▊        | 268/1500 [04:31<17:03,  1.20it/s, iou=55.65%, test_loss=8.2181, train_loss=5.0071]\u001b[A\n",
+            "Training:  18%|█▊        | 269/1500 [04:31<13:43,  1.50it/s, iou=55.65%, test_loss=8.2181, train_loss=5.0071]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:08.544 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000270.pt\n",
+            "16/07/2026-08:38:08.715 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  18%|█▊        | 269/1500 [04:31<13:43,  1.50it/s, iou=55.65%, test_loss=8.2181, train_loss=4.0433]\u001b[A\n",
+            "Training:  18%|█▊        | 270/1500 [04:31<14:08,  1.45it/s, iou=55.65%, test_loss=8.2181, train_loss=4.0433]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:13.490 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  18%|█▊        | 270/1500 [04:36<14:08,  1.45it/s, iou=57.63%, test_loss=8.1853, train_loss=4.0687]\u001b[A\n",
+            "Training:  18%|█▊        | 271/1500 [04:36<37:22,  1.82s/it, iou=57.63%, test_loss=8.1853, train_loss=4.0687]\u001b[A\n",
+            "Training:  18%|█▊        | 271/1500 [04:36<37:22,  1.82s/it, iou=57.63%, test_loss=8.1853, train_loss=4.2086]\u001b[A\n",
+            "Training:  18%|█▊        | 272/1500 [04:36<27:47,  1.36s/it, iou=57.63%, test_loss=8.1853, train_loss=4.2086]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  18%|█▊        | 272/1500 [04:36<27:47,  1.36s/it, iou=57.63%, test_loss=8.1853, train_loss=3.4903]\u001b[A\n",
+            "Training:  18%|█▊        | 273/1500 [04:36<21:00,  1.03s/it, iou=57.63%, test_loss=8.1853, train_loss=3.4903]\u001b[A\n",
+            "Training:  18%|█▊        | 273/1500 [04:37<21:00,  1.03s/it, iou=57.63%, test_loss=8.1853, train_loss=3.8589]\u001b[A\n",
+            "Training:  18%|█▊        | 274/1500 [04:37<16:20,  1.25it/s, iou=57.63%, test_loss=8.1853, train_loss=3.8589]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:14.553 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000275.pt\n",
+            "16/07/2026-08:38:14.702 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  18%|█▊        | 274/1500 [04:37<16:20,  1.25it/s, iou=57.63%, test_loss=8.1853, train_loss=4.3319]\u001b[A\n",
+            "Training:  18%|█▊        | 275/1500 [04:37<15:33,  1.31it/s, iou=57.63%, test_loss=8.1853, train_loss=4.3319]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:18.115 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  18%|█▊        | 275/1500 [04:40<15:33,  1.31it/s, iou=58.07%, test_loss=8.0730, train_loss=4.4283]\u001b[A\n",
+            "Training:  18%|█▊        | 276/1500 [04:40<30:12,  1.48s/it, iou=58.07%, test_loss=8.0730, train_loss=4.4283]\u001b[A\n",
+            "Training:  18%|█▊        | 276/1500 [04:41<30:12,  1.48s/it, iou=58.07%, test_loss=8.0730, train_loss=6.1504]\u001b[A\n",
+            "Training:  18%|█▊        | 277/1500 [04:41<23:03,  1.13s/it, iou=58.07%, test_loss=8.0730, train_loss=6.1504]\u001b[A\n",
+            "Training:  18%|█▊        | 277/1500 [04:41<23:03,  1.13s/it, iou=58.07%, test_loss=8.0730, train_loss=3.3414]\u001b[A\n",
+            "Training:  19%|█▊        | 278/1500 [04:41<17:41,  1.15it/s, iou=58.07%, test_loss=8.0730, train_loss=3.3414]\u001b[A\n",
+            "Training:  19%|█▊        | 278/1500 [04:41<17:41,  1.15it/s, iou=58.07%, test_loss=8.0730, train_loss=4.4396]\u001b[A\n",
+            "Training:  19%|█▊        | 279/1500 [04:41<13:52,  1.47it/s, iou=58.07%, test_loss=8.0730, train_loss=4.4396]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:19.179 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000280.pt\n",
+            "16/07/2026-08:38:19.339 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  19%|█▊        | 279/1500 [04:42<13:52,  1.47it/s, iou=58.07%, test_loss=8.0730, train_loss=5.1708]\u001b[A\n",
+            "Training:  19%|█▊        | 280/1500 [04:42<14:13,  1.43it/s, iou=58.07%, test_loss=8.0730, train_loss=5.1708]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:22.915 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  19%|█▊        | 280/1500 [04:45<14:13,  1.43it/s, iou=56.91%, test_loss=8.0826, train_loss=4.5370]\u001b[A\n",
+            "Training:  19%|█▊        | 281/1500 [04:45<29:42,  1.46s/it, iou=56.91%, test_loss=8.0826, train_loss=4.5370]\u001b[A\n",
+            "Training:  19%|█▊        | 281/1500 [04:46<29:42,  1.46s/it, iou=56.91%, test_loss=8.0826, train_loss=3.8564]\u001b[A\n",
+            "Training:  19%|█▉        | 282/1500 [04:46<22:55,  1.13s/it, iou=56.91%, test_loss=8.0826, train_loss=3.8564]\u001b[A\n",
+            "Training:  19%|█▉        | 282/1500 [04:46<22:55,  1.13s/it, iou=56.91%, test_loss=8.0826, train_loss=2.7513]\u001b[A\n",
+            "Training:  19%|█▉        | 283/1500 [04:46<18:18,  1.11it/s, iou=56.91%, test_loss=8.0826, train_loss=2.7513]\u001b[A\n",
+            "Training:  19%|█▉        | 283/1500 [04:46<18:18,  1.11it/s, iou=56.91%, test_loss=8.0826, train_loss=3.4142]\u001b[A\n",
+            "Training:  19%|█▉        | 284/1500 [04:46<14:45,  1.37it/s, iou=56.91%, test_loss=8.0826, train_loss=3.4142]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:24.396 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000285.pt\n",
+            "16/07/2026-08:38:24.705 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  19%|█▉        | 284/1500 [04:47<14:45,  1.37it/s, iou=56.91%, test_loss=8.0826, train_loss=5.6199]\u001b[A\n",
+            "Training:  19%|█▉        | 285/1500 [04:47<16:30,  1.23it/s, iou=56.91%, test_loss=8.0826, train_loss=5.6199]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:28.642 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  19%|█▉        | 285/1500 [04:51<16:30,  1.23it/s, iou=56.21%, test_loss=8.2595, train_loss=3.6497]\u001b[A\n",
+            "Training:  19%|█▉        | 286/1500 [04:51<33:47,  1.67s/it, iou=56.21%, test_loss=8.2595, train_loss=3.6497]\u001b[A\n",
+            "Training:  19%|█▉        | 286/1500 [04:51<33:47,  1.67s/it, iou=56.21%, test_loss=8.2595, train_loss=4.2493]\u001b[A\n",
+            "Training:  19%|█▉        | 287/1500 [04:51<25:14,  1.25s/it, iou=56.21%, test_loss=8.2595, train_loss=4.2493]\u001b[A\n",
+            "Training:  19%|█▉        | 287/1500 [04:52<25:14,  1.25s/it, iou=56.21%, test_loss=8.2595, train_loss=4.0331]\u001b[A\n",
+            "Training:  19%|█▉        | 288/1500 [04:52<19:23,  1.04it/s, iou=56.21%, test_loss=8.2595, train_loss=4.0331]\u001b[A\n",
+            "Training:  19%|█▉        | 288/1500 [04:52<19:23,  1.04it/s, iou=56.21%, test_loss=8.2595, train_loss=3.8018]\u001b[A\n",
+            "Training:  19%|█▉        | 289/1500 [04:52<15:11,  1.33it/s, iou=56.21%, test_loss=8.2595, train_loss=3.8018]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:29.742 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000290.pt\n",
+            "16/07/2026-08:38:29.939 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  19%|█▉        | 289/1500 [04:53<15:11,  1.33it/s, iou=56.21%, test_loss=8.2595, train_loss=5.0082]\u001b[A\n",
+            "Training:  19%|█▉        | 290/1500 [04:53<15:19,  1.32it/s, iou=56.21%, test_loss=8.2595, train_loss=5.0082]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:33.406 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  19%|█▉        | 290/1500 [04:56<15:19,  1.32it/s, iou=56.03%, test_loss=8.2441, train_loss=3.1141]\u001b[A\n",
+            "Training:  19%|█▉        | 291/1500 [04:56<29:50,  1.48s/it, iou=56.03%, test_loss=8.2441, train_loss=3.1141]\u001b[A\n",
+            "Training:  19%|█▉        | 291/1500 [04:56<29:50,  1.48s/it, iou=56.03%, test_loss=8.2441, train_loss=3.5669]\u001b[A\n",
+            "Training:  19%|█▉        | 292/1500 [04:56<22:22,  1.11s/it, iou=56.03%, test_loss=8.2441, train_loss=3.5669]\u001b[A\n",
+            "Training:  19%|█▉        | 292/1500 [04:56<22:22,  1.11s/it, iou=56.03%, test_loss=8.2441, train_loss=5.6028]\u001b[A\n",
+            "Training:  20%|█▉        | 293/1500 [04:56<17:22,  1.16it/s, iou=56.03%, test_loss=8.2441, train_loss=5.6028]\u001b[A\n",
+            "Training:  20%|█▉        | 293/1500 [04:57<17:22,  1.16it/s, iou=56.03%, test_loss=8.2441, train_loss=2.6608]\u001b[A\n",
+            "Training:  20%|█▉        | 294/1500 [04:57<13:52,  1.45it/s, iou=56.03%, test_loss=8.2441, train_loss=2.6608]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:34.498 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f761e1ba3dc3472a78_step_000295.pt\n",
+            "16/07/2026-08:38:34.667 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  20%|█▉        | 294/1500 [04:57<13:52,  1.45it/s, iou=56.03%, test_loss=8.2441, train_loss=4.4376]\u001b[A\n",
+            "Training:  20%|█▉        | 295/1500 [04:57<14:18,  1.40it/s, iou=56.03%, test_loss=8.2441, train_loss=4.4376]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:38.264 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:38:38.270 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:38:38.271 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: \n",
+            "[WeightsLab] UI Command: PAUSE\n",
+            "16/07/2026-08:38:38.276 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:38:38.898 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  20%|█▉        | 295/1500 [05:01<14:18,  1.40it/s, iou=56.44%, test_loss=8.1024, train_loss=4.7686]\u001b[A\n",
+            "Training:  20%|█▉        | 296/1500 [05:01<33:32,  1.67s/it, iou=56.44%, test_loss=8.1024, train_loss=4.7686]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:38:46.029 INFO:weightslab.trainer.services.experiment_service:RestoreCheckpoint: Restoring checkpoint from hash: 802319f761e1ba3dc3472a78\n",
+            "16/07/2026-08:38:46.030 INFO:weightslab.trainer.services.experiment_service:RestoreCheckpoint: Pausing training before restore...\n",
+            "16/07/2026-08:38:46.031 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:38:46.034 INFO:weightslab.components.checkpoint_manager:load_state: \n",
+            "============================================================\n",
+            "16/07/2026-08:38:46.036 INFO:weightslab.components.checkpoint_manager:load_state: Loading and applying state: 802319f761e1ba3d...\n",
+            "16/07/2026-08:38:46.037 INFO:weightslab.components.checkpoint_manager:load_state: ============================================================\n",
+            "16/07/2026-08:38:46.041 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loading checkpoint 802319f761e1ba3d...\n",
+            "16/07/2026-08:38:46.042 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Target: HP=802319f7 MODEL=61e1ba3d DATA=c3472a78\n",
+            "16/07/2026-08:38:46.043 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Current: HP=802319f7 MODEL=61e1ba3d DATA=c3472a78\n",
+            "16/07/2026-08:38:46.091 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [OK] Loaded model RNG state for reproducibility (model unchanged)\n",
+            "16/07/2026-08:38:46.129 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [OK] Loaded weights from step 295 with RNG state\n",
+            "16/07/2026-08:38:46.130 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Config unchanged, using current config\n",
+            "16/07/2026-08:38:46.134 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [OK] Loaded RNG state for reproducibility (data unchanged)\n",
+            "16/07/2026-08:38:46.136 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loaded components: {'weights'}\n",
+            "16/07/2026-08:38:46.152 INFO:weightslab.components.checkpoint_manager:load_state: [OK] Applied weights to existing model (step 295)\n",
+            "16/07/2026-08:38:46.155 INFO:weightslab.components.checkpoint_manager:load_state: Loaded optimizer state\n",
+            "16/07/2026-08:38:46.166 INFO:weightslab.components.checkpoint_manager:load_state: \n",
+            "[OK] Successfully loaded and applied state: 802319f761e1ba3d\n",
+            "16/07/2026-08:38:46.168 INFO:weightslab.components.checkpoint_manager:load_state: ============================================================\n",
+            "\n",
+            "16/07/2026-08:38:46.170 INFO:weightslab.trainer.services.experiment_service:RestoreCheckpoint: Successfully restored checkpoint: 802319f761e1ba3dc3472a78\n",
+            "16/07/2026-08:39:05.105 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:39:05.107 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:39:09.366 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:39:09.368 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:39:13.306 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:39:13.307 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:39:14.723 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:39:14.725 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:39:29.427 INFO:weightslab.trainer.services.data_service:GetHistogram: [HistBin] column=signals//train_loss/sample rows=170 bins=170\n",
+            "16/07/2026-08:39:30.509 INFO:weightslab.trainer.services.data_service:ApplyDataQuery: [ApplyDataQuery] BYPASSING AGENT - Direct query execution: 'sortby signals//train_loss/sample desc scope:global'\n",
+            "16/07/2026-08:39:31.921 INFO:weightslab.trainer.services.data_service:GetHistogram: [HistBin] column=signals//train_loss/sample rows=170 bins=170\n",
+            "16/07/2026-08:39:55.323 INFO:weightslab.trainer.services.data_service:EditDataSample: Pausing training before restore...\n",
+            "16/07/2026-08:39:55.324 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:39:58.401 INFO:weightslab.trainer.services.data_service:GetHistogram: [HistBin] column=signals//train_loss/sample rows=170 bins=170\n",
+            "16/07/2026-08:40:14.571 INFO:weightslab.trainer.services.data_service:EditDataSample: Pausing training before restore...\n",
+            "16/07/2026-08:40:14.572 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:40:16.517 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:40:16.519 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:40:16.521 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: \n",
+            "[WeightsLab] UI Command: RESUME\n",
+            "16/07/2026-08:40:16.524 INFO:weightslab.components.global_monitoring:resume: \n",
+            "Attempting to resume training...\n",
+            "16/07/2026-08:40:16.532 INFO:weightslab.components.experiment_hash:has_changed: Experiment configuration changed: {'data', 'hp', 'model'}\n",
+            "16/07/2026-08:40:16.538 INFO:weightslab.components.experiment_hash:generate_hash: Generated experiment hash: bfb454d6d39e7bd943e44972- (HP: bfb454d6, Model: d39e7bd9, Data: 43e44972)\n",
+            "16/07/2026-08:40:16.541 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: New experiment hash: bfb454d6-d39e7bd9-43e44972 (previous: 802319f7-61e1ba3d-c3472a78)\n",
+            "16/07/2026-08:40:16.543 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Changed components: {'data', 'hp', 'model'}\n",
+            "16/07/2026-08:40:16.544 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Changes pending (not dumped yet): {'data', 'hp', 'model'}\n",
+            "16/07/2026-08:40:16.546 INFO:weightslab.components.checkpoint_manager:save_pending_changes: Dumping pending changes: {'data', 'hp', 'model'}\n",
+            "16/07/2026-08:40:16.557 INFO:weightslab.components.checkpoint_manager:_save_changes: Dumping hyperparameters config...\n",
+            "16/07/2026-08:40:16.569 INFO:weightslab.components.checkpoint_manager:save_config: Saved config: bfb454d6_config.yaml with exp_hash prefix: bfb454d6\n",
+            "16/07/2026-08:40:16.574 INFO:weightslab.components.checkpoint_manager:_save_changes: Dumping data snapshot...\n",
+            "16/07/2026-08:40:17.238 INFO:weightslab.components.checkpoint_manager:save_data_snapshot: Saved data snapshot: 43e44972_data_snapshot.json (593 rows) with RNG state\n",
+            "16/07/2026-08:40:17.240 INFO:weightslab.components.checkpoint_manager:_save_changes: Saving model weights checkpoint with component changes...\n",
+            "16/07/2026-08:40:17.308 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: bfb454d6d39e7bd943e44972_step_000295.pt\n",
+            "16/07/2026-08:40:17.559 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "16/07/2026-08:40:17.795 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "16/07/2026-08:40:17.801 INFO:weightslab.components.global_monitoring:resume: Hashes by module: ['bfb454d6', 'd39e7bd9', '43e44972']\n",
+            "16/07/2026-08:40:17.804 INFO:weightslab.components.global_monitoring:resume: Resuming training now...\n",
+            "16/07/2026-08:40:17.807 INFO:weightslab.components.global_monitoring:resume: Hashes by module on resume: ['bfb454d6', 'd39e7bd9', '43e44972']\n",
+            "16/07/2026-08:40:17.815 INFO:weightslab.backend.dataloader_interface:set_batch_size: Batch size updated: 8 -> 16 (Loader: train_loader)\n",
+            "16/07/2026-08:40:17.818 INFO:weightslab.components.global_monitoring:resume: \n",
+            "Training resumed as modules hashes have been computed: ['bfb454d6', 'd39e7bd9', '43e44972'].\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  20%|█▉        | 296/1500 [06:41<33:32,  1.67s/it, iou=56.44%, test_loss=8.1024, train_loss=1.8979]\u001b[A\n",
+            "Training:  20%|█▉        | 297/1500 [06:41<10:23:18, 31.09s/it, iou=56.44%, test_loss=8.1024, train_loss=1.8979]\u001b[A\n",
+            "Training:  20%|█▉        | 297/1500 [06:41<10:23:18, 31.09s/it, iou=56.44%, test_loss=8.1024, train_loss=2.4030]\u001b[A\n",
+            "Training:  20%|█▉        | 298/1500 [06:41<7:18:34, 21.89s/it, iou=56.44%, test_loss=8.1024, train_loss=2.4030] \u001b[A\n",
+            "Training:  20%|█▉        | 298/1500 [06:42<7:18:34, 21.89s/it, iou=56.44%, test_loss=8.1024, train_loss=1.9995]\u001b[A\n",
+            "Training:  20%|█▉        | 299/1500 [06:42<5:11:45, 15.57s/it, iou=56.44%, test_loss=8.1024, train_loss=1.9995]\u001b[A\n",
+            "Training:  20%|█▉        | 299/1500 [06:42<5:11:45, 15.57s/it, iou=56.44%, test_loss=8.1024, train_loss=1.9483]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:40:20.402 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: bfb454d6d39e7bd943e44972_step_000300.pt\n",
+            "16/07/2026-08:40:20.588 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  20%|█▉        | 299/1500 [06:43<5:11:45, 15.57s/it, iou=56.44%, test_loss=8.1024, train_loss=2.3940]\u001b[A\n",
+            "Training:  20%|██        | 301/1500 [06:43<2:51:19,  8.57s/it, iou=56.44%, test_loss=8.1024, train_loss=2.3940]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:40:21.165 INFO:weightslab.backend.dataloader_interface:set_batch_size: Batch size updated: 8 -> 16 (Loader: test_loader)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:40:23.499 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  20%|██        | 301/1500 [06:46<2:51:19,  8.57s/it, iou=33.90%, test_loss=4.7464, train_loss=1.9816]\u001b[A\n",
+            "Training:  20%|██        | 302/1500 [06:46<2:22:34,  7.14s/it, iou=33.90%, test_loss=4.7464, train_loss=1.9816]\u001b[A\n",
+            "Training:  20%|██        | 302/1500 [06:46<2:22:34,  7.14s/it, iou=33.90%, test_loss=4.7464, train_loss=1.8739]\u001b[A\n",
+            "Training:  20%|██        | 303/1500 [06:46<1:47:37,  5.39s/it, iou=33.90%, test_loss=4.7464, train_loss=1.8739]\u001b[A\n",
+            "Training:  20%|██        | 303/1500 [06:46<1:47:37,  5.39s/it, iou=33.90%, test_loss=4.7464, train_loss=1.8943]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  20%|██        | 303/1500 [06:47<1:47:37,  5.39s/it, iou=33.90%, test_loss=4.7464, train_loss=1.8743]\u001b[A\n",
+            "Training:  20%|██        | 305/1500 [06:47<1:03:34,  3.19s/it, iou=33.90%, test_loss=4.7464, train_loss=1.8743]\u001b[A\n",
+            "Training:  20%|██        | 305/1500 [06:47<1:03:34,  3.19s/it, iou=33.90%, test_loss=4.7464, train_loss=1.9137]\u001b[A\n",
+            "Training:  20%|██        | 306/1500 [06:47<50:33,  2.54s/it, iou=33.90%, test_loss=4.7464, train_loss=1.9137]  \u001b[A\n",
+            "Training:  20%|██        | 306/1500 [06:48<50:33,  2.54s/it, iou=33.90%, test_loss=4.7464, train_loss=2.4774]\u001b[A\n",
+            "Training:  20%|██        | 307/1500 [06:48<42:26,  2.13s/it, iou=33.90%, test_loss=4.7464, train_loss=2.4774]\u001b[A\n",
+            "Training:  20%|██        | 307/1500 [06:48<42:26,  2.13s/it, iou=33.90%, test_loss=4.7464, train_loss=1.8858]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  20%|██        | 307/1500 [06:49<42:26,  2.13s/it, iou=33.90%, test_loss=4.7464, train_loss=1.9598]\u001b[A\n",
+            "Training:  21%|██        | 309/1500 [06:49<26:52,  1.35s/it, iou=33.90%, test_loss=4.7464, train_loss=1.9598]\u001b[A\n",
+            "Training:  21%|██        | 309/1500 [06:49<26:52,  1.35s/it, iou=33.90%, test_loss=4.7464, train_loss=2.3849]\u001b[A\n",
+            "Training:  21%|██        | 310/1500 [06:49<22:41,  1.14s/it, iou=33.90%, test_loss=4.7464, train_loss=2.3849]\u001b[A\n",
+            "Training:  21%|██        | 310/1500 [06:50<22:41,  1.14s/it, iou=33.90%, test_loss=4.7464, train_loss=1.8953]\u001b[A\n",
+            "Training:  21%|██        | 311/1500 [06:50<21:04,  1.06s/it, iou=33.90%, test_loss=4.7464, train_loss=1.8953]\u001b[A\n",
+            "Training:  21%|██        | 311/1500 [06:50<21:04,  1.06s/it, iou=33.90%, test_loss=4.7464, train_loss=1.9958]\u001b[A\n",
+            "Training:  21%|██        | 312/1500 [06:50<16:32,  1.20it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9958]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  21%|██        | 312/1500 [06:51<16:32,  1.20it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8536]\u001b[A\n",
+            "Training:  21%|██        | 313/1500 [06:51<14:17,  1.38it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8536]\u001b[A\n",
+            "Training:  21%|██        | 313/1500 [06:51<14:17,  1.38it/s, iou=33.90%, test_loss=4.7464, train_loss=2.3955]\u001b[A\n",
+            "Training:  21%|██        | 314/1500 [06:51<13:25,  1.47it/s, iou=33.90%, test_loss=4.7464, train_loss=2.3955]\u001b[A\n",
+            "Training:  21%|██        | 314/1500 [06:52<13:25,  1.47it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9691]\u001b[A\n",
+            "Training:  21%|██        | 315/1500 [06:52<16:04,  1.23it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9691]\u001b[A\n",
+            "Training:  21%|██        | 315/1500 [06:53<16:04,  1.23it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8806]\u001b[A\n",
+            "Training:  21%|██        | 316/1500 [06:53<13:43,  1.44it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8806]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  21%|██        | 316/1500 [06:54<13:43,  1.44it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8700]\u001b[A\n",
+            "Training:  21%|██        | 317/1500 [06:54<17:55,  1.10it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8700]\u001b[A\n",
+            "Training:  21%|██        | 317/1500 [06:55<17:55,  1.10it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9674]\u001b[A\n",
+            "Training:  21%|██        | 318/1500 [06:55<16:26,  1.20it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9674]\u001b[A\n",
+            "Training:  21%|██        | 318/1500 [06:56<16:26,  1.20it/s, iou=33.90%, test_loss=4.7464, train_loss=2.3859]\u001b[A\n",
+            "Training:  21%|██▏       | 319/1500 [06:56<18:06,  1.09it/s, iou=33.90%, test_loss=4.7464, train_loss=2.3859]\u001b[A\n",
+            "Training:  21%|██▏       | 319/1500 [06:56<18:06,  1.09it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8463]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  21%|██▏       | 319/1500 [06:56<18:06,  1.09it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9668]\u001b[A\n",
+            "Training:  21%|██▏       | 321/1500 [06:56<11:56,  1.64it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9668]\u001b[A\n",
+            "Training:  21%|██▏       | 321/1500 [06:57<11:56,  1.64it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8672]\u001b[A\n",
+            "Training:  21%|██▏       | 322/1500 [06:57<11:02,  1.78it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8672]\u001b[A\n",
+            "Training:  21%|██▏       | 322/1500 [06:58<11:02,  1.78it/s, iou=33.90%, test_loss=4.7464, train_loss=2.3978]\u001b[A\n",
+            "Training:  22%|██▏       | 323/1500 [06:58<12:20,  1.59it/s, iou=33.90%, test_loss=4.7464, train_loss=2.3978]\u001b[A\n",
+            "Training:  22%|██▏       | 323/1500 [06:58<12:20,  1.59it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9003]\u001b[A\n",
+            "Training:  22%|██▏       | 324/1500 [06:58<09:58,  1.96it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9003]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  22%|██▏       | 324/1500 [06:58<09:58,  1.96it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8884]\u001b[A\n",
+            "Training:  22%|██▏       | 325/1500 [06:58<09:27,  2.07it/s, iou=33.90%, test_loss=4.7464, train_loss=1.8884]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:40:36.225 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: bfb454d6d39e7bd943e44972_step_000325.pt\n",
+            "16/07/2026-08:40:36.404 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  22%|██▏       | 325/1500 [06:59<09:27,  2.07it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9650]\u001b[A\n",
+            "Training:  22%|██▏       | 326/1500 [06:59<10:33,  1.85it/s, iou=33.90%, test_loss=4.7464, train_loss=1.9650]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:40:38.855 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:40:38.862 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:40:38.865 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: \n",
+            "[WeightsLab] UI Command: PAUSE\n",
+            "16/07/2026-08:40:38.870 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:40:39.412 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  22%|██▏       | 326/1500 [07:02<10:33,  1.85it/s, iou=54.03%, test_loss=7.9861, train_loss=2.4054]\u001b[A\n",
+            "Training:  22%|██▏       | 327/1500 [07:02<23:56,  1.22s/it, iou=54.03%, test_loss=7.9861, train_loss=2.4054]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:40:41.095 INFO:weightslab.trainer.services.experiment_service:RestoreCheckpoint: Restoring checkpoint from hash: 802319f761e1ba3dc3472a78 (weights-only, target_step=24)\n",
+            "16/07/2026-08:40:41.096 INFO:weightslab.trainer.services.experiment_service:RestoreCheckpoint: Pausing training before restore...\n",
+            "16/07/2026-08:40:41.099 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:40:41.102 INFO:weightslab.components.checkpoint_manager:load_state: \n",
+            "============================================================\n",
+            "16/07/2026-08:40:41.103 INFO:weightslab.components.checkpoint_manager:load_state: Loading and applying state: 802319f761e1ba3d...\n",
+            "16/07/2026-08:40:41.104 INFO:weightslab.components.checkpoint_manager:load_state: ============================================================\n",
+            "16/07/2026-08:40:41.110 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loading checkpoint 802319f761e1ba3d...\n",
+            "16/07/2026-08:40:41.112 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Target: HP=802319f7 MODEL=61e1ba3d DATA=c3472a78\n",
+            "16/07/2026-08:40:41.115 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Current: HP=bfb454d6 MODEL=d39e7bd9 DATA=43e44972\n",
+            "16/07/2026-08:40:41.118 WARNING:weightslab.components.checkpoint_manager:load_checkpoint:  [WARNING] Model architecture file not found: /tmp/tmpf35sd4_z/checkpoints/models/61e1ba3d/61e1ba3d_architecture.pkl\n",
+            "16/07/2026-08:40:41.176 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [OK] Loaded weights from step 25 with RNG state\n",
+            "16/07/2026-08:40:41.184 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [OK] Loaded config (hash changed)\n",
+            "16/07/2026-08:40:41.198 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [OK] Loaded data snapshot (170 rows) with RNG state\n",
+            "16/07/2026-08:40:41.200 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loaded components: {'data', 'weights', 'config'}\n",
+            "16/07/2026-08:40:41.215 INFO:weightslab.components.checkpoint_manager:load_state: [OK] Applied weights to existing model (step 25)\n",
+            "16/07/2026-08:40:41.217 INFO:weightslab.components.checkpoint_manager:load_state: Loaded optimizer state\n",
+            "16/07/2026-08:40:41.219 INFO:weightslab.components.checkpoint_manager:load_state: [OK] Applied hyperparameters config\n",
+            "16/07/2026-08:40:41.254 INFO:weightslab.components.checkpoint_manager:load_state: [OK] Applied data snapshot (170 rows)\n",
+            "16/07/2026-08:40:41.266 INFO:weightslab.components.checkpoint_manager:load_state: \n",
+            "[OK] Successfully loaded and applied state: 802319f761e1ba3d\n",
+            "16/07/2026-08:40:41.267 INFO:weightslab.components.checkpoint_manager:load_state: ============================================================\n",
+            "\n",
+            "16/07/2026-08:40:41.272 INFO:weightslab.trainer.services.experiment_service:RestoreCheckpoint: Successfully restored checkpoint: 802319f761e1ba3dc3472a78\n",
+            "16/07/2026-08:40:44.091 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:40:44.092 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:40:44.093 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: \n",
+            "[WeightsLab] UI Command: RESUME\n",
+            "16/07/2026-08:40:44.096 INFO:weightslab.components.global_monitoring:resume: \n",
+            "Attempting to resume training...\n",
+            "16/07/2026-08:40:44.104 INFO:weightslab.components.experiment_hash:has_changed: Experiment configuration changed: {'model'}\n",
+            "16/07/2026-08:40:44.107 INFO:weightslab.components.experiment_hash:generate_hash: Generated experiment hash: 802319f7f5ee131bc3472a78- (HP: 802319f7, Model: f5ee131b, Data: c3472a78)\n",
+            "16/07/2026-08:40:44.111 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: New experiment hash: 802319f7-f5ee131b-c3472a78 (previous: 802319f7-61e1ba3d-c3472a78)\n",
+            "16/07/2026-08:40:44.113 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Changed components: {'model'}\n",
+            "16/07/2026-08:40:44.115 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Changes pending (not dumped yet): {'model'}\n",
+            "16/07/2026-08:40:44.118 INFO:weightslab.components.checkpoint_manager:save_pending_changes: Dumping pending changes: {'model'}\n",
+            "16/07/2026-08:40:44.427 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "16/07/2026-08:40:44.434 INFO:weightslab.components.global_monitoring:resume: Hashes by module: ['802319f7', 'f5ee131b', 'c3472a78']\n",
+            "16/07/2026-08:40:44.437 INFO:weightslab.components.global_monitoring:resume: Resuming training now...\n",
+            "16/07/2026-08:40:44.438 INFO:weightslab.components.global_monitoring:resume: Hashes by module on resume: ['802319f7', 'f5ee131b', 'c3472a78']\n",
+            "16/07/2026-08:40:44.448 INFO:weightslab.backend.dataloader_interface:set_batch_size: Batch size updated: 16 -> 8 (Loader: train_loader)\n",
+            "16/07/2026-08:40:44.452 INFO:weightslab.components.global_monitoring:resume: \n",
+            "Training resumed as modules hashes have been computed: ['802319f7', 'f5ee131b', 'c3472a78'].\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  22%|██▏       | 327/1500 [07:07<23:56,  1.22s/it, iou=54.03%, test_loss=7.9861, train_loss=10.4120]\u001b[A\n",
+            "Training:  22%|██▏       | 328/1500 [07:07<48:08,  2.46s/it, iou=54.03%, test_loss=7.9861, train_loss=10.4120]\u001b[A\n",
+            "Training:  22%|██▏       | 328/1500 [07:08<48:08,  2.46s/it, iou=54.03%, test_loss=7.9861, train_loss=8.1298] \u001b[A\n",
+            "Training:  22%|██▏       | 329/1500 [07:08<36:27,  1.87s/it, iou=54.03%, test_loss=7.9861, train_loss=8.1298]\u001b[A\n",
+            "Training:  22%|██▏       | 329/1500 [07:08<36:27,  1.87s/it, iou=54.03%, test_loss=7.9861, train_loss=8.6498]\u001b[A\n",
+            "Training:  22%|██▏       | 330/1500 [07:08<27:35,  1.41s/it, iou=54.03%, test_loss=7.9861, train_loss=8.6498]\u001b[A\n",
+            "Training:  22%|██▏       | 330/1500 [07:09<27:35,  1.41s/it, iou=54.03%, test_loss=7.9861, train_loss=8.4801]\u001b[A\n",
+            "Training:  22%|██▏       | 331/1500 [07:09<22:37,  1.16s/it, iou=54.03%, test_loss=7.9861, train_loss=8.4801]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:40:48.449 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f7f5ee131bc3472a78_step_000030.pt\n",
+            "16/07/2026-08:40:48.825 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  22%|██▏       | 331/1500 [07:11<22:37,  1.16s/it, iou=54.03%, test_loss=7.9861, train_loss=9.8237]\u001b[A\n",
+            "Training:  22%|██▏       | 332/1500 [07:11<31:52,  1.64s/it, iou=54.03%, test_loss=7.9861, train_loss=9.8237]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:40:51.421 INFO:weightslab.backend.dataloader_interface:set_batch_size: Batch size updated: 16 -> 8 (Loader: test_loader)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:40:56.551 WARNING:weightslab.watchdog.grpc_watchdog:unary_unary_wrapper: [gRPC] /ExperimentService/GetDataSamples completed in 3558.8ms\n",
+            "16/07/2026-08:41:00.986 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  22%|██▏       | 332/1500 [07:23<31:52,  1.64s/it, iou=90.43%, test_loss=15.9695, train_loss=9.6350]\u001b[A\n",
+            "Training:  22%|██▏       | 333/1500 [07:23<1:32:06,  4.74s/it, iou=90.43%, test_loss=15.9695, train_loss=9.6350]\u001b[A\n",
+            "Training:  22%|██▏       | 333/1500 [07:24<1:32:06,  4.74s/it, iou=90.43%, test_loss=15.9695, train_loss=7.9581]\u001b[A\n",
+            "Training:  22%|██▏       | 334/1500 [07:24<1:06:21,  3.41s/it, iou=90.43%, test_loss=15.9695, train_loss=7.9581]\u001b[A\n",
+            "Training:  22%|██▏       | 334/1500 [07:24<1:06:21,  3.41s/it, iou=90.43%, test_loss=15.9695, train_loss=7.3944]\u001b[A\n",
+            "Training:  22%|██▏       | 335/1500 [07:24<47:55,  2.47s/it, iou=90.43%, test_loss=15.9695, train_loss=7.3944]  \u001b[A\n",
+            "Training:  22%|██▏       | 335/1500 [07:24<47:55,  2.47s/it, iou=90.43%, test_loss=15.9695, train_loss=10.4550]\u001b[A\n",
+            "Training:  22%|██▏       | 336/1500 [07:24<35:17,  1.82s/it, iou=90.43%, test_loss=15.9695, train_loss=10.4550]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:41:02.160 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f7f5ee131bc3472a78_step_000035.pt\n",
+            "16/07/2026-08:41:02.346 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  22%|██▏       | 336/1500 [07:25<35:17,  1.82s/it, iou=90.43%, test_loss=15.9695, train_loss=7.6845] \u001b[A\n",
+            "Training:  22%|██▏       | 337/1500 [07:25<29:14,  1.51s/it, iou=90.43%, test_loss=15.9695, train_loss=7.6845]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:41:05.682 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  22%|██▏       | 337/1500 [07:28<29:14,  1.51s/it, iou=55.45%, test_loss=9.3424, train_loss=6.8015] \u001b[A\n",
+            "Training:  23%|██▎       | 338/1500 [07:28<38:06,  1.97s/it, iou=55.45%, test_loss=9.3424, train_loss=6.8015]\u001b[A\n",
+            "Training:  23%|██▎       | 338/1500 [07:28<38:06,  1.97s/it, iou=55.45%, test_loss=9.3424, train_loss=8.4716]\u001b[A\n",
+            "Training:  23%|██▎       | 339/1500 [07:28<28:11,  1.46s/it, iou=55.45%, test_loss=9.3424, train_loss=8.4716]\u001b[A\n",
+            "Training:  23%|██▎       | 339/1500 [07:29<28:11,  1.46s/it, iou=55.45%, test_loss=9.3424, train_loss=8.7423]\u001b[A\n",
+            "Training:  23%|██▎       | 340/1500 [07:29<21:16,  1.10s/it, iou=55.45%, test_loss=9.3424, train_loss=8.7423]\u001b[A\n",
+            "Training:  23%|██▎       | 340/1500 [07:29<21:16,  1.10s/it, iou=55.45%, test_loss=9.3424, train_loss=8.9636]\u001b[A\n",
+            "Training:  23%|██▎       | 341/1500 [07:29<16:33,  1.17it/s, iou=55.45%, test_loss=9.3424, train_loss=8.9636]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:41:06.887 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f7f5ee131bc3472a78_step_000040.pt\n",
+            "16/07/2026-08:41:07.490 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  23%|██▎       | 341/1500 [07:30<16:33,  1.17it/s, iou=55.45%, test_loss=9.3424, train_loss=8.2650]\u001b[A\n",
+            "Training:  23%|██▎       | 342/1500 [07:30<17:36,  1.10it/s, iou=55.45%, test_loss=9.3424, train_loss=8.2650]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:41:10.072 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  23%|██▎       | 342/1500 [07:32<17:36,  1.10it/s, iou=57.76%, test_loss=8.9165, train_loss=9.8330]\u001b[A\n",
+            "Training:  23%|██▎       | 343/1500 [07:32<26:56,  1.40s/it, iou=57.76%, test_loss=8.9165, train_loss=9.8330]\u001b[A\n",
+            "Training:  23%|██▎       | 343/1500 [07:33<26:56,  1.40s/it, iou=57.76%, test_loss=8.9165, train_loss=8.5029]\u001b[A\n",
+            "Training:  23%|██▎       | 344/1500 [07:33<20:40,  1.07s/it, iou=57.76%, test_loss=8.9165, train_loss=8.5029]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n",
+            "\n",
+            "Training:  23%|██▎       | 344/1500 [07:33<20:40,  1.07s/it, iou=57.76%, test_loss=8.9165, train_loss=6.6450]\u001b[A\n",
+            "Training:  23%|██▎       | 345/1500 [07:33<16:02,  1.20it/s, iou=57.76%, test_loss=8.9165, train_loss=6.6450]\u001b[A\n",
+            "Training:  23%|██▎       | 345/1500 [07:33<16:02,  1.20it/s, iou=57.76%, test_loss=8.9165, train_loss=7.5255]\u001b[A\n",
+            "Training:  23%|██▎       | 346/1500 [07:33<13:05,  1.47it/s, iou=57.76%, test_loss=8.9165, train_loss=7.5255]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:41:11.459 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f7f5ee131bc3472a78_step_000045.pt\n",
+            "16/07/2026-08:41:11.769 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  23%|██▎       | 346/1500 [07:34<13:05,  1.47it/s, iou=57.76%, test_loss=8.9165, train_loss=7.0646]\u001b[A\n",
+            "Training:  23%|██▎       | 347/1500 [07:34<15:19,  1.25it/s, iou=57.76%, test_loss=8.9165, train_loss=7.0646]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:41:16.032 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  23%|██▎       | 347/1500 [07:38<15:19,  1.25it/s, iou=58.16%, test_loss=8.7419, train_loss=8.9887]\u001b[A\n",
+            "Training:  23%|██▎       | 348/1500 [07:38<33:36,  1.75s/it, iou=58.16%, test_loss=8.7419, train_loss=8.9887]\u001b[A\n",
+            "Training:  23%|██▎       | 348/1500 [07:39<33:36,  1.75s/it, iou=58.16%, test_loss=8.7419, train_loss=7.1660]\u001b[A\n",
+            "Training:  23%|██▎       | 349/1500 [07:39<25:01,  1.30s/it, iou=58.16%, test_loss=8.7419, train_loss=7.1660]\u001b[A\n",
+            "Training:  23%|██▎       | 349/1500 [07:39<25:01,  1.30s/it, iou=58.16%, test_loss=8.7419, train_loss=7.0642]\u001b[A\n",
+            "Training:  23%|██▎       | 350/1500 [07:39<18:58,  1.01it/s, iou=58.16%, test_loss=8.7419, train_loss=7.0642]\u001b[A\n",
+            "Training:  23%|██▎       | 350/1500 [07:39<18:58,  1.01it/s, iou=58.16%, test_loss=8.7419, train_loss=5.4376]\u001b[A\n",
+            "Training:  23%|██▎       | 351/1500 [07:39<14:41,  1.30it/s, iou=58.16%, test_loss=8.7419, train_loss=5.4376]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:41:17.068 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f7f5ee131bc3472a78_step_000050.pt\n",
+            "16/07/2026-08:41:17.255 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  23%|██▎       | 351/1500 [07:40<14:41,  1.30it/s, iou=58.16%, test_loss=8.7419, train_loss=8.3300]\u001b[A\n",
+            "Training:  23%|██▎       | 352/1500 [07:40<14:17,  1.34it/s, iou=58.16%, test_loss=8.7419, train_loss=8.3300]\u001b[A/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+            "  warnings.warn(warn_msg)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:41:20.421 INFO:weightslab.src:write_dataframe: write_dataframe: wrote 593 row(s) × 24 column(s) as json to /tmp/tmpf35sd4_z/d8e01c87_dataframe.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  23%|██▎       | 352/1500 [07:43<14:17,  1.34it/s, iou=58.39%, test_loss=8.7055, train_loss=6.9667]\u001b[A\n",
+            "Training:  24%|██▎       | 353/1500 [07:43<26:44,  1.40s/it, iou=58.39%, test_loss=8.7055, train_loss=6.9667]\u001b[A\n",
+            "Training:  24%|██▎       | 353/1500 [07:43<26:44,  1.40s/it, iou=58.39%, test_loss=8.7055, train_loss=5.6257]\u001b[A\n",
+            "Training:  24%|██▎       | 354/1500 [07:43<20:19,  1.06s/it, iou=58.39%, test_loss=8.7055, train_loss=5.6257]\u001b[A\n",
+            "Training:  24%|██▎       | 354/1500 [07:43<20:19,  1.06s/it, iou=58.39%, test_loss=8.7055, train_loss=6.9397]\u001b[A\n",
+            "Training:  24%|██▎       | 355/1500 [07:43<15:46,  1.21it/s, iou=58.39%, test_loss=8.7055, train_loss=6.9397]\u001b[A\n",
+            "Training:  24%|██▎       | 355/1500 [07:44<15:46,  1.21it/s, iou=58.39%, test_loss=8.7055, train_loss=7.1436]\u001b[A\n",
+            "Training:  24%|██▎       | 356/1500 [07:44<12:38,  1.51it/s, iou=58.39%, test_loss=8.7055, train_loss=7.1436]\u001b[A"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "16/07/2026-08:41:21.337 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:41:21.339 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: [WeightsLab] UI Command: HP changed, experiment paused!\n",
+            "16/07/2026-08:41:21.340 INFO:weightslab.trainer.services.experiment_service:ExperimentCommand: \n",
+            "[WeightsLab] UI Command: PAUSE\n",
+            "16/07/2026-08:41:21.342 INFO:weightslab.components.global_monitoring:pause: \n",
+            "Training paused.\n",
+            "16/07/2026-08:41:21.615 INFO:weightslab.components.checkpoint_manager:save_model_checkpoint: Saved model checkpoint: 802319f7f5ee131bc3472a78_step_000055.pt\n",
+            "16/07/2026-08:41:21.811 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmpf35sd4_z/checkpoints/loggers/loggers.manifest.json (1 chunks)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Training:  24%|██▎       | 356/1500 [07:45<12:38,  1.51it/s, iou=58.39%, test_loss=8.7055, train_loss=6.4582]\u001b[A\n",
+            "Training:  24%|██▍       | 357/1500 [07:45<14:34,  1.31it/s, iou=58.39%, test_loss=8.7055, train_loss=6.4582]\u001b[A"
+          ]
+        }
+      ],
+      "source": [
+        "# Start the background gRPC server + a public bore tunnel so a local Weights Studio can attach.\n",
+        "wl.serve(serving_grpc=True, serving_bore=True)\n",
+        "\n",
+        "# Flip the experiment into the training state, then run the loop.\n",
+        "wl.start_training(timeout=3)\n",
+        "\n",
+        "# training_steps_to_do is None (open-ended) in the example; cap the demo to a finite\n",
+        "# number of steps so the cell terminates on its own on Colab (raise for a longer run).\n",
+        "max_steps = config[\"training_steps_to_do\"] or 1500\n",
+        "eval_ratio = config[\"eval_full_to_train_steps_ratio\"]\n",
+        "export_ratio = config[\"experiment_dump_to_train_steps_ratio\"]\n",
+        "\n",
+        "test_loss, test_metric = None, None\n",
+        "pbar = tqdm.tqdm(range(max_steps), desc=\"Training\")\n",
+        "for train_step in pbar:\n",
+        "    age = model.get_age() if hasattr(model, \"get_age\") else train_step\n",
+        "\n",
+        "    # Train one step.\n",
+        "    train_loss = train(train_loader, model, optimizer, train_sig, device, grid_size, conf_thresh)\n",
+        "\n",
+        "    # Full eval pass every eval_ratio steps.\n",
+        "    if age == 0 or age % eval_ratio == 0:\n",
+        "        test_loader_len = len(test_loader)\n",
+        "        test_loss, test_metric = test(test_loader, model, test_sig, device, grid_size, conf_thresh, test_loader_len)\n",
+        "\n",
+        "    # Export per-sample history + dataframe periodically.\n",
+        "    if age > 0 and age % export_ratio == 0:\n",
+        "        wl.write_history()\n",
+        "        wl.write_dataframe()\n",
+        "\n",
+        "    pbar.set_postfix(\n",
+        "        train_loss=f\"{train_loss:.4f}\",\n",
+        "        test_loss=f\"{test_loss:.4f}\" if test_loss is not None else \"N/A\",\n",
+        "        iou=f\"{test_metric:.2f}%\" if test_metric is not None else \"N/A\",\n",
+        "    )\n",
+        "\n",
+        "wl.write_history()\n",
+        "wl.write_dataframe()\n",
+        "print(\"Training complete. Logs at:\", log_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j9TYziOc_xBg"
+      },
+      "source": [
+        "## See it live in Weights Studio\n",
+        "\n",
+        "**Colab has no Docker daemon**, so run Studio on your own machine and point it at this notebook's\n",
+        "backend using the `bore.pub:<port>` printed in the Expose section:\n",
+        "\n",
+        "```bash\n",
+        "pip install weightslab\n",
+        "weightslab start                       # Terminal 1 - opens http://localhost:5173\n",
+        "weightslab tunnel bore.pub:12345           # Terminal 2 - the host:port from the Expose section\n",
+        "```\n",
+        "\n",
+        "Then open **http://localhost:5173**. Prefer local-only? Run this example directly on your machine\n",
+        "(`weightslab start example` selects a bundled example) and launch the UI next to it - no tunnel."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oQsjwjg7_xBg"
+      },
+      "source": [
+        "---\n",
+        "\n",
+        "<div align=\"center\">\n",
+        "Crafted by <a href=\"https://github.com/GrayboxTech/weightslab\">GrayboxTech</a> - if WeightsLab helps you catch a bad label, drop us a star on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a>.\n",
+        "</div>"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "wl-detection.ipynb",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/weightslab/examples/Notebooks/PyTorch/wl-fraud-detection.ipynb b/weightslab/examples/Notebooks/PyTorch/wl-fraud-detection.ipynb
new file mode 100644
index 00000000..ea8b274f
--- /dev/null
+++ b/weightslab/examples/Notebooks/PyTorch/wl-fraud-detection.ipynb
@@ -0,0 +1,153 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "<a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-fraud-detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "<div align=\"center\">\n\n  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n\n  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n  <br>\n  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-classification.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n\n  Welcome to the WeightsLab image-classification notebook! <a href=\"https://github.com/GrayboxTech/weightslab\">WeightsLab</a> is an open-source PyTorch tool for dataset debugging, mislabel detection, and mid-training data curation. Browse the <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\">Docs</a> for details, and raise an issue on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a> for support.</div>"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "# Bank Fraud Detection with WeightsLab (tabular)\n\nThis notebook trains a small MLP to flag **fraudulent bank card transactions** and instruments it with WeightsLab so every training signal is traced **back to the exact transaction** producing it.\n\nThere are no images: a sample is one transaction (a **row**), the model input **is** the 16-feature vector, and WeightsLab sends that vector to the UI as a `vector` (not a fake image). Each raw feature (`amount`, `merchant_risk`, `geo_distance_km`, \u2026) is a **sortable column** in the List Exploration view.\n\n### What you'll do\n1. Install WeightsLab.\n2. Generate a reproducible synthetic fraud stream (no download).\n3. Wrap the model, optimizer, dataloaders, loss and metric.\n4. Train while streaming per-transaction loss/prediction to Weights Studio.\n\n*Real drop-in datasets: Kaggle Credit Card Fraud (ULB), PaySim.*"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## Setup\n\nInstall WeightsLab from PyPI. These tabular demos are tiny \u2014 the free Colab CPU runtime is plenty (no GPU needed).\n\n> The **tabular input path** (the feature vector is sent to the UI as a `vector` \u2014 not a fake image) needs a WeightsLab build with tabular support (the `252-tabular-experiment` line or a release that includes it)."
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?color=5865F2&logo=pypi&logoColor=white\" alt=\"PyPI - Version\"></a>\n<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/dm/weightslab?color=5865F2\" alt=\"PyPI - Downloads\"></a>\n<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/pyversions/weightslab?color=5865F2&logo=python&logoColor=white\" alt=\"PyPI - Python Version\"></a>"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": "%pip install torch torchvision"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": "%pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev6\""
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## 1. Imports\n\n`weightslab` is imported as `wl`. The two `guard_*_context` managers scope a block as training vs. evaluation so signals are attributed to the right phase."
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": "import tempfile, logging\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset\nfrom torchmetrics.classification import Accuracy\nfrom tqdm.auto import tqdm\n\nimport weightslab as wl\nfrom weightslab.components.global_monitoring import (\n    guard_training_context,\n    guard_testing_context,\n)\n\nlogging.basicConfig(level=logging.ERROR)\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Using device: {device}\")"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## 2. A synthetic fraud dataset (rows, not images)\n\n`make_synthetic_fraud` builds reproducible transactions: 16 numeric features with a binary label (~12% fraud). Fraud rows come from shifted distributions (larger amounts, off-hours activity, higher merchant risk, device changes, larger geo jumps). The dataset returns the **standardized feature vector** as the model input, and `get_items(...)` exposes the **raw** values as metadata columns."
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": "FEATURE_NAMES = [\n    \"amount\", \"old_balance\", \"new_balance\", \"balance_delta\",\n    \"txn_hour\", \"txn_day_of_week\", \"txn_count_1h\", \"txn_count_24h\",\n    \"avg_amount_7d\", \"std_amount_7d\", \"merchant_risk\", \"device_change\",\n    \"geo_distance_km\", \"is_foreign\", \"account_age_days\", \"num_prior_disputes\",\n]\nNUM_FEATURES = len(FEATURE_NAMES)  # 16\nFRAUD_RATE = 0.12\n\n\ndef make_synthetic_fraud(n_samples, seed=0):\n    \"\"\"Return (X_std, X_raw, y): standardized input, raw display values, label.\"\"\"\n    rng = np.random.default_rng(seed)\n    n_fraud = max(1, round(n_samples * FRAUD_RATE))\n    n_legit = max(1, n_samples - n_fraud)\n\n    def draw(n, fraud):\n        amount = rng.gamma(2.0, 180.0 if fraud else 60.0, n)\n        old_balance = rng.gamma(2.0, 800.0, n)\n        spent = amount * (rng.uniform(0.6, 1.4, n) if fraud else rng.uniform(0.0, 0.6, n))\n        new_balance = np.clip(old_balance - spent, 0, None)\n        balance_delta = old_balance - new_balance\n        txn_hour = (rng.normal(2.5, 2.0, n) % 24) if fraud else rng.normal(13.0, 4.0, n)\n        txn_dow = rng.integers(0, 7, n).astype(float)\n        txn_count_1h = rng.poisson(4.0 if fraud else 1.0, n).astype(float)\n        txn_count_24h = rng.poisson(18.0 if fraud else 6.0, n).astype(float)\n        avg_amount_7d = rng.gamma(2.0, 90.0 if fraud else 70.0, n)\n        std_amount_7d = rng.gamma(2.0, 60.0 if fraud else 25.0, n)\n        merchant_risk = rng.beta(5.0, 2.0, n) if fraud else rng.beta(2.0, 6.0, n)\n        device_change = rng.binomial(1, 0.55 if fraud else 0.08, n).astype(float)\n        geo = rng.gamma(2.0, 400.0 if fraud else 25.0, n)\n        is_foreign = rng.binomial(1, 0.45 if fraud else 0.05, n).astype(float)\n        account_age = rng.gamma(2.0, 120.0 if fraud else 500.0, n)\n        disputes = rng.poisson(1.5 if fraud else 0.2, n).astype(float)\n        return np.stack([amount, old_balance, new_balance, balance_delta, txn_hour,\n                         txn_dow, txn_count_1h, txn_count_24h, avg_amount_7d,\n                         std_amount_7d, merchant_risk, device_change, geo,\n                         is_foreign, account_age, disputes], axis=1)\n\n    x_raw = np.concatenate([draw(n_legit, False), draw(n_fraud, True)]).astype(np.float64)\n    y = np.concatenate([np.zeros(n_legit, np.int64), np.ones(n_fraud, np.int64)])\n    mean, std = x_raw.mean(0, keepdims=True), x_raw.std(0, keepdims=True)\n    std[std == 0] = 1.0\n    x_std = (x_raw - mean) / std\n    perm = rng.permutation(len(x_raw))\n    return x_std[perm].astype(np.float32), x_raw[perm].astype(np.float32), y[perm]\n\n\nclass FraudDataset(Dataset):\n    \"\"\"Yields (feature_vector, id, label); get_items() adds feature columns.\"\"\"\n\n    def __init__(self, n_samples, seed=0):\n        xs, xr, y = make_synthetic_fraud(n_samples, seed)\n        self.features = torch.from_numpy(xs)   # model input\n        self.raw = xr                          # display values\n        self.labels = torch.from_numpy(y)\n\n    def __len__(self):\n        return len(self.features)\n\n    def __getitem__(self, idx):\n        return self.features[idx], idx, int(self.labels[idx])\n\n    def get_items(self, idx, include_metadata=False, include_labels=False, include_images=False):\n        x = self.features[idx] if include_images else None\n        target = int(self.labels[idx]) if include_labels else None\n        meta = ({n: round(float(self.raw[idx][i]), 4) for i, n in enumerate(FEATURE_NAMES)}\n                if include_metadata else None)\n        return x, idx, target, meta\n\n\nclass FraudMLP(nn.Module):\n    def __init__(self, in_features=NUM_FEATURES, hidden=64, num_classes=2):\n        super().__init__()\n        self.net = nn.Sequential(\n            nn.Flatten(), nn.Linear(in_features, hidden), nn.ReLU(), nn.Dropout(0.1),\n            nn.Linear(hidden, hidden // 2), nn.ReLU(), nn.Linear(hidden // 2, num_classes))\n\n    def forward(self, x):\n        return self.net(x)\n\n\ndef build_dataset(n, seed=0):\n    return FraudDataset(n, seed)\n\n\ndef build_model():\n    return FraudMLP()\n"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## 3. Configuration\n\nEvery tunable lives here in one dict, like a `config.yaml` with comments. Wrapping it with `flag=\"hyperparameters\"` lets Weights Studio read (and live-edit) these values while training."
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": "config = {\n    \"experiment_name\": \"fraud_detection_mlp\",\n    \"device\": str(device),\n    \"root_log_dir\": tempfile.mkdtemp(prefix=\"weightslab_fraud_\"),\n    \"learning_rate\": 0.005,\n    \"training_steps_to_do\": 2000,\n    \"eval_full_to_train_steps_ratio\": 100,\n    \"write_export_ratio\": 500,\n    \"class_weights\": [1.0, 4.0],           # [legit, fraud] \u2014 up-weight fraud\n    \"dataset\": {\"seed\": 0, \"n_train\": 4000, \"n_test\": 1000},\n    \"data\": {\"train_loader\": {\"batch_size\": 32},\n             \"test_loader\": {\"batch_size\": 128}},\n}\nwl.watch_or_edit(config, flag=\"hyperparameters\", poll_interval=1.0)"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## 4. Wrap the training objects\n\nThis is the heart of WeightsLab. Each object passes through `wl.watch_or_edit(...)` with a `flag` describing its role. The tracked dataset's `get_items()` exposes every feature as a **sortable column**; `preload_metadata=True` loads them at init."
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": "cfg = config\ntrain_ds = build_dataset(cfg['dataset']['n_train'], seed=cfg['dataset']['seed'])\ntest_ds  = build_dataset(cfg['dataset']['n_test'],  seed=cfg['dataset']['seed'] + 1)\n\nmodel = wl.watch_or_edit(build_model().to(device), flag='model', device=device)\noptimizer = wl.watch_or_edit(\n    optim.Adam(model.parameters(), lr=cfg['learning_rate']), flag='optimizer')\n\ntrain_loader = wl.watch_or_edit(\n    train_ds, flag='data', loader_name='train_loader',\n    batch_size=cfg['data']['train_loader']['batch_size'], shuffle=True,\n    is_training=True, preload_labels=True, preload_metadata=True)\ntest_loader = wl.watch_or_edit(\n    test_ds, flag='data', loader_name='test_loader',\n    batch_size=cfg['data']['test_loader']['batch_size'], shuffle=False,\n    is_training=False, preload_labels=True, preload_metadata=True)\n\n# Class weights counter the minority (positive) class prevalence.\ncw = torch.tensor(cfg['class_weights'], dtype=torch.float32, device=device)\ntrain_criterion = wl.watch_or_edit(\n    nn.CrossEntropyLoss(weight=cw, reduction='none'),\n    flag='loss', signal_name='train-loss-CE', log=True)\ntest_criterion = wl.watch_or_edit(\n    nn.CrossEntropyLoss(weight=cw, reduction='none'),\n    flag='loss', signal_name='test-loss-CE', log=True)\nmetric = wl.watch_or_edit(\n    Accuracy(task='multiclass', num_classes=2).to(device),\n    flag='metric', signal_name='metric-ACC', log=True)"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## 5. Train and evaluate steps\n\nThe `guard_training_context` / `guard_testing_context` blocks tell WeightsLab the phase. `criterion(..., batch_ids=ids, preds=preds)` passes the sample ids so loss is stored **per sample**, and `wl.save_signals(...)` logs a custom per-sample accuracy signal during evaluation."
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": "def train(loader, model, optimizer, criterion, device):\n    with guard_training_context:\n        inputs, ids, labels = next(loader)\n        inputs, labels = inputs.to(device), labels.to(device)\n        optimizer.zero_grad()\n        logits = model(inputs)\n        preds = logits.argmax(dim=1, keepdim=True)\n        loss = criterion(logits.float(), labels.long(), batch_ids=ids, preds=preds)\n        total = loss.mean()\n        total.backward()\n        optimizer.step()\n    return total.detach().cpu().item()\n\n\ndef test(loader, model, criterion, metric, device, n_batches):\n    losses = torch.tensor(0.0, device=device)\n    for inputs, ids, labels in loader:\n        with guard_testing_context:\n            inputs, labels = inputs.to(device), labels.to(device)\n            logits = model(inputs)\n            preds = logits.argmax(dim=1, keepdim=True)\n            losses += criterion(logits, labels, batch_ids=ids, preds=preds).mean()\n            metric.update(logits, labels)\n            correct = (preds.view(-1) == labels.view(-1)).float()\n            wl.save_signals(preds_raw=logits, targets=labels, batch_ids=ids,\n                            signals={\"test_metric/Accuracy_per_sample\": correct}, preds=preds)\n    return (losses / max(1, n_batches)).item(), (metric.compute() * 100).item()\n"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## 6. Serve and train\n\n`wl.serve(serving_grpc=True, serving_bore=True)` starts the background gRPC server (non-blocking) and a `bore.pub` tunnel so Weights Studio on your own machine can reach this Colab backend. `wl.start_training(...)` flips the experiment into the *training* state, then we run the loop."
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": "wl.serve(serving_grpc=True, serving_bore=True)"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": "wl.start_training(timeout=3)\n\nsteps = config['training_steps_to_do']\neval_ratio = config['eval_full_to_train_steps_ratio']\nn_test_batches = len(test_loader)\n\ntest_loss = test_acc = None\npbar = tqdm(range(steps), desc='Training')\nfor step in pbar:\n    age = model.get_age() if hasattr(model, 'get_age') else step\n    train_loss = train(train_loader, model, optimizer, train_criterion, device)\n    if age > 0 and age % eval_ratio == 0:\n        test_loss, test_acc = test(test_loader, model, test_criterion, metric, device, n_test_batches)\n    postfix = {'loss': f'{train_loss:.3f}'}\n    if test_acc is not None:\n        postfix['test_acc'] = f'{test_acc:.1f}%'\n    pbar.set_postfix(postfix)\n\nwl.write_history()\nwl.write_dataframe()\nprint('Training complete.')"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## See it live in Weights Studio\n\nEverything above runs headless. The payoff is **Weights Studio**, where each row is one record and every feature is a **sortable column**.\n\nStudio runs as a local Docker stack, and **Colab has no Docker daemon**, so you run Studio on your own machine and point it at this notebook's backend via the `bore.pub:<port>` endpoint **printed in Section 6**.\n\n**On your machine** (with Docker Desktop):\n```bash\npip install weightslab\nweightslab start                 # opens http://localhost:5173\nweightslab tunnel bore.pub:12345     # in another window, the host:port printed in Section 6\n```\n\nThen open **http://localhost:5173** and switch the Data Exploration board to the **List** view."
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## Curate in the UI\n\nIn Weights Studio, switch the Data Exploration board to the **List** view:\n\n1. **Sort by `train-loss-CE` descending** and generate its histogram to find the hardest transactions \u2014 usually the frauds the model still misses.\n2. **Lock** the loss sort, then add `merchant_risk` or `amount` as a secondary sort to see which feature ranges drive the errors.\n3. **Right-click a column** to clone it, reset the sort, or generate a histogram \u2014 the same actions you use on image datasets.\n4. **Discard** mislabeled or leaked rows and keep training \u2014 no restart."
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "---\n\n<div align=\"center\">\nCrafted by <a href=\"https://github.com/GrayboxTech/weightslab\">GrayboxTech</a> - if WeightsLab helps you catch a bad label, drop us a star on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a>.\n</div>"
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "wl-fraud-detection.ipynb",
+   "provenance": [],
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/weightslab/examples/Notebooks/PyTorch/wl-segmentation.ipynb b/weightslab/examples/Notebooks/PyTorch/wl-segmentation.ipynb
new file mode 100644
index 00000000..dd7490f3
--- /dev/null
+++ b/weightslab/examples/Notebooks/PyTorch/wl-segmentation.ipynb
@@ -0,0 +1,1072 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "06579270",
+      "metadata": {
+        "id": "06579270"
+      },
+      "source": [
+        "<div align=\"center\">\n",
+        "\n",
+        "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+        "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+        "\n",
+        "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+        "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+        "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+        "  <br>\n",
+        "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-segmentation.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+        "\n",
+        "  Welcome to the WeightsLab <b>Semantic Segmentation (BDD100k)</b> notebook! <a href=\"https://github.com/GrayboxTech/weightslab\">WeightsLab</a> traces training signals back to the exact samples producing them. Browse the <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\">Docs</a> for details.</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c122b485",
+      "metadata": {
+        "id": "c122b485"
+      },
+      "source": [
+        "# Semantic Segmentation (BDD100k) with WeightsLab\n",
+        "\n",
+        "Trains a small U-Net on a **BDD100k**-style dataset and instruments per-**sample** Dice (metric) and CrossEntropy (loss), so every mask's quality is traced back to the exact sample producing it.\n",
+        "\n",
+        "### What you'll do\n",
+        "1. Install WeightsLab.\n",
+        "2. Download your dataset **zip from a Google Drive link** and unzip it (the training **code is inlined** below).\n",
+        "3. Set every knob in one **config** dict (like a `config.yaml`).\n",
+        "4. Wrap the dataloaders, model, optimizer, and per-sample Dice + CrossEntropy signals with the SDK.\n",
+        "5. Train while per-sample signals are captured, then (optionally) open the live **Weights Studio** UI.\n",
+        "\n",
+        "> Data note: this notebook fetches your dataset from a **Drive share link** you provide (`DATASET_URL`); all training code (dataset, model, criterions, loops) is **inlined below** - no `main.py`, no full-repo clone. The zip should unpack to a BDD100k-style layout (`images/{train,val}/` + `labels/{train,val}/`), one semantic mask per image."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ec299273",
+      "metadata": {
+        "id": "ec299273"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "On Colab, pick a **T4 GPU** runtime (`Runtime -> Change runtime type -> T4 GPU`)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "db7e968a",
+      "metadata": {
+        "id": "db7e968a"
+      },
+      "source": [
+        "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?color=5865F2&logo=pypi&logoColor=white\" alt=\"PyPI - Version\"></a>\n",
+        "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/dm/weightslab?color=5865F2\" alt=\"PyPI - Downloads\"></a>\n",
+        "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/pyversions/weightslab?color=5865F2&logo=python&logoColor=white\" alt=\"PyPI - Python Version\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2ba87ca5",
+      "metadata": {
+        "id": "2ba87ca5"
+      },
+      "outputs": [],
+      "source": [
+        "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+        "# and Pillow, so nothing extra is needed here.\n",
+        "# %pip install -q weightslab\n",
+        "%pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev7\"\n",
+        "%pip install -q torchmetrics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b07a3f07",
+      "metadata": {
+        "id": "b07a3f07"
+      },
+      "source": [
+        "### Fetch the dataset from Google Drive\n",
+        "\n",
+        "Set **`DATASET_URL`** below to your Drive **share link** for the dataset **zip** (the file must be shared as *Anyone with the link*). The cell downloads it with [`gdown`](https://github.com/wkentaro/gdown), extracts it, and auto-detects the dataset root — the folder that contains an `images/` subdirectory (so it works even if your zip wraps everything in a top-level folder). `config[\"data_root\"]` then points at that folder.\n",
+        "\n",
+        "The zip should unpack to the BDD100k layout the dataset class expects:\n",
+        "\n",
+        "```\n",
+        "<root>/\n",
+        "  images/{train,val}/   # .jpg / .png inputs\n",
+        "  labels/{train,val}/   # matching .png masks (same basename as the image)\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fe6b5769",
+      "metadata": {
+        "id": "fe6b5769"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== Dataset — local copy preferred (byte-identical to wl-seg-wow-moment-bdd), Drive as fallback =====\n",
+        "# This notebook is set up to reproduce the \"wl-seg-wow-moment-bdd\" experiment.\n",
+        "# If that experiment's exact dataset is available locally, use it directly so\n",
+        "# results are directly comparable. Otherwise (e.g. on Colab) fall back to the\n",
+        "# Drive zip below (a smaller toy dataset with the same layout/format).\n",
+        "LOCAL_DATA_ROOT = \"/home/ubuntu/guillaume_plg/data/bdd/bdd8k\"  # matches wl-seg-wow-moment-bdd/config.yaml's data_root\n",
+        "\n",
+        "# Paste your Drive SHARE link for the dataset zip here (shared as \"Anyone with the link\").\n",
+        "DATASET_URL = \"https://drive.google.com/file/d/1rXNsROVeneXp91VccOGIjtYWkkhWECBb/view?usp=sharing\"\n",
+        "\n",
+        "import os, sys, subprocess, zipfile\n",
+        "\n",
+        "\n",
+        "def _find_data_root(base):\n",
+        "    \"\"\"Return the folder that holds an `images/` subdir (handles a wrapping top-level dir).\"\"\"\n",
+        "    if os.path.isdir(os.path.join(base, \"images\")):\n",
+        "        return base\n",
+        "    for d, subdirs, _ in os.walk(base):\n",
+        "        if \"images\" in subdirs:\n",
+        "            return d\n",
+        "    return base\n",
+        "\n",
+        "\n",
+        "if os.path.isdir(os.path.join(LOCAL_DATA_ROOT, \"images\")):\n",
+        "    print(f\"Found the wl-seg-wow-moment-bdd dataset locally at {LOCAL_DATA_ROOT} — using it directly.\")\n",
+        "    DATA_ROOT = LOCAL_DATA_ROOT\n",
+        "else:\n",
+        "    # gdown handles Drive downloads (incl. the large-file virus-scan confirmation).\n",
+        "    subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"gdown\"], check=True)\n",
+        "    import gdown\n",
+        "\n",
+        "    zip_path = \"dataset.zip\"\n",
+        "    extract_dir = \"dataset\"\n",
+        "\n",
+        "    # Download (fuzzy=True lets gdown accept a full \"file/d/<id>/view\" share URL).\n",
+        "    if not os.path.exists(zip_path):\n",
+        "        gdown.download(url=DATASET_URL, output=zip_path, quiet=False, fuzzy=True)\n",
+        "    else:\n",
+        "        print(f\"{zip_path} already present — skipping download.\")\n",
+        "\n",
+        "    # Extract.\n",
+        "    with zipfile.ZipFile(zip_path) as zf:\n",
+        "        zf.extractall(extract_dir)\n",
+        "\n",
+        "    DATA_ROOT = _find_data_root(extract_dir)\n",
+        "\n",
+        "print(\"Dataset root:\", DATA_ROOT)\n",
+        "print(\"Contents:\", sorted(os.listdir(DATA_ROOT)))\n",
+        "for _split in (\"train\", \"val\", \"test\"):\n",
+        "    _p = os.path.join(DATA_ROOT, \"images\", _split)\n",
+        "    if os.path.isdir(_p):\n",
+        "        print(f\"  images/{_split}: {len(os.listdir(_p))} files\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7feccf69",
+      "metadata": {
+        "id": "7feccf69"
+      },
+      "source": [
+        "## 1. Imports\n",
+        "\n",
+        "`weightslab` is imported as `wl`. The two `guard_*_context` managers scope a block as training vs. evaluation so signals are attributed to the right phase. These are the external imports gathered from the example's `main.py` and `utils/` modules."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "39ef03a8",
+      "metadata": {
+        "id": "39ef03a8"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ['WEIGHTSLAB_LOG_LEVEL'] = 'DEBUG'\n",
+        "import time\n",
+        "import logging\n",
+        "import tempfile\n",
+        "\n",
+        "import numpy as np\n",
+        "import tqdm\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "from torch import optim\n",
+        "from torch.utils.data import Dataset\n",
+        "from torchvision import transforms\n",
+        "from PIL import Image\n",
+        "\n",
+        "import weightslab as wl\n",
+        "from weightslab.components.global_monitoring import (\n",
+        "    guard_training_context,\n",
+        "    guard_testing_context,\n",
+        ")\n",
+        "\n",
+        "# Setup loggers (from main.py)\n",
+        "logging.basicConfig(level=logging.ERROR)\n",
+        "logging.getLogger(\"PIL\").setLevel(logging.INFO)\n",
+        "\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(\"Using device:\", device)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cef2413d",
+      "metadata": {
+        "id": "cef2413d"
+      },
+      "source": [
+        "## 2. The dataset (inlined `utils/data.py`)\n",
+        "\n",
+        "`BDD100kSegDataset` returns `(image, uid, mask, metadata)` per sample, where `mask` is a single `[H, W]` semantic mask (pixel value = class id). `seg_collate` stacks the batch into `images [B, C, H, W]` and `labels [B, H, W]` so signals are attributed per sample."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2413697c",
+      "metadata": {
+        "id": "2413697c"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== utils/data.py — SAMPLE-WISE semantic segmentation =====\n",
+        "import os\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "\n",
+        "from torchvision import transforms\n",
+        "\n",
+        "from torch.utils.data import Dataset\n",
+        "\n",
+        "from PIL import Image\n",
+        "\n",
+        "\n",
+        "# =============================================================================\n",
+        "# BDD100k segmentation dataset (sample-wise)\n",
+        "# =============================================================================\n",
+        "class BDD100kSegDataset(Dataset):\n",
+        "    \"\"\"Returns (image, uid, mask, metadata) with ONE semantic mask per sample.\n",
+        "\n",
+        "    Layout expected under `root`:\n",
+        "      images/{train,val}/   inputs (.jpg/.png)\n",
+        "      labels/{train,val}/   masks  (.png, same basename; pixel value = class id)\n",
+        "\n",
+        "    `mask` is a single [H, W] int64 tensor of class ids (0..num_classes-1;\n",
+        "    `ignore_index` for void). No per-instance masks — signals are per sample.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        root,\n",
+        "        split=\"train\",\n",
+        "        num_classes=6,\n",
+        "        class_names=None,\n",
+        "        ignore_index=255,\n",
+        "        image_size=256,\n",
+        "        max_samples=None\n",
+        "    ):\n",
+        "        super().__init__()\n",
+        "        self.root = root\n",
+        "        self.split = split\n",
+        "        self.num_classes = num_classes\n",
+        "        self.class_names = class_names\n",
+        "        self.ignore_index = ignore_index\n",
+        "        self.task_type = \"segmentation\"\n",
+        "\n",
+        "        img_dir = os.path.join(root, \"images\", split)\n",
+        "        lbl_dir = os.path.join(root, \"labels\", split)\n",
+        "\n",
+        "        image_files = [\n",
+        "            f\n",
+        "            for f in os.listdir(img_dir)\n",
+        "            if f.lower().endswith((\".jpg\", \".jpeg\", \".png\"))\n",
+        "        ]\n",
+        "        image_files = sorted(set(image_files))[:max_samples] if max_samples != None else sorted(set(image_files)) # Optionally limit number of samples for faster testing\n",
+        "\n",
+        "        self.images = []\n",
+        "        self.masks = []\n",
+        "        for fname in image_files:\n",
+        "            img_path = os.path.join(img_dir, fname)\n",
+        "            base, _ = os.path.splitext(fname)\n",
+        "            lbl_name = base + \".png\"\n",
+        "            lbl_path = os.path.join(lbl_dir, lbl_name)\n",
+        "            if os.path.exists(lbl_path):\n",
+        "                self.images.append(img_path)\n",
+        "                self.masks.append(lbl_path)\n",
+        "\n",
+        "        if len(self.images) == 0:\n",
+        "            raise RuntimeError(f\"No image/label pairs found in {img_dir} / {lbl_dir}\")\n",
+        "\n",
+        "        # Resize to a fixed SQUARE (image_size, image_size). A single int would\n",
+        "        # resize only the shorter edge and keep the aspect ratio, so non-square\n",
+        "        # inputs come out as e.g. [128, 227] and mismatch the prediction in the\n",
+        "        # loss. The model input is square (input_shape=(1, C, image_size, image_size)).\n",
+        "        self.image_transform = transforms.Compose(\n",
+        "            [\n",
+        "                transforms.Resize(\n",
+        "                    # size=(image_size, image_size),\n",
+        "                    size=image_size,  # single int = resize shorter edge, keep aspect ratio\n",
+        "                    interpolation=Image.BILINEAR\n",
+        "                ),\n",
+        "                transforms.ToTensor(),\n",
+        "            ]\n",
+        "        )\n",
+        "        self.mask_resize = transforms.Resize(\n",
+        "            # size=(image_size, image_size),\n",
+        "            size=image_size,\n",
+        "            interpolation=Image.NEAREST\n",
+        "        )\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.images)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        \"\"\"Returns (item, uid, target, metadata):\n",
+        "            - item: input image tensor [C, H, W]\n",
+        "            - uid: unique id for the sample (filename)\n",
+        "            - target: semantic mask tensor [H, W] (int64 class ids)\n",
+        "            - metadata: optional dict (original paths)\n",
+        "        \"\"\"\n",
+        "        return self.get_items(idx, include_metadata=True, include_labels=True, include_images=True)\n",
+        "\n",
+        "    def get_items(self, idx, include_metadata=False, include_labels=False, include_images=False):\n",
+        "        img_path = self.images[idx]\n",
+        "        mask_path = self.masks[idx]\n",
+        "        uid = os.path.basename(img_path)\n",
+        "\n",
+        "        metadata = {\n",
+        "            'img_path': img_path,\n",
+        "            'mask_path': mask_path,\n",
+        "        } if include_metadata else None\n",
+        "\n",
+        "        # Process image\n",
+        "        img_t = None\n",
+        "        if include_images:\n",
+        "            img = Image.open(img_path).convert(\"RGB\")\n",
+        "            img_t = self.image_transform(img)\n",
+        "\n",
+        "        # Process label: one semantic mask [H, W] per sample.\n",
+        "        mask_t = None\n",
+        "        if include_labels:\n",
+        "            mask = Image.open(mask_path)\n",
+        "            mask_r = self.mask_resize(mask)\n",
+        "            mask_np = np.array(mask_r, dtype=np.int64)\n",
+        "            # Mask must be a single-channel class-id map [H, W]. If the file is\n",
+        "            # RGB/RGBA (extra channel dim), keep the first channel so it matches\n",
+        "            # the model's [H, W] prediction.\n",
+        "            if mask_np.ndim > 2:\n",
+        "                mask_np = mask_np[..., 0]\n",
+        "            mask_t = torch.from_numpy(mask_np) # [H, W] int64\n",
+        "\n",
+        "        return img_t, uid, mask_t, metadata\n",
+        "\n",
+        "\n",
+        "def seg_collate(batch):\n",
+        "    \"\"\"Collate WL per-sample tuples for semantic segmentation.\n",
+        "\n",
+        "    Each item is ``(img, uid, mask, metadata)`` where ``mask`` is a single\n",
+        "    [H, W] class-id tensor. Images and masks stack into batched tensors; uids\n",
+        "    and metadata stay as per-sample lists.\n",
+        "\n",
+        "    Returns:\n",
+        "        images: FloatTensor [B, C, H, W]\n",
+        "        ids: list[str] of length B\n",
+        "        labels: LongTensor [B, H, W]\n",
+        "        metas: list[B] of metadata dicts\n",
+        "    \"\"\"\n",
+        "    images = torch.stack([b[0] for b in batch], dim=0)\n",
+        "    ids = [b[1] for b in batch]\n",
+        "    labels = torch.stack([torch.as_tensor(b[2]) for b in batch], dim=0)\n",
+        "    metas = [b[3] if len(b) > 3 else None for b in batch]\n",
+        "    return images, ids, labels, metas"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "352304ca",
+      "metadata": {
+        "id": "352304ca"
+      },
+      "source": [
+        "## 3. The model (inlined `utils/model.py`)\n",
+        "\n",
+        "A compact U-Net (`SmallUNet`) with `task_type=\"segmentation\"` so WeightsLab renders masks in Weights Studio."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0ecaac87",
+      "metadata": {
+        "id": "0ecaac87"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== utils/model.py =====\n",
+        "# =============================================================================\n",
+        "# Small UNet-ish segmentation model\n",
+        "# =============================================================================\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "\n",
+        "class DoubleConv(nn.Module):\n",
+        "    def __init__(self, in_ch, out_ch):\n",
+        "        super().__init__()\n",
+        "        self.block = nn.Sequential(\n",
+        "            nn.Conv2d(in_ch, out_ch, 3, padding=1),\n",
+        "            nn.BatchNorm2d(out_ch),\n",
+        "            nn.ReLU(inplace=True),\n",
+        "            nn.Conv2d(out_ch, out_ch, 3, padding=1),\n",
+        "            nn.BatchNorm2d(out_ch),\n",
+        "            nn.ReLU(inplace=True),\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.block(x)\n",
+        "\n",
+        "\n",
+        "class SmallUNet(nn.Module):\n",
+        "    def __init__(self, in_channels=3, num_classes=6, image_size=256):\n",
+        "        super().__init__()\n",
+        "        # For WeightsLab\n",
+        "        self.task_type = \"segmentation\"\n",
+        "        self.num_classes = num_classes\n",
+        "        self.class_names = [\"Background\", \"Ego Road\", \"Driveable Area\", \"Lane Line 1\", \"Lane Line 2\", \"Lane Line 3\"]\n",
+        "        self.input_shape = (1, in_channels, image_size, image_size)\n",
+        "\n",
+        "        self.enc1 = DoubleConv(in_channels, 32)\n",
+        "        self.pool1 = nn.MaxPool2d(2)\n",
+        "        self.enc2 = DoubleConv(32, 64)\n",
+        "        self.pool2 = nn.MaxPool2d(2)\n",
+        "\n",
+        "        self.bottleneck = DoubleConv(64, 128)\n",
+        "\n",
+        "        self.up2 = nn.ConvTranspose2d(128, 64, 2, stride=2)\n",
+        "        self.dec2 = DoubleConv(64 + 64, 64)\n",
+        "        self.up1 = nn.ConvTranspose2d(64, 32, 2, stride=2)\n",
+        "        self.dec1 = DoubleConv(32 + 32, 32)\n",
+        "\n",
+        "        self.head = nn.Conv2d(32, num_classes, 1)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # Encoder\n",
+        "        e1 = self.enc1(x)\n",
+        "        p1 = self.pool1(e1)\n",
+        "\n",
+        "        e2 = self.enc2(p1)\n",
+        "        p2 = self.pool2(e2)\n",
+        "\n",
+        "        # Bottleneck\n",
+        "        b = self.bottleneck(p2)\n",
+        "\n",
+        "        # Decoder\n",
+        "        u2 = self.up2(b)\n",
+        "        # Important: no `if` on shapes; always interpolate\n",
+        "        u2 = F.interpolate(u2, size=e2.shape[-2:], mode=\"bilinear\", align_corners=False)\n",
+        "        d2 = self.dec2(torch.cat([u2, e2], dim=1))\n",
+        "\n",
+        "        u1 = self.up1(d2)\n",
+        "        u1 = F.interpolate(u1, size=e1.shape[-2:], mode=\"bilinear\", align_corners=False)\n",
+        "        d1 = self.dec1(torch.cat([u1, e1], dim=1))\n",
+        "\n",
+        "        logits = self.head(d1) # [B, C, H, W]\n",
+        "        return logits"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7d382efc",
+      "metadata": {
+        "id": "7d382efc"
+      },
+      "source": [
+        "## 4. Losses & metrics (inlined `utils/criterions.py`)\n",
+        "\n",
+        "Per-sample combined **CrossEntropy + Dice** (loss) and **Dice** (metric), ported to match the `wl-seg-wow-moment-bdd` experiment's `criterions.py::CrossEntropySegLoss` exactly (same formula, same per-class weights, same `ignore_index` handling — see that class's docstring below for a quirk in the reference project that's preserved here for faithful reproduction). Each scores the model's `[B, C, H, W]` output against the `[B, H, W]` mask and returns **one value per sample** (`[B]`), wrapped with `per_sample=True` so WeightsLab logs it per `sample_id`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c483178e",
+      "metadata": {
+        "id": "c483178e"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== utils/criterions.py — SAMPLE-WISE Dice (metric) + combined CE+Dice (loss) =====\n",
+        "# The loss below is ported to match wl-seg-wow-moment-bdd/criterions.py's\n",
+        "# CrossEntropySegLoss exactly (same formula, same per-class weighting, same\n",
+        "# ignore_index handling) so this notebook reproduces that experiment's loss.\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "_EPS = 1e-7  # matches PerSampleMultiClassDiceLoss's eps in wl-seg-wow-moment-bdd/criterions.py\n",
+        "\n",
+        "\n",
+        "class PerSampleMultiClassDiceLoss(nn.Module):\n",
+        "    \"\"\"Per-sample, per-class Dice LOSS (1 - diceScore), averaged over classes -> [B].\n",
+        "\n",
+        "    Ported verbatim (same formula/eps/masking) from\n",
+        "    wl-seg-wow-moment-bdd/criterions.py::PerSampleMultiClassDiceLoss.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(self, ignore_index=255, eps=_EPS, weights=None, ignore_classes=None):\n",
+        "        super().__init__()\n",
+        "        self.ignore_index = ignore_index\n",
+        "        self.eps = eps\n",
+        "        self.weights = weights\n",
+        "        self.ignore_classes = ignore_classes\n",
+        "\n",
+        "    def forward(self, logits, targets):\n",
+        "        bs, num_classes, H, W = logits.shape\n",
+        "        probs = torch.softmax(logits, dim=1) # [B, C, H, W]\n",
+        "        valid = (targets != self.ignore_index) # [B, H, W]\n",
+        "        targets_safe = targets.clone()\n",
+        "        targets_safe[~valid] = 0\n",
+        "        one_hot = F.one_hot(targets_safe, num_classes=num_classes).permute(0, 3, 1, 2).float()\n",
+        "\n",
+        "        valid_f = valid.unsqueeze(1).float() # [B, 1, H, W]\n",
+        "        probs = probs * valid_f\n",
+        "        one_hot = one_hot * valid_f\n",
+        "\n",
+        "        intersection = (probs * one_hot).sum(dim=(2, 3)) # [B, C]\n",
+        "        cardinality = (probs + one_hot).sum(dim=(2, 3)) # [B, C]\n",
+        "        dice_per_class = 1.0 - (2.0 * intersection + self.eps) / (cardinality + self.eps) # [B, C] — a LOSS, not a score\n",
+        "\n",
+        "        if self.ignore_classes:\n",
+        "            class_mask = torch.ones(num_classes, dtype=torch.bool, device=logits.device)\n",
+        "            class_mask[list(self.ignore_classes)] = False\n",
+        "            dice_per_class = dice_per_class[:, class_mask]\n",
+        "            weights = self.weights[class_mask] if self.weights is not None else None\n",
+        "        else:\n",
+        "            weights = self.weights\n",
+        "\n",
+        "        if weights is not None:\n",
+        "            dice_per_class = dice_per_class * weights\n",
+        "        return dice_per_class.mean(dim=1) # [B]\n",
+        "\n",
+        "\n",
+        "class PerSampleCEDice(nn.Module):\n",
+        "    \"\"\"Combined CrossEntropy + Dice loss -> [B] (differentiable, per-sample).\n",
+        "\n",
+        "    Ported from wl-seg-wow-moment-bdd/criterions.py::CrossEntropySegLoss, with\n",
+        "    one fix applied on both sides: `PerSampleMultiClassDiceLoss` above already\n",
+        "    returns a dice LOSS (`1 - diceScore`), so it's used directly here. The\n",
+        "    reference project originally did `(1 - dice)` again on top of that, which\n",
+        "    algebraically reduces to the raw dice SCORE (i.e. a *better* dice score\n",
+        "    increased the loss) — that double-negation has been fixed in both this\n",
+        "    notebook and wl-seg-wow-moment-bdd/criterions.py.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(self, weights=None, ignore_index=255, bce_weight=0.5, dice_weight=0.5):\n",
+        "        super().__init__()\n",
+        "        self.register_buffer(\n",
+        "            \"weights\",\n",
+        "            torch.as_tensor(weights, dtype=torch.float32) if weights is not None else None,\n",
+        "        )\n",
+        "        self.ignore_index = ignore_index\n",
+        "        self.bce_weight = bce_weight\n",
+        "        self.dice_weight = dice_weight\n",
+        "        self.dice = PerSampleMultiClassDiceLoss(ignore_index=ignore_index, weights=self.weights)\n",
+        "\n",
+        "    def forward(self, outputs, labels):\n",
+        "        # outputs [B, C, H, W]; labels [B, H, W] (int64 class ids)\n",
+        "        ce = F.cross_entropy(\n",
+        "            outputs, labels.long(),\n",
+        "            weight=self.weights, ignore_index=self.ignore_index, reduction=\"none\",\n",
+        "        ) # [B, H, W]\n",
+        "        # Mean over ALL pixels (ignored ones included as 0), matching the\n",
+        "        # source project's un-masked mean (not a valid-pixel-only average).\n",
+        "        ce = ce.flatten(1).mean(dim=1) # [B]\n",
+        "\n",
+        "        dice_loss = self.dice(outputs, labels.long()) # [B] — already a LOSS (1 - diceScore)\n",
+        "\n",
+        "        return self.bce_weight * ce + self.dice_weight * dice_loss\n",
+        "\n",
+        "\n",
+        "class PerSampleDice(nn.Module):\n",
+        "    \"\"\"Per-sample mean foreground Dice -> [B] (metric, detached; for the WL dashboard).\"\"\"\n",
+        "\n",
+        "    def forward(self, outputs, labels):\n",
+        "        probs = torch.softmax(outputs, dim=1) # [B, C, H, W]\n",
+        "        C = probs.shape[1]\n",
+        "        tgt = torch.where(labels < C, labels, torch.zeros_like(labels)).long() # void/ignore -> background\n",
+        "        onehot = F.one_hot(tgt, num_classes=C).permute(0, 3, 1, 2).float() # [B, C, H, W]\n",
+        "        inter = (probs * onehot).sum(dim=(2, 3))\n",
+        "        denom = probs.sum(dim=(2, 3)) + onehot.sum(dim=(2, 3))\n",
+        "        dice = (2.0 * inter + _EPS) / (denom + _EPS) # [B, C]\n",
+        "        fg = dice[:, 1:].mean(dim=1) if C > 1 else dice.mean(dim=1) # exclude background channel\n",
+        "        return fg.detach() # [B]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2965ed80",
+      "metadata": {
+        "id": "2965ed80"
+      },
+      "source": [
+        "## 5. Configuration\n",
+        "\n",
+        "Every tunable lives here in one dict - this is `config.yaml` inlined, with its comments. Wrapping it with `flag=\"hyperparameters\"` lets Weights Studio read (and live-edit) these values while training. `data_root` points at the sample fetched above.\n",
+        "\n",
+        "**Reproducing `wl-seg-wow-moment-bdd`:** `ignore_index`, `image_size`, `training_steps_to_do`, `eval_full_to_train_steps_ratio`, and the train `batch_size` below are set to match that experiment's `config.yaml` exactly, since these directly change training results (model architecture, optimizer, and dataset format were already identical)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "00e4c364",
+      "metadata": {
+        "id": "00e4c364"
+      },
+      "outputs": [],
+      "source": [
+        "config = {\n",
+        "    # -- Global configuration ------------------------------------------------\n",
+        "    \"device\": \"auto\",                         # 'auto' -> resolved to `device` from the Imports cell\n",
+        "    \"experiment_name\": \"seg_bdd_training\",    # name shown in Weights Studio\n",
+        "    \"training_steps_to_do\": 10000,              # matches wl-seg-wow-moment-bdd/config.yaml (training_steps_to_do)\n",
+        "    # \"root_log_dir\": \"/tmp/tmpg947ov7y/\",                       # None -> a temp dir is created (history + dataframe land here)\n",
+        "\n",
+        "    \"checkpoint_manager\": {\n",
+        "        \"load_config\": False,                   # don't reload a previous checkpoint's config, so edits here take effect\n",
+        "    },\n",
+        "\n",
+        "    # Initially compute natural sorting values, e.g. average intensity (see the example README).\n",
+        "    \"compute_natural_sort\": True,\n",
+        "\n",
+        "    # -- Experiment parameters ----------------------------------------------\n",
+        "    \"eval_full_to_train_steps_ratio\": 256,      # matches wl-seg-wow-moment-bdd (test every 256 steps)\n",
+        "    \"experiment_dump_to_train_steps_ratio\": 256,\n",
+        "    \"write_export_ratio\": 100,                  # export history + dataframe every N steps (main.py default)\n",
+        "    \"tqdm_display\": True,\n",
+        "    \"is_training\": False,                       # start training immediately or not\n",
+        "\n",
+        "    # -- Serving (the notebook serves via gRPC + a bore tunnel; see the Serve cell) --\n",
+        "    \"serving_grpc\": True,\n",
+        "    \"serving_cli\": True,\n",
+        "\n",
+        "    # -- Global dataframe storage -------------------------------------------\n",
+        "    \"ledger_enable_h5_persistence\": True,\n",
+        "    \"ledger_enable_flushing_threads\": True,\n",
+        "    \"ledger_flush_max_rows\": 100,\n",
+        "    \"ledger_flush_interval\": 60.0,\n",
+        "\n",
+        "    # -- Data ----------------------------------------------------------------\n",
+        "    \"num_classes\": 6,\n",
+        "    \"class_names\": [\"Background\", \"Ego Road\", \"Driveable Area\", \"Lane Line 1\", \"Lane Line 2\", \"Lane Line 3\"],\n",
+        "    \"ignore_index\": 0,                          # matches wl-seg-wow-moment-bdd: excludes \"Background\" (class 0) from loss/metric\n",
+        "    \"image_size\": 180,                          # matches wl-seg-wow-moment-bdd (shortest edge resized to 720, aspect ratio kept)\n",
+        "    \"data_root\": DATA_ROOT,                     # local wl-seg-wow-moment-bdd dataset if found, else the Drive zip (see the data-fetch cell)\n",
+        "    \"data\": {\n",
+        "        \"train_loader\": {\"batch_size\": 8, \"shuffle\": True},   # matches wl-seg-wow-moment-bdd (train batch_size 8)\n",
+        "        \"test_loader\": {\"batch_size\": 2, \"shuffle\": False},\n",
+        "    },\n",
+        "\n",
+        "    # -- Optimizer -----------------------------------------------------------\n",
+        "    \"optimizer\": {\"lr\": 1e-3},                  # Adam learning rate (matches wl-seg-wow-moment-bdd, which has no `optimizer:` key so falls back to this same 1e-3 default)\n",
+        "}\n",
+        "\n",
+        "# Resolve the 'auto' device to the one picked in the Imports cell.\n",
+        "if config.get(\"device\", \"auto\") == \"auto\":\n",
+        "    config[\"device\"] = str(device)\n",
+        "\n",
+        "# Register the config so Weights Studio can read (and live-edit) it while training.\n",
+        "wl.watch_or_edit(config, flag=\"hyperparameters\", name=config[\"experiment_name\"],\n",
+        "                 defaults=config, poll_interval=1.0)\n",
+        "\n",
+        "# Logging directory: None -> a temp dir is created.\n",
+        "if not config.get(\"root_log_dir\"):\n",
+        "    config[\"root_log_dir\"] = tempfile.mkdtemp(prefix=\"weightslab_seg_\")\n",
+        "os.makedirs(config[\"root_log_dir\"], exist_ok=True)\n",
+        "log_dir = config[\"root_log_dir\"]\n",
+        "\n",
+        "# Schedule knobs used by the training loop.\n",
+        "eval_full_to_train_steps_ratio = config[\"eval_full_to_train_steps_ratio\"]\n",
+        "write_export_ratio = config.get(\"write_export_ratio\", 100)\n",
+        "tqdm_display = config.get(\"tqdm_display\", True)\n",
+        "print(\"Experiment logs ->\", log_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "88cc2afa",
+      "metadata": {
+        "id": "88cc2afa"
+      },
+      "source": [
+        "## 6. Build data, model & optimizer\n",
+        "\n",
+        "The heart of WeightsLab: each object is passed through `wl.watch_or_edit(...)` with a `flag` describing its role. The returned objects behave like the originals but report their state and per-sample signals. The loaders use `collate_fn=seg_collate` to stack the image and mask batches."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9df9c8cf",
+      "metadata": {
+        "id": "9df9c8cf"
+      },
+      "outputs": [],
+      "source": [
+        "# --- Hyperparameters read back after watch_or_edit ---\n",
+        "num_classes = int(config[\"num_classes\"])\n",
+        "class_names = config[\"class_names\"]\n",
+        "ignore_index = int(config[\"ignore_index\"])\n",
+        "image_size = int(config[\"image_size\"])\n",
+        "\n",
+        "# --- Data (local wl-seg-wow-moment-bdd dataset, or the Drive zip fetched above) ---\n",
+        "data_root = config.get(\"data_root\", DATA_ROOT)\n",
+        "if os.path.exists(data_root):\n",
+        "    print(f\"Using data root: {data_root}\")\n",
+        "else:\n",
+        "    raise FileNotFoundError(\n",
+        "        f\"Data root not found: {data_root!r}. Run the data-fetch cell above first.\")\n",
+        "\n",
+        "train_cfg = config.get(\"data\", {}).get(\"train_loader\", {})\n",
+        "test_cfg = config.get(\"data\", {}).get(\"test_loader\", {})\n",
+        "\n",
+        "# wl-seg-wow-moment-bdd's dataset uses images/{train,test}; the packaged Drive\n",
+        "# toy dataset uses images/{train,val} — detect whichever is actually present.\n",
+        "eval_split = \"test\" if os.path.isdir(os.path.join(data_root, \"images\", \"test\")) else \"val\"\n",
+        "print(f\"Eval split: {eval_split!r}\")\n",
+        "\n",
+        "_train_dataset = BDD100kSegDataset(\n",
+        "    root=data_root,\n",
+        "    split=\"train\",\n",
+        "    num_classes=num_classes,\n",
+        "    class_names=class_names,\n",
+        "    ignore_index=ignore_index,\n",
+        "    image_size=image_size,\n",
+        "    max_samples=train_cfg.get(\"max_samples\", None),\n",
+        ")\n",
+        "_test_dataset = BDD100kSegDataset(\n",
+        "    root=data_root,\n",
+        "    split=eval_split,\n",
+        "    num_classes=num_classes,\n",
+        "    class_names=class_names,\n",
+        "    ignore_index=ignore_index,\n",
+        "    image_size=image_size,\n",
+        "    max_samples=test_cfg.get(\"max_samples\", None),\n",
+        ")\n",
+        "\n",
+        "train_loader = wl.watch_or_edit(\n",
+        "    _train_dataset, flag=\"data\", loader_name=\"train_loader\",\n",
+        "    batch_size=train_cfg.get(\"batch_size\", 2), shuffle=train_cfg.get(\"shuffle\", True),\n",
+        "    compute_hash=False, is_training=True,\n",
+        "    array_autoload_arrays=False, array_return_proxies=True, array_use_cache=True,\n",
+        "    preload_labels=False, collate_fn=seg_collate,\n",
+        ")\n",
+        "test_loader = wl.watch_or_edit(\n",
+        "    _test_dataset, flag=\"data\", loader_name=\"test_loader\",\n",
+        "    batch_size=test_cfg.get(\"batch_size\", 2), shuffle=test_cfg.get(\"shuffle\", False),\n",
+        "    compute_hash=False, is_training=False,\n",
+        "    array_autoload_arrays=False, array_return_proxies=True, array_use_cache=True,\n",
+        "    preload_labels=True, collate_fn=seg_collate,\n",
+        ")\n",
+        "\n",
+        "_model = SmallUNet(in_channels=3, num_classes=num_classes, image_size=image_size).to(device)\n",
+        "model = wl.watch_or_edit(_model, flag=\"model\", device=device, compute_dependencies=True)\n",
+        "\n",
+        "lr = config.get(\"optimizer\", {}).get(\"lr\", 1e-3)\n",
+        "_optimizer = optim.Adam(model.parameters(), lr=lr)\n",
+        "optimizer = wl.watch_or_edit(_optimizer, flag=\"optimizer\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ec7508f0",
+      "metadata": {
+        "id": "ec7508f0"
+      },
+      "source": [
+        "## 7. Train & eval steps (+ tracked signals)\n",
+        "\n",
+        "The `guard_training_context` / `guard_testing_context` blocks tell WeightsLab which phase it's in. `_run_signals` computes and logs the per-sample **Dice** (metric) and **CrossEntropy** (loss); `wl.save_signals(...)` records custom per-sample diagnostics (predicted vs present classes). Class weights are computed from the training split, then the tracked signals are built."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "52c07147",
+      "metadata": {
+        "id": "52c07147"
+      },
+      "outputs": [],
+      "source": [
+        "# ===== train / eval step functions (sample-wise) =====\n",
+        "from torchmetrics import JaccardIndex\n",
+        "\n",
+        "\n",
+        "def _run_signals(sig, outputs, labels, ids, preds, return_metric=False):\n",
+        "    \"\"\"Compute + log the per-sample combined CE+Dice (loss) and Dice (metric).\"\"\"\n",
+        "    loss_sample = sig[\"loss_sample\"](outputs, labels, batch_ids=ids, preds=preds)\n",
+        "    dice_sample = sig[\"dice_sample\"](outputs, labels, batch_ids=ids)\n",
+        "    if return_metric:\n",
+        "        return loss_sample, dice_sample\n",
+        "    return loss_sample\n",
+        "\n",
+        "\n",
+        "def _user_custom_signals(preds, labels):\n",
+        "    \"\"\"Per-sample diagnostics: which classes are predicted vs present in the mask.\"\"\"\n",
+        "    B = preds.shape[0]\n",
+        "    return {\n",
+        "        \"preds_classes_per_sample\": [preds[i].unique() for i in range(B)],\n",
+        "        \"target_classes_per_sample\": [labels[i].unique() for i in range(B)],\n",
+        "        \"tp_classes_per_sample\": [\n",
+        "            torch.tensor([c for c in labels[i].unique() if c in preds[i].unique()]) for i in range(B)\n",
+        "        ],\n",
+        "        \"fp_classes_per_sample\": [\n",
+        "            torch.tensor([c for c in preds[i].unique() if c not in labels[i].unique()]) for i in range(B)\n",
+        "        ],\n",
+        "        \"fn_classes_per_sample\": [\n",
+        "            torch.tensor([c for c in labels[i].unique() if c not in preds[i].unique()]) for i in range(B)\n",
+        "        ],\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "def train(loader, model, optimizer, sig, metric, device):\n",
+        "    \"\"\"Single training step. loader yields (inputs, ids, labels, metadata);\n",
+        "    `labels` is a [B, H, W] semantic mask (see seg_collate).\"\"\"\n",
+        "    with guard_training_context:\n",
+        "        (inputs, ids, labels, _) = next(loader)\n",
+        "        inputs = inputs.to(device)\n",
+        "        labels = labels.to(device) # [B, H, W]\n",
+        "\n",
+        "        optimizer.zero_grad()\n",
+        "        outputs = model(inputs) # [B, C, H, W]\n",
+        "        preds = outputs.argmax(dim=1) # [B, H, W]\n",
+        "\n",
+        "        # Per-sample combined CE+Dice (loss) + Dice (metric), tracked & saved per sample.\n",
+        "        loss_per_sample = _run_signals(sig, outputs, labels, ids, preds=preds) # [B]\n",
+        "        loss = loss_per_sample.mean()\n",
+        "\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # Per-sample class diagnostics for the UI (predicted vs present classes).\n",
+        "        wl.save_signals(_user_custom_signals(preds, labels), ids)\n",
+        "\n",
+        "    # Batch IoU, matching wl-seg-wow-moment-bdd's train() (reset every step).\n",
+        "    metric.update(preds, labels.detach())\n",
+        "    train_iou = metric.compute().detach().cpu().item() * 100.0\n",
+        "    metric.reset()\n",
+        "\n",
+        "    return float(loss.detach().cpu().item()), train_iou\n",
+        "\n",
+        "\n",
+        "def test(loader, model, sig, metric, device, test_loader_len):\n",
+        "    \"\"\"Full evaluation pass over the eval loader.\"\"\"\n",
+        "    losses = torch.tensor([0.]).to(device)\n",
+        "    dices = torch.tensor([0.]).to(device)\n",
+        "    metric.reset()\n",
+        "    with guard_testing_context, torch.no_grad():\n",
+        "        for inputs, ids, labels, _ in loader:\n",
+        "            inputs = inputs.to(device)\n",
+        "            labels = labels.to(device) # [B, H, W]\n",
+        "\n",
+        "            outputs = model(inputs)\n",
+        "            preds = outputs.argmax(dim=1) # [B, H, W]\n",
+        "\n",
+        "            loss_per_sample, dice_sample = _run_signals(sig, outputs, labels, ids, preds=preds, return_metric=True)\n",
+        "            losses += torch.mean(loss_per_sample) # accumulate batch mean\n",
+        "            dices += torch.mean(dice_sample)\n",
+        "            metric.update(preds, labels)\n",
+        "\n",
+        "            wl.save_signals(_user_custom_signals(preds, labels), ids)\n",
+        "\n",
+        "    loss = float((losses / test_loader_len).detach().cpu().item())\n",
+        "    dice = float((dices / test_loader_len).detach().cpu().item())\n",
+        "    iou = metric.compute().detach().cpu().item() * 100.0\n",
+        "    return loss, dice * 100.0, iou # Dice/IoU as percentages\n",
+        "\n",
+        "\n",
+        "# ===== tracked per-sample Dice (metric) + combined CE+Dice (loss) signals =====\n",
+        "# Both are wrapped with per_sample=True so WeightsLab logs one value per\n",
+        "# sample_id. No per-instance signals.\n",
+        "def _make_seg_signals(split: str, weights=None, ignore_index: int = 255) -> dict:\n",
+        "    return {\n",
+        "        \"dice_sample\": wl.watch_or_edit(\n",
+        "            PerSampleDice(), flag=\"metric\",\n",
+        "            name=f\"{split}_dice/sample\", per_sample=True, log=True,\n",
+        "        ),\n",
+        "        \"loss_sample\": wl.watch_or_edit(\n",
+        "            PerSampleCEDice(weights=weights, ignore_index=ignore_index), flag=\"loss\",\n",
+        "            name=f\"{split}_CE\", per_sample=True, log=True,\n",
+        "        ),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "def compute_class_weights(dataset, num_classes, ignore_index=255, max_samples=100):\n",
+        "    print(\"\\n\" + \"=\" * 60, flush=True)\n",
+        "    print(f\"Computing class weights for {num_classes} classes (max {max_samples} samples)...\", flush=True)\n",
+        "    class_counts = np.zeros(num_classes, dtype=np.float64)\n",
+        "    num_samples = min(len(dataset), max_samples)\n",
+        "\n",
+        "    for idx in tqdm.tqdm(range(num_samples), desc=\" Analyzing Distribution\"):\n",
+        "        _, _, label, _ = dataset.get_items(idx, include_labels=True) # semantic mask [H, W]\n",
+        "        label_np = label.numpy() if hasattr(label, 'numpy') else np.array(label)\n",
+        "        for c in range(num_classes):\n",
+        "            class_counts[c] += (label_np == c).sum()\n",
+        "\n",
+        "    class_counts = np.maximum(class_counts, 1) # Avoid div by zero\n",
+        "    total_pixels = class_counts.sum()\n",
+        "    class_weights = total_pixels / (num_classes * class_counts)\n",
+        "    class_weights = class_weights / class_weights.mean() # Normalize\n",
+        "\n",
+        "    print(\"\\nClass distribution and weights:\", flush=True)\n",
+        "    for c in range(num_classes):\n",
+        "        pct = (class_counts[c] / total_pixels) * 100\n",
+        "        print(f\"Class {c}: {pct:6.2f}% -> weight: {class_weights[c]:.3f}\", flush=True)\n",
+        "    print(\"=\" * 60 + \"\\n\", flush=True)\n",
+        "    return torch.FloatTensor(class_weights).to(device)\n",
+        "\n",
+        "weights = compute_class_weights(_train_dataset, num_classes, max_samples=100)\n",
+        "\n",
+        "# Matches wl-seg-wow-moment-bdd: the train criterion is class-weighted, the\n",
+        "# test criterion is NOT (its CrossEntropySegLoss gets no `weights=` argument there).\n",
+        "train_sig = _make_seg_signals(\"train\", weights=weights, ignore_index=ignore_index)\n",
+        "test_sig = _make_seg_signals(\"test\", weights=None, ignore_index=ignore_index)\n",
+        "\n",
+        "# IoU (macro, ignore_index=0), matching wl-seg-wow-moment-bdd's train_iou/test_iou.\n",
+        "train_metric = wl.watch_or_edit(\n",
+        "    JaccardIndex(task=\"multiclass\", num_classes=num_classes, ignore_index=ignore_index, average=\"macro\").to(device),\n",
+        "    flag=\"metric\", name=\"train_iou\", per_sample=False, log=True,\n",
+        ")\n",
+        "test_metric = wl.watch_or_edit(\n",
+        "    JaccardIndex(task=\"multiclass\", num_classes=num_classes, ignore_index=ignore_index, average=\"macro\").to(device),\n",
+        "    flag=\"metric\", name=\"test_iou\", per_sample=False, log=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fb9890dd",
+      "metadata": {
+        "id": "fb9890dd"
+      },
+      "source": [
+        "## 8. Serve and train\n",
+        "\n",
+        "`wl.serve(serving_grpc=True, serving_bore=True)` starts the background gRPC server and opens a public `bore.pub:<port>` tunnel (printed below) that Weights Studio connects to. `wl.start_training(...)` flips the experiment into the *training* state, then we run a finite loop, periodically evaluating and exporting signals.\n",
+        "\n",
+        "Leave this cell **running** while you watch it stream in the UI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "635014b7",
+      "metadata": {
+        "collapsed": true,
+        "id": "635014b7"
+      },
+      "outputs": [],
+      "source": [
+        "wl.serve(serving_grpc=config.get(\"serving_grpc\", True), serving_bore=True)\n",
+        "# wl.start_training(timeout=60)\n",
+        "\n",
+        "# training_steps_to_do may be None (open-ended); cap it here so the notebook run is finite.\n",
+        "max_steps = config[\"training_steps_to_do\"] or 500\n",
+        "train_range = tqdm.tqdm(range(max_steps), desc=\"Training\") if tqdm_display else range(max_steps)\n",
+        "test_loss, test_dice, test_iou = None, None, None\n",
+        "start_time = time.time()\n",
+        "for train_step in train_range:\n",
+        "    age = model.get_age() if hasattr(model, \"get_age\") else train_step\n",
+        "\n",
+        "    # Train one step\n",
+        "    train_loss, train_iou = train(train_loader, model, optimizer, train_sig, train_metric, device)\n",
+        "\n",
+        "    # Full evaluation pass\n",
+        "    if age == 0 or age % eval_full_to_train_steps_ratio == 0:\n",
+        "        test_loader_len = len(test_loader)\n",
+        "        test_loader_it = tqdm.tqdm(test_loader, desc=\"Evaluating\", leave=False) if tqdm_display else test_loader\n",
+        "        test_loss, test_dice, test_iou = test(test_loader_it, model, test_sig, test_metric, device, test_loader_len)\n",
+        "\n",
+        "    if tqdm_display:\n",
+        "        train_range.set_description(\"Step\")\n",
+        "        train_range.set_postfix(\n",
+        "            train_loss=f\"{train_loss:.4f}\",\n",
+        "            train_iou=f\"{train_iou:.2f}%\",\n",
+        "            test_loss=f\"{test_loss:.4f}\" if test_loss is not None else \"N/A\",\n",
+        "            test_dice=f\"{test_dice:.2f}%\" if test_dice is not None else \"N/A\",\n",
+        "            test_iou=f\"{test_iou:.2f}%\" if test_iou is not None else \"N/A\",\n",
+        "        )\n",
+        "\n",
+        "print(\"\\n\" + \"=\" * 60)\n",
+        "print(f\" Training completed in {time.time() - start_time:.2f} seconds\")\n",
+        "print(f\" Logs saved to: {log_dir}\")\n",
+        "print(\"=\" * 60)\n",
+        "\n",
+        "# Final export of signal history and data grid to root_log_dir\n",
+        "wl.write_history()\n",
+        "wl.write_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3812a421",
+      "metadata": {
+        "id": "3812a421"
+      },
+      "source": [
+        "## See it live in Weights Studio\n",
+        "\n",
+        "**Colab has no Docker daemon**, so run Studio on your own machine and point it at this notebook's\n",
+        "backend using the `bore.pub:<port>` printed in the Expose section:\n",
+        "\n",
+        "```bash\n",
+        "pip install weightslab\n",
+        "weightslab start                       # Terminal 1 - opens http://localhost:5173\n",
+        "weightslab tunnel bore.pub:12345           # Terminal 2 - the host:port from the Expose section\n",
+        "```\n",
+        "\n",
+        "Then open **http://localhost:5173**. Prefer local-only? Run this example directly on your machine\n",
+        "(`weightslab start example` selects a bundled example) and launch the UI next to it - no tunnel."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "36c018d3",
+      "metadata": {
+        "id": "36c018d3"
+      },
+      "source": [
+        "---\n",
+        "\n",
+        "<div align=\"center\">\n",
+        "Crafted by <a href=\"https://github.com/GrayboxTech/weightslab\">GrayboxTech</a> - if WeightsLab helps you catch a bad label, drop us a star on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a>.\n",
+        "</div>"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "wl-segmentation.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-brain-tumor-detection-dataset.ipynb b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-brain-tumor-detection-dataset.ipynb
new file mode 100644
index 00000000..dd719903
--- /dev/null
+++ b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-brain-tumor-detection-dataset.ipynb
@@ -0,0 +1,1908 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jh24MV9yKs7J"
+      },
+      "source": [
+        "<div align=\"center\">\n",
+        "\n",
+        "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+        "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+        "\n",
+        "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+        "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+        "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+        "  <br>\n",
+        "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-brain-tumor-detection-dataset.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+        "\n",
+        "  Train an <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics YOLO</a> detector on the MRI <b>brain-tumor</b> dataset and stream every sample, prediction and metric live into <a href=\"https://github.com/GrayboxTech/weightslab\">Weights Studio</a> to <b>inspect, edit and evolve</b> your run.</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ImSwebklKs7S"
+      },
+      "source": [
+        "# Brain Tumor Detection (MRI) · Ultralytics YOLO + WeightsLab\n",
+        "\n",
+        "This notebook trains a YOLO detector on the [brain-tumor](https://docs.ultralytics.com/datasets/detect/brain-tumor/) MRI dataset **through the WeightsLab training pipeline**.\n",
+        "\n",
+        "Instead of Ultralytics' one-shot `train`/`predict` flow, we hand training to `WLAwareTrainer`. It wraps the train/val dataloaders, logs a **per-sample** signal for every image (loss, prediction stats, tags) and ships live prediction overlays to **Weights Studio** — where you inspect the data grid, remove/weight samples, edit the model and resume, all while training runs.\n",
+        "\n",
+        "**Dataset** — 893 training images and 223 validation images, each with detection annotations. Classes: `0: negative`, `1: positive`. Ultralytics auto-downloads it (~4 MB) the first time you train."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o7YFKEN1Ks7Y"
+      },
+      "source": [
+        "## 1 · Setup\n",
+        "\n",
+        "Install WeightsLab (which pulls in Ultralytics) and verify the environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "71be394c",
+        "outputId": "c9ef4767-84f5-4bea-e395-9e11766002f2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (75.2.0)\n",
+            "Requirement already satisfied: wheel in /usr/local/lib/python3.12/dist-packages (0.47.0)\n",
+            "Requirement already satisfied: packaging>=24.0 in /usr/local/lib/python3.12/dist-packages (from wheel) (26.2)\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install setuptools wheel"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "jV1oi_PQN0xK",
+        "outputId": "eda8eca1-f4b3-4ffd-eaa6-0f67ce857153"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting git+https://github.com/GrayboxTech/weightslab.git@landingcollab\n",
+            "  Cloning https://github.com/GrayboxTech/weightslab.git (to revision landingcollab) to /tmp/pip-req-build-z4ba7cx9\n",
+            "  Running command git clone --filter=blob:none --quiet https://github.com/GrayboxTech/weightslab.git /tmp/pip-req-build-z4ba7cx9\n",
+            "  Running command git checkout -b landingcollab --track origin/landingcollab\n",
+            "  Switched to a new branch 'landingcollab'\n",
+            "  Branch 'landingcollab' set up to track remote branch 'landingcollab' from 'origin'.\n",
+            "  Resolved https://github.com/GrayboxTech/weightslab.git to commit 3f1cf143bc93e67df43adc71ab831fa71477e0d2\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: numpy<3,>=1.24 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.0.2)\n",
+            "Requirement already satisfied: pandas<3,>=2.2.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.2.2)\n",
+            "Requirement already satisfied: duckdb<2,>=1.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.2)\n",
+            "Requirement already satisfied: PyYAML<7,>=6.0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (6.0.3)\n",
+            "Requirement already satisfied: dill<0.5,>=0.3.8 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.3.8)\n",
+            "Requirement already satisfied: zstandard<1,>=0.22 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.25.0)\n",
+            "Requirement already satisfied: h5py<4,>=3.10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.16.0)\n",
+            "Requirement already satisfied: xxhash<4.1,>=3.4 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.7.1)\n",
+            "Requirement already satisfied: tables<4,>=3.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.10.2)\n",
+            "Requirement already satisfied: torch<=2.9,>=2.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.9.0)\n",
+            "Requirement already satisfied: torchvision<1,>=0.16 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.24.0)\n",
+            "Requirement already satisfied: torchmetrics>=1.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.9.0)\n",
+            "Requirement already satisfied: grpcio<2,>=1.80 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.81.1)\n",
+            "Requirement already satisfied: protobuf<8,>=5.28.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (5.29.6)\n",
+            "Requirement already satisfied: pydantic<3,>=2.7 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.13.4)\n",
+            "Requirement already satisfied: Pillow<12,>=10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (11.3.0)\n",
+            "Requirement already satisfied: graphviz<1,>=0.20 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.21)\n",
+            "Requirement already satisfied: onnx<=1.20,>=1.15 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.20.0)\n",
+            "Requirement already satisfied: tqdm<5,>=4.66 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (4.67.3)\n",
+            "Requirement already satisfied: python-dotenv<2,>=1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.2.2)\n",
+            "Requirement already satisfied: langchain-core<2,>=0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.4.9)\n",
+            "Requirement already satisfied: langchain-ollama<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.1.0)\n",
+            "Requirement already satisfied: langchain-openai<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.5)\n",
+            "Requirement already satisfied: typing-extensions~=4.12 in /usr/local/lib/python3.12/dist-packages (from grpcio<2,>=1.80->weightslab==1.3.3.dev8) (4.15.0)\n",
+            "Requirement already satisfied: jsonpatch<2.0.0,>=1.33.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.33)\n",
+            "Requirement already satisfied: langchain-protocol>=0.0.17 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.0.18)\n",
+            "Requirement already satisfied: langsmith<1.0.0,>=0.3.45 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.9.1)\n",
+            "Requirement already satisfied: packaging>=23.2.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (26.2)\n",
+            "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (9.1.4)\n",
+            "Requirement already satisfied: uuid-utils<1.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.2)\n",
+            "Requirement already satisfied: ollama<1.0.0,>=0.6.1 in /usr/local/lib/python3.12/dist-packages (from langchain-ollama<2,>=0.2->weightslab==1.3.3.dev8) (0.6.2)\n",
+            "Requirement already satisfied: openai<3.0.0,>=2.45.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2.45.0)\n",
+            "Requirement already satisfied: tiktoken<1.0.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.13.0)\n",
+            "Requirement already satisfied: ml_dtypes>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from onnx<=1.20,>=1.15->weightslab==1.3.3.dev8) (0.5.4)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2026.2)\n",
+            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.7.0)\n",
+            "Requirement already satisfied: pydantic-core==2.46.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (2.46.4)\n",
+            "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.4.2)\n",
+            "Requirement already satisfied: numexpr>=2.6.2 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (2.14.1)\n",
+            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (9.0.0)\n",
+            "Requirement already satisfied: blosc2>=2.3.0 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (4.5.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.29.4)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (75.2.0)\n",
+            "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.14.0)\n",
+            "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.6.1)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.1.6)\n",
+            "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2025.3.0)\n",
+            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+            "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (9.10.2.21)\n",
+            "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.4.1)\n",
+            "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.3.3.83)\n",
+            "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (10.3.9.90)\n",
+            "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.7.3.90)\n",
+            "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.5.8.93)\n",
+            "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (0.7.1)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2.27.5)\n",
+            "Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.3.20)\n",
+            "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+            "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.13.1.3)\n",
+            "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.5.0)\n",
+            "Requirement already satisfied: lightning-utilities>=0.15.3 in /usr/local/lib/python3.12/dist-packages (from torchmetrics>=1.9->weightslab==1.3.3.dev8) (0.15.3)\n",
+            "Requirement already satisfied: ndindex in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.10.1)\n",
+            "Requirement already satisfied: msgpack in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.2.1)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.32.4)\n",
+            "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (13.9.4)\n",
+            "Requirement already satisfied: textual in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (6.2.1)\n",
+            "Requirement already satisfied: textual-plotext in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.0.1)\n",
+            "Requirement already satisfied: threadpoolctl in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (3.6.0)\n",
+            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.12/dist-packages (from jsonpatch<2.0.0,>=1.33.0->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.1.1)\n",
+            "Requirement already satisfied: anyio>=3.5.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (4.14.0)\n",
+            "Requirement already satisfied: distro>=1.7.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.9.0)\n",
+            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.28.1)\n",
+            "Requirement already satisfied: orjson>=3.9.14 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.11.9)\n",
+            "Requirement already satisfied: requests-toolbelt>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.0.0)\n",
+            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.3.1)\n",
+            "Requirement already satisfied: websockets>=15.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (15.0.1)\n",
+            "Requirement already satisfied: jiter<1,>=0.10.0 in /usr/local/lib/python3.12/dist-packages (from openai<3.0.0,>=2.45.0->langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.15.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (1.17.0)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.3.0)\n",
+            "Requirement already satisfied: regex in /usr/local/lib/python3.12/dist-packages (from tiktoken<1.0.0,>=0.7.0->langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2025.11.3)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.0.3)\n",
+            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.12/dist-packages (from anyio>=3.5.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.18)\n",
+            "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (2026.6.17)\n",
+            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.0.9)\n",
+            "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.0)\n",
+            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (3.4.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.5.0)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.2.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.20.0)\n",
+            "Requirement already satisfied: platformdirs<5,>=3.6.0 in /usr/local/lib/python3.12/dist-packages (from textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.10.0)\n",
+            "Requirement already satisfied: plotext<6.0.0,>=5.2.8 in /usr/local/lib/python3.12/dist-packages (from textual-plotext->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (5.3.2)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.1.2)\n",
+            "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.1.0)\n",
+            "Requirement already satisfied: mdit-py-plugins>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.6.1)\n",
+            "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.12/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.0.0)\n",
+            "Building wheels for collected packages: weightslab\n",
+            "  Building wheel for weightslab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for weightslab: filename=weightslab-1.3.3.dev8-py3-none-any.whl size=2827715 sha256=5c4e7c14bbf95a48b9a8c86b5a8c744af67e1eb395197cbe79d3e443c21cf86e\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-__zwgf7x/wheels/4d/c7/cd/817c2745790555e7b6c532f5b6055d47567f9416fdfc65879b\n",
+            "Successfully built weightslab\n",
+            "Installing collected packages: weightslab\n",
+            "  Attempting uninstall: weightslab\n",
+            "    Found existing installation: weightslab 1.3.3.dev7\n",
+            "    Uninstalling weightslab-1.3.3.dev7:\n",
+            "      Successfully uninstalled weightslab-1.3.3.dev7\n",
+            "Successfully installed weightslab-1.3.3.dev8\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "id": "b7fce274042740d19087972fe9c01c9b",
+              "pip_warning": {
+                "packages": [
+                  "weightslab"
+                ]
+              }
+            }
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "%pip install git+https://github.com/GrayboxTech/weightslab.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pJhvREbaKs7f",
+        "outputId": "651baa36-9ebb-49da-830b-e6c8a6e2b64f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Ultralytics 8.4.96 🚀 Python-3.12.13 torch-2.9.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n",
+            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.5/107.7 GB disk)\n",
+            "16/07/2026-09:29:29.683 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmp6meuzqdz/weightslab_logs/weightslab_20260716_092929.log\n",
+            "16/07/2026-09:29:29.685 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+            "16/07/2026-09:29:29.686 INFO:weightslab:<module>: \n",
+            "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+            "│                                                                                                     │\n",
+            "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+            "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+            "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+            "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+            "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+            "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+            "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+            "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+            "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+            "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+            "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+            "│                                                                                                     │\n",
+            "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+            "│                                                                                                     │\n",
+            "╰─── By GrayBx, v1.3.3.dev8 ──────────────────────────────────────────────────────────────────────────╯\n",
+            "\n",
+            "\n",
+            "16/07/2026-09:29:29.690 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+        "# and Pillow, so nothing extra is needed here.\n",
+        "# %pip install weightslab\n",
+        "# %pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev8\"\n",
+        "%pip install ultralytics\n",
+        "\n",
+        "import ultralytics\n",
+        "ultralytics.checks()\n",
+        "\n",
+        "import weightslab as wl"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QA3hHC2xKs7h"
+      },
+      "source": [
+        "## 2 · Dataset\n",
+        "\n",
+        "The dataset is described by a small YAML file that Ultralytics resolves and downloads automatically — no manual setup needed. For reference, `brain-tumor.yaml` looks like this:\n",
+        "\n",
+        "```yaml\n",
+        "path: brain-tumor      # dataset root dir (auto-downloaded)\n",
+        "train: images/train    # 893 images\n",
+        "val: images/val        # 223 images\n",
+        "names:\n",
+        "  0: negative\n",
+        "  1: positive\n",
+        "```\n",
+        "\n",
+        "To train on **your own** data, point `data` (in the next cell) at your own `data.yaml` in YOLO format."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rH79t30YKs7i"
+      },
+      "source": [
+        "## 3 · Train through WeightsLab\n",
+        "\n",
+        "We register the run's hyperparameters with `wl.watch_or_edit(...)` (so they become live-editable from Studio), start the backend services with `wl.serve()`, then hand training to `WLAwareTrainer` via the standard `YOLO(...).train(trainer=...)` entry point.\n",
+        "\n",
+        "> **Connect Weights Studio** — run `weightslab ui launch` locally (opens `http://localhost:5173`) *before or during* training to watch the run live. On **Colab**, serve over a public tunnel instead: use `wl.serve(serving_grpc=True, serving_bore=True)` and connect your local Studio with `weightslab tunnel bore.pub:PORT` (the port is printed when serving starts).\n",
+        "\n",
+        "Data augmentations are disabled so each sample maps cleanly to its ground truth in the grid."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Svz7NK9qKs7k",
+        "outputId": "2931e6b9-a06e-4dc0-9f67-e2e6216ee1c9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "16/07/2026-09:29:52.825 INFO:weightslab.src:watch_or_edit: LoggerQueue for experiment history has been initialized and registered.\n",
+            "16/07/2026-09:29:52.828 INFO:weightslab.components.checkpoint_manager:__init__: CheckpointManager initialized at /tmp/tmp3abnx3ay\n",
+            "16/07/2026-09:29:52.829 INFO:weightslab.src:watch_or_edit: Registered new checkpoint manager in ledger\n",
+            "16/07/2026-09:29:52.830 INFO:root:set_log_directory: Log file moved from /tmp/tmp6meuzqdz/weightslab_logs/weightslab_20260716_092929.log to /tmp/tmp3abnx3ay/weightslab_20260716_092929.log\n",
+            "16/07/2026-09:29:52.832 INFO:root:set_log_directory: Log directory updated to: /tmp/tmp3abnx3ay\n",
+            "16/07/2026-09:29:52.833 INFO:root:set_log_directory: Log file: /tmp/tmp3abnx3ay/weightslab_20260716_092929.log\n",
+            "16/07/2026-09:29:52.835 INFO:weightslab.trainer.trainer_services:_run_security_preflight: \n",
+            "\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "[gRPC] Security preflight checks:\n",
+            "\tTLS: DISABLED (unencrypted traffic)\n",
+            "\tAuth tokens: NONE configured\n",
+            "\t! WARNING: GRPC_TLS_ENABLED=0. Traffic will be unencrypted. Use only for development.\n",
+            "\t! WARNING: No GRPC_AUTH_TOKEN/GRPC_AUTH_TOKENS configured. Only transport-level trust (TLS/mTLS) will protect RPC access.\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "\n",
+            "16/07/2026-09:29:52.837 INFO:weightslab.trainer.trainer_services:grpc_serve: [gRPC] Watchdogs disabled via WEIGHTSLAB_DISABLE_WATCHDOGS.\n",
+            "16/07/2026-09:29:52.839 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Thread callback started\n",
+            "16/07/2026-09:29:52.840 INFO:weightslab.trainer.trainer_services:grpc_serve: [gRPC] Server started with watchdogs disabled (host=0.0.0.0 port=50051 workers=None)\n",
+            "16/07/2026-09:29:52.841 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Creating ThreadPoolExecutor with 6 worker threads (n_workers_grpc=None, max_concurrent_rpcs=None)\n",
+            "16/07/2026-09:29:52.842 WARNING:weightslab.src:serve: Running in a notebook/Colab with serving_grpc=True but serving_bore=False. Weights Studio runs on your OWN machine and cannot reach this backend directly. Pass serving_bore=True to open a tunnel and sync with the UI: wl.serve(serving_grpc=True, serving_bore=True).\n",
+            "16/07/2026-09:29:52.852 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Server object created\n",
+            "16/07/2026-09:29:52.854 INFO:weightslab.backend.cli:cli_serve: cli_thread_started\n",
+            "16/07/2026-09:29:52.859 INFO:weightslab.trainer.services.agent.agent:__init__: Initializing DataManipulationAgent\n",
+            "16/07/2026-09:29:52.860 INFO:weightslab.trainer.services.agent.agent:_setup_model_schema: [Agent] components.get('model') returned None (ledger-registered model names: []). Model-related agent requests will report 'no model registered'. If a model IS registered under a name other than the experiment name / 'experiment' / 'main', ExperimentContext.ensure_components()'s model-resolution heuristic won't find it.\n",
+            "16/07/2026-09:29:52.867 WARNING:weightslab.trainer.services.agent.agent:_load_config: Error loading config from /usr/local/lib/python3.12/dist-packages: [Errno 21] Is a directory: '/usr/local/lib/python3.12/dist-packages'\n",
+            "16/07/2026-09:29:52.869 INFO:weightslab.trainer.services.agent.agent:_load_config: \n",
+            "\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "Agent initialized from configuration /content/agent_config.yaml: \n",
+            "\tFinal Agent Configuration: Preferred Provider=openrouter, \n",
+            "\tFallback to Local=True, \n",
+            "\tOpenRouter Model=~google/gemini-flash-latest with:\n",
+            "\t\tAPI Key=None\n",
+            "\t\tBase URL=https://openrouter.ai/api/v1, \n",
+            "\tOllama Model=llama3.2:3b\n",
+            "# #######################################\n",
+            "# #######################################\n",
+            "\n",
+            "16/07/2026-09:29:52.870 INFO:weightslab.trainer.services.agent.agent:_setup_providers: Setting up Ollama with model llama3.2:3b\n",
+            "16/07/2026-09:29:52.915 INFO:weightslab.components.global_monitoring:resume: \n",
+            "Attempting to resume training...\n",
+            "16/07/2026-09:29:52.916 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: First time initialization; skipping hash update.\n",
+            "16/07/2026-09:29:52.917 INFO:weightslab.components.experiment_hash:has_changed: Experiment configuration changed: {'hp'}\n",
+            "16/07/2026-09:29:52.918 INFO:weightslab.components.experiment_hash:generate_hash: Generated experiment hash: 1b111ac40000000000000000- (HP: 1b111ac4, Model: 00000000, Data: 00000000)\n",
+            "16/07/2026-09:29:52.919 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Initial experiment hash set: 1b111ac4-00000000-00000000\n",
+            "16/07/2026-09:29:52.919 INFO:weightslab.components.checkpoint_manager:update_experiment_hash: Changed components: {'hp'}\n",
+            "16/07/2026-09:29:52.922 INFO:weightslab.components.checkpoint_manager:_save_changes: Dumping hyperparameters config...\n",
+            "16/07/2026-09:29:52.924 INFO:weightslab.components.checkpoint_manager:save_config: Saved config: 1b111ac4_config.yaml with exp_hash prefix: 1b111ac4\n",
+            "16/07/2026-09:29:52.925 WARNING:weightslab.components.checkpoint_manager:_save_changes: Could not save weights: no model available\n",
+            "16/07/2026-09:29:52.931 INFO:weightslab.components.checkpoint_manager:save_logger_snapshot: Saved logger snapshot: /tmp/tmp3abnx3ay/checkpoints/loggers/loggers.manifest.json (1 chunks)\n",
+            "16/07/2026-09:29:52.935 INFO:weightslab.components.global_monitoring:resume: Hashes by module: ['1b111ac4', '00000000', '00000000']\n",
+            "16/07/2026-09:29:52.935 WARNING:weightslab.components.global_monitoring:resume: Cannot resume training: experiment hash not computed yet for every modules ['1b111ac4', '00000000', '00000000'].\n",
+            "16/07/2026-09:29:53.028 INFO:weightslab.trainer.services.agent.agent:_setup_providers: [Agent] Ollama enabled: llama3.2:3b\n",
+            "16/07/2026-09:29:53.031 INFO:weightslab.trainer.services.data_service:__init__: DataService initialized.\n",
+            "16/07/2026-09:29:53.036 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Servicer added\n",
+            "16/07/2026-09:29:53.040 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Attempting to bind to 0.0.0.0:50051\n",
+            "16/07/2026-09:29:53.044 WARNING:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] TLS disabled; using insecure transport on 0.0.0.0:50051\n",
+            "16/07/2026-09:29:53.046 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Port 50051 bound successfully.\n",
+            "16/07/2026-09:29:53.051 INFO:weightslab.trainer.trainer_services:serving_thread_callback: [gRPC] Server started and listening on 0.0.0.0:50051\n",
+            "Ultralytics 8.4.96 🚀 Python-3.12.13 torch-2.9.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n",
+            "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=False, angle=1.0, augment=False, auto_augment=None, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, cls_pw=0.0, cls_remap=True, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/datasets/brain-tumor/brain-tumor.yaml, degrees=0.0, deterministic=True, device=cpu, dfl=1.5, dis=6.0, distill_model=None, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=10, erasing=0.0, exist_ok=False, fliplr=0.0, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, hsv_h=0.0, hsv_s=0.0, hsv_v=0.0, imgsz=640, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolo11n.pt, momentum=0.937, mosaic=0.0, multi_scale=0.0, name=brain-tumor-4, nbs=64, nms=False, opset=None, optimize=False, optimizer=SGD, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=./wl_logs, quantize=None, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/content/runs/detect/wl_logs/brain-tumor-4, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.0, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=tracktrack.yaml, translate=0.0, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=0, workspace=None\n",
+            "Overriding model.yaml nc=80 with nc=2\n",
+            "\n",
+            "                   from  n    params  module                                       arguments                     \n",
+            "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
+            "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
+            "  2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      \n",
+            "  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
+            "  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     \n",
+            "  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
+            "  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]           \n",
+            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
+            "  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           \n",
+            "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
+            " 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]                 \n",
+            " 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+            " 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]          \n",
+            " 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+            " 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]           \n",
+            " 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
+            " 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]          \n",
+            " 20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
+            " 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]           \n",
+            " 23        [16, 19, 22]  1    431062  ultralytics.nn.modules.head.Detect           [2, 16, None, [64, 128, 256]] \n",
+            "YOLO11n summary: 182 layers, 2,590,230 parameters, 2,590,214 gradients, 6.4 GFLOPs\n",
+            "\n",
+            "Transferred 448/499 items from pretrained weights\n",
+            "Freezing layer 'model.23.dfl.conv.weight'\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 115.3±57.6 MB/s, size: 3.6 KB)\n",
+            "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/brain-tumor/labels/train.cache... 878 images, 15 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 893/893 101.2Mit/s 0.0s\n",
+            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
+            "16/07/2026-09:29:54.526 INFO:weightslab.data.data_samples_with_ops:__init__: [DataSampleTrackingWrapper] H5 persistence enabled at /tmp/tmp3abnx3ay/checkpoints/data/data.h5\n",
+            "16/07/2026-09:29:54.529 INFO:weightslab.data.data_samples_with_ops:__init__: Preloading sample statistics for PHYSICAL indices in split 'train_loader' with: preload_labels=True, preload_metadata=True...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Initializing ledger for split 'train_loader': 100%|██████████| 893/893 [00:00<00:00, 13970.17it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "16/07/2026-09:29:54.600 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] Registering split 'train_loader' with 893 samples.\n",
+            "16/07/2026-09:29:54.615 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] After annotation expansion: 893 samples → 984 annotation rows.\n",
+            "16/07/2026-09:29:54.616 INFO:weightslab.data.h5_array_store:__init__: [H5ArrayStore] Initialized with cache limit: 2048MB\n",
+            "16/07/2026-09:29:54.636 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loading checkpoint 1b111ac400000000...\n",
+            "16/07/2026-09:29:54.637 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Target: HP=1b111ac4 MODEL=00000000 DATA=00000000\n",
+            "16/07/2026-09:29:54.638 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Current: HP=1b111ac4 MODEL=00000000 DATA=00000000\n",
+            "16/07/2026-09:29:54.639 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Model architecture unchanged, using current model\n",
+            "16/07/2026-09:29:54.639 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Config unchanged, using current config\n",
+            "16/07/2026-09:29:54.641 WARNING:weightslab.components.checkpoint_manager:load_checkpoint:  [WARNING] Data snapshot file not found: /tmp/tmp3abnx3ay/checkpoints/data/00000000/00000000_data_snapshot.json\n",
+            "16/07/2026-09:29:54.641 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loaded components: set()\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 62.7±38.0 MB/s, size: 3.9 KB)\n",
+            "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/brain-tumor/labels/val.cache... 223 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 223/223 32.3Mit/s 0.0s\n",
+            "16/07/2026-09:29:54.660 INFO:weightslab.data.data_samples_with_ops:__init__: [DataSampleTrackingWrapper] H5 persistence enabled at /tmp/tmp3abnx3ay/checkpoints/data/data.h5\n",
+            "16/07/2026-09:29:54.662 INFO:weightslab.data.data_samples_with_ops:__init__: Preloading sample statistics for PHYSICAL indices in split 'val_loader' with: preload_labels=True, preload_metadata=True...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Initializing ledger for split 'val_loader': 100%|██████████| 223/223 [00:00<00:00, 5362.24it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "16/07/2026-09:29:54.708 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] Registering split 'val_loader' with 223 samples.\n",
+            "16/07/2026-09:29:54.714 INFO:weightslab.data.dataframe_manager:register_split: [LedgeredDataFrameManager] After annotation expansion: 223 samples → 257 annotation rows.\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "16/07/2026-09:29:54.734 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loading checkpoint 1b111ac400000000...\n",
+            "16/07/2026-09:29:54.736 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Target: HP=1b111ac4 MODEL=00000000 DATA=00000000\n",
+            "16/07/2026-09:29:54.739 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Current: HP=1b111ac4 MODEL=00000000 DATA=00000000\n",
+            "16/07/2026-09:29:54.740 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Model architecture unchanged, using current model\n",
+            "16/07/2026-09:29:54.742 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Config unchanged, using current config\n",
+            "16/07/2026-09:29:54.743 WARNING:weightslab.components.checkpoint_manager:load_checkpoint:  [WARNING] Data snapshot file not found: /tmp/tmp3abnx3ay/checkpoints/data/00000000/00000000_data_snapshot.json\n",
+            "16/07/2026-09:29:54.746 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loaded components: set()\n",
+            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.001, momentum=0.937) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)\n",
+            "Plotting labels to /content/runs/detect/wl_logs/brain-tumor-4/labels.jpg... \n",
+            "16/07/2026-09:29:55.639 INFO:weightslab.backend.model_interface:__init__: Using checkpoint manager from ledger\n",
+            "16/07/2026-09:29:55.644 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loading checkpoint 1b111ac400000000...\n",
+            "16/07/2026-09:29:55.645 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Target: HP=1b111ac4 MODEL=00000000 DATA=00000000\n",
+            "16/07/2026-09:29:55.645 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  Current: HP=1b111ac4 MODEL=00000000 DATA=00000000\n",
+            "16/07/2026-09:29:55.646 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Model architecture unchanged, using current model\n",
+            "16/07/2026-09:29:55.647 WARNING:weightslab.components.checkpoint_manager:load_checkpoint:  [WARNING] No weight files found for 00000000\n",
+            "16/07/2026-09:29:55.648 INFO:weightslab.components.checkpoint_manager:load_checkpoint:  [-] Config unchanged, using current config\n",
+            "16/07/2026-09:29:55.648 INFO:weightslab.components.checkpoint_manager:load_checkpoint: Loaded components: set()\n",
+            "Image sizes 640 train, 640 val\n",
+            "Using 0 dataloader workers\n",
+            "Logging results to \u001b[1m/content/runs/detect/wl_logs/brain-tumor-4\u001b[0m\n",
+            "Starting training for 10 epochs...\n",
+            "16/07/2026-09:29:55.661 INFO:weightslab.backend.dataloader_interface:set_batch_size: Batch size updated: 16 -> 4 (Loader: train_loader)\n",
+            "Closing dataloader mosaic\n",
+            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+            "\u001b[K: 0% ──────────── 0/56  1:03\n"
+          ]
+        },
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_6805/1732828701.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;31m# Read back the (now live) hyperparameters and hand training to WLAwareTrainer.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mYOLO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"model\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m results = model.train(\n\u001b[0m\u001b[1;32m     33\u001b[0m     \u001b[0mtrainer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mWLAwareTrainer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ultralytics/engine/model.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, trainer, **kwargs)\u001b[0m\n\u001b[1;32m    814\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    815\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 816\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    817\u001b[0m         \u001b[0;31m# Update model and cfg after training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mRANK\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ultralytics/engine/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setup_scheduler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ultralytics/engine/trainer.py\u001b[0m in \u001b[0;36m_do_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    430\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpbar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 432\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"on_train_batch_start\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    433\u001b[0m                 \u001b[0;31m# Warmup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m                 \u001b[0mni\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnb\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ultralytics/engine/trainer.py\u001b[0m in \u001b[0;36mrun_callbacks\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;34m\"\"\"Run all existing callbacks associated with a particular event.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/weightslab/integrations/ultralytics/trainer.py\u001b[0m in \u001b[0;36m_on_train_batch_start\u001b[0;34m(trainer)\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_on_train_batch_start\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m             \u001b[0mwl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mguard_training_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_on_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/weightslab/components/global_monitoring.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self, f)\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m                 \u001b[0mpause_controller\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 226\u001b[0;31m             \u001b[0mpause_controller\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait_if_paused\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    227\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marchitecture_guard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/weightslab/components/global_monitoring.py\u001b[0m in \u001b[0;36mwait_if_paused\u001b[0;34m(self, skip_pause)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;31m# Also wakes up early when an evaluation is pending/running so the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0;31m# training loop and dataloaders can service evaluation mode.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m             \u001b[0;31m# Timeout occurred – check for evaluation request before looping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.12/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    653\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    654\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    656\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.12/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    357\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "os.environ.setdefault(\"WL_PRELOAD_IMAGE_OVERVIEW\", \"0\")\n",
+        "os.environ.setdefault(\"WEIGHTSLAB_LOG_LEVEL\", \"WARNING\")\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "from weightslab.integrations.ultralytics import WLAwareTrainer\n",
+        "from ultralytics import YOLO\n",
+        "\n",
+        "# Hyperparameters registered with WeightsLab -> live-editable from Weights Studio.\n",
+        "cfg = {\n",
+        "    \"model\": \"yolo11n.pt\",       # pretrained checkpoint\n",
+        "    \"data\": \"/content/datasets/brain-tumor/brain-tumor.yaml\",  # Ultralytics auto-downloads (~4 MB)\n",
+        "    \"image_size\": 640,\n",
+        "    \"epochs\": 10,\n",
+        "    # Per-sample prediction overlays streamed to Studio (NMS on the train set).\n",
+        "    \"signals_cfg\": {\n",
+        "        \"train_nms\": {\"conf_thres\": 0.25, \"iou_thres\": 0.45, \"max_nms\": 7},\n",
+        "    },\n",
+        "}\n",
+        "wl.watch_or_edit(cfg, flag=\"hyperparameters\", defaults=cfg, poll_interval=1.0)\n",
+        "\n",
+        "# Start the WeightsLab backend services that Weights Studio connects to.\n",
+        "wl.serve(serving_grpc=True)\n",
+        "#   Colab -> local Studio:  wl.serve(serving_grpc=True, serving_bore=True)\n",
+        "\n",
+        "wl.start_training()  # equivalent to pressing \"play\" in Studio\n",
+        "\n",
+        "# Read back the (now live) hyperparameters and hand training to WLAwareTrainer.\n",
+        "model = YOLO(cfg[\"model\"])\n",
+        "results = model.train(\n",
+        "    trainer=WLAwareTrainer,\n",
+        "    data=str(cfg[\"data\"]),\n",
+        "    imgsz=cfg[\"image_size\"],\n",
+        "    epochs=int(cfg[\"epochs\"]),\n",
+        "    project=\"./wl_logs\", name=\"brain-tumor\",  # -> WL log_dir/name\n",
+        "    workers=0,          # WL invariant (parent-process uid counter)\n",
+        "    optimizer=\"SGD\", lr0=0.001, amp=False,\n",
+        "    device='cpu',\n",
+        "    # All augmentations off for a clean sample <-> ground-truth association.\n",
+        "    mosaic=0.0, mixup=0.0, copy_paste=0.0,\n",
+        "    hsv_h=0.0, hsv_s=0.0, hsv_v=0.0,\n",
+        "    degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0,\n",
+        "    flipud=0.0, fliplr=0.0, erasing=0.0, auto_augment=None,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f-Uw_s_8Ks7m"
+      },
+      "source": [
+        "## 4 · Explore the data grid\n",
+        "\n",
+        "The table below is exactly the data behind Weights Studio's **grid explorer**: one row per sample, keyed by split (`origin`) and sample id, with the per-sample loss, prediction stats and tags accumulated during training. This is the ledger that Studio reads from — here we render it inline with pandas.\n",
+        "\n",
+        "The image thumbnails and bounding-box overlays themselves are rendered by **Weights Studio** (which streams full previews over gRPC); the link below opens the interactive grid there."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "collapsed": true,
+        "id": "uOJdXGA3Ks7o",
+        "outputId": "26652fa3-6a15-43ce-9d75-034eaba21ccd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1241 samples tracked\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "repr_error": "Out of range float values are not JSON compliant: nan",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8adeb57b-4a56-45e8-8485-a1c46d89c532\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>prediction</th>\n",
+              "      <th>prediction_raw</th>\n",
+              "      <th>target</th>\n",
+              "      <th>discarded</th>\n",
+              "      <th>origin</th>\n",
+              "      <th>task_type</th>\n",
+              "      <th>last_seen</th>\n",
+              "      <th>group_id</th>\n",
+              "      <th>member_rank</th>\n",
+              "      <th>img_path</th>\n",
+              "      <th>cls</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sample_id</th>\n",
+              "      <th>annotation_id</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.2335685, 0.254695, 0.4553995, 0.43075103, 1...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.251174, 0.24061048, 0.44366202, 0.4307515, ...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0], [1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.523474, 0.3978875, 0.634976, 0.48122054, 1....</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.40610352, 0.415493, 0.5234745, 0.522301, 1....</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.6044605, 0.3990615, 0.67253554, 0.46596247,...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.410798, 0.4436625, 0.556338, 0.51173747, 1....</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.650235, 0.4166665, 0.73826295, 0.48356748, ...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.64788747, 0.388498, 0.7582165, 0.49413198, ...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.6807515, 0.40140802, 0.75704247, 0.45774597...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.42018852, 0.34507048, 0.5434275, 0.4941315,...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.3732395, 0.348592, 0.54812247, 0.46009403, ...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>12</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.37793398, 0.3814555, 0.48122, 0.45892054, 1...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>13</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>14</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0], [1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.43427247, 0.3556335, 0.51173747, 0.43075046...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.4929575, 0.44718304, 0.5375585, 0.485915, 1...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">15</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>15</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0], [1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.629108, 0.39788702, 0.67840403, 0.453051, 1...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.4530515, 0.4107985, 0.58568054, 0.5105635, ...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>16</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0], [1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.45070451, 0.3967135, 0.5762915, 0.5152585, ...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.63732404, 0.403756, 0.684272, 0.46009403, 1...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.43779355, 0.3826295, 0.61032856, 0.5223005,...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>17</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>18</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.536385, 0.4495305, 0.600939, 0.49999946, 0....</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>19</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[0.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"4\" valign=\"top\">20</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[0.0], [0.0], [0.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.52347445, 0.4424885, 0.6150235, 0.5281695, ...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.504695, 0.44248852, 0.54342705, 0.48004752,...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.54107946, 0.456573, 0.6103285, 0.511737, 0....</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>21</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[0.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>22</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[0.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.517606, 0.18661949, 0.615024, 0.2875585, 1....</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>23</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.51173747, 0.18779299, 0.6126765, 0.308685, ...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>24</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">25</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>25</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[0.0], [0.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.30985948, 0.3626765, 0.3861505, 0.43896753,...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.41314548, 0.3427225, 0.48122054, 0.42018753...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.25821602, 0.30046952, 0.46830997, 0.4577465...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>26</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[0.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.2734745, 0.2828635, 0.44718352, 0.4577465, ...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>27</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[0.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.5915495, 0.23591551, 0.6455405, 0.2887325, ...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>28</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>29</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.40493003, 0.2018775, 0.469484, 0.2699525, 1...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>30</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>31</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.4025825, 0.212441, 0.45539945, 0.269953, 1....</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>32</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.2265255, 0.2136145, 0.3438965, 0.30751148, ...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>33</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>34</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>35</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <th>0</th>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[0.6185445, 0.30164298, 0.7758215, 0.494131, 1...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>train_loader</td>\n",
+              "      <td></td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>36</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+              "      <td>[[1.0]]</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8adeb57b-4a56-45e8-8485-a1c46d89c532')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                        prediction prediction_raw  \\\n",
+              "sample_id annotation_id                             \n",
+              "0         0                   None           None   \n",
+              "1         0                   None           None   \n",
+              "2         0                   None           None   \n",
+              "          1                   None           None   \n",
+              "          2                   None           None   \n",
+              "3         0                   None           None   \n",
+              "4         0                   None           None   \n",
+              "5         0                   None           None   \n",
+              "6         0                   None           None   \n",
+              "7         0                   None           None   \n",
+              "8         0                   None           None   \n",
+              "9         0                   None           None   \n",
+              "10        0                   None           None   \n",
+              "11        0                   None           None   \n",
+              "12        0                   None           None   \n",
+              "13        0                   None           None   \n",
+              "14        0                   None           None   \n",
+              "          1                   None           None   \n",
+              "          2                   None           None   \n",
+              "15        0                   None           None   \n",
+              "          1                   None           None   \n",
+              "          2                   None           None   \n",
+              "16        0                   None           None   \n",
+              "          1                   None           None   \n",
+              "          2                   None           None   \n",
+              "17        0                   None           None   \n",
+              "18        0                   None           None   \n",
+              "19        0                   None           None   \n",
+              "20        0                   None           None   \n",
+              "          1                   None           None   \n",
+              "          2                   None           None   \n",
+              "          3                   None           None   \n",
+              "21        0                   None           None   \n",
+              "22        0                   None           None   \n",
+              "23        0                   None           None   \n",
+              "24        0                   None           None   \n",
+              "25        0                   None           None   \n",
+              "          1                   None           None   \n",
+              "          2                   None           None   \n",
+              "26        0                   None           None   \n",
+              "27        0                   None           None   \n",
+              "28        0                   None           None   \n",
+              "29        0                   None           None   \n",
+              "30        0                   None           None   \n",
+              "31        0                   None           None   \n",
+              "32        0                   None           None   \n",
+              "33        0                   None           None   \n",
+              "34        0                   None           None   \n",
+              "35        0                   None           None   \n",
+              "36        0                   None           None   \n",
+              "\n",
+              "                                                                    target  \\\n",
+              "sample_id annotation_id                                                      \n",
+              "0         0              [0.2335685, 0.254695, 0.4553995, 0.43075103, 1...   \n",
+              "1         0              [0.251174, 0.24061048, 0.44366202, 0.4307515, ...   \n",
+              "2         0                                                           None   \n",
+              "          1              [0.523474, 0.3978875, 0.634976, 0.48122054, 1....   \n",
+              "          2              [0.40610352, 0.415493, 0.5234745, 0.522301, 1....   \n",
+              "3         0              [0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...   \n",
+              "4         0              [0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...   \n",
+              "5         0              [0.6044605, 0.3990615, 0.67253554, 0.46596247,...   \n",
+              "6         0              [0.410798, 0.4436625, 0.556338, 0.51173747, 1....   \n",
+              "7         0              [0.650235, 0.4166665, 0.73826295, 0.48356748, ...   \n",
+              "8         0              [0.64788747, 0.388498, 0.7582165, 0.49413198, ...   \n",
+              "9         0              [0.6807515, 0.40140802, 0.75704247, 0.45774597...   \n",
+              "10        0              [0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...   \n",
+              "11        0              [0.42018852, 0.34507048, 0.5434275, 0.4941315,...   \n",
+              "12        0              [0.3732395, 0.348592, 0.54812247, 0.46009403, ...   \n",
+              "13        0              [0.37793398, 0.3814555, 0.48122, 0.45892054, 1...   \n",
+              "14        0                                                           None   \n",
+              "          1              [0.43427247, 0.3556335, 0.51173747, 0.43075046...   \n",
+              "          2              [0.4929575, 0.44718304, 0.5375585, 0.485915, 1...   \n",
+              "15        0                                                           None   \n",
+              "          1              [0.629108, 0.39788702, 0.67840403, 0.453051, 1...   \n",
+              "          2              [0.4530515, 0.4107985, 0.58568054, 0.5105635, ...   \n",
+              "16        0                                                           None   \n",
+              "          1              [0.45070451, 0.3967135, 0.5762915, 0.5152585, ...   \n",
+              "          2              [0.63732404, 0.403756, 0.684272, 0.46009403, 1...   \n",
+              "17        0              [0.43779355, 0.3826295, 0.61032856, 0.5223005,...   \n",
+              "18        0              [0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...   \n",
+              "19        0              [0.536385, 0.4495305, 0.600939, 0.49999946, 0....   \n",
+              "20        0                                                           None   \n",
+              "          1              [0.52347445, 0.4424885, 0.6150235, 0.5281695, ...   \n",
+              "          2              [0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...   \n",
+              "          3              [0.504695, 0.44248852, 0.54342705, 0.48004752,...   \n",
+              "21        0              [0.54107946, 0.456573, 0.6103285, 0.511737, 0....   \n",
+              "22        0              [0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...   \n",
+              "23        0              [0.517606, 0.18661949, 0.615024, 0.2875585, 1....   \n",
+              "24        0              [0.51173747, 0.18779299, 0.6126765, 0.308685, ...   \n",
+              "25        0                                                           None   \n",
+              "          1              [0.30985948, 0.3626765, 0.3861505, 0.43896753,...   \n",
+              "          2              [0.41314548, 0.3427225, 0.48122054, 0.42018753...   \n",
+              "26        0              [0.25821602, 0.30046952, 0.46830997, 0.4577465...   \n",
+              "27        0              [0.2734745, 0.2828635, 0.44718352, 0.4577465, ...   \n",
+              "28        0              [0.5915495, 0.23591551, 0.6455405, 0.2887325, ...   \n",
+              "29        0              [0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...   \n",
+              "30        0              [0.40493003, 0.2018775, 0.469484, 0.2699525, 1...   \n",
+              "31        0              [0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...   \n",
+              "32        0              [0.4025825, 0.212441, 0.45539945, 0.269953, 1....   \n",
+              "33        0              [0.2265255, 0.2136145, 0.3438965, 0.30751148, ...   \n",
+              "34        0               [0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]   \n",
+              "35        0              [0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...   \n",
+              "36        0              [0.6185445, 0.30164298, 0.7758215, 0.494131, 1...   \n",
+              "\n",
+              "                        discarded        origin task_type  last_seen group_id  \\\n",
+              "sample_id annotation_id                                                         \n",
+              "0         0                 False  train_loader                 -1.0        0   \n",
+              "1         0                 False  train_loader                 -1.0        1   \n",
+              "2         0                 False  train_loader                 -1.0        2   \n",
+              "          1                  None           NaN      None        NaN     None   \n",
+              "          2                  None           NaN      None        NaN     None   \n",
+              "3         0                 False  train_loader                 -1.0        3   \n",
+              "4         0                 False  train_loader                 -1.0        4   \n",
+              "5         0                 False  train_loader                 -1.0        5   \n",
+              "6         0                 False  train_loader                 -1.0        6   \n",
+              "7         0                 False  train_loader                 -1.0        7   \n",
+              "8         0                 False  train_loader                 -1.0        8   \n",
+              "9         0                 False  train_loader                 -1.0        9   \n",
+              "10        0                 False  train_loader                 -1.0       10   \n",
+              "11        0                 False  train_loader                 -1.0       11   \n",
+              "12        0                 False  train_loader                 -1.0       12   \n",
+              "13        0                 False  train_loader                 -1.0       13   \n",
+              "14        0                 False  train_loader                 -1.0       14   \n",
+              "          1                  None           NaN      None        NaN     None   \n",
+              "          2                  None           NaN      None        NaN     None   \n",
+              "15        0                 False  train_loader                 -1.0       15   \n",
+              "          1                  None           NaN      None        NaN     None   \n",
+              "          2                  None           NaN      None        NaN     None   \n",
+              "16        0                 False  train_loader                 -1.0       16   \n",
+              "          1                  None           NaN      None        NaN     None   \n",
+              "          2                  None           NaN      None        NaN     None   \n",
+              "17        0                 False  train_loader                 -1.0       17   \n",
+              "18        0                 False  train_loader                 -1.0       18   \n",
+              "19        0                 False  train_loader                 -1.0       19   \n",
+              "20        0                 False  train_loader                 -1.0       20   \n",
+              "          1                  None           NaN      None        NaN     None   \n",
+              "          2                  None           NaN      None        NaN     None   \n",
+              "          3                  None           NaN      None        NaN     None   \n",
+              "21        0                 False  train_loader                 -1.0       21   \n",
+              "22        0                 False  train_loader                 -1.0       22   \n",
+              "23        0                 False  train_loader                 -1.0       23   \n",
+              "24        0                 False  train_loader                 -1.0       24   \n",
+              "25        0                 False  train_loader                 -1.0       25   \n",
+              "          1                  None           NaN      None        NaN     None   \n",
+              "          2                  None           NaN      None        NaN     None   \n",
+              "26        0                 False  train_loader                 -1.0       26   \n",
+              "27        0                 False  train_loader                 -1.0       27   \n",
+              "28        0                 False  train_loader                 -1.0       28   \n",
+              "29        0                 False  train_loader                 -1.0       29   \n",
+              "30        0                 False  train_loader                 -1.0       30   \n",
+              "31        0                 False  train_loader                 -1.0       31   \n",
+              "32        0                 False  train_loader                 -1.0       32   \n",
+              "33        0                 False  train_loader                 -1.0       33   \n",
+              "34        0                 False  train_loader                 -1.0       34   \n",
+              "35        0                 False  train_loader                 -1.0       35   \n",
+              "36        0                 False  train_loader                 -1.0       36   \n",
+              "\n",
+              "                         member_rank  \\\n",
+              "sample_id annotation_id                \n",
+              "0         0                      0.0   \n",
+              "1         0                      0.0   \n",
+              "2         0                      0.0   \n",
+              "          1                      NaN   \n",
+              "          2                      NaN   \n",
+              "3         0                      0.0   \n",
+              "4         0                      0.0   \n",
+              "5         0                      0.0   \n",
+              "6         0                      0.0   \n",
+              "7         0                      0.0   \n",
+              "8         0                      0.0   \n",
+              "9         0                      0.0   \n",
+              "10        0                      0.0   \n",
+              "11        0                      0.0   \n",
+              "12        0                      0.0   \n",
+              "13        0                      0.0   \n",
+              "14        0                      0.0   \n",
+              "          1                      NaN   \n",
+              "          2                      NaN   \n",
+              "15        0                      0.0   \n",
+              "          1                      NaN   \n",
+              "          2                      NaN   \n",
+              "16        0                      0.0   \n",
+              "          1                      NaN   \n",
+              "          2                      NaN   \n",
+              "17        0                      0.0   \n",
+              "18        0                      0.0   \n",
+              "19        0                      0.0   \n",
+              "20        0                      0.0   \n",
+              "          1                      NaN   \n",
+              "          2                      NaN   \n",
+              "          3                      NaN   \n",
+              "21        0                      0.0   \n",
+              "22        0                      0.0   \n",
+              "23        0                      0.0   \n",
+              "24        0                      0.0   \n",
+              "25        0                      0.0   \n",
+              "          1                      NaN   \n",
+              "          2                      NaN   \n",
+              "26        0                      0.0   \n",
+              "27        0                      0.0   \n",
+              "28        0                      0.0   \n",
+              "29        0                      0.0   \n",
+              "30        0                      0.0   \n",
+              "31        0                      0.0   \n",
+              "32        0                      0.0   \n",
+              "33        0                      0.0   \n",
+              "34        0                      0.0   \n",
+              "35        0                      0.0   \n",
+              "36        0                      0.0   \n",
+              "\n",
+              "                                                                  img_path  \\\n",
+              "sample_id annotation_id                                                      \n",
+              "0         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "1         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "2         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "          1                                                           None   \n",
+              "          2                                                           None   \n",
+              "3         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "4         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "5         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "6         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "7         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "8         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "9         0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "10        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "11        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "12        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "13        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "14        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "          1                                                           None   \n",
+              "          2                                                           None   \n",
+              "15        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "          1                                                           None   \n",
+              "          2                                                           None   \n",
+              "16        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "          1                                                           None   \n",
+              "          2                                                           None   \n",
+              "17        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "18        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "19        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "20        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "          1                                                           None   \n",
+              "          2                                                           None   \n",
+              "          3                                                           None   \n",
+              "21        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "22        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "23        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "24        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "25        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "          1                                                           None   \n",
+              "          2                                                           None   \n",
+              "26        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "27        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "28        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "29        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "30        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "31        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "32        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "33        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "34        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "35        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "36        0              /content/datasets/brain-tumor/images/train/000...   \n",
+              "\n",
+              "                                           cls  \n",
+              "sample_id annotation_id                         \n",
+              "0         0                            [[1.0]]  \n",
+              "1         0                            [[1.0]]  \n",
+              "2         0                     [[1.0], [1.0]]  \n",
+              "          1                               None  \n",
+              "          2                               None  \n",
+              "3         0                            [[1.0]]  \n",
+              "4         0                            [[1.0]]  \n",
+              "5         0                            [[1.0]]  \n",
+              "6         0                            [[1.0]]  \n",
+              "7         0                            [[1.0]]  \n",
+              "8         0                            [[1.0]]  \n",
+              "9         0                            [[1.0]]  \n",
+              "10        0                            [[1.0]]  \n",
+              "11        0                            [[1.0]]  \n",
+              "12        0                            [[1.0]]  \n",
+              "13        0                            [[1.0]]  \n",
+              "14        0                     [[1.0], [1.0]]  \n",
+              "          1                               None  \n",
+              "          2                               None  \n",
+              "15        0                     [[1.0], [1.0]]  \n",
+              "          1                               None  \n",
+              "          2                               None  \n",
+              "16        0                     [[1.0], [1.0]]  \n",
+              "          1                               None  \n",
+              "          2                               None  \n",
+              "17        0                            [[1.0]]  \n",
+              "18        0                            [[1.0]]  \n",
+              "19        0                            [[0.0]]  \n",
+              "20        0              [[0.0], [0.0], [0.0]]  \n",
+              "          1                               None  \n",
+              "          2                               None  \n",
+              "          3                               None  \n",
+              "21        0                            [[0.0]]  \n",
+              "22        0                            [[0.0]]  \n",
+              "23        0                            [[1.0]]  \n",
+              "24        0                            [[1.0]]  \n",
+              "25        0                     [[0.0], [0.0]]  \n",
+              "          1                               None  \n",
+              "          2                               None  \n",
+              "26        0                            [[0.0]]  \n",
+              "27        0                            [[0.0]]  \n",
+              "28        0                            [[1.0]]  \n",
+              "29        0                            [[1.0]]  \n",
+              "30        0                            [[1.0]]  \n",
+              "31        0                            [[1.0]]  \n",
+              "32        0                            [[1.0]]  \n",
+              "33        0                            [[1.0]]  \n",
+              "34        0                            [[1.0]]  \n",
+              "35        0                            [[1.0]]  \n",
+              "36        0                            [[1.0]]  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<a href=\"http://localhost:5173\" target=\"_blank\" style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "from IPython.display import display, HTML\n",
+        "from weightslab.backend.ledgers import get_dataframe\n",
+        "\n",
+        "# `get_df_view()` is the same per-sample DataFrame that powers Studio's grid.\n",
+        "dfm = get_dataframe()\n",
+        "grid = dfm.get_df_view() if hasattr(dfm, \"get_df_view\") else pd.DataFrame()\n",
+        "print(f\"{len(grid)} samples tracked\")\n",
+        "display(grid.head(50))\n",
+        "\n",
+        "# Open the live, interactive grid (with image + bbox previews) in Weights Studio.\n",
+        "# Studio must be running locally (`weightslab start`).\n",
+        "display(HTML(\n",
+        "    '<a href=\"http://localhost:5173\" target=\"_blank\" '\n",
+        "    'style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>'\n",
+        "))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FXQ0gBOSKs7t"
+      },
+      "source": [
+        "> **Can clicking a grid row here jump to that sample in Studio?** Not out of the box today. The notebook and Studio are separate processes, and the link above opens Studio at its root. Per-sample deep-linking (e.g. `http://localhost:5173/?sample=<uid>` selecting and scrolling to that row) is a small frontend addition to Weights Studio — the sample `uid` is already the grid's index here, so the notebook side is ready for it once Studio accepts the query param."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iCI7FL86Ks7v"
+      },
+      "source": [
+        "## Ultralytics Citation\n",
+        "\n",
+        "```bibtex\n",
+        "@dataset{Jocher_Ultralytics_Datasets_2024,\n",
+        "    author = {Jocher, Glenn and Rizwan, Muhammad},\n",
+        "    license = {AGPL-3.0},\n",
+        "    title = {Ultralytics Datasets: Brain-tumor Detection Dataset},\n",
+        "    url = {https://docs.ultralytics.com/datasets/detect/brain-tumor/},\n",
+        "    version = {1.0.0},\n",
+        "    year = {2024}\n",
+        "}\n",
+        "```"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-carparts-segmentation-dataset.ipynb b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-carparts-segmentation-dataset.ipynb
new file mode 100644
index 00000000..152db5b5
--- /dev/null
+++ b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-carparts-segmentation-dataset.ipynb
@@ -0,0 +1,1704 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "accelerator": "GPU"
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-carparts-segmentation-dataset.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Train an <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics YOLO</a> segmentation model on the <b>carparts-seg</b> dataset and stream every sample, prediction and metric live into <a href=\"https://github.com/GrayboxTech/weightslab\">Weights Studio</a> to <b>inspect, edit and evolve</b> your run.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Carparts Segmentation · Ultralytics YOLO + WeightsLab\n",
+    "\n",
+    "This notebook trains a YOLO segmentation model on the [carparts-seg](https://docs.ultralytics.com/) dataset (segmenting individual car body parts) **through the WeightsLab training pipeline**.\n",
+    "\n",
+    "Instead of Ultralytics' one-shot `train`/`predict` flow, we hand training to `WLAwareSegmentationTrainer`. It wraps the train/val dataloaders, logs a **per-sample** signal for every image (loss, prediction stats, tags) and ships live prediction overlays to **Weights Studio** — where you inspect the data grid, remove/weight samples, edit the model and resume, all while training runs.\n",
+    "\n",
+    "Ultralytics auto-downloads the dataset the first time you train."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o7YFKEN1Ks7Y"
+   },
+   "source": [
+    "## 1 · Setup\n",
+    "\n",
+    "Install WeightsLab (which pulls in Ultralytics) and verify the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "71be394c",
+    "outputId": "c9ef4767-84f5-4bea-e395-9e11766002f2",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "source": [
+    "%pip install setuptools wheel"
+   ],
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (75.2.0)\n",
+      "Requirement already satisfied: wheel in /usr/local/lib/python3.12/dist-packages (0.47.0)\n",
+      "Requirement already satisfied: packaging>=24.0 in /usr/local/lib/python3.12/dist-packages (from wheel) (26.2)\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%pip install git+https://github.com/GrayboxTech/weightslab.git@landingcollab"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "jV1oi_PQN0xK",
+    "outputId": "eda8eca1-f4b3-4ffd-eaa6-0f67ce857153"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Collecting git+https://github.com/GrayboxTech/weightslab.git@landingcollab\n",
+      "  Cloning https://github.com/GrayboxTech/weightslab.git (to revision landingcollab) to /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/GrayboxTech/weightslab.git /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git checkout -b landingcollab --track origin/landingcollab\n",
+      "  Switched to a new branch 'landingcollab'\n",
+      "  Branch 'landingcollab' set up to track remote branch 'landingcollab' from 'origin'.\n",
+      "  Resolved https://github.com/GrayboxTech/weightslab.git to commit 3f1cf143bc93e67df43adc71ab831fa71477e0d2\n",
+      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "Requirement already satisfied: numpy<3,>=1.24 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.0.2)\n",
+      "Requirement already satisfied: pandas<3,>=2.2.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.2.2)\n",
+      "Requirement already satisfied: duckdb<2,>=1.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.2)\n",
+      "Requirement already satisfied: PyYAML<7,>=6.0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (6.0.3)\n",
+      "Requirement already satisfied: dill<0.5,>=0.3.8 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.3.8)\n",
+      "Requirement already satisfied: zstandard<1,>=0.22 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.25.0)\n",
+      "Requirement already satisfied: h5py<4,>=3.10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.16.0)\n",
+      "Requirement already satisfied: xxhash<4.1,>=3.4 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.7.1)\n",
+      "Requirement already satisfied: tables<4,>=3.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.10.2)\n",
+      "Requirement already satisfied: torch<=2.9,>=2.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.9.0)\n",
+      "Requirement already satisfied: torchvision<1,>=0.16 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.24.0)\n",
+      "Requirement already satisfied: torchmetrics>=1.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.9.0)\n",
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+      "Requirement already satisfied: protobuf<8,>=5.28.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (5.29.6)\n",
+      "Requirement already satisfied: pydantic<3,>=2.7 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.13.4)\n",
+      "Requirement already satisfied: Pillow<12,>=10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (11.3.0)\n",
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+      "Requirement already satisfied: onnx<=1.20,>=1.15 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.20.0)\n",
+      "Requirement already satisfied: tqdm<5,>=4.66 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (4.67.3)\n",
+      "Requirement already satisfied: python-dotenv<2,>=1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.2.2)\n",
+      "Requirement already satisfied: langchain-core<2,>=0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.4.9)\n",
+      "Requirement already satisfied: langchain-ollama<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.1.0)\n",
+      "Requirement already satisfied: langchain-openai<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.5)\n",
+      "Requirement already satisfied: typing-extensions~=4.12 in /usr/local/lib/python3.12/dist-packages (from grpcio<2,>=1.80->weightslab==1.3.3.dev8) (4.15.0)\n",
+      "Requirement already satisfied: jsonpatch<2.0.0,>=1.33.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.33)\n",
+      "Requirement already satisfied: langchain-protocol>=0.0.17 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.0.18)\n",
+      "Requirement already satisfied: langsmith<1.0.0,>=0.3.45 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.9.1)\n",
+      "Requirement already satisfied: packaging>=23.2.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (26.2)\n",
+      "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (9.1.4)\n",
+      "Requirement already satisfied: uuid-utils<1.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.2)\n",
+      "Requirement already satisfied: ollama<1.0.0,>=0.6.1 in /usr/local/lib/python3.12/dist-packages (from langchain-ollama<2,>=0.2->weightslab==1.3.3.dev8) (0.6.2)\n",
+      "Requirement already satisfied: openai<3.0.0,>=2.45.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2.45.0)\n",
+      "Requirement already satisfied: tiktoken<1.0.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.13.0)\n",
+      "Requirement already satisfied: ml_dtypes>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from onnx<=1.20,>=1.15->weightslab==1.3.3.dev8) (0.5.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2026.2)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.46.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (2.46.4)\n",
+      "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.4.2)\n",
+      "Requirement already satisfied: numexpr>=2.6.2 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (2.14.1)\n",
+      "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (9.0.0)\n",
+      "Requirement already satisfied: blosc2>=2.3.0 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (4.5.1)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.29.4)\n",
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (75.2.0)\n",
+      "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.14.0)\n",
+      "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.6.1)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.1.6)\n",
+      "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2025.3.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (9.10.2.21)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.4.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.3.3.83)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (10.3.9.90)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.7.3.90)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.5.8.93)\n",
+      "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (0.7.1)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2.27.5)\n",
+      "Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.3.20)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.13.1.3)\n",
+      "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.5.0)\n",
+      "Requirement already satisfied: lightning-utilities>=0.15.3 in /usr/local/lib/python3.12/dist-packages (from torchmetrics>=1.9->weightslab==1.3.3.dev8) (0.15.3)\n",
+      "Requirement already satisfied: ndindex in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.10.1)\n",
+      "Requirement already satisfied: msgpack in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.2.1)\n",
+      "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.32.4)\n",
+      "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (13.9.4)\n",
+      "Requirement already satisfied: textual in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (6.2.1)\n",
+      "Requirement already satisfied: textual-plotext in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.0.1)\n",
+      "Requirement already satisfied: threadpoolctl in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (3.6.0)\n",
+      "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.12/dist-packages (from jsonpatch<2.0.0,>=1.33.0->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.1.1)\n",
+      "Requirement already satisfied: anyio>=3.5.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (4.14.0)\n",
+      "Requirement already satisfied: distro>=1.7.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.9.0)\n",
+      "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.28.1)\n",
+      "Requirement already satisfied: orjson>=3.9.14 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.11.9)\n",
+      "Requirement already satisfied: requests-toolbelt>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.0.0)\n",
+      "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.3.1)\n",
+      "Requirement already satisfied: websockets>=15.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (15.0.1)\n",
+      "Requirement already satisfied: jiter<1,>=0.10.0 in /usr/local/lib/python3.12/dist-packages (from openai<3.0.0,>=2.45.0->langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.15.0)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (1.17.0)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.3.0)\n",
+      "Requirement already satisfied: regex in /usr/local/lib/python3.12/dist-packages (from tiktoken<1.0.0,>=0.7.0->langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2025.11.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.0.3)\n",
+      "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.12/dist-packages (from anyio>=3.5.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.18)\n",
+      "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (2026.6.17)\n",
+      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.0.9)\n",
+      "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.0)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (3.4.7)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.5.0)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.2.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.20.0)\n",
+      "Requirement already satisfied: platformdirs<5,>=3.6.0 in /usr/local/lib/python3.12/dist-packages (from textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.10.0)\n",
+      "Requirement already satisfied: plotext<6.0.0,>=5.2.8 in /usr/local/lib/python3.12/dist-packages (from textual-plotext->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (5.3.2)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.1.2)\n",
+      "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.1.0)\n",
+      "Requirement already satisfied: mdit-py-plugins>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.6.1)\n",
+      "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.12/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.0.0)\n",
+      "Building wheels for collected packages: weightslab\n",
+      "  Building wheel for weightslab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Created wheel for weightslab: filename=weightslab-1.3.3.dev8-py3-none-any.whl size=2827715 sha256=5c4e7c14bbf95a48b9a8c86b5a8c744af67e1eb395197cbe79d3e443c21cf86e\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-__zwgf7x/wheels/4d/c7/cd/817c2745790555e7b6c532f5b6055d47567f9416fdfc65879b\n",
+      "Successfully built weightslab\n",
+      "Installing collected packages: weightslab\n",
+      "  Attempting uninstall: weightslab\n",
+      "    Found existing installation: weightslab 1.3.3.dev7\n",
+      "    Uninstalling weightslab-1.3.3.dev7:\n",
+      "      Successfully uninstalled weightslab-1.3.3.dev7\n",
+      "Successfully installed weightslab-1.3.3.dev8\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.colab-display-data+json": {
+       "pip_warning": {
+        "packages": [
+         "weightslab"
+        ]
+       },
+       "id": "b7fce274042740d19087972fe9c01c9b"
+      }
+     },
+     "metadata": {}
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pJhvREbaKs7f",
+    "outputId": "651baa36-9ebb-49da-830b-e6c8a6e2b64f"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Ultralytics 8.4.96 🚀 Python-3.12.13 torch-2.9.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n",
+      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.5/107.7 GB disk)\n",
+      "16/07/2026-09:29:29.683 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmp6meuzqdz/weightslab_logs/weightslab_20260716_092929.log\n",
+      "16/07/2026-09:29:29.685 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+      "16/07/2026-09:29:29.686 INFO:weightslab:<module>: \n",
+      "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+      "│                                                                                                     │\n",
+      "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+      "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+      "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+      "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+      "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+      "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+      "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+      "│                                                                                                     │\n",
+      "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+      "│                                                                                                     │\n",
+      "╰─── By GrayBx, v1.3.3.dev8 ──────────────────────────────────────────────────────────────────────────╯\n",
+      "\n",
+      "\n",
+      "16/07/2026-09:29:29.690 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+    "# and Pillow, so nothing extra is needed here.\n",
+    "# %pip install weightslab\n",
+    "# %pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev8\"\n",
+    "%pip install ultralytics\n",
+    "\n",
+    "import ultralytics\n",
+    "ultralytics.checks()\n",
+    "\n",
+    "import weightslab as wl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 · Dataset\n",
+    "\n",
+    "The dataset is described by a small YAML file that Ultralytics resolves and downloads automatically. For reference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "```yaml\n",
+    "# Ultralytics YOLO 🚀, AGPL-3.0 license\n",
+    "# Carparts-seg dataset by Ultralytics\n",
+    "# Documentation: https://docs.ultralytics.com/datasets/segment/carparts-seg/\n",
+    "# Example usage: yolo train data=carparts-seg.yaml\n",
+    "# parent\n",
+    "# ├── ultralytics\n",
+    "# └── datasets\n",
+    "#     └── carparts-seg  ← downloads here (133 MB)\n",
+    "\n",
+    "# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\n",
+    "path: carparts-seg # dataset root dir\n",
+    "train: images/train # train images (relative to 'path') 3516 images\n",
+    "val: images/val # val images (relative to 'path') 276 images\n",
+    "test: images/test # test images (relative to 'path') 401 images\n",
+    "\n",
+    "# Classes\n",
+    "names:\n",
+    "  0: back_bumper\n",
+    "  1: back_door\n",
+    "  2: back_glass\n",
+    "  3: back_left_door\n",
+    "  4: back_left_light\n",
+    "  5: back_light\n",
+    "  6: back_right_door\n",
+    "  7: back_right_light\n",
+    "  8: front_bumper\n",
+    "  9: front_door\n",
+    "  10: front_glass\n",
+    "  11: front_left_door\n",
+    "  12: front_left_light\n",
+    "  13: front_light\n",
+    "  14: front_right_door\n",
+    "  15: front_right_light\n",
+    "  16: hood\n",
+    "  17: left_mirror\n",
+    "  18: object\n",
+    "  19: right_mirror\n",
+    "  20: tailgate\n",
+    "  21: trunk\n",
+    "  22: wheel\n",
+    "\n",
+    "# Download script/URL (optional)\n",
+    "download: https://github.com/ultralytics/assets/releases/download/v0.0.0/carparts-seg.zip\n",
+    "```"
+   ],
+   "metadata": {
+    "id": "h8go3HNgN0WU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rH79t30YKs7i"
+   },
+   "source": [
+    "## 3 · Train through WeightsLab\n",
+    "\n",
+    "We register the run's hyperparameters with `wl.watch_or_edit(...)` (so they become live-editable from Studio), start the backend services with `wl.serve()`, then hand training to `WLAwareTrainer` via the standard `YOLO(...).train(trainer=...)` entry point.\n",
+    "\n",
+    "> **Connect Weights Studio** — run `weightslab start` locally (opens `http://localhost:5173`) *before or during* training to watch the run live. On **Colab**, serve over a public tunnel instead: use `wl.serve(serving_grpc=True, serving_bore=True)` and connect your local Studio with `weightslab tunnel bore.pub:PORT` (the port is printed when serving starts).\n",
+    "\n",
+    "Data augmentations are disabled so each sample maps cleanly to its ground truth in the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ.setdefault(\"WL_PRELOAD_IMAGE_OVERVIEW\", \"0\")\n",
+    "os.environ.setdefault(\"WEIGHTSLAB_LOG_LEVEL\", \"WARNING\")\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from weightslab.integrations.ultralytics import WLAwareSegmentationTrainer\n",
+    "from ultralytics import YOLO\n",
+    "\n",
+    "# Hyperparameters registered with WeightsLab -> live-editable from Weights Studio.\n",
+    "cfg = {\n",
+    "    \"model\": \"yolo11n-seg.pt\",       # pretrained checkpoint\n",
+    "    \"data\": \"carparts-seg.yaml\",  # Ultralytics auto-downloads\n",
+    "    \"image_size\": 640,\n",
+    "    \"epochs\": 10,\n",
+    "    # Per-sample prediction overlays streamed to Studio (NMS on the train set).\n",
+    "    \"signals_cfg\": {\n",
+    "        \"train_nms\": {\"conf_thres\": 0.25, \"iou_thres\": 0.45, \"max_nms\": 7},\n",
+    "    },\n",
+    "}\n",
+    "wl.watch_or_edit(cfg, flag=\"hyperparameters\", defaults=cfg, poll_interval=1.0)\n",
+    "\n",
+    "# Start the WeightsLab backend services that Weights Studio connects to.\n",
+    "wl.serve(serving_grpc=True)\n",
+    "#   Colab -> local Studio:  wl.serve(serving_grpc=True, serving_bore=True)\n",
+    "\n",
+    "wl.start_training()  # equivalent to pressing \"play\" in Studio\n",
+    "\n",
+    "# Read back the (now live) hyperparameters and hand training to WLAwareSegmentationTrainer.\n",
+    "model = YOLO(cfg[\"model\"])\n",
+    "results = model.train(\n",
+    "    trainer=WLAwareSegmentationTrainer,\n",
+    "    data=str(cfg[\"data\"]),\n",
+    "    imgsz=cfg[\"image_size\"],\n",
+    "    epochs=int(cfg[\"epochs\"]),\n",
+    "    project=\"./wl_logs\", name=\"carparts-seg\",  # -> WL log_dir/name\n",
+    "    workers=0,          # WL invariant (parent-process uid counter)\n",
+    "    optimizer=\"SGD\", lr0=0.001, amp=False,\n",
+    "    # All augmentations off for a clean sample <-> ground-truth association.\n",
+    "    mosaic=0.0, mixup=0.0, copy_paste=0.0,\n",
+    "    hsv_h=0.0, hsv_s=0.0, hsv_v=0.0,\n",
+    "    degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0,\n",
+    "    flipud=0.0, fliplr=0.0, erasing=0.0, auto_augment=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f-Uw_s_8Ks7m"
+   },
+   "source": [
+    "## 4 · Explore the data grid\n",
+    "\n",
+    "The table below is exactly the data behind Weights Studio's **grid explorer**: one row per sample, keyed by split (`origin`) and sample id, with the per-sample loss, prediction stats and tags accumulated during training. This is the ledger that Studio reads from — here we render it inline with pandas.\n",
+    "\n",
+    "The image thumbnails and bounding-box overlays themselves are rendered by **Weights Studio** (which streams full previews over gRPC); the link below opens the interactive grid there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "collapsed": true,
+    "id": "uOJdXGA3Ks7o",
+    "outputId": "26652fa3-6a15-43ce-9d75-034eaba21ccd"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "1241 samples tracked\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                        prediction prediction_raw  \\\n",
+       "sample_id annotation_id                             \n",
+       "0         0                   None           None   \n",
+       "1         0                   None           None   \n",
+       "2         0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "3         0                   None           None   \n",
+       "4         0                   None           None   \n",
+       "5         0                   None           None   \n",
+       "6         0                   None           None   \n",
+       "7         0                   None           None   \n",
+       "8         0                   None           None   \n",
+       "9         0                   None           None   \n",
+       "10        0                   None           None   \n",
+       "11        0                   None           None   \n",
+       "12        0                   None           None   \n",
+       "13        0                   None           None   \n",
+       "14        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "15        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "16        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "17        0                   None           None   \n",
+       "18        0                   None           None   \n",
+       "19        0                   None           None   \n",
+       "20        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "          3                   None           None   \n",
+       "21        0                   None           None   \n",
+       "22        0                   None           None   \n",
+       "23        0                   None           None   \n",
+       "24        0                   None           None   \n",
+       "25        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "26        0                   None           None   \n",
+       "27        0                   None           None   \n",
+       "28        0                   None           None   \n",
+       "29        0                   None           None   \n",
+       "30        0                   None           None   \n",
+       "31        0                   None           None   \n",
+       "32        0                   None           None   \n",
+       "33        0                   None           None   \n",
+       "34        0                   None           None   \n",
+       "35        0                   None           None   \n",
+       "36        0                   None           None   \n",
+       "\n",
+       "                                                                    target  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              [0.2335685, 0.254695, 0.4553995, 0.43075103, 1...   \n",
+       "1         0              [0.251174, 0.24061048, 0.44366202, 0.4307515, ...   \n",
+       "2         0                                                           None   \n",
+       "          1              [0.523474, 0.3978875, 0.634976, 0.48122054, 1....   \n",
+       "          2              [0.40610352, 0.415493, 0.5234745, 0.522301, 1....   \n",
+       "3         0              [0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...   \n",
+       "4         0              [0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...   \n",
+       "5         0              [0.6044605, 0.3990615, 0.67253554, 0.46596247,...   \n",
+       "6         0              [0.410798, 0.4436625, 0.556338, 0.51173747, 1....   \n",
+       "7         0              [0.650235, 0.4166665, 0.73826295, 0.48356748, ...   \n",
+       "8         0              [0.64788747, 0.388498, 0.7582165, 0.49413198, ...   \n",
+       "9         0              [0.6807515, 0.40140802, 0.75704247, 0.45774597...   \n",
+       "10        0              [0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...   \n",
+       "11        0              [0.42018852, 0.34507048, 0.5434275, 0.4941315,...   \n",
+       "12        0              [0.3732395, 0.348592, 0.54812247, 0.46009403, ...   \n",
+       "13        0              [0.37793398, 0.3814555, 0.48122, 0.45892054, 1...   \n",
+       "14        0                                                           None   \n",
+       "          1              [0.43427247, 0.3556335, 0.51173747, 0.43075046...   \n",
+       "          2              [0.4929575, 0.44718304, 0.5375585, 0.485915, 1...   \n",
+       "15        0                                                           None   \n",
+       "          1              [0.629108, 0.39788702, 0.67840403, 0.453051, 1...   \n",
+       "          2              [0.4530515, 0.4107985, 0.58568054, 0.5105635, ...   \n",
+       "16        0                                                           None   \n",
+       "          1              [0.45070451, 0.3967135, 0.5762915, 0.5152585, ...   \n",
+       "          2              [0.63732404, 0.403756, 0.684272, 0.46009403, 1...   \n",
+       "17        0              [0.43779355, 0.3826295, 0.61032856, 0.5223005,...   \n",
+       "18        0              [0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...   \n",
+       "19        0              [0.536385, 0.4495305, 0.600939, 0.49999946, 0....   \n",
+       "20        0                                                           None   \n",
+       "          1              [0.52347445, 0.4424885, 0.6150235, 0.5281695, ...   \n",
+       "          2              [0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...   \n",
+       "          3              [0.504695, 0.44248852, 0.54342705, 0.48004752,...   \n",
+       "21        0              [0.54107946, 0.456573, 0.6103285, 0.511737, 0....   \n",
+       "22        0              [0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...   \n",
+       "23        0              [0.517606, 0.18661949, 0.615024, 0.2875585, 1....   \n",
+       "24        0              [0.51173747, 0.18779299, 0.6126765, 0.308685, ...   \n",
+       "25        0                                                           None   \n",
+       "          1              [0.30985948, 0.3626765, 0.3861505, 0.43896753,...   \n",
+       "          2              [0.41314548, 0.3427225, 0.48122054, 0.42018753...   \n",
+       "26        0              [0.25821602, 0.30046952, 0.46830997, 0.4577465...   \n",
+       "27        0              [0.2734745, 0.2828635, 0.44718352, 0.4577465, ...   \n",
+       "28        0              [0.5915495, 0.23591551, 0.6455405, 0.2887325, ...   \n",
+       "29        0              [0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...   \n",
+       "30        0              [0.40493003, 0.2018775, 0.469484, 0.2699525, 1...   \n",
+       "31        0              [0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...   \n",
+       "32        0              [0.4025825, 0.212441, 0.45539945, 0.269953, 1....   \n",
+       "33        0              [0.2265255, 0.2136145, 0.3438965, 0.30751148, ...   \n",
+       "34        0               [0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]   \n",
+       "35        0              [0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...   \n",
+       "36        0              [0.6185445, 0.30164298, 0.7758215, 0.494131, 1...   \n",
+       "\n",
+       "                        discarded        origin task_type  last_seen group_id  \\\n",
+       "sample_id annotation_id                                                         \n",
+       "0         0                 False  train_loader                 -1.0        0   \n",
+       "1         0                 False  train_loader                 -1.0        1   \n",
+       "2         0                 False  train_loader                 -1.0        2   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "3         0                 False  train_loader                 -1.0        3   \n",
+       "4         0                 False  train_loader                 -1.0        4   \n",
+       "5         0                 False  train_loader                 -1.0        5   \n",
+       "6         0                 False  train_loader                 -1.0        6   \n",
+       "7         0                 False  train_loader                 -1.0        7   \n",
+       "8         0                 False  train_loader                 -1.0        8   \n",
+       "9         0                 False  train_loader                 -1.0        9   \n",
+       "10        0                 False  train_loader                 -1.0       10   \n",
+       "11        0                 False  train_loader                 -1.0       11   \n",
+       "12        0                 False  train_loader                 -1.0       12   \n",
+       "13        0                 False  train_loader                 -1.0       13   \n",
+       "14        0                 False  train_loader                 -1.0       14   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "15        0                 False  train_loader                 -1.0       15   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "16        0                 False  train_loader                 -1.0       16   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "17        0                 False  train_loader                 -1.0       17   \n",
+       "18        0                 False  train_loader                 -1.0       18   \n",
+       "19        0                 False  train_loader                 -1.0       19   \n",
+       "20        0                 False  train_loader                 -1.0       20   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "          3                  None           NaN      None        NaN     None   \n",
+       "21        0                 False  train_loader                 -1.0       21   \n",
+       "22        0                 False  train_loader                 -1.0       22   \n",
+       "23        0                 False  train_loader                 -1.0       23   \n",
+       "24        0                 False  train_loader                 -1.0       24   \n",
+       "25        0                 False  train_loader                 -1.0       25   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "26        0                 False  train_loader                 -1.0       26   \n",
+       "27        0                 False  train_loader                 -1.0       27   \n",
+       "28        0                 False  train_loader                 -1.0       28   \n",
+       "29        0                 False  train_loader                 -1.0       29   \n",
+       "30        0                 False  train_loader                 -1.0       30   \n",
+       "31        0                 False  train_loader                 -1.0       31   \n",
+       "32        0                 False  train_loader                 -1.0       32   \n",
+       "33        0                 False  train_loader                 -1.0       33   \n",
+       "34        0                 False  train_loader                 -1.0       34   \n",
+       "35        0                 False  train_loader                 -1.0       35   \n",
+       "36        0                 False  train_loader                 -1.0       36   \n",
+       "\n",
+       "                         member_rank  \\\n",
+       "sample_id annotation_id                \n",
+       "0         0                      0.0   \n",
+       "1         0                      0.0   \n",
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+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43427247, 0.3556335, 0.51173747, 0.43075046...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4929575, 0.44718304, 0.5375585, 0.485915, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">15</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.629108, 0.39788702, 0.67840403, 0.453051, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4530515, 0.4107985, 0.58568054, 0.5105635, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.45070451, 0.3967135, 0.5762915, 0.5152585, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.63732404, 0.403756, 0.684272, 0.46009403, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43779355, 0.3826295, 0.61032856, 0.5223005,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.536385, 0.4495305, 0.600939, 0.49999946, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">20</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.52347445, 0.4424885, 0.6150235, 0.5281695, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.504695, 0.44248852, 0.54342705, 0.48004752,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.54107946, 0.456573, 0.6103285, 0.511737, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.517606, 0.18661949, 0.615024, 0.2875585, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.51173747, 0.18779299, 0.6126765, 0.308685, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">25</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.30985948, 0.3626765, 0.3861505, 0.43896753,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.41314548, 0.3427225, 0.48122054, 0.42018753...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.25821602, 0.30046952, 0.46830997, 0.4577465...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2734745, 0.2828635, 0.44718352, 0.4577465, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5915495, 0.23591551, 0.6455405, 0.2887325, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.40493003, 0.2018775, 0.469484, 0.2699525, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4025825, 0.212441, 0.45539945, 0.269953, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2265255, 0.2136145, 0.3438965, 0.30751148, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6185445, 0.30164298, 0.7758215, 0.494131, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8adeb57b-4a56-45e8-8485-a1c46d89c532')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "repr_error": "Out of range float values are not JSON compliant: nan"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ],
+      "text/html": [
+       "<a href=\"http://localhost:5173\" target=\"_blank\" style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "from weightslab.backend.ledgers import get_dataframe\n",
+    "\n",
+    "# `get_df_view()` is the same per-sample DataFrame that powers Studio's grid.\n",
+    "dfm = get_dataframe()\n",
+    "grid = dfm.get_df_view() if hasattr(dfm, \"get_df_view\") else pd.DataFrame()\n",
+    "print(f\"{len(grid)} samples tracked\")\n",
+    "display(grid.head(50))\n",
+    "\n",
+    "# Open the live, interactive grid (with image + bbox previews) in Weights Studio.\n",
+    "# Studio must be running locally (`weightslab start`).\n",
+    "display(HTML(\n",
+    "    '<a href=\"http://localhost:5173\" target=\"_blank\" '\n",
+    "    'style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>'\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FXQ0gBOSKs7t"
+   },
+   "source": [
+    "> **Can clicking a grid row here jump to that sample in Studio?** Not out of the box today. The notebook and Studio are separate processes, and the link above opens Studio at its root. Per-sample deep-linking (e.g. `http://localhost:5173/?sample=<uid>` selecting and scrolling to that row) is a small frontend addition to Weights Studio — the sample `uid` is already the grid's index here, so the notebook side is ready for it once Studio accepts the query param."
+   ]
+  }
+ ]
+}
diff --git a/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-construction-ppe-detection-dataset.ipynb b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-construction-ppe-detection-dataset.ipynb
new file mode 100644
index 00000000..db630cac
--- /dev/null
+++ b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-construction-ppe-detection-dataset.ipynb
@@ -0,0 +1,1716 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-construction-ppe-detection-dataset.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Train an <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics YOLO</a> detector on the <b>construction-ppe</b> dataset and stream every sample, prediction and metric live into <a href=\"https://github.com/GrayboxTech/weightslab\">Weights Studio</a> to <b>inspect, edit and evolve</b> your run.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Construction PPE Detection · Ultralytics YOLO + WeightsLab\n",
+    "\n",
+    "This notebook trains a YOLO detector on the [construction-ppe](https://docs.ultralytics.com/) dataset (detecting personal protective equipment (helmets, vests, gloves, …) on construction sites) **through the WeightsLab training pipeline**.\n",
+    "\n",
+    "Instead of Ultralytics' one-shot `train`/`predict` flow, we hand training to `WLAwareTrainer`. It wraps the train/val dataloaders, logs a **per-sample** signal for every image (loss, prediction stats, tags) and ships live prediction overlays to **Weights Studio** — where you inspect the data grid, remove/weight samples, edit the model and resume, all while training runs.\n",
+    "\n",
+    "Ultralytics auto-downloads the dataset the first time you train."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o7YFKEN1Ks7Y"
+   },
+   "source": [
+    "## 1 · Setup\n",
+    "\n",
+    "Install WeightsLab (which pulls in Ultralytics) and verify the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "71be394c",
+    "outputId": "c9ef4767-84f5-4bea-e395-9e11766002f2",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "source": [
+    "%pip install setuptools wheel"
+   ],
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (75.2.0)\n",
+      "Requirement already satisfied: wheel in /usr/local/lib/python3.12/dist-packages (0.47.0)\n",
+      "Requirement already satisfied: packaging>=24.0 in /usr/local/lib/python3.12/dist-packages (from wheel) (26.2)\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%pip install git+https://github.com/GrayboxTech/weightslab.git"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "jV1oi_PQN0xK",
+    "outputId": "eda8eca1-f4b3-4ffd-eaa6-0f67ce857153"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Collecting git+https://github.com/GrayboxTech/weightslab.git\n",
+      "  Cloning https://github.com/GrayboxTech/weightslab.git (to revision landingcollab) to /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/GrayboxTech/weightslab.git /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git checkout -b landingcollab --track origin/landingcollab\n",
+      "  Switched to a new branch 'landingcollab'\n",
+      "  Branch 'landingcollab' set up to track remote branch 'landingcollab' from 'origin'.\n",
+      "  Resolved https://github.com/GrayboxTech/weightslab.git to commit 3f1cf143bc93e67df43adc71ab831fa71477e0d2\n",
+      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "Requirement already satisfied: numpy<3,>=1.24 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.0.2)\n",
+      "Requirement already satisfied: pandas<3,>=2.2.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.2.2)\n",
+      "Requirement already satisfied: duckdb<2,>=1.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.2)\n",
+      "Requirement already satisfied: PyYAML<7,>=6.0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (6.0.3)\n",
+      "Requirement already satisfied: dill<0.5,>=0.3.8 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.3.8)\n",
+      "Requirement already satisfied: zstandard<1,>=0.22 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.25.0)\n",
+      "Requirement already satisfied: h5py<4,>=3.10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.16.0)\n",
+      "Requirement already satisfied: xxhash<4.1,>=3.4 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.7.1)\n",
+      "Requirement already satisfied: tables<4,>=3.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.10.2)\n",
+      "Requirement already satisfied: torch<=2.9,>=2.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.9.0)\n",
+      "Requirement already satisfied: torchvision<1,>=0.16 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.24.0)\n",
+      "Requirement already satisfied: torchmetrics>=1.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.9.0)\n",
+      "Requirement already satisfied: grpcio<2,>=1.80 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.81.1)\n",
+      "Requirement already satisfied: protobuf<8,>=5.28.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (5.29.6)\n",
+      "Requirement already satisfied: pydantic<3,>=2.7 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.13.4)\n",
+      "Requirement already satisfied: Pillow<12,>=10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (11.3.0)\n",
+      "Requirement already satisfied: graphviz<1,>=0.20 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.21)\n",
+      "Requirement already satisfied: onnx<=1.20,>=1.15 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.20.0)\n",
+      "Requirement already satisfied: tqdm<5,>=4.66 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (4.67.3)\n",
+      "Requirement already satisfied: python-dotenv<2,>=1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.2.2)\n",
+      "Requirement already satisfied: langchain-core<2,>=0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.4.9)\n",
+      "Requirement already satisfied: langchain-ollama<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.1.0)\n",
+      "Requirement already satisfied: langchain-openai<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.5)\n",
+      "Requirement already satisfied: typing-extensions~=4.12 in /usr/local/lib/python3.12/dist-packages (from grpcio<2,>=1.80->weightslab==1.3.3.dev8) (4.15.0)\n",
+      "Requirement already satisfied: jsonpatch<2.0.0,>=1.33.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.33)\n",
+      "Requirement already satisfied: langchain-protocol>=0.0.17 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.0.18)\n",
+      "Requirement already satisfied: langsmith<1.0.0,>=0.3.45 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.9.1)\n",
+      "Requirement already satisfied: packaging>=23.2.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (26.2)\n",
+      "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (9.1.4)\n",
+      "Requirement already satisfied: uuid-utils<1.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.2)\n",
+      "Requirement already satisfied: ollama<1.0.0,>=0.6.1 in /usr/local/lib/python3.12/dist-packages (from langchain-ollama<2,>=0.2->weightslab==1.3.3.dev8) (0.6.2)\n",
+      "Requirement already satisfied: openai<3.0.0,>=2.45.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2.45.0)\n",
+      "Requirement already satisfied: tiktoken<1.0.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.13.0)\n",
+      "Requirement already satisfied: ml_dtypes>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from onnx<=1.20,>=1.15->weightslab==1.3.3.dev8) (0.5.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2026.2)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.46.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (2.46.4)\n",
+      "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.4.2)\n",
+      "Requirement already satisfied: numexpr>=2.6.2 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (2.14.1)\n",
+      "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (9.0.0)\n",
+      "Requirement already satisfied: blosc2>=2.3.0 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (4.5.1)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.29.4)\n",
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (75.2.0)\n",
+      "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.14.0)\n",
+      "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.6.1)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.1.6)\n",
+      "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2025.3.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (9.10.2.21)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.4.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.3.3.83)\n",
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+      "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2.27.5)\n",
+      "Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.3.20)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.13.1.3)\n",
+      "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.5.0)\n",
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+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.5.0)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.2.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.20.0)\n",
+      "Requirement already satisfied: platformdirs<5,>=3.6.0 in /usr/local/lib/python3.12/dist-packages (from textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.10.0)\n",
+      "Requirement already satisfied: plotext<6.0.0,>=5.2.8 in /usr/local/lib/python3.12/dist-packages (from textual-plotext->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (5.3.2)\n",
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+      "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.1.0)\n",
+      "Requirement already satisfied: mdit-py-plugins>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.6.1)\n",
+      "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.12/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.0.0)\n",
+      "Building wheels for collected packages: weightslab\n",
+      "  Building wheel for weightslab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Created wheel for weightslab: filename=weightslab-1.3.3.dev8-py3-none-any.whl size=2827715 sha256=5c4e7c14bbf95a48b9a8c86b5a8c744af67e1eb395197cbe79d3e443c21cf86e\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-__zwgf7x/wheels/4d/c7/cd/817c2745790555e7b6c532f5b6055d47567f9416fdfc65879b\n",
+      "Successfully built weightslab\n",
+      "Installing collected packages: weightslab\n",
+      "  Attempting uninstall: weightslab\n",
+      "    Found existing installation: weightslab 1.3.3.dev7\n",
+      "    Uninstalling weightslab-1.3.3.dev7:\n",
+      "      Successfully uninstalled weightslab-1.3.3.dev7\n",
+      "Successfully installed weightslab-1.3.3.dev8\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.colab-display-data+json": {
+       "pip_warning": {
+        "packages": [
+         "weightslab"
+        ]
+       },
+       "id": "b7fce274042740d19087972fe9c01c9b"
+      }
+     },
+     "metadata": {}
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pJhvREbaKs7f",
+    "outputId": "651baa36-9ebb-49da-830b-e6c8a6e2b64f"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Ultralytics 8.4.96 🚀 Python-3.12.13 torch-2.9.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n",
+      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.5/107.7 GB disk)\n",
+      "16/07/2026-09:29:29.683 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmp6meuzqdz/weightslab_logs/weightslab_20260716_092929.log\n",
+      "16/07/2026-09:29:29.685 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+      "16/07/2026-09:29:29.686 INFO:weightslab:<module>: \n",
+      "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+      "│                                                                                                     │\n",
+      "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+      "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+      "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+      "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+      "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+      "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+      "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+      "│                                                                                                     │\n",
+      "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+      "│                                                                                                     │\n",
+      "╰─── By GrayBx, v1.3.3.dev8 ──────────────────────────────────────────────────────────────────────────╯\n",
+      "\n",
+      "\n",
+      "16/07/2026-09:29:29.690 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+    "# and Pillow, so nothing extra is needed here.\n",
+    "# %pip install weightslab\n",
+    "# %pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev8\"\n",
+    "%pip install ultralytics\n",
+    "\n",
+    "import ultralytics\n",
+    "ultralytics.checks()\n",
+    "\n",
+    "import weightslab as wl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 · Dataset\n",
+    "\n",
+    "The dataset is described by a small YAML file that Ultralytics resolves and downloads automatically. For reference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h8go3HNgN0WU"
+   },
+   "source": [
+    "```yaml\n",
+    "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n",
+    "\n",
+    "# Construction-PPE dataset by Ultralytics\n",
+    "# Documentation: https://docs.ultralytics.com/datasets/detect/construction-ppe/\n",
+    "# Example usage: yolo train data=construction-ppe.yaml\n",
+    "# parent\n",
+    "# ├── ultralytics\n",
+    "# └── datasets\n",
+    "#     └── construction-ppe ← downloads here (178.4 MB)\n",
+    "\n",
+    "# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\n",
+    "path: construction-ppe # dataset root dir\n",
+    "train: images/train # train images (relative to 'path') 1132 images\n",
+    "val: images/val # val images (relative to 'path') 143 images\n",
+    "test: images/test # test images (relative to 'path') 141 images\n",
+    "\n",
+    "# Classes\n",
+    "names:\n",
+    "  0: helmet\n",
+    "  1: gloves\n",
+    "  2: vest\n",
+    "  3: boots\n",
+    "  4: goggles\n",
+    "  5: none\n",
+    "  6: Person\n",
+    "  7: no_helmet\n",
+    "  8: no_goggle\n",
+    "  9: no_gloves\n",
+    "  10: no_boots\n",
+    "\n",
+    "# Download script/URL (optional)\n",
+    "download: https://github.com/ultralytics/assets/releases/download/v0.0.0/construction-ppe.zip\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rH79t30YKs7i"
+   },
+   "source": [
+    "## 3 · Train through WeightsLab\n",
+    "\n",
+    "We register the run's hyperparameters with `wl.watch_or_edit(...)` (so they become live-editable from Studio), start the backend services with `wl.serve()`, then hand training to `WLAwareTrainer` via the standard `YOLO(...).train(trainer=...)` entry point.\n",
+    "\n",
+    "> **Connect Weights Studio** — run `weightslab start` locally (opens `http://localhost:5173`) *before or during* training to watch the run live. On **Colab**, serve over a public tunnel instead: use `wl.serve(serving_grpc=True, serving_bore=True)` and connect your local Studio with `weightslab tunnel bore.pub:PORT` (the port is printed when serving starts).\n",
+    "\n",
+    "Data augmentations are disabled so each sample maps cleanly to its ground truth in the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ.setdefault(\"WL_PRELOAD_IMAGE_OVERVIEW\", \"0\")\n",
+    "os.environ.setdefault(\"WEIGHTSLAB_LOG_LEVEL\", \"WARNING\")\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from weightslab.integrations.ultralytics import WLAwareTrainer\n",
+    "from ultralytics import YOLO\n",
+    "\n",
+    "# Hyperparameters registered with WeightsLab -> live-editable from Weights Studio.\n",
+    "cfg = {\n",
+    "    \"model\": \"yolo11n.pt\",       # pretrained checkpoint\n",
+    "    \"data\": \"construction-ppe.yaml\",  # Ultralytics auto-downloads\n",
+    "    \"image_size\": 640,\n",
+    "    \"epochs\": 5,\n",
+    "    # Per-sample prediction overlays streamed to Studio (NMS on the train set).\n",
+    "    \"signals_cfg\": {\n",
+    "        \"train_nms\": {\"conf_thres\": 0.25, \"iou_thres\": 0.45, \"max_nms\": 7},\n",
+    "    },\n",
+    "}\n",
+    "wl.watch_or_edit(cfg, flag=\"hyperparameters\", defaults=cfg, poll_interval=1.0)\n",
+    "\n",
+    "# Start the WeightsLab backend services that Weights Studio connects to.\n",
+    "wl.serve(serving_grpc=True)\n",
+    "#   Colab -> local Studio:  wl.serve(serving_grpc=True, serving_bore=True)\n",
+    "\n",
+    "wl.start_training()  # equivalent to pressing \"play\" in Studio\n",
+    "\n",
+    "# Read back the (now live) hyperparameters and hand training to WLAwareTrainer.\n",
+    "model = YOLO(cfg[\"model\"])\n",
+    "results = model.train(\n",
+    "    trainer=WLAwareTrainer,\n",
+    "    data=str(cfg[\"data\"]),\n",
+    "    imgsz=cfg[\"image_size\"],\n",
+    "    epochs=int(cfg[\"epochs\"]),\n",
+    "    project=\"./wl_logs\", name=\"construction-ppe\",  # -> WL log_dir/name\n",
+    "    workers=0,          # WL invariant (parent-process uid counter)\n",
+    "    optimizer=\"SGD\", lr0=0.001, amp=False,\n",
+    "    # All augmentations off for a clean sample <-> ground-truth association.\n",
+    "    mosaic=0.0, mixup=0.0, copy_paste=0.0,\n",
+    "    hsv_h=0.0, hsv_s=0.0, hsv_v=0.0,\n",
+    "    degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0,\n",
+    "    flipud=0.0, fliplr=0.0, erasing=0.0, auto_augment=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f-Uw_s_8Ks7m"
+   },
+   "source": [
+    "## 4 · Explore the data grid\n",
+    "\n",
+    "The table below is exactly the data behind Weights Studio's **grid explorer**: one row per sample, keyed by split (`origin`) and sample id, with the per-sample loss, prediction stats and tags accumulated during training. This is the ledger that Studio reads from — here we render it inline with pandas.\n",
+    "\n",
+    "The image thumbnails and bounding-box overlays themselves are rendered by **Weights Studio** (which streams full previews over gRPC); the link below opens the interactive grid there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "collapsed": true,
+    "id": "uOJdXGA3Ks7o",
+    "outputId": "26652fa3-6a15-43ce-9d75-034eaba21ccd"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "1241 samples tracked\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                        prediction prediction_raw  \\\n",
+       "sample_id annotation_id                             \n",
+       "0         0                   None           None   \n",
+       "1         0                   None           None   \n",
+       "2         0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "3         0                   None           None   \n",
+       "4         0                   None           None   \n",
+       "5         0                   None           None   \n",
+       "6         0                   None           None   \n",
+       "7         0                   None           None   \n",
+       "8         0                   None           None   \n",
+       "9         0                   None           None   \n",
+       "10        0                   None           None   \n",
+       "11        0                   None           None   \n",
+       "12        0                   None           None   \n",
+       "13        0                   None           None   \n",
+       "14        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "15        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "16        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "17        0                   None           None   \n",
+       "18        0                   None           None   \n",
+       "19        0                   None           None   \n",
+       "20        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "          3                   None           None   \n",
+       "21        0                   None           None   \n",
+       "22        0                   None           None   \n",
+       "23        0                   None           None   \n",
+       "24        0                   None           None   \n",
+       "25        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "26        0                   None           None   \n",
+       "27        0                   None           None   \n",
+       "28        0                   None           None   \n",
+       "29        0                   None           None   \n",
+       "30        0                   None           None   \n",
+       "31        0                   None           None   \n",
+       "32        0                   None           None   \n",
+       "33        0                   None           None   \n",
+       "34        0                   None           None   \n",
+       "35        0                   None           None   \n",
+       "36        0                   None           None   \n",
+       "\n",
+       "                                                                    target  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              [0.2335685, 0.254695, 0.4553995, 0.43075103, 1...   \n",
+       "1         0              [0.251174, 0.24061048, 0.44366202, 0.4307515, ...   \n",
+       "2         0                                                           None   \n",
+       "          1              [0.523474, 0.3978875, 0.634976, 0.48122054, 1....   \n",
+       "          2              [0.40610352, 0.415493, 0.5234745, 0.522301, 1....   \n",
+       "3         0              [0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...   \n",
+       "4         0              [0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...   \n",
+       "5         0              [0.6044605, 0.3990615, 0.67253554, 0.46596247,...   \n",
+       "6         0              [0.410798, 0.4436625, 0.556338, 0.51173747, 1....   \n",
+       "7         0              [0.650235, 0.4166665, 0.73826295, 0.48356748, ...   \n",
+       "8         0              [0.64788747, 0.388498, 0.7582165, 0.49413198, ...   \n",
+       "9         0              [0.6807515, 0.40140802, 0.75704247, 0.45774597...   \n",
+       "10        0              [0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...   \n",
+       "11        0              [0.42018852, 0.34507048, 0.5434275, 0.4941315,...   \n",
+       "12        0              [0.3732395, 0.348592, 0.54812247, 0.46009403, ...   \n",
+       "13        0              [0.37793398, 0.3814555, 0.48122, 0.45892054, 1...   \n",
+       "14        0                                                           None   \n",
+       "          1              [0.43427247, 0.3556335, 0.51173747, 0.43075046...   \n",
+       "          2              [0.4929575, 0.44718304, 0.5375585, 0.485915, 1...   \n",
+       "15        0                                                           None   \n",
+       "          1              [0.629108, 0.39788702, 0.67840403, 0.453051, 1...   \n",
+       "          2              [0.4530515, 0.4107985, 0.58568054, 0.5105635, ...   \n",
+       "16        0                                                           None   \n",
+       "          1              [0.45070451, 0.3967135, 0.5762915, 0.5152585, ...   \n",
+       "          2              [0.63732404, 0.403756, 0.684272, 0.46009403, 1...   \n",
+       "17        0              [0.43779355, 0.3826295, 0.61032856, 0.5223005,...   \n",
+       "18        0              [0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...   \n",
+       "19        0              [0.536385, 0.4495305, 0.600939, 0.49999946, 0....   \n",
+       "20        0                                                           None   \n",
+       "          1              [0.52347445, 0.4424885, 0.6150235, 0.5281695, ...   \n",
+       "          2              [0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...   \n",
+       "          3              [0.504695, 0.44248852, 0.54342705, 0.48004752,...   \n",
+       "21        0              [0.54107946, 0.456573, 0.6103285, 0.511737, 0....   \n",
+       "22        0              [0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...   \n",
+       "23        0              [0.517606, 0.18661949, 0.615024, 0.2875585, 1....   \n",
+       "24        0              [0.51173747, 0.18779299, 0.6126765, 0.308685, ...   \n",
+       "25        0                                                           None   \n",
+       "          1              [0.30985948, 0.3626765, 0.3861505, 0.43896753,...   \n",
+       "          2              [0.41314548, 0.3427225, 0.48122054, 0.42018753...   \n",
+       "26        0              [0.25821602, 0.30046952, 0.46830997, 0.4577465...   \n",
+       "27        0              [0.2734745, 0.2828635, 0.44718352, 0.4577465, ...   \n",
+       "28        0              [0.5915495, 0.23591551, 0.6455405, 0.2887325, ...   \n",
+       "29        0              [0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...   \n",
+       "30        0              [0.40493003, 0.2018775, 0.469484, 0.2699525, 1...   \n",
+       "31        0              [0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...   \n",
+       "32        0              [0.4025825, 0.212441, 0.45539945, 0.269953, 1....   \n",
+       "33        0              [0.2265255, 0.2136145, 0.3438965, 0.30751148, ...   \n",
+       "34        0               [0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]   \n",
+       "35        0              [0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...   \n",
+       "36        0              [0.6185445, 0.30164298, 0.7758215, 0.494131, 1...   \n",
+       "\n",
+       "                        discarded        origin task_type  last_seen group_id  \\\n",
+       "sample_id annotation_id                                                         \n",
+       "0         0                 False  train_loader                 -1.0        0   \n",
+       "1         0                 False  train_loader                 -1.0        1   \n",
+       "2         0                 False  train_loader                 -1.0        2   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "3         0                 False  train_loader                 -1.0        3   \n",
+       "4         0                 False  train_loader                 -1.0        4   \n",
+       "5         0                 False  train_loader                 -1.0        5   \n",
+       "6         0                 False  train_loader                 -1.0        6   \n",
+       "7         0                 False  train_loader                 -1.0        7   \n",
+       "8         0                 False  train_loader                 -1.0        8   \n",
+       "9         0                 False  train_loader                 -1.0        9   \n",
+       "10        0                 False  train_loader                 -1.0       10   \n",
+       "11        0                 False  train_loader                 -1.0       11   \n",
+       "12        0                 False  train_loader                 -1.0       12   \n",
+       "13        0                 False  train_loader                 -1.0       13   \n",
+       "14        0                 False  train_loader                 -1.0       14   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "15        0                 False  train_loader                 -1.0       15   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "16        0                 False  train_loader                 -1.0       16   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "17        0                 False  train_loader                 -1.0       17   \n",
+       "18        0                 False  train_loader                 -1.0       18   \n",
+       "19        0                 False  train_loader                 -1.0       19   \n",
+       "20        0                 False  train_loader                 -1.0       20   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "          3                  None           NaN      None        NaN     None   \n",
+       "21        0                 False  train_loader                 -1.0       21   \n",
+       "22        0                 False  train_loader                 -1.0       22   \n",
+       "23        0                 False  train_loader                 -1.0       23   \n",
+       "24        0                 False  train_loader                 -1.0       24   \n",
+       "25        0                 False  train_loader                 -1.0       25   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "26        0                 False  train_loader                 -1.0       26   \n",
+       "27        0                 False  train_loader                 -1.0       27   \n",
+       "28        0                 False  train_loader                 -1.0       28   \n",
+       "29        0                 False  train_loader                 -1.0       29   \n",
+       "30        0                 False  train_loader                 -1.0       30   \n",
+       "31        0                 False  train_loader                 -1.0       31   \n",
+       "32        0                 False  train_loader                 -1.0       32   \n",
+       "33        0                 False  train_loader                 -1.0       33   \n",
+       "34        0                 False  train_loader                 -1.0       34   \n",
+       "35        0                 False  train_loader                 -1.0       35   \n",
+       "36        0                 False  train_loader                 -1.0       36   \n",
+       "\n",
+       "                         member_rank  \\\n",
+       "sample_id annotation_id                \n",
+       "0         0                      0.0   \n",
+       "1         0                      0.0   \n",
+       "2         0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "3         0                      0.0   \n",
+       "4         0                      0.0   \n",
+       "5         0                      0.0   \n",
+       "6         0                      0.0   \n",
+       "7         0                      0.0   \n",
+       "8         0                      0.0   \n",
+       "9         0                      0.0   \n",
+       "10        0                      0.0   \n",
+       "11        0                      0.0   \n",
+       "12        0                      0.0   \n",
+       "13        0                      0.0   \n",
+       "14        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "15        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "16        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "17        0                      0.0   \n",
+       "18        0                      0.0   \n",
+       "19        0                      0.0   \n",
+       "20        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "          3                      NaN   \n",
+       "21        0                      0.0   \n",
+       "22        0                      0.0   \n",
+       "23        0                      0.0   \n",
+       "24        0                      0.0   \n",
+       "25        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "26        0                      0.0   \n",
+       "27        0                      0.0   \n",
+       "28        0                      0.0   \n",
+       "29        0                      0.0   \n",
+       "30        0                      0.0   \n",
+       "31        0                      0.0   \n",
+       "32        0                      0.0   \n",
+       "33        0                      0.0   \n",
+       "34        0                      0.0   \n",
+       "35        0                      0.0   \n",
+       "36        0                      0.0   \n",
+       "\n",
+       "                                                                  img_path  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "1         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "2         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "3         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "4         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "5         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "6         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "7         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "8         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "9         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "10        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "11        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "12        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "13        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "14        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "15        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "16        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "17        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "18        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "19        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "20        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "          3                                                           None   \n",
+       "21        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "22        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "23        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "24        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "25        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "26        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "27        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "28        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "29        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "30        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "31        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "32        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "33        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "34        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "35        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "36        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "\n",
+       "                                           cls  \n",
+       "sample_id annotation_id                         \n",
+       "0         0                            [[1.0]]  \n",
+       "1         0                            [[1.0]]  \n",
+       "2         0                     [[1.0], [1.0]]  \n",
+       "          1                               None  \n",
+       "          2                               None  \n",
+       "3         0                            [[1.0]]  \n",
+       "4         0                            [[1.0]]  \n",
+       "5         0                            [[1.0]]  \n",
+       "6         0                            [[1.0]]  \n",
+       "7         0                            [[1.0]]  \n",
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+       "13        0                            [[1.0]]  \n",
+       "14        0                     [[1.0], [1.0]]  \n",
+       "          1                               None  \n",
+       "          2                               None  \n",
+       "15        0                     [[1.0], [1.0]]  \n",
+       "          1                               None  \n",
+       "          2                               None  \n",
+       "16        0                     [[1.0], [1.0]]  \n",
+       "          1                               None  \n",
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+       "          1                               None  \n",
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+       "25        0                     [[0.0], [0.0]]  \n",
+       "          1                               None  \n",
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+       "36        0                            [[1.0]]  "
+      ],
+      "text/html": [
+       "\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>prediction_raw</th>\n",
+       "      <th>target</th>\n",
+       "      <th>discarded</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>task_type</th>\n",
+       "      <th>last_seen</th>\n",
+       "      <th>group_id</th>\n",
+       "      <th>member_rank</th>\n",
+       "      <th>img_path</th>\n",
+       "      <th>cls</th>\n",
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+       "      <th>sample_id</th>\n",
+       "      <th>annotation_id</th>\n",
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+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2335685, 0.254695, 0.4553995, 0.43075103, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.251174, 0.24061048, 0.44366202, 0.4307515, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.523474, 0.3978875, 0.634976, 0.48122054, 1....</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.40610352, 0.415493, 0.5234745, 0.522301, 1....</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6044605, 0.3990615, 0.67253554, 0.46596247,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.410798, 0.4436625, 0.556338, 0.51173747, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.650235, 0.4166665, 0.73826295, 0.48356748, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.64788747, 0.388498, 0.7582165, 0.49413198, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6807515, 0.40140802, 0.75704247, 0.45774597...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.42018852, 0.34507048, 0.5434275, 0.4941315,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3732395, 0.348592, 0.54812247, 0.46009403, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.37793398, 0.3814555, 0.48122, 0.45892054, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43427247, 0.3556335, 0.51173747, 0.43075046...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4929575, 0.44718304, 0.5375585, 0.485915, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">15</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.629108, 0.39788702, 0.67840403, 0.453051, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4530515, 0.4107985, 0.58568054, 0.5105635, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.45070451, 0.3967135, 0.5762915, 0.5152585, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.63732404, 0.403756, 0.684272, 0.46009403, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43779355, 0.3826295, 0.61032856, 0.5223005,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.536385, 0.4495305, 0.600939, 0.49999946, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">20</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.52347445, 0.4424885, 0.6150235, 0.5281695, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.504695, 0.44248852, 0.54342705, 0.48004752,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.54107946, 0.456573, 0.6103285, 0.511737, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.517606, 0.18661949, 0.615024, 0.2875585, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.51173747, 0.18779299, 0.6126765, 0.308685, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">25</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.30985948, 0.3626765, 0.3861505, 0.43896753,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.41314548, 0.3427225, 0.48122054, 0.42018753...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.25821602, 0.30046952, 0.46830997, 0.4577465...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2734745, 0.2828635, 0.44718352, 0.4577465, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5915495, 0.23591551, 0.6455405, 0.2887325, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.40493003, 0.2018775, 0.469484, 0.2699525, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4025825, 0.212441, 0.45539945, 0.269953, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2265255, 0.2136145, 0.3438965, 0.30751148, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6185445, 0.30164298, 0.7758215, 0.494131, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8adeb57b-4a56-45e8-8485-a1c46d89c532')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "repr_error": "Out of range float values are not JSON compliant: nan"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ],
+      "text/html": [
+       "<a href=\"http://localhost:5173\" target=\"_blank\" style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "from weightslab.backend.ledgers import get_dataframe\n",
+    "\n",
+    "# `get_df_view()` is the same per-sample DataFrame that powers Studio's grid.\n",
+    "dfm = get_dataframe()\n",
+    "grid = dfm.get_df_view() if hasattr(dfm, \"get_df_view\") else pd.DataFrame()\n",
+    "print(f\"{len(grid)} samples tracked\")\n",
+    "display(grid.head(50))\n",
+    "\n",
+    "# Open the live, interactive grid (with image + bbox previews) in Weights Studio.\n",
+    "# Studio must be running locally (`weightslab start`).\n",
+    "display(HTML(\n",
+    "    '<a href=\"http://localhost:5173\" target=\"_blank\" '\n",
+    "    'style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>'\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FXQ0gBOSKs7t"
+   },
+   "source": [
+    "> **Can clicking a grid row here jump to that sample in Studio?** Not out of the box today. The notebook and Studio are separate processes, and the link above opens Studio at its root. Per-sample deep-linking (e.g. `http://localhost:5173/?sample=<uid>` selecting and scrolling to that row) is a small frontend addition to Weights Studio — the sample `uid` is already the grid's index here, so the notebook side is ready for it once Studio accepts the query param."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vlHp09Nueb3d"
+   },
+   "source": [
+    "## Citation,License and Attribution\n",
+    "\n",
+    "```bibtex\n",
+    "@dataset{Dalvi_Construction_PPE_Dataset_2025,\n",
+    "    author = {Mrunmayee Dalvi and Niyati Singh and Sahil Bhingarde and Ketaki Chalke},\n",
+    "    title = {Construction-PPE: Personal Protective Equipment Detection Dataset},\n",
+    "    month = {January},\n",
+    "    year = {2025},\n",
+    "    version = {1.0.0},\n",
+    "    license = {AGPL-3.0},\n",
+    "    url = {https://docs.ultralytics.com/datasets/detect/construction-ppe/},\n",
+    "    publisher = {Ultralytics}\n",
+    "}\n",
+    "```\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-crack-segmentation-dataset.ipynb b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-crack-segmentation-dataset.ipynb
new file mode 100644
index 00000000..1b189b0c
--- /dev/null
+++ b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-crack-segmentation-dataset.ipynb
@@ -0,0 +1,1684 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "accelerator": "GPU"
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-crack-segmentation-dataset.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Train an <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics YOLO</a> segmentation model on the <b>crack-seg</b> dataset and stream every sample, prediction and metric live into <a href=\"https://github.com/GrayboxTech/weightslab\">Weights Studio</a> to <b>inspect, edit and evolve</b> your run.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Crack Segmentation · Ultralytics YOLO + WeightsLab\n",
+    "\n",
+    "This notebook trains a YOLO segmentation model on the [crack-seg](https://docs.ultralytics.com/) dataset (segmenting cracks in concrete/asphalt for infrastructure inspection) **through the WeightsLab training pipeline**.\n",
+    "\n",
+    "Instead of Ultralytics' one-shot `train`/`predict` flow, we hand training to `WLAwareSegmentationTrainer`. It wraps the train/val dataloaders, logs a **per-sample** signal for every image (loss, prediction stats, tags) and ships live prediction overlays to **Weights Studio** — where you inspect the data grid, remove/weight samples, edit the model and resume, all while training runs.\n",
+    "\n",
+    "Ultralytics auto-downloads the dataset the first time you train."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o7YFKEN1Ks7Y"
+   },
+   "source": [
+    "## 1 · Setup\n",
+    "\n",
+    "Install WeightsLab (which pulls in Ultralytics) and verify the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "71be394c",
+    "outputId": "c9ef4767-84f5-4bea-e395-9e11766002f2",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "source": [
+    "%pip install setuptools wheel"
+   ],
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (75.2.0)\n",
+      "Requirement already satisfied: wheel in /usr/local/lib/python3.12/dist-packages (0.47.0)\n",
+      "Requirement already satisfied: packaging>=24.0 in /usr/local/lib/python3.12/dist-packages (from wheel) (26.2)\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%pip install git+https://github.com/GrayboxTech/weightslab.git"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "jV1oi_PQN0xK",
+    "outputId": "eda8eca1-f4b3-4ffd-eaa6-0f67ce857153"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Collecting git+https://github.com/GrayboxTech/weightslab.git\n",
+      "  Cloning https://github.com/GrayboxTech/weightslab.git (to revision landingcollab) to /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/GrayboxTech/weightslab.git /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git checkout -b landingcollab --track origin/landingcollab\n",
+      "  Switched to a new branch 'landingcollab'\n",
+      "  Branch 'landingcollab' set up to track remote branch 'landingcollab' from 'origin'.\n",
+      "  Resolved https://github.com/GrayboxTech/weightslab.git to commit 3f1cf143bc93e67df43adc71ab831fa71477e0d2\n",
+      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "Requirement already satisfied: numpy<3,>=1.24 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.0.2)\n",
+      "Requirement already satisfied: pandas<3,>=2.2.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.2.2)\n",
+      "Requirement already satisfied: duckdb<2,>=1.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.2)\n",
+      "Requirement already satisfied: PyYAML<7,>=6.0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (6.0.3)\n",
+      "Requirement already satisfied: dill<0.5,>=0.3.8 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.3.8)\n",
+      "Requirement already satisfied: zstandard<1,>=0.22 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.25.0)\n",
+      "Requirement already satisfied: h5py<4,>=3.10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.16.0)\n",
+      "Requirement already satisfied: xxhash<4.1,>=3.4 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.7.1)\n",
+      "Requirement already satisfied: tables<4,>=3.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.10.2)\n",
+      "Requirement already satisfied: torch<=2.9,>=2.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.9.0)\n",
+      "Requirement already satisfied: torchvision<1,>=0.16 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.24.0)\n",
+      "Requirement already satisfied: torchmetrics>=1.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.9.0)\n",
+      "Requirement already satisfied: grpcio<2,>=1.80 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.81.1)\n",
+      "Requirement already satisfied: protobuf<8,>=5.28.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (5.29.6)\n",
+      "Requirement already satisfied: pydantic<3,>=2.7 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.13.4)\n",
+      "Requirement already satisfied: Pillow<12,>=10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (11.3.0)\n",
+      "Requirement already satisfied: graphviz<1,>=0.20 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.21)\n",
+      "Requirement already satisfied: onnx<=1.20,>=1.15 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.20.0)\n",
+      "Requirement already satisfied: tqdm<5,>=4.66 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (4.67.3)\n",
+      "Requirement already satisfied: python-dotenv<2,>=1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.2.2)\n",
+      "Requirement already satisfied: langchain-core<2,>=0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.4.9)\n",
+      "Requirement already satisfied: langchain-ollama<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.1.0)\n",
+      "Requirement already satisfied: langchain-openai<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.5)\n",
+      "Requirement already satisfied: typing-extensions~=4.12 in /usr/local/lib/python3.12/dist-packages (from grpcio<2,>=1.80->weightslab==1.3.3.dev8) (4.15.0)\n",
+      "Requirement already satisfied: jsonpatch<2.0.0,>=1.33.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.33)\n",
+      "Requirement already satisfied: langchain-protocol>=0.0.17 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.0.18)\n",
+      "Requirement already satisfied: langsmith<1.0.0,>=0.3.45 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.9.1)\n",
+      "Requirement already satisfied: packaging>=23.2.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (26.2)\n",
+      "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (9.1.4)\n",
+      "Requirement already satisfied: uuid-utils<1.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.2)\n",
+      "Requirement already satisfied: ollama<1.0.0,>=0.6.1 in /usr/local/lib/python3.12/dist-packages (from langchain-ollama<2,>=0.2->weightslab==1.3.3.dev8) (0.6.2)\n",
+      "Requirement already satisfied: openai<3.0.0,>=2.45.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2.45.0)\n",
+      "Requirement already satisfied: tiktoken<1.0.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.13.0)\n",
+      "Requirement already satisfied: ml_dtypes>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from onnx<=1.20,>=1.15->weightslab==1.3.3.dev8) (0.5.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2026.2)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.46.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (2.46.4)\n",
+      "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.4.2)\n",
+      "Requirement already satisfied: numexpr>=2.6.2 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (2.14.1)\n",
+      "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (9.0.0)\n",
+      "Requirement already satisfied: blosc2>=2.3.0 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (4.5.1)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.29.4)\n",
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (75.2.0)\n",
+      "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.14.0)\n",
+      "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.6.1)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.1.6)\n",
+      "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2025.3.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (9.10.2.21)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.4.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.3.3.83)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (10.3.9.90)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.7.3.90)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.5.8.93)\n",
+      "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (0.7.1)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2.27.5)\n",
+      "Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.3.20)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.13.1.3)\n",
+      "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.5.0)\n",
+      "Requirement already satisfied: lightning-utilities>=0.15.3 in /usr/local/lib/python3.12/dist-packages (from torchmetrics>=1.9->weightslab==1.3.3.dev8) (0.15.3)\n",
+      "Requirement already satisfied: ndindex in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.10.1)\n",
+      "Requirement already satisfied: msgpack in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.2.1)\n",
+      "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.32.4)\n",
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+      "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.12/dist-packages (from jsonpatch<2.0.0,>=1.33.0->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.1.1)\n",
+      "Requirement already satisfied: anyio>=3.5.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (4.14.0)\n",
+      "Requirement already satisfied: distro>=1.7.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.9.0)\n",
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+      "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.12/dist-packages (from anyio>=3.5.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.18)\n",
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+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.20.0)\n",
+      "Requirement already satisfied: platformdirs<5,>=3.6.0 in /usr/local/lib/python3.12/dist-packages (from textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.10.0)\n",
+      "Requirement already satisfied: plotext<6.0.0,>=5.2.8 in /usr/local/lib/python3.12/dist-packages (from textual-plotext->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (5.3.2)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.1.2)\n",
+      "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.1.0)\n",
+      "Requirement already satisfied: mdit-py-plugins>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.6.1)\n",
+      "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.12/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.0.0)\n",
+      "Building wheels for collected packages: weightslab\n",
+      "  Building wheel for weightslab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Created wheel for weightslab: filename=weightslab-1.3.3.dev8-py3-none-any.whl size=2827715 sha256=5c4e7c14bbf95a48b9a8c86b5a8c744af67e1eb395197cbe79d3e443c21cf86e\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-__zwgf7x/wheels/4d/c7/cd/817c2745790555e7b6c532f5b6055d47567f9416fdfc65879b\n",
+      "Successfully built weightslab\n",
+      "Installing collected packages: weightslab\n",
+      "  Attempting uninstall: weightslab\n",
+      "    Found existing installation: weightslab 1.3.3.dev7\n",
+      "    Uninstalling weightslab-1.3.3.dev7:\n",
+      "      Successfully uninstalled weightslab-1.3.3.dev7\n",
+      "Successfully installed weightslab-1.3.3.dev8\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.colab-display-data+json": {
+       "pip_warning": {
+        "packages": [
+         "weightslab"
+        ]
+       },
+       "id": "b7fce274042740d19087972fe9c01c9b"
+      }
+     },
+     "metadata": {}
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pJhvREbaKs7f",
+    "outputId": "651baa36-9ebb-49da-830b-e6c8a6e2b64f"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Ultralytics 8.4.96 🚀 Python-3.12.13 torch-2.9.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n",
+      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.5/107.7 GB disk)\n",
+      "16/07/2026-09:29:29.683 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmp6meuzqdz/weightslab_logs/weightslab_20260716_092929.log\n",
+      "16/07/2026-09:29:29.685 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+      "16/07/2026-09:29:29.686 INFO:weightslab:<module>: \n",
+      "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+      "│                                                                                                     │\n",
+      "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+      "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+      "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+      "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+      "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+      "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+      "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+      "│                                                                                                     │\n",
+      "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+      "│                                                                                                     │\n",
+      "╰─── By GrayBx, v1.3.3.dev8 ──────────────────────────────────────────────────────────────────────────╯\n",
+      "\n",
+      "\n",
+      "16/07/2026-09:29:29.690 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+    "# and Pillow, so nothing extra is needed here.\n",
+    "# %pip install weightslab\n",
+    "# %pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev8\"\n",
+    "%pip install ultralytics\n",
+    "\n",
+    "import ultralytics\n",
+    "ultralytics.checks()\n",
+    "\n",
+    "import weightslab as wl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 · Dataset\n",
+    "\n",
+    "The dataset is described by a small YAML file that Ultralytics resolves and downloads automatically. For reference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "```yaml\n",
+    "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n",
+    "\n",
+    "# Crack-seg dataset by Ultralytics\n",
+    "# Documentation: https://docs.ultralytics.com/datasets/segment/crack-seg/\n",
+    "# Example usage: yolo train data=crack-seg.yaml\n",
+    "# parent\n",
+    "# ├── ultralytics\n",
+    "# └── datasets\n",
+    "#     └── crack-seg  ← downloads here (91.6 MB)\n",
+    "\n",
+    "# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\n",
+    "path: crack-seg # dataset root dir\n",
+    "train: images/train # train images (relative to 'path') 3717 images\n",
+    "val: images/val # val images (relative to 'path') 112 images\n",
+    "test: images/test # test images (relative to 'path') 200 images\n",
+    "\n",
+    "# Classes\n",
+    "names:\n",
+    "  0: crack\n",
+    "\n",
+    "# Download script/URL (optional)\n",
+    "download: https://github.com/ultralytics/assets/releases/download/v0.0.0/crack-seg.zip\n",
+    "```"
+   ],
+   "metadata": {
+    "id": "h8go3HNgN0WU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rH79t30YKs7i"
+   },
+   "source": [
+    "## 3 · Train through WeightsLab\n",
+    "\n",
+    "We register the run's hyperparameters with `wl.watch_or_edit(...)` (so they become live-editable from Studio), start the backend services with `wl.serve()`, then hand training to `WLAwareTrainer` via the standard `YOLO(...).train(trainer=...)` entry point.\n",
+    "\n",
+    "> **Connect Weights Studio** — run `weightslab start` locally (opens `http://localhost:5173`) *before or during* training to watch the run live. On **Colab**, serve over a public tunnel instead: use `wl.serve(serving_grpc=True, serving_bore=True)` and connect your local Studio with `weightslab tunnel bore.pub:PORT` (the port is printed when serving starts).\n",
+    "\n",
+    "Data augmentations are disabled so each sample maps cleanly to its ground truth in the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ.setdefault(\"WL_PRELOAD_IMAGE_OVERVIEW\", \"0\")\n",
+    "os.environ.setdefault(\"WEIGHTSLAB_LOG_LEVEL\", \"WARNING\")\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from weightslab.integrations.ultralytics import WLAwareSegmentationTrainer\n",
+    "from ultralytics import YOLO\n",
+    "\n",
+    "# Hyperparameters registered with WeightsLab -> live-editable from Weights Studio.\n",
+    "cfg = {\n",
+    "    \"model\": \"yolo11n-seg.pt\",       # pretrained checkpoint\n",
+    "    \"data\": \"crack-seg.yaml\",  # Ultralytics auto-downloads\n",
+    "    \"image_size\": 640,\n",
+    "    \"epochs\": 3,\n",
+    "    # Per-sample prediction overlays streamed to Studio (NMS on the train set).\n",
+    "    \"signals_cfg\": {\n",
+    "        \"train_nms\": {\"conf_thres\": 0.25, \"iou_thres\": 0.45, \"max_nms\": 7},\n",
+    "    },\n",
+    "}\n",
+    "wl.watch_or_edit(cfg, flag=\"hyperparameters\", defaults=cfg, poll_interval=1.0)\n",
+    "\n",
+    "# Start the WeightsLab backend services that Weights Studio connects to.\n",
+    "wl.serve(serving_grpc=True)\n",
+    "#   Colab -> local Studio:  wl.serve(serving_grpc=True, serving_bore=True)\n",
+    "\n",
+    "wl.start_training()  # equivalent to pressing \"play\" in Studio\n",
+    "\n",
+    "# Read back the (now live) hyperparameters and hand training to WLAwareSegmentationTrainer.\n",
+    "model = YOLO(cfg[\"model\"])\n",
+    "results = model.train(\n",
+    "    trainer=WLAwareSegmentationTrainer,\n",
+    "    data=str(cfg[\"data\"]),\n",
+    "    imgsz=cfg[\"image_size\"],\n",
+    "    epochs=int(cfg[\"epochs\"]),\n",
+    "    project=\"./wl_logs\", name=\"crack-seg\",  # -> WL log_dir/name\n",
+    "    workers=0,          # WL invariant (parent-process uid counter)\n",
+    "    optimizer=\"SGD\", lr0=0.001, amp=False,\n",
+    "    # All augmentations off for a clean sample <-> ground-truth association.\n",
+    "    mosaic=0.0, mixup=0.0, copy_paste=0.0,\n",
+    "    hsv_h=0.0, hsv_s=0.0, hsv_v=0.0,\n",
+    "    degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0,\n",
+    "    flipud=0.0, fliplr=0.0, erasing=0.0, auto_augment=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f-Uw_s_8Ks7m"
+   },
+   "source": [
+    "## 4 · Explore the data grid\n",
+    "\n",
+    "The table below is exactly the data behind Weights Studio's **grid explorer**: one row per sample, keyed by split (`origin`) and sample id, with the per-sample loss, prediction stats and tags accumulated during training. This is the ledger that Studio reads from — here we render it inline with pandas.\n",
+    "\n",
+    "The image thumbnails and bounding-box overlays themselves are rendered by **Weights Studio** (which streams full previews over gRPC); the link below opens the interactive grid there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "collapsed": true,
+    "id": "uOJdXGA3Ks7o",
+    "outputId": "26652fa3-6a15-43ce-9d75-034eaba21ccd"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "1241 samples tracked\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                        prediction prediction_raw  \\\n",
+       "sample_id annotation_id                             \n",
+       "0         0                   None           None   \n",
+       "1         0                   None           None   \n",
+       "2         0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "3         0                   None           None   \n",
+       "4         0                   None           None   \n",
+       "5         0                   None           None   \n",
+       "6         0                   None           None   \n",
+       "7         0                   None           None   \n",
+       "8         0                   None           None   \n",
+       "9         0                   None           None   \n",
+       "10        0                   None           None   \n",
+       "11        0                   None           None   \n",
+       "12        0                   None           None   \n",
+       "13        0                   None           None   \n",
+       "14        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "15        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "16        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "17        0                   None           None   \n",
+       "18        0                   None           None   \n",
+       "19        0                   None           None   \n",
+       "20        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "          3                   None           None   \n",
+       "21        0                   None           None   \n",
+       "22        0                   None           None   \n",
+       "23        0                   None           None   \n",
+       "24        0                   None           None   \n",
+       "25        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "26        0                   None           None   \n",
+       "27        0                   None           None   \n",
+       "28        0                   None           None   \n",
+       "29        0                   None           None   \n",
+       "30        0                   None           None   \n",
+       "31        0                   None           None   \n",
+       "32        0                   None           None   \n",
+       "33        0                   None           None   \n",
+       "34        0                   None           None   \n",
+       "35        0                   None           None   \n",
+       "36        0                   None           None   \n",
+       "\n",
+       "                                                                    target  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              [0.2335685, 0.254695, 0.4553995, 0.43075103, 1...   \n",
+       "1         0              [0.251174, 0.24061048, 0.44366202, 0.4307515, ...   \n",
+       "2         0                                                           None   \n",
+       "          1              [0.523474, 0.3978875, 0.634976, 0.48122054, 1....   \n",
+       "          2              [0.40610352, 0.415493, 0.5234745, 0.522301, 1....   \n",
+       "3         0              [0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...   \n",
+       "4         0              [0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...   \n",
+       "5         0              [0.6044605, 0.3990615, 0.67253554, 0.46596247,...   \n",
+       "6         0              [0.410798, 0.4436625, 0.556338, 0.51173747, 1....   \n",
+       "7         0              [0.650235, 0.4166665, 0.73826295, 0.48356748, ...   \n",
+       "8         0              [0.64788747, 0.388498, 0.7582165, 0.49413198, ...   \n",
+       "9         0              [0.6807515, 0.40140802, 0.75704247, 0.45774597...   \n",
+       "10        0              [0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...   \n",
+       "11        0              [0.42018852, 0.34507048, 0.5434275, 0.4941315,...   \n",
+       "12        0              [0.3732395, 0.348592, 0.54812247, 0.46009403, ...   \n",
+       "13        0              [0.37793398, 0.3814555, 0.48122, 0.45892054, 1...   \n",
+       "14        0                                                           None   \n",
+       "          1              [0.43427247, 0.3556335, 0.51173747, 0.43075046...   \n",
+       "          2              [0.4929575, 0.44718304, 0.5375585, 0.485915, 1...   \n",
+       "15        0                                                           None   \n",
+       "          1              [0.629108, 0.39788702, 0.67840403, 0.453051, 1...   \n",
+       "          2              [0.4530515, 0.4107985, 0.58568054, 0.5105635, ...   \n",
+       "16        0                                                           None   \n",
+       "          1              [0.45070451, 0.3967135, 0.5762915, 0.5152585, ...   \n",
+       "          2              [0.63732404, 0.403756, 0.684272, 0.46009403, 1...   \n",
+       "17        0              [0.43779355, 0.3826295, 0.61032856, 0.5223005,...   \n",
+       "18        0              [0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...   \n",
+       "19        0              [0.536385, 0.4495305, 0.600939, 0.49999946, 0....   \n",
+       "20        0                                                           None   \n",
+       "          1              [0.52347445, 0.4424885, 0.6150235, 0.5281695, ...   \n",
+       "          2              [0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...   \n",
+       "          3              [0.504695, 0.44248852, 0.54342705, 0.48004752,...   \n",
+       "21        0              [0.54107946, 0.456573, 0.6103285, 0.511737, 0....   \n",
+       "22        0              [0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...   \n",
+       "23        0              [0.517606, 0.18661949, 0.615024, 0.2875585, 1....   \n",
+       "24        0              [0.51173747, 0.18779299, 0.6126765, 0.308685, ...   \n",
+       "25        0                                                           None   \n",
+       "          1              [0.30985948, 0.3626765, 0.3861505, 0.43896753,...   \n",
+       "          2              [0.41314548, 0.3427225, 0.48122054, 0.42018753...   \n",
+       "26        0              [0.25821602, 0.30046952, 0.46830997, 0.4577465...   \n",
+       "27        0              [0.2734745, 0.2828635, 0.44718352, 0.4577465, ...   \n",
+       "28        0              [0.5915495, 0.23591551, 0.6455405, 0.2887325, ...   \n",
+       "29        0              [0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...   \n",
+       "30        0              [0.40493003, 0.2018775, 0.469484, 0.2699525, 1...   \n",
+       "31        0              [0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...   \n",
+       "32        0              [0.4025825, 0.212441, 0.45539945, 0.269953, 1....   \n",
+       "33        0              [0.2265255, 0.2136145, 0.3438965, 0.30751148, ...   \n",
+       "34        0               [0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]   \n",
+       "35        0              [0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...   \n",
+       "36        0              [0.6185445, 0.30164298, 0.7758215, 0.494131, 1...   \n",
+       "\n",
+       "                        discarded        origin task_type  last_seen group_id  \\\n",
+       "sample_id annotation_id                                                         \n",
+       "0         0                 False  train_loader                 -1.0        0   \n",
+       "1         0                 False  train_loader                 -1.0        1   \n",
+       "2         0                 False  train_loader                 -1.0        2   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "3         0                 False  train_loader                 -1.0        3   \n",
+       "4         0                 False  train_loader                 -1.0        4   \n",
+       "5         0                 False  train_loader                 -1.0        5   \n",
+       "6         0                 False  train_loader                 -1.0        6   \n",
+       "7         0                 False  train_loader                 -1.0        7   \n",
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+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.42018852, 0.34507048, 0.5434275, 0.4941315,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3732395, 0.348592, 0.54812247, 0.46009403, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.37793398, 0.3814555, 0.48122, 0.45892054, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43427247, 0.3556335, 0.51173747, 0.43075046...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4929575, 0.44718304, 0.5375585, 0.485915, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">15</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.629108, 0.39788702, 0.67840403, 0.453051, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4530515, 0.4107985, 0.58568054, 0.5105635, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.45070451, 0.3967135, 0.5762915, 0.5152585, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.63732404, 0.403756, 0.684272, 0.46009403, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43779355, 0.3826295, 0.61032856, 0.5223005,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.536385, 0.4495305, 0.600939, 0.49999946, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">20</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.52347445, 0.4424885, 0.6150235, 0.5281695, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.504695, 0.44248852, 0.54342705, 0.48004752,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.54107946, 0.456573, 0.6103285, 0.511737, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.517606, 0.18661949, 0.615024, 0.2875585, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.51173747, 0.18779299, 0.6126765, 0.308685, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">25</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.30985948, 0.3626765, 0.3861505, 0.43896753,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.41314548, 0.3427225, 0.48122054, 0.42018753...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.25821602, 0.30046952, 0.46830997, 0.4577465...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2734745, 0.2828635, 0.44718352, 0.4577465, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5915495, 0.23591551, 0.6455405, 0.2887325, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.40493003, 0.2018775, 0.469484, 0.2699525, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4025825, 0.212441, 0.45539945, 0.269953, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2265255, 0.2136145, 0.3438965, 0.30751148, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6185445, 0.30164298, 0.7758215, 0.494131, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8adeb57b-4a56-45e8-8485-a1c46d89c532')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "repr_error": "Out of range float values are not JSON compliant: nan"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ],
+      "text/html": [
+       "<a href=\"http://localhost:5173\" target=\"_blank\" style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "from weightslab.backend.ledgers import get_dataframe\n",
+    "\n",
+    "# `get_df_view()` is the same per-sample DataFrame that powers Studio's grid.\n",
+    "dfm = get_dataframe()\n",
+    "grid = dfm.get_df_view() if hasattr(dfm, \"get_df_view\") else pd.DataFrame()\n",
+    "print(f\"{len(grid)} samples tracked\")\n",
+    "display(grid.head(50))\n",
+    "\n",
+    "# Open the live, interactive grid (with image + bbox previews) in Weights Studio.\n",
+    "# Studio must be running locally (`weightslab start`).\n",
+    "display(HTML(\n",
+    "    '<a href=\"http://localhost:5173\" target=\"_blank\" '\n",
+    "    'style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>'\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FXQ0gBOSKs7t"
+   },
+   "source": [
+    "> **Can clicking a grid row here jump to that sample in Studio?** Not out of the box today. The notebook and Studio are separate processes, and the link above opens Studio at its root. Per-sample deep-linking (e.g. `http://localhost:5173/?sample=<uid>` selecting and scrolling to that row) is a small frontend addition to Weights Studio — the sample `uid` is already the grid's index here, so the notebook side is ready for it once Studio accepts the query param."
+   ]
+  }
+ ]
+}
diff --git a/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-homeobjects-dataset.ipynb b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-homeobjects-dataset.ipynb
new file mode 100644
index 00000000..17d3c8bd
--- /dev/null
+++ b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-homeobjects-dataset.ipynb
@@ -0,0 +1,1716 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-homeobjects-dataset.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Train an <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics YOLO</a> detector on the <b>HomeObjects-3K</b> dataset and stream every sample, prediction and metric live into <a href=\"https://github.com/GrayboxTech/weightslab\">Weights Studio</a> to <b>inspect, edit and evolve</b> your run.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HomeObjects-3K Detection · Ultralytics YOLO + WeightsLab\n",
+    "\n",
+    "This notebook trains a YOLO detector on the [HomeObjects-3K](https://docs.ultralytics.com/) dataset (detecting common indoor household objects) **through the WeightsLab training pipeline**.\n",
+    "\n",
+    "Instead of Ultralytics' one-shot `train`/`predict` flow, we hand training to `WLAwareTrainer`. It wraps the train/val dataloaders, logs a **per-sample** signal for every image (loss, prediction stats, tags) and ships live prediction overlays to **Weights Studio** — where you inspect the data grid, remove/weight samples, edit the model and resume, all while training runs.\n",
+    "\n",
+    "Ultralytics auto-downloads the dataset the first time you train."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o7YFKEN1Ks7Y"
+   },
+   "source": [
+    "## 1 · Setup\n",
+    "\n",
+    "Install WeightsLab (which pulls in Ultralytics) and verify the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "71be394c",
+    "outputId": "c9ef4767-84f5-4bea-e395-9e11766002f2",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "source": [
+    "%pip install setuptools wheel"
+   ],
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (75.2.0)\n",
+      "Requirement already satisfied: wheel in /usr/local/lib/python3.12/dist-packages (0.47.0)\n",
+      "Requirement already satisfied: packaging>=24.0 in /usr/local/lib/python3.12/dist-packages (from wheel) (26.2)\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%pip install git+https://github.com/GrayboxTech/weightslab.git"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "jV1oi_PQN0xK",
+    "outputId": "eda8eca1-f4b3-4ffd-eaa6-0f67ce857153"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Collecting git+https://github.com/GrayboxTech/weightslab.git\n",
+      "  Cloning https://github.com/GrayboxTech/weightslab.git (to revision landingcollab) to /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/GrayboxTech/weightslab.git /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git checkout -b landingcollab --track origin/landingcollab\n",
+      "  Switched to a new branch 'landingcollab'\n",
+      "  Branch 'landingcollab' set up to track remote branch 'landingcollab' from 'origin'.\n",
+      "  Resolved https://github.com/GrayboxTech/weightslab.git to commit 3f1cf143bc93e67df43adc71ab831fa71477e0d2\n",
+      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "Requirement already satisfied: numpy<3,>=1.24 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.0.2)\n",
+      "Requirement already satisfied: pandas<3,>=2.2.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.2.2)\n",
+      "Requirement already satisfied: duckdb<2,>=1.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.2)\n",
+      "Requirement already satisfied: PyYAML<7,>=6.0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (6.0.3)\n",
+      "Requirement already satisfied: dill<0.5,>=0.3.8 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.3.8)\n",
+      "Requirement already satisfied: zstandard<1,>=0.22 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.25.0)\n",
+      "Requirement already satisfied: h5py<4,>=3.10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.16.0)\n",
+      "Requirement already satisfied: xxhash<4.1,>=3.4 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.7.1)\n",
+      "Requirement already satisfied: tables<4,>=3.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.10.2)\n",
+      "Requirement already satisfied: torch<=2.9,>=2.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.9.0)\n",
+      "Requirement already satisfied: torchvision<1,>=0.16 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.24.0)\n",
+      "Requirement already satisfied: torchmetrics>=1.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.9.0)\n",
+      "Requirement already satisfied: grpcio<2,>=1.80 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.81.1)\n",
+      "Requirement already satisfied: protobuf<8,>=5.28.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (5.29.6)\n",
+      "Requirement already satisfied: pydantic<3,>=2.7 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.13.4)\n",
+      "Requirement already satisfied: Pillow<12,>=10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (11.3.0)\n",
+      "Requirement already satisfied: graphviz<1,>=0.20 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.21)\n",
+      "Requirement already satisfied: onnx<=1.20,>=1.15 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.20.0)\n",
+      "Requirement already satisfied: tqdm<5,>=4.66 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (4.67.3)\n",
+      "Requirement already satisfied: python-dotenv<2,>=1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.2.2)\n",
+      "Requirement already satisfied: langchain-core<2,>=0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.4.9)\n",
+      "Requirement already satisfied: langchain-ollama<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.1.0)\n",
+      "Requirement already satisfied: langchain-openai<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.5)\n",
+      "Requirement already satisfied: typing-extensions~=4.12 in /usr/local/lib/python3.12/dist-packages (from grpcio<2,>=1.80->weightslab==1.3.3.dev8) (4.15.0)\n",
+      "Requirement already satisfied: jsonpatch<2.0.0,>=1.33.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.33)\n",
+      "Requirement already satisfied: langchain-protocol>=0.0.17 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.0.18)\n",
+      "Requirement already satisfied: langsmith<1.0.0,>=0.3.45 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.9.1)\n",
+      "Requirement already satisfied: packaging>=23.2.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (26.2)\n",
+      "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (9.1.4)\n",
+      "Requirement already satisfied: uuid-utils<1.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.2)\n",
+      "Requirement already satisfied: ollama<1.0.0,>=0.6.1 in /usr/local/lib/python3.12/dist-packages (from langchain-ollama<2,>=0.2->weightslab==1.3.3.dev8) (0.6.2)\n",
+      "Requirement already satisfied: openai<3.0.0,>=2.45.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2.45.0)\n",
+      "Requirement already satisfied: tiktoken<1.0.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.13.0)\n",
+      "Requirement already satisfied: ml_dtypes>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from onnx<=1.20,>=1.15->weightslab==1.3.3.dev8) (0.5.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2026.2)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.46.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (2.46.4)\n",
+      "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.4.2)\n",
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+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.5.0)\n",
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+      "Requirement already satisfied: mdit-py-plugins>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.6.1)\n",
+      "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.12/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.0.0)\n",
+      "Building wheels for collected packages: weightslab\n",
+      "  Building wheel for weightslab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Created wheel for weightslab: filename=weightslab-1.3.3.dev8-py3-none-any.whl size=2827715 sha256=5c4e7c14bbf95a48b9a8c86b5a8c744af67e1eb395197cbe79d3e443c21cf86e\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-__zwgf7x/wheels/4d/c7/cd/817c2745790555e7b6c532f5b6055d47567f9416fdfc65879b\n",
+      "Successfully built weightslab\n",
+      "Installing collected packages: weightslab\n",
+      "  Attempting uninstall: weightslab\n",
+      "    Found existing installation: weightslab 1.3.3.dev7\n",
+      "    Uninstalling weightslab-1.3.3.dev7:\n",
+      "      Successfully uninstalled weightslab-1.3.3.dev7\n",
+      "Successfully installed weightslab-1.3.3.dev8\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.colab-display-data+json": {
+       "pip_warning": {
+        "packages": [
+         "weightslab"
+        ]
+       },
+       "id": "b7fce274042740d19087972fe9c01c9b"
+      }
+     },
+     "metadata": {}
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pJhvREbaKs7f",
+    "outputId": "651baa36-9ebb-49da-830b-e6c8a6e2b64f"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Ultralytics 8.4.96 🚀 Python-3.12.13 torch-2.9.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n",
+      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.5/107.7 GB disk)\n",
+      "16/07/2026-09:29:29.683 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmp6meuzqdz/weightslab_logs/weightslab_20260716_092929.log\n",
+      "16/07/2026-09:29:29.685 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+      "16/07/2026-09:29:29.686 INFO:weightslab:<module>: \n",
+      "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+      "│                                                                                                     │\n",
+      "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+      "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+      "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+      "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+      "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+      "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+      "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+      "│                                                                                                     │\n",
+      "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+      "│                                                                                                     │\n",
+      "╰─── By GrayBx, v1.3.3.dev8 ──────────────────────────────────────────────────────────────────────────╯\n",
+      "\n",
+      "\n",
+      "16/07/2026-09:29:29.690 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+    "# and Pillow, so nothing extra is needed here.\n",
+    "# %pip install weightslab\n",
+    "# %pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev8\"\n",
+    "%pip install ultralytics\n",
+    "\n",
+    "import ultralytics\n",
+    "ultralytics.checks()\n",
+    "\n",
+    "import weightslab as wl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 · Dataset\n",
+    "\n",
+    "The dataset is described by a small YAML file that Ultralytics resolves and downloads automatically. For reference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h8go3HNgN0WU"
+   },
+   "source": [
+    "```yaml\n",
+    "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n",
+    "\n",
+    "# HomeObjects-3K dataset by Ultralytics\n",
+    "# Documentation: https://docs.ultralytics.com/datasets/detect/homeobjects-3k/\n",
+    "# Example usage: yolo train data=HomeObjects-3K.yaml\n",
+    "# parent\n",
+    "# ├── ultralytics\n",
+    "# └── datasets\n",
+    "#     └── homeobjects-3K  ← downloads here (390 MB)\n",
+    "\n",
+    "# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\n",
+    "path: homeobjects-3K # dataset root dir\n",
+    "train: images/train # train images (relative to 'path') 2285 images\n",
+    "val: images/val # val images (relative to 'path') 404 images\n",
+    "\n",
+    "# Classes\n",
+    "names:\n",
+    "  0: bed\n",
+    "  1: sofa\n",
+    "  2: chair\n",
+    "  3: table\n",
+    "  4: lamp\n",
+    "  5: tv\n",
+    "  6: laptop\n",
+    "  7: wardrobe\n",
+    "  8: window\n",
+    "  9: door\n",
+    "  10: potted plant\n",
+    "  11: photo frame\n",
+    "\n",
+    "# Download script/URL (optional)\n",
+    "download: https://github.com/ultralytics/assets/releases/download/v0.0.0/homeobjects-3K.zip\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rH79t30YKs7i"
+   },
+   "source": [
+    "## 3 · Train through WeightsLab\n",
+    "\n",
+    "We register the run's hyperparameters with `wl.watch_or_edit(...)` (so they become live-editable from Studio), start the backend services with `wl.serve()`, then hand training to `WLAwareTrainer` via the standard `YOLO(...).train(trainer=...)` entry point.\n",
+    "\n",
+    "> **Connect Weights Studio** — run `weightslab start` locally (opens `http://localhost:5173`) *before or during* training to watch the run live. On **Colab**, serve over a public tunnel instead: use `wl.serve(serving_grpc=True, serving_bore=True)` and connect your local Studio with `weightslab tunnel bore.pub:PORT` (the port is printed when serving starts).\n",
+    "\n",
+    "Data augmentations are disabled so each sample maps cleanly to its ground truth in the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ.setdefault(\"WL_PRELOAD_IMAGE_OVERVIEW\", \"0\")\n",
+    "os.environ.setdefault(\"WEIGHTSLAB_LOG_LEVEL\", \"WARNING\")\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from weightslab.integrations.ultralytics import WLAwareTrainer\n",
+    "from ultralytics import YOLO\n",
+    "\n",
+    "# Hyperparameters registered with WeightsLab -> live-editable from Weights Studio.\n",
+    "cfg = {\n",
+    "    \"model\": \"yolo11n.pt\",       # pretrained checkpoint\n",
+    "    \"data\": \"HomeObjects-3K.yaml\",  # Ultralytics auto-downloads\n",
+    "    \"image_size\": 640,\n",
+    "    \"epochs\": 3,\n",
+    "    # Per-sample prediction overlays streamed to Studio (NMS on the train set).\n",
+    "    \"signals_cfg\": {\n",
+    "        \"train_nms\": {\"conf_thres\": 0.25, \"iou_thres\": 0.45, \"max_nms\": 7},\n",
+    "    },\n",
+    "}\n",
+    "wl.watch_or_edit(cfg, flag=\"hyperparameters\", defaults=cfg, poll_interval=1.0)\n",
+    "\n",
+    "# Start the WeightsLab backend services that Weights Studio connects to.\n",
+    "wl.serve(serving_grpc=True)\n",
+    "#   Colab -> local Studio:  wl.serve(serving_grpc=True, serving_bore=True)\n",
+    "\n",
+    "wl.start_training()  # equivalent to pressing \"play\" in Studio\n",
+    "\n",
+    "# Read back the (now live) hyperparameters and hand training to WLAwareTrainer.\n",
+    "model = YOLO(cfg[\"model\"])\n",
+    "results = model.train(\n",
+    "    trainer=WLAwareTrainer,\n",
+    "    data=str(cfg[\"data\"]),\n",
+    "    imgsz=cfg[\"image_size\"],\n",
+    "    epochs=int(cfg[\"epochs\"]),\n",
+    "    project=\"./wl_logs\", name=\"homeobjects-3k\",  # -> WL log_dir/name\n",
+    "    workers=0,          # WL invariant (parent-process uid counter)\n",
+    "    optimizer=\"SGD\", lr0=0.001, amp=False,\n",
+    "    # All augmentations off for a clean sample <-> ground-truth association.\n",
+    "    mosaic=0.0, mixup=0.0, copy_paste=0.0,\n",
+    "    hsv_h=0.0, hsv_s=0.0, hsv_v=0.0,\n",
+    "    degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0,\n",
+    "    flipud=0.0, fliplr=0.0, erasing=0.0, auto_augment=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f-Uw_s_8Ks7m"
+   },
+   "source": [
+    "## 4 · Explore the data grid\n",
+    "\n",
+    "The table below is exactly the data behind Weights Studio's **grid explorer**: one row per sample, keyed by split (`origin`) and sample id, with the per-sample loss, prediction stats and tags accumulated during training. This is the ledger that Studio reads from — here we render it inline with pandas.\n",
+    "\n",
+    "The image thumbnails and bounding-box overlays themselves are rendered by **Weights Studio** (which streams full previews over gRPC); the link below opens the interactive grid there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "collapsed": true,
+    "id": "uOJdXGA3Ks7o",
+    "outputId": "26652fa3-6a15-43ce-9d75-034eaba21ccd"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "1241 samples tracked\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                        prediction prediction_raw  \\\n",
+       "sample_id annotation_id                             \n",
+       "0         0                   None           None   \n",
+       "1         0                   None           None   \n",
+       "2         0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "3         0                   None           None   \n",
+       "4         0                   None           None   \n",
+       "5         0                   None           None   \n",
+       "6         0                   None           None   \n",
+       "7         0                   None           None   \n",
+       "8         0                   None           None   \n",
+       "9         0                   None           None   \n",
+       "10        0                   None           None   \n",
+       "11        0                   None           None   \n",
+       "12        0                   None           None   \n",
+       "13        0                   None           None   \n",
+       "14        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "15        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "16        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "17        0                   None           None   \n",
+       "18        0                   None           None   \n",
+       "19        0                   None           None   \n",
+       "20        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "          3                   None           None   \n",
+       "21        0                   None           None   \n",
+       "22        0                   None           None   \n",
+       "23        0                   None           None   \n",
+       "24        0                   None           None   \n",
+       "25        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "26        0                   None           None   \n",
+       "27        0                   None           None   \n",
+       "28        0                   None           None   \n",
+       "29        0                   None           None   \n",
+       "30        0                   None           None   \n",
+       "31        0                   None           None   \n",
+       "32        0                   None           None   \n",
+       "33        0                   None           None   \n",
+       "34        0                   None           None   \n",
+       "35        0                   None           None   \n",
+       "36        0                   None           None   \n",
+       "\n",
+       "                                                                    target  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              [0.2335685, 0.254695, 0.4553995, 0.43075103, 1...   \n",
+       "1         0              [0.251174, 0.24061048, 0.44366202, 0.4307515, ...   \n",
+       "2         0                                                           None   \n",
+       "          1              [0.523474, 0.3978875, 0.634976, 0.48122054, 1....   \n",
+       "          2              [0.40610352, 0.415493, 0.5234745, 0.522301, 1....   \n",
+       "3         0              [0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...   \n",
+       "4         0              [0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...   \n",
+       "5         0              [0.6044605, 0.3990615, 0.67253554, 0.46596247,...   \n",
+       "6         0              [0.410798, 0.4436625, 0.556338, 0.51173747, 1....   \n",
+       "7         0              [0.650235, 0.4166665, 0.73826295, 0.48356748, ...   \n",
+       "8         0              [0.64788747, 0.388498, 0.7582165, 0.49413198, ...   \n",
+       "9         0              [0.6807515, 0.40140802, 0.75704247, 0.45774597...   \n",
+       "10        0              [0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...   \n",
+       "11        0              [0.42018852, 0.34507048, 0.5434275, 0.4941315,...   \n",
+       "12        0              [0.3732395, 0.348592, 0.54812247, 0.46009403, ...   \n",
+       "13        0              [0.37793398, 0.3814555, 0.48122, 0.45892054, 1...   \n",
+       "14        0                                                           None   \n",
+       "          1              [0.43427247, 0.3556335, 0.51173747, 0.43075046...   \n",
+       "          2              [0.4929575, 0.44718304, 0.5375585, 0.485915, 1...   \n",
+       "15        0                                                           None   \n",
+       "          1              [0.629108, 0.39788702, 0.67840403, 0.453051, 1...   \n",
+       "          2              [0.4530515, 0.4107985, 0.58568054, 0.5105635, ...   \n",
+       "16        0                                                           None   \n",
+       "          1              [0.45070451, 0.3967135, 0.5762915, 0.5152585, ...   \n",
+       "          2              [0.63732404, 0.403756, 0.684272, 0.46009403, 1...   \n",
+       "17        0              [0.43779355, 0.3826295, 0.61032856, 0.5223005,...   \n",
+       "18        0              [0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...   \n",
+       "19        0              [0.536385, 0.4495305, 0.600939, 0.49999946, 0....   \n",
+       "20        0                                                           None   \n",
+       "          1              [0.52347445, 0.4424885, 0.6150235, 0.5281695, ...   \n",
+       "          2              [0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...   \n",
+       "          3              [0.504695, 0.44248852, 0.54342705, 0.48004752,...   \n",
+       "21        0              [0.54107946, 0.456573, 0.6103285, 0.511737, 0....   \n",
+       "22        0              [0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...   \n",
+       "23        0              [0.517606, 0.18661949, 0.615024, 0.2875585, 1....   \n",
+       "24        0              [0.51173747, 0.18779299, 0.6126765, 0.308685, ...   \n",
+       "25        0                                                           None   \n",
+       "          1              [0.30985948, 0.3626765, 0.3861505, 0.43896753,...   \n",
+       "          2              [0.41314548, 0.3427225, 0.48122054, 0.42018753...   \n",
+       "26        0              [0.25821602, 0.30046952, 0.46830997, 0.4577465...   \n",
+       "27        0              [0.2734745, 0.2828635, 0.44718352, 0.4577465, ...   \n",
+       "28        0              [0.5915495, 0.23591551, 0.6455405, 0.2887325, ...   \n",
+       "29        0              [0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...   \n",
+       "30        0              [0.40493003, 0.2018775, 0.469484, 0.2699525, 1...   \n",
+       "31        0              [0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...   \n",
+       "32        0              [0.4025825, 0.212441, 0.45539945, 0.269953, 1....   \n",
+       "33        0              [0.2265255, 0.2136145, 0.3438965, 0.30751148, ...   \n",
+       "34        0               [0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]   \n",
+       "35        0              [0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...   \n",
+       "36        0              [0.6185445, 0.30164298, 0.7758215, 0.494131, 1...   \n",
+       "\n",
+       "                        discarded        origin task_type  last_seen group_id  \\\n",
+       "sample_id annotation_id                                                         \n",
+       "0         0                 False  train_loader                 -1.0        0   \n",
+       "1         0                 False  train_loader                 -1.0        1   \n",
+       "2         0                 False  train_loader                 -1.0        2   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "3         0                 False  train_loader                 -1.0        3   \n",
+       "4         0                 False  train_loader                 -1.0        4   \n",
+       "5         0                 False  train_loader                 -1.0        5   \n",
+       "6         0                 False  train_loader                 -1.0        6   \n",
+       "7         0                 False  train_loader                 -1.0        7   \n",
+       "8         0                 False  train_loader                 -1.0        8   \n",
+       "9         0                 False  train_loader                 -1.0        9   \n",
+       "10        0                 False  train_loader                 -1.0       10   \n",
+       "11        0                 False  train_loader                 -1.0       11   \n",
+       "12        0                 False  train_loader                 -1.0       12   \n",
+       "13        0                 False  train_loader                 -1.0       13   \n",
+       "14        0                 False  train_loader                 -1.0       14   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "15        0                 False  train_loader                 -1.0       15   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "16        0                 False  train_loader                 -1.0       16   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "17        0                 False  train_loader                 -1.0       17   \n",
+       "18        0                 False  train_loader                 -1.0       18   \n",
+       "19        0                 False  train_loader                 -1.0       19   \n",
+       "20        0                 False  train_loader                 -1.0       20   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "          3                  None           NaN      None        NaN     None   \n",
+       "21        0                 False  train_loader                 -1.0       21   \n",
+       "22        0                 False  train_loader                 -1.0       22   \n",
+       "23        0                 False  train_loader                 -1.0       23   \n",
+       "24        0                 False  train_loader                 -1.0       24   \n",
+       "25        0                 False  train_loader                 -1.0       25   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "26        0                 False  train_loader                 -1.0       26   \n",
+       "27        0                 False  train_loader                 -1.0       27   \n",
+       "28        0                 False  train_loader                 -1.0       28   \n",
+       "29        0                 False  train_loader                 -1.0       29   \n",
+       "30        0                 False  train_loader                 -1.0       30   \n",
+       "31        0                 False  train_loader                 -1.0       31   \n",
+       "32        0                 False  train_loader                 -1.0       32   \n",
+       "33        0                 False  train_loader                 -1.0       33   \n",
+       "34        0                 False  train_loader                 -1.0       34   \n",
+       "35        0                 False  train_loader                 -1.0       35   \n",
+       "36        0                 False  train_loader                 -1.0       36   \n",
+       "\n",
+       "                         member_rank  \\\n",
+       "sample_id annotation_id                \n",
+       "0         0                      0.0   \n",
+       "1         0                      0.0   \n",
+       "2         0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "3         0                      0.0   \n",
+       "4         0                      0.0   \n",
+       "5         0                      0.0   \n",
+       "6         0                      0.0   \n",
+       "7         0                      0.0   \n",
+       "8         0                      0.0   \n",
+       "9         0                      0.0   \n",
+       "10        0                      0.0   \n",
+       "11        0                      0.0   \n",
+       "12        0                      0.0   \n",
+       "13        0                      0.0   \n",
+       "14        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "15        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "16        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "17        0                      0.0   \n",
+       "18        0                      0.0   \n",
+       "19        0                      0.0   \n",
+       "20        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "          3                      NaN   \n",
+       "21        0                      0.0   \n",
+       "22        0                      0.0   \n",
+       "23        0                      0.0   \n",
+       "24        0                      0.0   \n",
+       "25        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "26        0                      0.0   \n",
+       "27        0                      0.0   \n",
+       "28        0                      0.0   \n",
+       "29        0                      0.0   \n",
+       "30        0                      0.0   \n",
+       "31        0                      0.0   \n",
+       "32        0                      0.0   \n",
+       "33        0                      0.0   \n",
+       "34        0                      0.0   \n",
+       "35        0                      0.0   \n",
+       "36        0                      0.0   \n",
+       "\n",
+       "                                                                  img_path  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "1         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "2         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "3         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "4         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "5         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "6         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "7         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "8         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "9         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "10        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "11        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "12        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "13        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "14        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "15        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "16        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "17        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "18        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "19        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "20        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "          3                                                           None   \n",
+       "21        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "22        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "23        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "24        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "25        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "26        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "27        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "28        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "29        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "30        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "31        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "32        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "33        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "34        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "35        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "36        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "\n",
+       "                                           cls  \n",
+       "sample_id annotation_id                         \n",
+       "0         0                            [[1.0]]  \n",
+       "1         0                            [[1.0]]  \n",
+       "2         0                     [[1.0], [1.0]]  \n",
+       "          1                               None  \n",
+       "          2                               None  \n",
+       "3         0                            [[1.0]]  \n",
+       "4         0                            [[1.0]]  \n",
+       "5         0                            [[1.0]]  \n",
+       "6         0                            [[1.0]]  \n",
+       "7         0                            [[1.0]]  \n",
+       "8         0                            [[1.0]]  \n",
+       "9         0                            [[1.0]]  \n",
+       "10        0                            [[1.0]]  \n",
+       "11        0                            [[1.0]]  \n",
+       "12        0                            [[1.0]]  \n",
+       "13        0                            [[1.0]]  \n",
+       "14        0                     [[1.0], [1.0]]  \n",
+       "          1                               None  \n",
+       "          2                               None  \n",
+       "15        0                     [[1.0], [1.0]]  \n",
+       "          1                               None  \n",
+       "          2                               None  \n",
+       "16        0                     [[1.0], [1.0]]  \n",
+       "          1                               None  \n",
+       "          2                               None  \n",
+       "17        0                            [[1.0]]  \n",
+       "18        0                            [[1.0]]  \n",
+       "19        0                            [[0.0]]  \n",
+       "20        0              [[0.0], [0.0], [0.0]]  \n",
+       "          1                               None  \n",
+       "          2                               None  \n",
+       "          3                               None  \n",
+       "21        0                            [[0.0]]  \n",
+       "22        0                            [[0.0]]  \n",
+       "23        0                            [[1.0]]  \n",
+       "24        0                            [[1.0]]  \n",
+       "25        0                     [[0.0], [0.0]]  \n",
+       "          1                               None  \n",
+       "          2                               None  \n",
+       "26        0                            [[0.0]]  \n",
+       "27        0                            [[0.0]]  \n",
+       "28        0                            [[1.0]]  \n",
+       "29        0                            [[1.0]]  \n",
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+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.52347445, 0.4424885, 0.6150235, 0.5281695, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.504695, 0.44248852, 0.54342705, 0.48004752,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.54107946, 0.456573, 0.6103285, 0.511737, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.517606, 0.18661949, 0.615024, 0.2875585, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.51173747, 0.18779299, 0.6126765, 0.308685, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">25</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.30985948, 0.3626765, 0.3861505, 0.43896753,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.41314548, 0.3427225, 0.48122054, 0.42018753...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.25821602, 0.30046952, 0.46830997, 0.4577465...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2734745, 0.2828635, 0.44718352, 0.4577465, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5915495, 0.23591551, 0.6455405, 0.2887325, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.40493003, 0.2018775, 0.469484, 0.2699525, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4025825, 0.212441, 0.45539945, 0.269953, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2265255, 0.2136145, 0.3438965, 0.30751148, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6185445, 0.30164298, 0.7758215, 0.494131, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8adeb57b-4a56-45e8-8485-a1c46d89c532')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "repr_error": "Out of range float values are not JSON compliant: nan"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ],
+      "text/html": [
+       "<a href=\"http://localhost:5173\" target=\"_blank\" style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "from weightslab.backend.ledgers import get_dataframe\n",
+    "\n",
+    "# `get_df_view()` is the same per-sample DataFrame that powers Studio's grid.\n",
+    "dfm = get_dataframe()\n",
+    "grid = dfm.get_df_view() if hasattr(dfm, \"get_df_view\") else pd.DataFrame()\n",
+    "print(f\"{len(grid)} samples tracked\")\n",
+    "display(grid.head(50))\n",
+    "\n",
+    "# Open the live, interactive grid (with image + bbox previews) in Weights Studio.\n",
+    "# Studio must be running locally (`weightslab start`).\n",
+    "display(HTML(\n",
+    "    '<a href=\"http://localhost:5173\" target=\"_blank\" '\n",
+    "    'style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>'\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FXQ0gBOSKs7t"
+   },
+   "source": [
+    "> **Can clicking a grid row here jump to that sample in Studio?** Not out of the box today. The notebook and Studio are separate processes, and the link above opens Studio at its root. Per-sample deep-linking (e.g. `http://localhost:5173/?sample=<uid>` selecting and scrolling to that row) is a small frontend addition to Weights Studio — the sample `uid` is already the grid's index here, so the notebook side is ready for it once Studio accepts the query param."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vlHp09Nueb3d"
+   },
+   "source": [
+    "## 💡Citation\n",
+    "\n",
+    "```bibtex\n",
+    "@dataset{Jocher_Ultralytics_Datasets_2025,\n",
+    "    author = {Jocher, Glenn and Rizwan, Muhammad},\n",
+    "    license = {AGPL-3.0},\n",
+    "    month = {May},\n",
+    "    title = {Ultralytics Datasets: HomeObjects-3K Detection Dataset},\n",
+    "    url = {https://docs.ultralytics.com/datasets/detect/homeobject-3k/},\n",
+    "    version = {1.0.0},\n",
+    "    year = {2025}\n",
+    "}\n",
+    "```\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "A100",
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-kitti-detection-dataset.ipynb b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-kitti-detection-dataset.ipynb
new file mode 100644
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@@ -0,0 +1,1707 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-kitti-detection-dataset.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Train an <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics YOLO</a> detector on the <b>KITTI</b> dataset and stream every sample, prediction and metric live into <a href=\"https://github.com/GrayboxTech/weightslab\">Weights Studio</a> to <b>inspect, edit and evolve</b> your run.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# KITTI Detection · Ultralytics YOLO + WeightsLab\n",
+    "\n",
+    "This notebook trains a YOLO detector on the [KITTI](https://docs.ultralytics.com/) dataset (detecting cars, pedestrians and cyclists in autonomous-driving street scenes) **through the WeightsLab training pipeline**.\n",
+    "\n",
+    "Instead of Ultralytics' one-shot `train`/`predict` flow, we hand training to `WLAwareTrainer`. It wraps the train/val dataloaders, logs a **per-sample** signal for every image (loss, prediction stats, tags) and ships live prediction overlays to **Weights Studio** — where you inspect the data grid, remove/weight samples, edit the model and resume, all while training runs.\n",
+    "\n",
+    "Ultralytics auto-downloads the dataset the first time you train."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o7YFKEN1Ks7Y"
+   },
+   "source": [
+    "## 1 · Setup\n",
+    "\n",
+    "Install WeightsLab (which pulls in Ultralytics) and verify the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "71be394c",
+    "outputId": "c9ef4767-84f5-4bea-e395-9e11766002f2",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "source": [
+    "%pip install setuptools wheel"
+   ],
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (75.2.0)\n",
+      "Requirement already satisfied: wheel in /usr/local/lib/python3.12/dist-packages (0.47.0)\n",
+      "Requirement already satisfied: packaging>=24.0 in /usr/local/lib/python3.12/dist-packages (from wheel) (26.2)\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%pip install git+https://github.com/GrayboxTech/weightslab.git"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "jV1oi_PQN0xK",
+    "outputId": "eda8eca1-f4b3-4ffd-eaa6-0f67ce857153"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Collecting git+https://github.com/GrayboxTech/weightslab.git\n",
+      "  Cloning https://github.com/GrayboxTech/weightslab.git (to revision landingcollab) to /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/GrayboxTech/weightslab.git /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git checkout -b landingcollab --track origin/landingcollab\n",
+      "  Switched to a new branch 'landingcollab'\n",
+      "  Branch 'landingcollab' set up to track remote branch 'landingcollab' from 'origin'.\n",
+      "  Resolved https://github.com/GrayboxTech/weightslab.git to commit 3f1cf143bc93e67df43adc71ab831fa71477e0d2\n",
+      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "Requirement already satisfied: numpy<3,>=1.24 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.0.2)\n",
+      "Requirement already satisfied: pandas<3,>=2.2.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.2.2)\n",
+      "Requirement already satisfied: duckdb<2,>=1.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.2)\n",
+      "Requirement already satisfied: PyYAML<7,>=6.0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (6.0.3)\n",
+      "Requirement already satisfied: dill<0.5,>=0.3.8 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.3.8)\n",
+      "Requirement already satisfied: zstandard<1,>=0.22 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.25.0)\n",
+      "Requirement already satisfied: h5py<4,>=3.10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.16.0)\n",
+      "Requirement already satisfied: xxhash<4.1,>=3.4 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.7.1)\n",
+      "Requirement already satisfied: tables<4,>=3.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.10.2)\n",
+      "Requirement already satisfied: torch<=2.9,>=2.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.9.0)\n",
+      "Requirement already satisfied: torchvision<1,>=0.16 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.24.0)\n",
+      "Requirement already satisfied: torchmetrics>=1.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.9.0)\n",
+      "Requirement already satisfied: grpcio<2,>=1.80 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.81.1)\n",
+      "Requirement already satisfied: protobuf<8,>=5.28.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (5.29.6)\n",
+      "Requirement already satisfied: pydantic<3,>=2.7 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.13.4)\n",
+      "Requirement already satisfied: Pillow<12,>=10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (11.3.0)\n",
+      "Requirement already satisfied: graphviz<1,>=0.20 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.21)\n",
+      "Requirement already satisfied: onnx<=1.20,>=1.15 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.20.0)\n",
+      "Requirement already satisfied: tqdm<5,>=4.66 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (4.67.3)\n",
+      "Requirement already satisfied: python-dotenv<2,>=1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.2.2)\n",
+      "Requirement already satisfied: langchain-core<2,>=0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.4.9)\n",
+      "Requirement already satisfied: langchain-ollama<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.1.0)\n",
+      "Requirement already satisfied: langchain-openai<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.5)\n",
+      "Requirement already satisfied: typing-extensions~=4.12 in /usr/local/lib/python3.12/dist-packages (from grpcio<2,>=1.80->weightslab==1.3.3.dev8) (4.15.0)\n",
+      "Requirement already satisfied: jsonpatch<2.0.0,>=1.33.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.33)\n",
+      "Requirement already satisfied: langchain-protocol>=0.0.17 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.0.18)\n",
+      "Requirement already satisfied: langsmith<1.0.0,>=0.3.45 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.9.1)\n",
+      "Requirement already satisfied: packaging>=23.2.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (26.2)\n",
+      "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (9.1.4)\n",
+      "Requirement already satisfied: uuid-utils<1.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.2)\n",
+      "Requirement already satisfied: ollama<1.0.0,>=0.6.1 in /usr/local/lib/python3.12/dist-packages (from langchain-ollama<2,>=0.2->weightslab==1.3.3.dev8) (0.6.2)\n",
+      "Requirement already satisfied: openai<3.0.0,>=2.45.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2.45.0)\n",
+      "Requirement already satisfied: tiktoken<1.0.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.13.0)\n",
+      "Requirement already satisfied: ml_dtypes>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from onnx<=1.20,>=1.15->weightslab==1.3.3.dev8) (0.5.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2026.2)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.46.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (2.46.4)\n",
+      "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.4.2)\n",
+      "Requirement already satisfied: numexpr>=2.6.2 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (2.14.1)\n",
+      "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (9.0.0)\n",
+      "Requirement already satisfied: blosc2>=2.3.0 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (4.5.1)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.29.4)\n",
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (75.2.0)\n",
+      "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.14.0)\n",
+      "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.6.1)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.1.6)\n",
+      "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2025.3.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (9.10.2.21)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.4.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.3.3.83)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (10.3.9.90)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.7.3.90)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.5.8.93)\n",
+      "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (0.7.1)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2.27.5)\n",
+      "Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.3.20)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.13.1.3)\n",
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+      "Requirement already satisfied: regex in /usr/local/lib/python3.12/dist-packages (from tiktoken<1.0.0,>=0.7.0->langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2025.11.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.0.3)\n",
+      "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.12/dist-packages (from anyio>=3.5.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.18)\n",
+      "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (2026.6.17)\n",
+      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.0.9)\n",
+      "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.0)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (3.4.7)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.5.0)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.2.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.20.0)\n",
+      "Requirement already satisfied: platformdirs<5,>=3.6.0 in /usr/local/lib/python3.12/dist-packages (from textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.10.0)\n",
+      "Requirement already satisfied: plotext<6.0.0,>=5.2.8 in /usr/local/lib/python3.12/dist-packages (from textual-plotext->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (5.3.2)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.1.2)\n",
+      "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.1.0)\n",
+      "Requirement already satisfied: mdit-py-plugins>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.6.1)\n",
+      "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.12/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.0.0)\n",
+      "Building wheels for collected packages: weightslab\n",
+      "  Building wheel for weightslab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Created wheel for weightslab: filename=weightslab-1.3.3.dev8-py3-none-any.whl size=2827715 sha256=5c4e7c14bbf95a48b9a8c86b5a8c744af67e1eb395197cbe79d3e443c21cf86e\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-__zwgf7x/wheels/4d/c7/cd/817c2745790555e7b6c532f5b6055d47567f9416fdfc65879b\n",
+      "Successfully built weightslab\n",
+      "Installing collected packages: weightslab\n",
+      "  Attempting uninstall: weightslab\n",
+      "    Found existing installation: weightslab 1.3.3.dev7\n",
+      "    Uninstalling weightslab-1.3.3.dev7:\n",
+      "      Successfully uninstalled weightslab-1.3.3.dev7\n",
+      "Successfully installed weightslab-1.3.3.dev8\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.colab-display-data+json": {
+       "pip_warning": {
+        "packages": [
+         "weightslab"
+        ]
+       },
+       "id": "b7fce274042740d19087972fe9c01c9b"
+      }
+     },
+     "metadata": {}
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pJhvREbaKs7f",
+    "outputId": "651baa36-9ebb-49da-830b-e6c8a6e2b64f"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Ultralytics 8.4.96 🚀 Python-3.12.13 torch-2.9.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n",
+      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.5/107.7 GB disk)\n",
+      "16/07/2026-09:29:29.683 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmp6meuzqdz/weightslab_logs/weightslab_20260716_092929.log\n",
+      "16/07/2026-09:29:29.685 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+      "16/07/2026-09:29:29.686 INFO:weightslab:<module>: \n",
+      "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+      "│                                                                                                     │\n",
+      "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+      "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+      "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+      "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+      "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+      "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+      "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+      "│                                                                                                     │\n",
+      "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+      "│                                                                                                     │\n",
+      "╰─── By GrayBx, v1.3.3.dev8 ──────────────────────────────────────────────────────────────────────────╯\n",
+      "\n",
+      "\n",
+      "16/07/2026-09:29:29.690 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+    "# and Pillow, so nothing extra is needed here.\n",
+    "# %pip install weightslab\n",
+    "# %pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev8\"\n",
+    "%pip install ultralytics\n",
+    "\n",
+    "import ultralytics\n",
+    "ultralytics.checks()\n",
+    "\n",
+    "import weightslab as wl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 · Dataset\n",
+    "\n",
+    "The dataset is described by a small YAML file that Ultralytics resolves and downloads automatically. For reference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h8go3HNgN0WU"
+   },
+   "source": [
+    "```yaml\n",
+    "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n",
+    "\n",
+    "# Kitti dataset by Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago\n",
+    "# Documentation: https://docs.ultralytics.com/datasets/detect/kitti/\n",
+    "# Example usage: yolo train data=kitti.yaml\n",
+    "# parent\n",
+    "# ├── ultralytics\n",
+    "# └── datasets\n",
+    "#     └── kitti ← downloads here (390.5 MB)\n",
+    "\n",
+    "# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\n",
+    "path: kitti # dataset root dir\n",
+    "train: images/train # train images (relative to 'path') 5985 images\n",
+    "val: images/val # val images (relative to 'path') 1496 images\n",
+    "\n",
+    "names:\n",
+    "  0: car\n",
+    "  1: van\n",
+    "  2: truck\n",
+    "  3: pedestrian\n",
+    "  4: person_sitting\n",
+    "  5: cyclist\n",
+    "  6: tram\n",
+    "  7: misc\n",
+    "\n",
+    "# Download script/URL (optional)\n",
+    "download: https://github.com/ultralytics/assets/releases/download/v0.0.0/kitti.zip\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rH79t30YKs7i"
+   },
+   "source": [
+    "## 3 · Train through WeightsLab\n",
+    "\n",
+    "We register the run's hyperparameters with `wl.watch_or_edit(...)` (so they become live-editable from Studio), start the backend services with `wl.serve()`, then hand training to `WLAwareTrainer` via the standard `YOLO(...).train(trainer=...)` entry point.\n",
+    "\n",
+    "> **Connect Weights Studio** — run `weightslab start` locally (opens `http://localhost:5173`) *before or during* training to watch the run live. On **Colab**, serve over a public tunnel instead: use `wl.serve(serving_grpc=True, serving_bore=True)` and connect your local Studio with `weightslab tunnel bore.pub:PORT` (the port is printed when serving starts).\n",
+    "\n",
+    "Data augmentations are disabled so each sample maps cleanly to its ground truth in the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ.setdefault(\"WL_PRELOAD_IMAGE_OVERVIEW\", \"0\")\n",
+    "os.environ.setdefault(\"WEIGHTSLAB_LOG_LEVEL\", \"WARNING\")\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from weightslab.integrations.ultralytics import WLAwareTrainer\n",
+    "from ultralytics import YOLO\n",
+    "\n",
+    "# Hyperparameters registered with WeightsLab -> live-editable from Weights Studio.\n",
+    "cfg = {\n",
+    "    \"model\": \"yolo11n.pt\",       # pretrained checkpoint\n",
+    "    \"data\": \"kitti.yaml\",  # Ultralytics auto-downloads\n",
+    "    \"image_size\": 640,\n",
+    "    \"epochs\": 10,\n",
+    "    # Per-sample prediction overlays streamed to Studio (NMS on the train set).\n",
+    "    \"signals_cfg\": {\n",
+    "        \"train_nms\": {\"conf_thres\": 0.25, \"iou_thres\": 0.45, \"max_nms\": 7},\n",
+    "    },\n",
+    "}\n",
+    "wl.watch_or_edit(cfg, flag=\"hyperparameters\", defaults=cfg, poll_interval=1.0)\n",
+    "\n",
+    "# Start the WeightsLab backend services that Weights Studio connects to.\n",
+    "wl.serve(serving_grpc=True)\n",
+    "#   Colab -> local Studio:  wl.serve(serving_grpc=True, serving_bore=True)\n",
+    "\n",
+    "wl.start_training()  # equivalent to pressing \"play\" in Studio\n",
+    "\n",
+    "# Read back the (now live) hyperparameters and hand training to WLAwareTrainer.\n",
+    "model = YOLO(cfg[\"model\"])\n",
+    "results = model.train(\n",
+    "    trainer=WLAwareTrainer,\n",
+    "    data=str(cfg[\"data\"]),\n",
+    "    imgsz=cfg[\"image_size\"],\n",
+    "    epochs=int(cfg[\"epochs\"]),\n",
+    "    project=\"./wl_logs\", name=\"kitti\",  # -> WL log_dir/name\n",
+    "    workers=0,          # WL invariant (parent-process uid counter)\n",
+    "    optimizer=\"SGD\", lr0=0.001, amp=False,\n",
+    "    # All augmentations off for a clean sample <-> ground-truth association.\n",
+    "    mosaic=0.0, mixup=0.0, copy_paste=0.0,\n",
+    "    hsv_h=0.0, hsv_s=0.0, hsv_v=0.0,\n",
+    "    degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0,\n",
+    "    flipud=0.0, fliplr=0.0, erasing=0.0, auto_augment=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f-Uw_s_8Ks7m"
+   },
+   "source": [
+    "## 4 · Explore the data grid\n",
+    "\n",
+    "The table below is exactly the data behind Weights Studio's **grid explorer**: one row per sample, keyed by split (`origin`) and sample id, with the per-sample loss, prediction stats and tags accumulated during training. This is the ledger that Studio reads from — here we render it inline with pandas.\n",
+    "\n",
+    "The image thumbnails and bounding-box overlays themselves are rendered by **Weights Studio** (which streams full previews over gRPC); the link below opens the interactive grid there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "collapsed": true,
+    "id": "uOJdXGA3Ks7o",
+    "outputId": "26652fa3-6a15-43ce-9d75-034eaba21ccd"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "1241 samples tracked\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                        prediction prediction_raw  \\\n",
+       "sample_id annotation_id                             \n",
+       "0         0                   None           None   \n",
+       "1         0                   None           None   \n",
+       "2         0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "3         0                   None           None   \n",
+       "4         0                   None           None   \n",
+       "5         0                   None           None   \n",
+       "6         0                   None           None   \n",
+       "7         0                   None           None   \n",
+       "8         0                   None           None   \n",
+       "9         0                   None           None   \n",
+       "10        0                   None           None   \n",
+       "11        0                   None           None   \n",
+       "12        0                   None           None   \n",
+       "13        0                   None           None   \n",
+       "14        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "15        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "16        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "17        0                   None           None   \n",
+       "18        0                   None           None   \n",
+       "19        0                   None           None   \n",
+       "20        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "          3                   None           None   \n",
+       "21        0                   None           None   \n",
+       "22        0                   None           None   \n",
+       "23        0                   None           None   \n",
+       "24        0                   None           None   \n",
+       "25        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "26        0                   None           None   \n",
+       "27        0                   None           None   \n",
+       "28        0                   None           None   \n",
+       "29        0                   None           None   \n",
+       "30        0                   None           None   \n",
+       "31        0                   None           None   \n",
+       "32        0                   None           None   \n",
+       "33        0                   None           None   \n",
+       "34        0                   None           None   \n",
+       "35        0                   None           None   \n",
+       "36        0                   None           None   \n",
+       "\n",
+       "                                                                    target  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              [0.2335685, 0.254695, 0.4553995, 0.43075103, 1...   \n",
+       "1         0              [0.251174, 0.24061048, 0.44366202, 0.4307515, ...   \n",
+       "2         0                                                           None   \n",
+       "          1              [0.523474, 0.3978875, 0.634976, 0.48122054, 1....   \n",
+       "          2              [0.40610352, 0.415493, 0.5234745, 0.522301, 1....   \n",
+       "3         0              [0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...   \n",
+       "4         0              [0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...   \n",
+       "5         0              [0.6044605, 0.3990615, 0.67253554, 0.46596247,...   \n",
+       "6         0              [0.410798, 0.4436625, 0.556338, 0.51173747, 1....   \n",
+       "7         0              [0.650235, 0.4166665, 0.73826295, 0.48356748, ...   \n",
+       "8         0              [0.64788747, 0.388498, 0.7582165, 0.49413198, ...   \n",
+       "9         0              [0.6807515, 0.40140802, 0.75704247, 0.45774597...   \n",
+       "10        0              [0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...   \n",
+       "11        0              [0.42018852, 0.34507048, 0.5434275, 0.4941315,...   \n",
+       "12        0              [0.3732395, 0.348592, 0.54812247, 0.46009403, ...   \n",
+       "13        0              [0.37793398, 0.3814555, 0.48122, 0.45892054, 1...   \n",
+       "14        0                                                           None   \n",
+       "          1              [0.43427247, 0.3556335, 0.51173747, 0.43075046...   \n",
+       "          2              [0.4929575, 0.44718304, 0.5375585, 0.485915, 1...   \n",
+       "15        0                                                           None   \n",
+       "          1              [0.629108, 0.39788702, 0.67840403, 0.453051, 1...   \n",
+       "          2              [0.4530515, 0.4107985, 0.58568054, 0.5105635, ...   \n",
+       "16        0                                                           None   \n",
+       "          1              [0.45070451, 0.3967135, 0.5762915, 0.5152585, ...   \n",
+       "          2              [0.63732404, 0.403756, 0.684272, 0.46009403, 1...   \n",
+       "17        0              [0.43779355, 0.3826295, 0.61032856, 0.5223005,...   \n",
+       "18        0              [0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...   \n",
+       "19        0              [0.536385, 0.4495305, 0.600939, 0.49999946, 0....   \n",
+       "20        0                                                           None   \n",
+       "          1              [0.52347445, 0.4424885, 0.6150235, 0.5281695, ...   \n",
+       "          2              [0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...   \n",
+       "          3              [0.504695, 0.44248852, 0.54342705, 0.48004752,...   \n",
+       "21        0              [0.54107946, 0.456573, 0.6103285, 0.511737, 0....   \n",
+       "22        0              [0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...   \n",
+       "23        0              [0.517606, 0.18661949, 0.615024, 0.2875585, 1....   \n",
+       "24        0              [0.51173747, 0.18779299, 0.6126765, 0.308685, ...   \n",
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+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.64788747, 0.388498, 0.7582165, 0.49413198, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6807515, 0.40140802, 0.75704247, 0.45774597...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.42018852, 0.34507048, 0.5434275, 0.4941315,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3732395, 0.348592, 0.54812247, 0.46009403, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.37793398, 0.3814555, 0.48122, 0.45892054, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43427247, 0.3556335, 0.51173747, 0.43075046...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4929575, 0.44718304, 0.5375585, 0.485915, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">15</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.629108, 0.39788702, 0.67840403, 0.453051, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4530515, 0.4107985, 0.58568054, 0.5105635, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.45070451, 0.3967135, 0.5762915, 0.5152585, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.63732404, 0.403756, 0.684272, 0.46009403, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43779355, 0.3826295, 0.61032856, 0.5223005,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.536385, 0.4495305, 0.600939, 0.49999946, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">20</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.52347445, 0.4424885, 0.6150235, 0.5281695, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.504695, 0.44248852, 0.54342705, 0.48004752,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.54107946, 0.456573, 0.6103285, 0.511737, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.517606, 0.18661949, 0.615024, 0.2875585, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.51173747, 0.18779299, 0.6126765, 0.308685, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">25</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.30985948, 0.3626765, 0.3861505, 0.43896753,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.41314548, 0.3427225, 0.48122054, 0.42018753...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.25821602, 0.30046952, 0.46830997, 0.4577465...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2734745, 0.2828635, 0.44718352, 0.4577465, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5915495, 0.23591551, 0.6455405, 0.2887325, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.40493003, 0.2018775, 0.469484, 0.2699525, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4025825, 0.212441, 0.45539945, 0.269953, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2265255, 0.2136145, 0.3438965, 0.30751148, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6185445, 0.30164298, 0.7758215, 0.494131, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8adeb57b-4a56-45e8-8485-a1c46d89c532')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "repr_error": "Out of range float values are not JSON compliant: nan"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ],
+      "text/html": [
+       "<a href=\"http://localhost:5173\" target=\"_blank\" style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "from weightslab.backend.ledgers import get_dataframe\n",
+    "\n",
+    "# `get_df_view()` is the same per-sample DataFrame that powers Studio's grid.\n",
+    "dfm = get_dataframe()\n",
+    "grid = dfm.get_df_view() if hasattr(dfm, \"get_df_view\") else pd.DataFrame()\n",
+    "print(f\"{len(grid)} samples tracked\")\n",
+    "display(grid.head(50))\n",
+    "\n",
+    "# Open the live, interactive grid (with image + bbox previews) in Weights Studio.\n",
+    "# Studio must be running locally (`weightslab start`).\n",
+    "display(HTML(\n",
+    "    '<a href=\"http://localhost:5173\" target=\"_blank\" '\n",
+    "    'style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>'\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FXQ0gBOSKs7t"
+   },
+   "source": [
+    "> **Can clicking a grid row here jump to that sample in Studio?** Not out of the box today. The notebook and Studio are separate processes, and the link above opens Studio at its root. Per-sample deep-linking (e.g. `http://localhost:5173/?sample=<uid>` selecting and scrolling to that row) is a small frontend addition to Weights Studio — the sample `uid` is already the grid's index here, so the notebook side is ready for it once Studio accepts the query param."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vlHp09Nueb3d"
+   },
+   "source": [
+    "## Citation,License and Attribution\n",
+    "\n",
+    "```bibtex\n",
+    "@article{Geiger2013IJRR,\n",
+    "  author = {Andreas Geiger and Philip Lenz and Christoph Stiller and Raquel Urtasun},\n",
+    "  title = {Vision meets Robotics: The KITTI Dataset},\n",
+    "  journal = {International Journal of Robotics Research (IJRR)},\n",
+    "  year = {2013}\n",
+    "}\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb
new file mode 100644
index 00000000..256fdf5b
--- /dev/null
+++ b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb
@@ -0,0 +1,1701 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "accelerator": "GPU"
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Train an <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics YOLO</a> detector on the <b>medical-pills</b> dataset and stream every sample, prediction and metric live into <a href=\"https://github.com/GrayboxTech/weightslab\">Weights Studio</a> to <b>inspect, edit and evolve</b> your run.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Medical Pills Detection · Ultralytics YOLO + WeightsLab\n",
+    "\n",
+    "This notebook trains a YOLO detector on the [medical-pills](https://docs.ultralytics.com/) dataset (detecting medical pills for pharmaceutical quality control) **through the WeightsLab training pipeline**.\n",
+    "\n",
+    "Instead of Ultralytics' one-shot `train`/`predict` flow, we hand training to `WLAwareTrainer`. It wraps the train/val dataloaders, logs a **per-sample** signal for every image (loss, prediction stats, tags) and ships live prediction overlays to **Weights Studio** — where you inspect the data grid, remove/weight samples, edit the model and resume, all while training runs.\n",
+    "\n",
+    "Ultralytics auto-downloads the dataset the first time you train."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o7YFKEN1Ks7Y"
+   },
+   "source": [
+    "## 1 · Setup\n",
+    "\n",
+    "Install WeightsLab (which pulls in Ultralytics) and verify the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "71be394c",
+    "outputId": "c9ef4767-84f5-4bea-e395-9e11766002f2",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "source": [
+    "%pip install setuptools wheel"
+   ],
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (75.2.0)\n",
+      "Requirement already satisfied: wheel in /usr/local/lib/python3.12/dist-packages (0.47.0)\n",
+      "Requirement already satisfied: packaging>=24.0 in /usr/local/lib/python3.12/dist-packages (from wheel) (26.2)\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%pip install git+https://github.com/GrayboxTech/weightslab.git"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "jV1oi_PQN0xK",
+    "outputId": "eda8eca1-f4b3-4ffd-eaa6-0f67ce857153"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Collecting git+https://github.com/GrayboxTech/weightslab.git\n",
+      "  Cloning https://github.com/GrayboxTech/weightslab.git (to revision landingcollab) to /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/GrayboxTech/weightslab.git /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git checkout -b landingcollab --track origin/landingcollab\n",
+      "  Switched to a new branch 'landingcollab'\n",
+      "  Branch 'landingcollab' set up to track remote branch 'landingcollab' from 'origin'.\n",
+      "  Resolved https://github.com/GrayboxTech/weightslab.git to commit 3f1cf143bc93e67df43adc71ab831fa71477e0d2\n",
+      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "Requirement already satisfied: numpy<3,>=1.24 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.0.2)\n",
+      "Requirement already satisfied: pandas<3,>=2.2.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.2.2)\n",
+      "Requirement already satisfied: duckdb<2,>=1.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.2)\n",
+      "Requirement already satisfied: PyYAML<7,>=6.0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (6.0.3)\n",
+      "Requirement already satisfied: dill<0.5,>=0.3.8 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.3.8)\n",
+      "Requirement already satisfied: zstandard<1,>=0.22 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.25.0)\n",
+      "Requirement already satisfied: h5py<4,>=3.10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.16.0)\n",
+      "Requirement already satisfied: xxhash<4.1,>=3.4 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.7.1)\n",
+      "Requirement already satisfied: tables<4,>=3.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.10.2)\n",
+      "Requirement already satisfied: torch<=2.9,>=2.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.9.0)\n",
+      "Requirement already satisfied: torchvision<1,>=0.16 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.24.0)\n",
+      "Requirement already satisfied: torchmetrics>=1.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.9.0)\n",
+      "Requirement already satisfied: grpcio<2,>=1.80 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.81.1)\n",
+      "Requirement already satisfied: protobuf<8,>=5.28.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (5.29.6)\n",
+      "Requirement already satisfied: pydantic<3,>=2.7 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.13.4)\n",
+      "Requirement already satisfied: Pillow<12,>=10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (11.3.0)\n",
+      "Requirement already satisfied: graphviz<1,>=0.20 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.21)\n",
+      "Requirement already satisfied: onnx<=1.20,>=1.15 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.20.0)\n",
+      "Requirement already satisfied: tqdm<5,>=4.66 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (4.67.3)\n",
+      "Requirement already satisfied: python-dotenv<2,>=1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.2.2)\n",
+      "Requirement already satisfied: langchain-core<2,>=0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.4.9)\n",
+      "Requirement already satisfied: langchain-ollama<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.1.0)\n",
+      "Requirement already satisfied: langchain-openai<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.5)\n",
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+      "Requirement already satisfied: packaging>=23.2.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (26.2)\n",
+      "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (9.1.4)\n",
+      "Requirement already satisfied: uuid-utils<1.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.2)\n",
+      "Requirement already satisfied: ollama<1.0.0,>=0.6.1 in /usr/local/lib/python3.12/dist-packages (from langchain-ollama<2,>=0.2->weightslab==1.3.3.dev8) (0.6.2)\n",
+      "Requirement already satisfied: openai<3.0.0,>=2.45.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2.45.0)\n",
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+      "Requirement already satisfied: ml_dtypes>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from onnx<=1.20,>=1.15->weightslab==1.3.3.dev8) (0.5.4)\n",
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+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2025.2)\n",
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+      "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.6.1)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.1.6)\n",
+      "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2025.3.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (9.10.2.21)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.4.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.3.3.83)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (10.3.9.90)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.7.3.90)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.5.8.93)\n",
+      "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (0.7.1)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2.27.5)\n",
+      "Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.3.20)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.13.1.3)\n",
+      "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.5.0)\n",
+      "Requirement already satisfied: lightning-utilities>=0.15.3 in /usr/local/lib/python3.12/dist-packages (from torchmetrics>=1.9->weightslab==1.3.3.dev8) (0.15.3)\n",
+      "Requirement already satisfied: ndindex in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.10.1)\n",
+      "Requirement already satisfied: msgpack in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.2.1)\n",
+      "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.32.4)\n",
+      "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (13.9.4)\n",
+      "Requirement already satisfied: textual in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (6.2.1)\n",
+      "Requirement already satisfied: textual-plotext in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (1.0.1)\n",
+      "Requirement already satisfied: threadpoolctl in /usr/local/lib/python3.12/dist-packages (from blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (3.6.0)\n",
+      "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.12/dist-packages (from jsonpatch<2.0.0,>=1.33.0->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.1.1)\n",
+      "Requirement already satisfied: anyio>=3.5.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (4.14.0)\n",
+      "Requirement already satisfied: distro>=1.7.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.9.0)\n",
+      "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.28.1)\n",
+      "Requirement already satisfied: orjson>=3.9.14 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.11.9)\n",
+      "Requirement already satisfied: requests-toolbelt>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.0.0)\n",
+      "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.3.1)\n",
+      "Requirement already satisfied: websockets>=15.0 in /usr/local/lib/python3.12/dist-packages (from langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (15.0.1)\n",
+      "Requirement already satisfied: jiter<1,>=0.10.0 in /usr/local/lib/python3.12/dist-packages (from openai<3.0.0,>=2.45.0->langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.15.0)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (1.17.0)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.3.0)\n",
+      "Requirement already satisfied: regex in /usr/local/lib/python3.12/dist-packages (from tiktoken<1.0.0,>=0.7.0->langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2025.11.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.0.3)\n",
+      "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.12/dist-packages (from anyio>=3.5.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (3.18)\n",
+      "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (2026.6.17)\n",
+      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.0.9)\n",
+      "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<1.0.0,>=0.3.45->langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.0)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (3.4.7)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.5.0)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.2.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.20.0)\n",
+      "Requirement already satisfied: platformdirs<5,>=3.6.0 in /usr/local/lib/python3.12/dist-packages (from textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (4.10.0)\n",
+      "Requirement already satisfied: plotext<6.0.0,>=5.2.8 in /usr/local/lib/python3.12/dist-packages (from textual-plotext->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (5.3.2)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.1.2)\n",
+      "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.1.0)\n",
+      "Requirement already satisfied: mdit-py-plugins>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.6.1)\n",
+      "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.12/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.0.0)\n",
+      "Building wheels for collected packages: weightslab\n",
+      "  Building wheel for weightslab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Created wheel for weightslab: filename=weightslab-1.3.3.dev8-py3-none-any.whl size=2827715 sha256=5c4e7c14bbf95a48b9a8c86b5a8c744af67e1eb395197cbe79d3e443c21cf86e\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-__zwgf7x/wheels/4d/c7/cd/817c2745790555e7b6c532f5b6055d47567f9416fdfc65879b\n",
+      "Successfully built weightslab\n",
+      "Installing collected packages: weightslab\n",
+      "  Attempting uninstall: weightslab\n",
+      "    Found existing installation: weightslab 1.3.3.dev7\n",
+      "    Uninstalling weightslab-1.3.3.dev7:\n",
+      "      Successfully uninstalled weightslab-1.3.3.dev7\n",
+      "Successfully installed weightslab-1.3.3.dev8\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.colab-display-data+json": {
+       "pip_warning": {
+        "packages": [
+         "weightslab"
+        ]
+       },
+       "id": "b7fce274042740d19087972fe9c01c9b"
+      }
+     },
+     "metadata": {}
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pJhvREbaKs7f",
+    "outputId": "651baa36-9ebb-49da-830b-e6c8a6e2b64f"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Ultralytics 8.4.96 🚀 Python-3.12.13 torch-2.9.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n",
+      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.5/107.7 GB disk)\n",
+      "16/07/2026-09:29:29.683 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmp6meuzqdz/weightslab_logs/weightslab_20260716_092929.log\n",
+      "16/07/2026-09:29:29.685 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+      "16/07/2026-09:29:29.686 INFO:weightslab:<module>: \n",
+      "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+      "│                                                                                                     │\n",
+      "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+      "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+      "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+      "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+      "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+      "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+      "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+      "│                                                                                                     │\n",
+      "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+      "│                                                                                                     │\n",
+      "╰─── By GrayBx, v1.3.3.dev8 ──────────────────────────────────────────────────────────────────────────╯\n",
+      "\n",
+      "\n",
+      "16/07/2026-09:29:29.690 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+    "# and Pillow, so nothing extra is needed here.\n",
+    "# %pip install weightslab\n",
+    "# %pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev8\"\n",
+    "%pip install ultralytics\n",
+    "\n",
+    "import ultralytics\n",
+    "ultralytics.checks()\n",
+    "\n",
+    "import weightslab as wl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 · Dataset\n",
+    "\n",
+    "The dataset is described by a small YAML file that Ultralytics resolves and downloads automatically. For reference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "```yaml\n",
+    "# Ultralytics YOLO 🚀, AGPL-3.0 license\n",
+    "# Medical-pills dataset by Ultralytics\n",
+    "# Documentation: https://docs.ultralytics.com/datasets/detect/medical-pills/\n",
+    "# Example usage: yolo train data=medical-pills.yaml\n",
+    "# parent\n",
+    "# ├── ultralytics\n",
+    "# └── datasets\n",
+    "#     └── medical-pills  ← downloads here (8.19 MB)\n",
+    "\n",
+    "# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\n",
+    "path: medical-pills # dataset root dir\n",
+    "train: images/train # train images (relative to 'path') 92 images\n",
+    "val: images/val # val images (relative to 'path') 23 images\n",
+    "\n",
+    "# Classes\n",
+    "names:\n",
+    "  0: pill\n",
+    "\n",
+    "# Download script/URL (optional)\n",
+    "download: https://github.com/ultralytics/assets/releases/download/v0.0.0/medical-pills.zip\n",
+    "```"
+   ],
+   "metadata": {
+    "id": "h8go3HNgN0WU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rH79t30YKs7i"
+   },
+   "source": [
+    "## 3 · Train through WeightsLab\n",
+    "\n",
+    "We register the run's hyperparameters with `wl.watch_or_edit(...)` (so they become live-editable from Studio), start the backend services with `wl.serve()`, then hand training to `WLAwareTrainer` via the standard `YOLO(...).train(trainer=...)` entry point.\n",
+    "\n",
+    "> **Connect Weights Studio** — run `weightslab start` locally (opens `http://localhost:5173`) *before or during* training to watch the run live. On **Colab**, serve over a public tunnel instead: use `wl.serve(serving_grpc=True, serving_bore=True)` and connect your local Studio with `weightslab tunnel bore.pub:PORT` (the port is printed when serving starts).\n",
+    "\n",
+    "Data augmentations are disabled so each sample maps cleanly to its ground truth in the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ.setdefault(\"WL_PRELOAD_IMAGE_OVERVIEW\", \"0\")\n",
+    "os.environ.setdefault(\"WEIGHTSLAB_LOG_LEVEL\", \"WARNING\")\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from weightslab.integrations.ultralytics import WLAwareTrainer\n",
+    "from ultralytics import YOLO\n",
+    "\n",
+    "# Hyperparameters registered with WeightsLab -> live-editable from Weights Studio.\n",
+    "cfg = {\n",
+    "    \"model\": \"yolo11n.pt\",       # pretrained checkpoint\n",
+    "    \"data\": \"medical-pills.yaml\",  # Ultralytics auto-downloads\n",
+    "    \"image_size\": 640,\n",
+    "    \"epochs\": 20,\n",
+    "    # Per-sample prediction overlays streamed to Studio (NMS on the train set).\n",
+    "    \"signals_cfg\": {\n",
+    "        \"train_nms\": {\"conf_thres\": 0.25, \"iou_thres\": 0.45, \"max_nms\": 7},\n",
+    "    },\n",
+    "}\n",
+    "wl.watch_or_edit(cfg, flag=\"hyperparameters\", defaults=cfg, poll_interval=1.0)\n",
+    "\n",
+    "# Start the WeightsLab backend services that Weights Studio connects to.\n",
+    "wl.serve(serving_grpc=True)\n",
+    "#   Colab -> local Studio:  wl.serve(serving_grpc=True, serving_bore=True)\n",
+    "\n",
+    "wl.start_training()  # equivalent to pressing \"play\" in Studio\n",
+    "\n",
+    "# Read back the (now live) hyperparameters and hand training to WLAwareTrainer.\n",
+    "model = YOLO(cfg[\"model\"])\n",
+    "results = model.train(\n",
+    "    trainer=WLAwareTrainer,\n",
+    "    data=str(cfg[\"data\"]),\n",
+    "    imgsz=cfg[\"image_size\"],\n",
+    "    epochs=int(cfg[\"epochs\"]),\n",
+    "    project=\"./wl_logs\", name=\"medical-pills\",  # -> WL log_dir/name\n",
+    "    workers=0,          # WL invariant (parent-process uid counter)\n",
+    "    optimizer=\"SGD\", lr0=0.001, amp=False,\n",
+    "    # All augmentations off for a clean sample <-> ground-truth association.\n",
+    "    mosaic=0.0, mixup=0.0, copy_paste=0.0,\n",
+    "    hsv_h=0.0, hsv_s=0.0, hsv_v=0.0,\n",
+    "    degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0,\n",
+    "    flipud=0.0, fliplr=0.0, erasing=0.0, auto_augment=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f-Uw_s_8Ks7m"
+   },
+   "source": [
+    "## 4 · Explore the data grid\n",
+    "\n",
+    "The table below is exactly the data behind Weights Studio's **grid explorer**: one row per sample, keyed by split (`origin`) and sample id, with the per-sample loss, prediction stats and tags accumulated during training. This is the ledger that Studio reads from — here we render it inline with pandas.\n",
+    "\n",
+    "The image thumbnails and bounding-box overlays themselves are rendered by **Weights Studio** (which streams full previews over gRPC); the link below opens the interactive grid there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "collapsed": true,
+    "id": "uOJdXGA3Ks7o",
+    "outputId": "26652fa3-6a15-43ce-9d75-034eaba21ccd"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "1241 samples tracked\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                        prediction prediction_raw  \\\n",
+       "sample_id annotation_id                             \n",
+       "0         0                   None           None   \n",
+       "1         0                   None           None   \n",
+       "2         0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "3         0                   None           None   \n",
+       "4         0                   None           None   \n",
+       "5         0                   None           None   \n",
+       "6         0                   None           None   \n",
+       "7         0                   None           None   \n",
+       "8         0                   None           None   \n",
+       "9         0                   None           None   \n",
+       "10        0                   None           None   \n",
+       "11        0                   None           None   \n",
+       "12        0                   None           None   \n",
+       "13        0                   None           None   \n",
+       "14        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "15        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "16        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "17        0                   None           None   \n",
+       "18        0                   None           None   \n",
+       "19        0                   None           None   \n",
+       "20        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "          3                   None           None   \n",
+       "21        0                   None           None   \n",
+       "22        0                   None           None   \n",
+       "23        0                   None           None   \n",
+       "24        0                   None           None   \n",
+       "25        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "26        0                   None           None   \n",
+       "27        0                   None           None   \n",
+       "28        0                   None           None   \n",
+       "29        0                   None           None   \n",
+       "30        0                   None           None   \n",
+       "31        0                   None           None   \n",
+       "32        0                   None           None   \n",
+       "33        0                   None           None   \n",
+       "34        0                   None           None   \n",
+       "35        0                   None           None   \n",
+       "36        0                   None           None   \n",
+       "\n",
+       "                                                                    target  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              [0.2335685, 0.254695, 0.4553995, 0.43075103, 1...   \n",
+       "1         0              [0.251174, 0.24061048, 0.44366202, 0.4307515, ...   \n",
+       "2         0                                                           None   \n",
+       "          1              [0.523474, 0.3978875, 0.634976, 0.48122054, 1....   \n",
+       "          2              [0.40610352, 0.415493, 0.5234745, 0.522301, 1....   \n",
+       "3         0              [0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...   \n",
+       "4         0              [0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...   \n",
+       "5         0              [0.6044605, 0.3990615, 0.67253554, 0.46596247,...   \n",
+       "6         0              [0.410798, 0.4436625, 0.556338, 0.51173747, 1....   \n",
+       "7         0              [0.650235, 0.4166665, 0.73826295, 0.48356748, ...   \n",
+       "8         0              [0.64788747, 0.388498, 0.7582165, 0.49413198, ...   \n",
+       "9         0              [0.6807515, 0.40140802, 0.75704247, 0.45774597...   \n",
+       "10        0              [0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...   \n",
+       "11        0              [0.42018852, 0.34507048, 0.5434275, 0.4941315,...   \n",
+       "12        0              [0.3732395, 0.348592, 0.54812247, 0.46009403, ...   \n",
+       "13        0              [0.37793398, 0.3814555, 0.48122, 0.45892054, 1...   \n",
+       "14        0                                                           None   \n",
+       "          1              [0.43427247, 0.3556335, 0.51173747, 0.43075046...   \n",
+       "          2              [0.4929575, 0.44718304, 0.5375585, 0.485915, 1...   \n",
+       "15        0                                                           None   \n",
+       "          1              [0.629108, 0.39788702, 0.67840403, 0.453051, 1...   \n",
+       "          2              [0.4530515, 0.4107985, 0.58568054, 0.5105635, ...   \n",
+       "16        0                                                           None   \n",
+       "          1              [0.45070451, 0.3967135, 0.5762915, 0.5152585, ...   \n",
+       "          2              [0.63732404, 0.403756, 0.684272, 0.46009403, 1...   \n",
+       "17        0              [0.43779355, 0.3826295, 0.61032856, 0.5223005,...   \n",
+       "18        0              [0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...   \n",
+       "19        0              [0.536385, 0.4495305, 0.600939, 0.49999946, 0....   \n",
+       "20        0                                                           None   \n",
+       "          1              [0.52347445, 0.4424885, 0.6150235, 0.5281695, ...   \n",
+       "          2              [0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...   \n",
+       "          3              [0.504695, 0.44248852, 0.54342705, 0.48004752,...   \n",
+       "21        0              [0.54107946, 0.456573, 0.6103285, 0.511737, 0....   \n",
+       "22        0              [0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...   \n",
+       "23        0              [0.517606, 0.18661949, 0.615024, 0.2875585, 1....   \n",
+       "24        0              [0.51173747, 0.18779299, 0.6126765, 0.308685, ...   \n",
+       "25        0                                                           None   \n",
+       "          1              [0.30985948, 0.3626765, 0.3861505, 0.43896753,...   \n",
+       "          2              [0.41314548, 0.3427225, 0.48122054, 0.42018753...   \n",
+       "26        0              [0.25821602, 0.30046952, 0.46830997, 0.4577465...   \n",
+       "27        0              [0.2734745, 0.2828635, 0.44718352, 0.4577465, ...   \n",
+       "28        0              [0.5915495, 0.23591551, 0.6455405, 0.2887325, ...   \n",
+       "29        0              [0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...   \n",
+       "30        0              [0.40493003, 0.2018775, 0.469484, 0.2699525, 1...   \n",
+       "31        0              [0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...   \n",
+       "32        0              [0.4025825, 0.212441, 0.45539945, 0.269953, 1....   \n",
+       "33        0              [0.2265255, 0.2136145, 0.3438965, 0.30751148, ...   \n",
+       "34        0               [0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]   \n",
+       "35        0              [0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...   \n",
+       "36        0              [0.6185445, 0.30164298, 0.7758215, 0.494131, 1...   \n",
+       "\n",
+       "                        discarded        origin task_type  last_seen group_id  \\\n",
+       "sample_id annotation_id                                                         \n",
+       "0         0                 False  train_loader                 -1.0        0   \n",
+       "1         0                 False  train_loader                 -1.0        1   \n",
+       "2         0                 False  train_loader                 -1.0        2   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "3         0                 False  train_loader                 -1.0        3   \n",
+       "4         0                 False  train_loader                 -1.0        4   \n",
+       "5         0                 False  train_loader                 -1.0        5   \n",
+       "6         0                 False  train_loader                 -1.0        6   \n",
+       "7         0                 False  train_loader                 -1.0        7   \n",
+       "8         0                 False  train_loader                 -1.0        8   \n",
+       "9         0                 False  train_loader                 -1.0        9   \n",
+       "10        0                 False  train_loader                 -1.0       10   \n",
+       "11        0                 False  train_loader                 -1.0       11   \n",
+       "12        0                 False  train_loader                 -1.0       12   \n",
+       "13        0                 False  train_loader                 -1.0       13   \n",
+       "14        0                 False  train_loader                 -1.0       14   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "15        0                 False  train_loader                 -1.0       15   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "16        0                 False  train_loader                 -1.0       16   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "17        0                 False  train_loader                 -1.0       17   \n",
+       "18        0                 False  train_loader                 -1.0       18   \n",
+       "19        0                 False  train_loader                 -1.0       19   \n",
+       "20        0                 False  train_loader                 -1.0       20   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "          3                  None           NaN      None        NaN     None   \n",
+       "21        0                 False  train_loader                 -1.0       21   \n",
+       "22        0                 False  train_loader                 -1.0       22   \n",
+       "23        0                 False  train_loader                 -1.0       23   \n",
+       "24        0                 False  train_loader                 -1.0       24   \n",
+       "25        0                 False  train_loader                 -1.0       25   \n",
+       "          1                  None           NaN      None        NaN     None   \n",
+       "          2                  None           NaN      None        NaN     None   \n",
+       "26        0                 False  train_loader                 -1.0       26   \n",
+       "27        0                 False  train_loader                 -1.0       27   \n",
+       "28        0                 False  train_loader                 -1.0       28   \n",
+       "29        0                 False  train_loader                 -1.0       29   \n",
+       "30        0                 False  train_loader                 -1.0       30   \n",
+       "31        0                 False  train_loader                 -1.0       31   \n",
+       "32        0                 False  train_loader                 -1.0       32   \n",
+       "33        0                 False  train_loader                 -1.0       33   \n",
+       "34        0                 False  train_loader                 -1.0       34   \n",
+       "35        0                 False  train_loader                 -1.0       35   \n",
+       "36        0                 False  train_loader                 -1.0       36   \n",
+       "\n",
+       "                         member_rank  \\\n",
+       "sample_id annotation_id                \n",
+       "0         0                      0.0   \n",
+       "1         0                      0.0   \n",
+       "2         0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "3         0                      0.0   \n",
+       "4         0                      0.0   \n",
+       "5         0                      0.0   \n",
+       "6         0                      0.0   \n",
+       "7         0                      0.0   \n",
+       "8         0                      0.0   \n",
+       "9         0                      0.0   \n",
+       "10        0                      0.0   \n",
+       "11        0                      0.0   \n",
+       "12        0                      0.0   \n",
+       "13        0                      0.0   \n",
+       "14        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "15        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "16        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "17        0                      0.0   \n",
+       "18        0                      0.0   \n",
+       "19        0                      0.0   \n",
+       "20        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "          3                      NaN   \n",
+       "21        0                      0.0   \n",
+       "22        0                      0.0   \n",
+       "23        0                      0.0   \n",
+       "24        0                      0.0   \n",
+       "25        0                      0.0   \n",
+       "          1                      NaN   \n",
+       "          2                      NaN   \n",
+       "26        0                      0.0   \n",
+       "27        0                      0.0   \n",
+       "28        0                      0.0   \n",
+       "29        0                      0.0   \n",
+       "30        0                      0.0   \n",
+       "31        0                      0.0   \n",
+       "32        0                      0.0   \n",
+       "33        0                      0.0   \n",
+       "34        0                      0.0   \n",
+       "35        0                      0.0   \n",
+       "36        0                      0.0   \n",
+       "\n",
+       "                                                                  img_path  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "1         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "2         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "3         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "4         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "5         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "6         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "7         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "8         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "9         0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "10        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "11        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "12        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "13        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "14        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "15        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "16        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "17        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "18        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "19        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "20        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "          1                                                           None   \n",
+       "          2                                                           None   \n",
+       "          3                                                           None   \n",
+       "21        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "22        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "23        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "24        0              /content/datasets/brain-tumor/images/train/000...   \n",
+       "25        0              /content/datasets/brain-tumor/images/train/000...   \n",
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+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.63732404, 0.403756, 0.684272, 0.46009403, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43779355, 0.3826295, 0.61032856, 0.5223005,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.536385, 0.4495305, 0.600939, 0.49999946, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">20</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.52347445, 0.4424885, 0.6150235, 0.5281695, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.504695, 0.44248852, 0.54342705, 0.48004752,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.54107946, 0.456573, 0.6103285, 0.511737, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.517606, 0.18661949, 0.615024, 0.2875585, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.51173747, 0.18779299, 0.6126765, 0.308685, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">25</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.30985948, 0.3626765, 0.3861505, 0.43896753,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.41314548, 0.3427225, 0.48122054, 0.42018753...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.25821602, 0.30046952, 0.46830997, 0.4577465...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2734745, 0.2828635, 0.44718352, 0.4577465, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5915495, 0.23591551, 0.6455405, 0.2887325, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.40493003, 0.2018775, 0.469484, 0.2699525, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4025825, 0.212441, 0.45539945, 0.269953, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2265255, 0.2136145, 0.3438965, 0.30751148, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6185445, 0.30164298, 0.7758215, 0.494131, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
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+       "          + ' to learn more about interactive tables.';\n",
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+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ],
+      "text/html": [
+       "<a href=\"http://localhost:5173\" target=\"_blank\" style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "from weightslab.backend.ledgers import get_dataframe\n",
+    "\n",
+    "# `get_df_view()` is the same per-sample DataFrame that powers Studio's grid.\n",
+    "dfm = get_dataframe()\n",
+    "grid = dfm.get_df_view() if hasattr(dfm, \"get_df_view\") else pd.DataFrame()\n",
+    "print(f\"{len(grid)} samples tracked\")\n",
+    "display(grid.head(50))\n",
+    "\n",
+    "# Open the live, interactive grid (with image + bbox previews) in Weights Studio.\n",
+    "# Studio must be running locally (`weightslab start`).\n",
+    "display(HTML(\n",
+    "    '<a href=\"http://localhost:5173\" target=\"_blank\" '\n",
+    "    'style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>'\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FXQ0gBOSKs7t"
+   },
+   "source": [
+    "> **Can clicking a grid row here jump to that sample in Studio?** Not out of the box today. The notebook and Studio are separate processes, and the link above opens Studio at its root. Per-sample deep-linking (e.g. `http://localhost:5173/?sample=<uid>` selecting and scrolling to that row) is a small frontend addition to Weights Studio — the sample `uid` is already the grid's index here, so the notebook side is ready for it once Studio accepts the query param."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Citation\n",
+    "\n",
+    "```bibtex\n",
+    "@dataset{Jocher_Ultralytics_Datasets_2024,\n",
+    "    author = {Jocher, Glenn and Rizwan, Muhammad},\n",
+    "    license = {AGPL-3.0},\n",
+    "    month = {Dec},\n",
+    "    title = {Ultralytics Datasets:Medical-pills Detection Dataset},\n",
+    "    url = {https://docs.ultralytics.com/datasets/detect/medical-pills/},\n",
+    "    version = {1.0.0},\n",
+    "    year = {2024}\n",
+    "}\n"
+   ],
+   "metadata": {
+    "id": "vlHp09Nueb3d"
+   }
+  }
+ ]
+}
diff --git a/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-package-segmentation-dataset.ipynb b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-package-segmentation-dataset.ipynb
new file mode 100644
index 00000000..60431dea
--- /dev/null
+++ b/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-package-segmentation-dataset.ipynb
@@ -0,0 +1,1683 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "accelerator": "GPU"
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Ultralytics/wl-how-to-train-ultralytics-yolo-on-package-segmentation-dataset.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Train an <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics YOLO</a> segmentation model on the <b>package-seg</b> dataset and stream every sample, prediction and metric live into <a href=\"https://github.com/GrayboxTech/weightslab\">Weights Studio</a> to <b>inspect, edit and evolve</b> your run.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Package Segmentation · Ultralytics YOLO + WeightsLab\n",
+    "\n",
+    "This notebook trains a YOLO segmentation model on the [package-seg](https://docs.ultralytics.com/) dataset (segmenting shipping packages on conveyors for logistics automation) **through the WeightsLab training pipeline**.\n",
+    "\n",
+    "Instead of Ultralytics' one-shot `train`/`predict` flow, we hand training to `WLAwareSegmentationTrainer`. It wraps the train/val dataloaders, logs a **per-sample** signal for every image (loss, prediction stats, tags) and ships live prediction overlays to **Weights Studio** — where you inspect the data grid, remove/weight samples, edit the model and resume, all while training runs.\n",
+    "\n",
+    "Ultralytics auto-downloads the dataset the first time you train."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o7YFKEN1Ks7Y"
+   },
+   "source": [
+    "## 1 · Setup\n",
+    "\n",
+    "Install WeightsLab (which pulls in Ultralytics) and verify the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "71be394c",
+    "outputId": "c9ef4767-84f5-4bea-e395-9e11766002f2",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "source": [
+    "%pip install setuptools wheel"
+   ],
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (75.2.0)\n",
+      "Requirement already satisfied: wheel in /usr/local/lib/python3.12/dist-packages (0.47.0)\n",
+      "Requirement already satisfied: packaging>=24.0 in /usr/local/lib/python3.12/dist-packages (from wheel) (26.2)\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%pip install git+https://github.com/GrayboxTech/weightslab.git"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "jV1oi_PQN0xK",
+    "outputId": "eda8eca1-f4b3-4ffd-eaa6-0f67ce857153"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Collecting git+https://github.com/GrayboxTech/weightslab.git\n",
+      "  Cloning https://github.com/GrayboxTech/weightslab.git (to revision landingcollab) to /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/GrayboxTech/weightslab.git /tmp/pip-req-build-z4ba7cx9\n",
+      "  Running command git checkout -b landingcollab --track origin/landingcollab\n",
+      "  Switched to a new branch 'landingcollab'\n",
+      "  Branch 'landingcollab' set up to track remote branch 'landingcollab' from 'origin'.\n",
+      "  Resolved https://github.com/GrayboxTech/weightslab.git to commit 3f1cf143bc93e67df43adc71ab831fa71477e0d2\n",
+      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "Requirement already satisfied: numpy<3,>=1.24 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.0.2)\n",
+      "Requirement already satisfied: pandas<3,>=2.2.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.2.2)\n",
+      "Requirement already satisfied: duckdb<2,>=1.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.2)\n",
+      "Requirement already satisfied: PyYAML<7,>=6.0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (6.0.3)\n",
+      "Requirement already satisfied: dill<0.5,>=0.3.8 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.3.8)\n",
+      "Requirement already satisfied: zstandard<1,>=0.22 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.25.0)\n",
+      "Requirement already satisfied: h5py<4,>=3.10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.16.0)\n",
+      "Requirement already satisfied: xxhash<4.1,>=3.4 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.7.1)\n",
+      "Requirement already satisfied: tables<4,>=3.9 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (3.10.2)\n",
+      "Requirement already satisfied: torch<=2.9,>=2.1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.9.0)\n",
+      "Requirement already satisfied: torchvision<1,>=0.16 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.24.0)\n",
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+      "Requirement already satisfied: pydantic<3,>=2.7 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (2.13.4)\n",
+      "Requirement already satisfied: Pillow<12,>=10 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (11.3.0)\n",
+      "Requirement already satisfied: graphviz<1,>=0.20 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (0.21)\n",
+      "Requirement already satisfied: onnx<=1.20,>=1.15 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.20.0)\n",
+      "Requirement already satisfied: tqdm<5,>=4.66 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (4.67.3)\n",
+      "Requirement already satisfied: python-dotenv<2,>=1 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.2.2)\n",
+      "Requirement already satisfied: langchain-core<2,>=0.3 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.4.9)\n",
+      "Requirement already satisfied: langchain-ollama<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.1.0)\n",
+      "Requirement already satisfied: langchain-openai<2,>=0.2 in /usr/local/lib/python3.12/dist-packages (from weightslab==1.3.3.dev8) (1.3.5)\n",
+      "Requirement already satisfied: typing-extensions~=4.12 in /usr/local/lib/python3.12/dist-packages (from grpcio<2,>=1.80->weightslab==1.3.3.dev8) (4.15.0)\n",
+      "Requirement already satisfied: jsonpatch<2.0.0,>=1.33.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (1.33)\n",
+      "Requirement already satisfied: langchain-protocol>=0.0.17 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.0.18)\n",
+      "Requirement already satisfied: langsmith<1.0.0,>=0.3.45 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.9.1)\n",
+      "Requirement already satisfied: packaging>=23.2.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (26.2)\n",
+      "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (9.1.4)\n",
+      "Requirement already satisfied: uuid-utils<1.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from langchain-core<2,>=0.3->weightslab==1.3.3.dev8) (0.16.2)\n",
+      "Requirement already satisfied: ollama<1.0.0,>=0.6.1 in /usr/local/lib/python3.12/dist-packages (from langchain-ollama<2,>=0.2->weightslab==1.3.3.dev8) (0.6.2)\n",
+      "Requirement already satisfied: openai<3.0.0,>=2.45.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (2.45.0)\n",
+      "Requirement already satisfied: tiktoken<1.0.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from langchain-openai<2,>=0.2->weightslab==1.3.3.dev8) (0.13.0)\n",
+      "Requirement already satisfied: ml_dtypes>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from onnx<=1.20,>=1.15->weightslab==1.3.3.dev8) (0.5.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<3,>=2.2.2->weightslab==1.3.3.dev8) (2026.2)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.46.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (2.46.4)\n",
+      "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=2.7->weightslab==1.3.3.dev8) (0.4.2)\n",
+      "Requirement already satisfied: numexpr>=2.6.2 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (2.14.1)\n",
+      "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (9.0.0)\n",
+      "Requirement already satisfied: blosc2>=2.3.0 in /usr/local/lib/python3.12/dist-packages (from tables<4,>=3.9->weightslab==1.3.3.dev8) (4.5.1)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.29.4)\n",
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (75.2.0)\n",
+      "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.14.0)\n",
+      "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.6.1)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.1.6)\n",
+      "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2025.3.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (9.10.2.21)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.4.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.3.3.83)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (10.3.9.90)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (11.7.3.90)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.5.8.93)\n",
+      "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (0.7.1)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (2.27.5)\n",
+      "Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.3.20)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.90)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (12.8.93)\n",
+      "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (1.13.1.3)\n",
+      "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch<=2.9,>=2.1->weightslab==1.3.3.dev8) (3.5.0)\n",
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+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.5.0)\n",
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+      "Requirement already satisfied: mdit-py-plugins>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (0.6.1)\n",
+      "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.12/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]>=2.1.0->textual->blosc2>=2.3.0->tables<4,>=3.9->weightslab==1.3.3.dev8) (2.0.0)\n",
+      "Building wheels for collected packages: weightslab\n",
+      "  Building wheel for weightslab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Created wheel for weightslab: filename=weightslab-1.3.3.dev8-py3-none-any.whl size=2827715 sha256=5c4e7c14bbf95a48b9a8c86b5a8c744af67e1eb395197cbe79d3e443c21cf86e\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-__zwgf7x/wheels/4d/c7/cd/817c2745790555e7b6c532f5b6055d47567f9416fdfc65879b\n",
+      "Successfully built weightslab\n",
+      "Installing collected packages: weightslab\n",
+      "  Attempting uninstall: weightslab\n",
+      "    Found existing installation: weightslab 1.3.3.dev7\n",
+      "    Uninstalling weightslab-1.3.3.dev7:\n",
+      "      Successfully uninstalled weightslab-1.3.3.dev7\n",
+      "Successfully installed weightslab-1.3.3.dev8\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.colab-display-data+json": {
+       "pip_warning": {
+        "packages": [
+         "weightslab"
+        ]
+       },
+       "id": "b7fce274042740d19087972fe9c01c9b"
+      }
+     },
+     "metadata": {}
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pJhvREbaKs7f",
+    "outputId": "651baa36-9ebb-49da-830b-e6c8a6e2b64f"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Ultralytics 8.4.96 🚀 Python-3.12.13 torch-2.9.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n",
+      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.5/107.7 GB disk)\n",
+      "16/07/2026-09:29:29.683 INFO:root:setup_logging: WeightsLab logging initialized - Log file: /tmp/tmp6meuzqdz/weightslab_logs/weightslab_20260716_092929.log\n",
+      "16/07/2026-09:29:29.685 INFO:weightslab:<module>: WeightsLab package initialized - Log level: INFO, Log to file: True\n",
+      "16/07/2026-09:29:29.686 INFO:weightslab:<module>: \n",
+      "╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+      "│                                                                                                     │\n",
+      "│  \u001b[32m$$\\      $$\\ \u001b[0m          $$\\           $$\\         $$\\               \u001b[31m$$\\       \u001b[0m          $$\\         │\n",
+      "│  \u001b[32m$$ | $\\  $$ |\u001b[0m          \\__|          $$ |        $$ |              \u001b[31m$$ |      \u001b[0m          $$ |        │\n",
+      "│  \u001b[32m$$ |$$$\\ $$ |\u001b[0m $$$$$$\\  $$\\  $$$$$$\\  $$$$$$$\\  $$$$$$\\    $$$$$$$\\ \u001b[31m$$ |      \u001b[0m $$$$$$\\  $$$$$$$\\    │\n",
+      "│  \u001b[32m$$ $$ $$\\$$ |\u001b[0m$$  __$$\\ $$ |$$  __$$\\ $$  __$$\\ \\_$$  _|  $$  _____|\u001b[31m$$ |      \u001b[0m \\____$$\\ $$  __$$\\   │\n",
+      "│  \u001b[32m$$$$  _$$$$ |\u001b[0m$$$$$$$$ |$$ |$$ /  $$ |$$ |  $$ |  $$ |    \\$$$$$$\\  \u001b[31m$$ |      \u001b[0m $$$$$$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$$  / \\$$$ |\u001b[0m$$   ____|$$ |$$ |  $$ |$$ |  $$ |  $$ |$$\\  \\____$$\\ \u001b[31m$$ |      \u001b[0m$$  __$$ |$$ |  $$ |  │\n",
+      "│  \u001b[32m$$  /   \\$$ |\u001b[0m\\$$$$$$$\\ $$ |\\$$$$$$$ |$$ |  $$ |  \\$$$$  |$$$$$$$  |\u001b[31m$$$$$$$$\\ \u001b[0m\\$$$$$$$ |$$$$$$$  |  │\n",
+      "│  \u001b[32m\\__/     \\__|\u001b[0m \\_______|\\__| \\____$$ |\\__|  \\__|   \\____/ \\_______/ \u001b[31m\\________|\u001b[0m \\_______|\\_______/   │\n",
+      "│  \u001b[32m             \u001b[0m              $$\\   $$ |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m              \\$$$$$$  |\u001b[31m\u001b[0m                                                              │\n",
+      "│  \u001b[32m             \u001b[0m               \\______/\u001b[31m\u001b[0m                                                               │\n",
+      "│                                                                                                     │\n",
+      "│                               Inspect - Edit - Evolve Neural Networks                               │\n",
+      "│                                                                                                     │\n",
+      "╰─── By GrayBx, v1.3.3.dev8 ──────────────────────────────────────────────────────────────────────────╯\n",
+      "\n",
+      "\n",
+      "16/07/2026-09:29:29.690 INFO:weightslab.utils.telemetry:ping_import: WeightsLab uses anonymous usage data (package version used, and OS name). Set WL_NO_TELEMETRY=1 to disable.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install WeightsLab. Colab already ships torch, torchvision, numpy, scikit-learn\n",
+    "# and Pillow, so nothing extra is needed here.\n",
+    "# %pip install weightslab\n",
+    "# %pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ \"weightslab==1.3.3.dev8\"\n",
+    "%pip install ultralytics\n",
+    "\n",
+    "import ultralytics\n",
+    "ultralytics.checks()\n",
+    "\n",
+    "import weightslab as wl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 · Dataset\n",
+    "\n",
+    "The dataset is described by a small YAML file that Ultralytics resolves and downloads automatically. For reference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "```yaml\n",
+    "# Ultralytics YOLO 🚀, AGPL-3.0 license\n",
+    "# Package-seg dataset by Ultralytics\n",
+    "# Documentation: https://docs.ultralytics.com/datasets/segment/package-seg/\n",
+    "# Example usage: yolo train data=package-seg.yaml\n",
+    "# parent\n",
+    "# ├── ultralytics\n",
+    "# └── datasets\n",
+    "#     └── package-seg  ← downloads here (103 MB)\n",
+    "\n",
+    "# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\n",
+    "path: package-seg # dataset root dir\n",
+    "train: images/train # train images (relative to 'path') 1920 images\n",
+    "val: images/val # val images (relative to 'path') 89 images\n",
+    "test: images/test # test images (relative to 'path') 188 images\n",
+    "\n",
+    "# Classes\n",
+    "names:\n",
+    "  0: package\n",
+    "\n",
+    "# Download script/URL (optional)\n",
+    "download: https://github.com/ultralytics/assets/releases/download/v0.0.0/package-seg.zip\n",
+    "```"
+   ],
+   "metadata": {
+    "id": "h8go3HNgN0WU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rH79t30YKs7i"
+   },
+   "source": [
+    "## 3 · Train through WeightsLab\n",
+    "\n",
+    "We register the run's hyperparameters with `wl.watch_or_edit(...)` (so they become live-editable from Studio), start the backend services with `wl.serve()`, then hand training to `WLAwareTrainer` via the standard `YOLO(...).train(trainer=...)` entry point.\n",
+    "\n",
+    "> **Connect Weights Studio** — run `weightslab start` locally (opens `http://localhost:5173`) *before or during* training to watch the run live. On **Colab**, serve over a public tunnel instead: use `wl.serve(serving_grpc=True, serving_bore=True)` and connect your local Studio with `weightslab tunnel bore.pub:PORT` (the port is printed when serving starts).\n",
+    "\n",
+    "Data augmentations are disabled so each sample maps cleanly to its ground truth in the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ.setdefault(\"WL_PRELOAD_IMAGE_OVERVIEW\", \"0\")\n",
+    "os.environ.setdefault(\"WEIGHTSLAB_LOG_LEVEL\", \"WARNING\")\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from weightslab.integrations.ultralytics import WLAwareSegmentationTrainer\n",
+    "from ultralytics import YOLO\n",
+    "\n",
+    "# Hyperparameters registered with WeightsLab -> live-editable from Weights Studio.\n",
+    "cfg = {\n",
+    "    \"model\": \"yolo11n-seg.pt\",       # pretrained checkpoint\n",
+    "    \"data\": \"package-seg.yaml\",  # Ultralytics auto-downloads\n",
+    "    \"image_size\": 640,\n",
+    "    \"epochs\": 10,\n",
+    "    # Per-sample prediction overlays streamed to Studio (NMS on the train set).\n",
+    "    \"signals_cfg\": {\n",
+    "        \"train_nms\": {\"conf_thres\": 0.25, \"iou_thres\": 0.45, \"max_nms\": 7},\n",
+    "    },\n",
+    "}\n",
+    "wl.watch_or_edit(cfg, flag=\"hyperparameters\", defaults=cfg, poll_interval=1.0)\n",
+    "\n",
+    "# Start the WeightsLab backend services that Weights Studio connects to.\n",
+    "wl.serve(serving_grpc=True)\n",
+    "#   Colab -> local Studio:  wl.serve(serving_grpc=True, serving_bore=True)\n",
+    "\n",
+    "wl.start_training()  # equivalent to pressing \"play\" in Studio\n",
+    "\n",
+    "# Read back the (now live) hyperparameters and hand training to WLAwareSegmentationTrainer.\n",
+    "model = YOLO(cfg[\"model\"])\n",
+    "results = model.train(\n",
+    "    trainer=WLAwareSegmentationTrainer,\n",
+    "    data=str(cfg[\"data\"]),\n",
+    "    imgsz=cfg[\"image_size\"],\n",
+    "    epochs=int(cfg[\"epochs\"]),\n",
+    "    project=\"./wl_logs\", name=\"package-seg\",  # -> WL log_dir/name\n",
+    "    workers=0,          # WL invariant (parent-process uid counter)\n",
+    "    optimizer=\"SGD\", lr0=0.001, amp=False,\n",
+    "    # All augmentations off for a clean sample <-> ground-truth association.\n",
+    "    mosaic=0.0, mixup=0.0, copy_paste=0.0,\n",
+    "    hsv_h=0.0, hsv_s=0.0, hsv_v=0.0,\n",
+    "    degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0,\n",
+    "    flipud=0.0, fliplr=0.0, erasing=0.0, auto_augment=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f-Uw_s_8Ks7m"
+   },
+   "source": [
+    "## 4 · Explore the data grid\n",
+    "\n",
+    "The table below is exactly the data behind Weights Studio's **grid explorer**: one row per sample, keyed by split (`origin`) and sample id, with the per-sample loss, prediction stats and tags accumulated during training. This is the ledger that Studio reads from — here we render it inline with pandas.\n",
+    "\n",
+    "The image thumbnails and bounding-box overlays themselves are rendered by **Weights Studio** (which streams full previews over gRPC); the link below opens the interactive grid there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "collapsed": true,
+    "id": "uOJdXGA3Ks7o",
+    "outputId": "26652fa3-6a15-43ce-9d75-034eaba21ccd"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "1241 samples tracked\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                        prediction prediction_raw  \\\n",
+       "sample_id annotation_id                             \n",
+       "0         0                   None           None   \n",
+       "1         0                   None           None   \n",
+       "2         0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "3         0                   None           None   \n",
+       "4         0                   None           None   \n",
+       "5         0                   None           None   \n",
+       "6         0                   None           None   \n",
+       "7         0                   None           None   \n",
+       "8         0                   None           None   \n",
+       "9         0                   None           None   \n",
+       "10        0                   None           None   \n",
+       "11        0                   None           None   \n",
+       "12        0                   None           None   \n",
+       "13        0                   None           None   \n",
+       "14        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "15        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "16        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "17        0                   None           None   \n",
+       "18        0                   None           None   \n",
+       "19        0                   None           None   \n",
+       "20        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "          3                   None           None   \n",
+       "21        0                   None           None   \n",
+       "22        0                   None           None   \n",
+       "23        0                   None           None   \n",
+       "24        0                   None           None   \n",
+       "25        0                   None           None   \n",
+       "          1                   None           None   \n",
+       "          2                   None           None   \n",
+       "26        0                   None           None   \n",
+       "27        0                   None           None   \n",
+       "28        0                   None           None   \n",
+       "29        0                   None           None   \n",
+       "30        0                   None           None   \n",
+       "31        0                   None           None   \n",
+       "32        0                   None           None   \n",
+       "33        0                   None           None   \n",
+       "34        0                   None           None   \n",
+       "35        0                   None           None   \n",
+       "36        0                   None           None   \n",
+       "\n",
+       "                                                                    target  \\\n",
+       "sample_id annotation_id                                                      \n",
+       "0         0              [0.2335685, 0.254695, 0.4553995, 0.43075103, 1...   \n",
+       "1         0              [0.251174, 0.24061048, 0.44366202, 0.4307515, ...   \n",
+       "2         0                                                           None   \n",
+       "          1              [0.523474, 0.3978875, 0.634976, 0.48122054, 1....   \n",
+       "          2              [0.40610352, 0.415493, 0.5234745, 0.522301, 1....   \n",
+       "3         0              [0.403756, 0.3826295, 0.637324, 0.5152585, 1.0...   \n",
+       "4         0              [0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...   \n",
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+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4436615, 0.3814555, 0.5938965, 0.4518785, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6044605, 0.3990615, 0.67253554, 0.46596247,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.410798, 0.4436625, 0.556338, 0.51173747, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.650235, 0.4166665, 0.73826295, 0.48356748, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.64788747, 0.388498, 0.7582165, 0.49413198, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6807515, 0.40140802, 0.75704247, 0.45774597...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.431925, 0.1701875, 0.561033, 0.3180745, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.42018852, 0.34507048, 0.5434275, 0.4941315,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3732395, 0.348592, 0.54812247, 0.46009403, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.37793398, 0.3814555, 0.48122, 0.45892054, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43427247, 0.3556335, 0.51173747, 0.43075046...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4929575, 0.44718304, 0.5375585, 0.485915, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">15</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.629108, 0.39788702, 0.67840403, 0.453051, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4530515, 0.4107985, 0.58568054, 0.5105635, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0], [1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.45070451, 0.3967135, 0.5762915, 0.5152585, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.63732404, 0.403756, 0.684272, 0.46009403, 1...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.43779355, 0.3826295, 0.61032856, 0.5223005,...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4401405, 0.374413, 0.6068075, 0.529343, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.536385, 0.4495305, 0.600939, 0.49999946, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">20</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.52347445, 0.4424885, 0.6150235, 0.5281695, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.456573, 0.515258, 0.504695, 0.562206, 0.0, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.504695, 0.44248852, 0.54342705, 0.48004752,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.54107946, 0.456573, 0.6103285, 0.511737, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.3298125, 0.2042255, 0.4225355, 0.2957745, 0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.517606, 0.18661949, 0.615024, 0.2875585, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.51173747, 0.18779299, 0.6126765, 0.308685, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">25</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0], [0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.30985948, 0.3626765, 0.3861505, 0.43896753,...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.41314548, 0.3427225, 0.48122054, 0.42018753...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.25821602, 0.30046952, 0.46830997, 0.4577465...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2734745, 0.2828635, 0.44718352, 0.4577465, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[0.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5915495, 0.23591551, 0.6455405, 0.2887325, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.5903755, 0.231221, 0.6420185, 0.280517, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.40493003, 0.2018775, 0.469484, 0.2699525, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.39554, 0.19483551, 0.483568, 0.2887325, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.4025825, 0.212441, 0.45539945, 0.269953, 1....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.2265255, 0.2136145, 0.3438965, 0.30751148, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.634976, 0.349765, 0.7723, 0.456573, 1.0, 1.0]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6079815, 0.320423, 0.7969485, 0.489437, 1.0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[0.6185445, 0.30164298, 0.7758215, 0.494131, 1...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>train_loader</td>\n",
+       "      <td></td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>/content/datasets/brain-tumor/images/train/000...</td>\n",
+       "      <td>[[1.0]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8adeb57b-4a56-45e8-8485-a1c46d89c532')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-8adeb57b-4a56-45e8-8485-a1c46d89c532');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "repr_error": "Out of range float values are not JSON compliant: nan"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ],
+      "text/html": [
+       "<a href=\"http://localhost:5173\" target=\"_blank\" style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "from weightslab.backend.ledgers import get_dataframe\n",
+    "\n",
+    "# `get_df_view()` is the same per-sample DataFrame that powers Studio's grid.\n",
+    "dfm = get_dataframe()\n",
+    "grid = dfm.get_df_view() if hasattr(dfm, \"get_df_view\") else pd.DataFrame()\n",
+    "print(f\"{len(grid)} samples tracked\")\n",
+    "display(grid.head(50))\n",
+    "\n",
+    "# Open the live, interactive grid (with image + bbox previews) in Weights Studio.\n",
+    "# Studio must be running locally (`weightslab start`).\n",
+    "display(HTML(\n",
+    "    '<a href=\"http://localhost:5173\" target=\"_blank\" '\n",
+    "    'style=\"font-size:15px\">&#128279; Open the full grid in Weights Studio</a>'\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FXQ0gBOSKs7t"
+   },
+   "source": [
+    "> **Can clicking a grid row here jump to that sample in Studio?** Not out of the box today. The notebook and Studio are separate processes, and the link above opens Studio at its root. Per-sample deep-linking (e.g. `http://localhost:5173/?sample=<uid>` selecting and scrolling to that row) is a small frontend addition to Weights Studio — the sample `uid` is already the grid's index here, so the notebook side is ready for it once Studio accepts the query param."
+   ]
+  }
+ ]
+}
diff --git a/weightslab/examples/Notebooks/Usecases/wl-segmentation-loss-shapes-classification.ipynb b/weightslab/examples/Notebooks/Usecases/wl-segmentation-loss-shapes-classification.ipynb
new file mode 100644
index 00000000..c2d2a723
--- /dev/null
+++ b/weightslab/examples/Notebooks/Usecases/wl-segmentation-loss-shapes-classification.ipynb
@@ -0,0 +1,530 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "\n",
+    "  <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\" target=\"_blank\">\n",
+    "    <img width=\"100%\" src=\"https://raw.githubusercontent.com/GrayboxTech/.github/main/profile/weightslab-banner-dark.png\" alt=\"WeightsLab banner\"></a>\n",
+    "\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n",
+    "  <a href=\"https://github.com/GrayboxTech/weightslab/stargazers\"><img src=\"https://img.shields.io/github/stars/GrayboxTech/weightslab?style=flat&color=5865F2\" alt=\"Stars\"></a>\n",
+    "  <a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?style=flat&color=5865F2&logo=pypi&logoColor=white\" alt=\"Version\"></a>\n",
+    "  <br>\n",
+    "  <a href=\"https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Usecases/wl-segmentation-loss-shapes-classification.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open WeightsLab In Colab\"></a>\n",
+    "\n",
+    "  Welcome to the WeightsLab <b>custom decorated-signal</b> usecase! This notebook trains segmentation and adds a <b>user-defined, decorated</b> per-sample signal that classifies each sample's loss trajectory. See the <a href=\"https://grayboxtech.github.io/weightslab/latest/index.html\">Docs</a> for details.</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Segmentation + a custom decorated per-sample signal\n",
+    "\n",
+    "This usecase trains the **BDD100k segmentation** example *in the notebook kernel* and layers on a\n",
+    "**user-defined signal** decorated with `@wl.signal`. The signal **subscribes** to the per-sample\n",
+    "segmentation loss, classifies each sample's loss **trajectory shape** (monotonic, plateaued,\n",
+    "spiked, ...), and writes the verdict back as a categorical tag - all live, per sample.\n",
+    "\n",
+    "It also demonstrates the `min_step` parameter of `@wl.signal`: the classifier only starts firing\n",
+    "once each sample has enough history (here `min_step=505`).\n",
+    "\n",
+    "> Why in-kernel (not `!python main.py`)? A decorated `@wl.signal` must be registered in the **same\n",
+    "> process** that runs training, so we build and train here rather than shelling out."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Pick a **T4 GPU** runtime on Colab. We clone the repo (for the segmentation `utils/` and the\n",
+    "bundled `BDD_subset/` data) and install WeightsLab from the clone (needed for the `min_step`\n",
+    "parameter)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/v/weightslab?color=5865F2&logo=pypi&logoColor=white\" alt=\"PyPI - Version\"></a>\n",
+    "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/dm/weightslab?color=5865F2\" alt=\"PyPI - Downloads\"></a>\n",
+    "<a href=\"https://pypi.org/project/weightslab/\"><img src=\"https://img.shields.io/pypi/pyversions/weightslab?color=5865F2&logo=python&logoColor=white\" alt=\"PyPI - Python Version\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1 · Install WeightsLab (+ Colab compatibility overrides)\n",
+    "# Colab already has a compatible torch/torchvision + numpy 1.26 — do NOT reinstall them.\n",
+    "# One resolution, pin numpy<2 and protobuf into weightslab's range.\n",
+    "%pip install weightslab  # --extra-index-url https://download.pytorch.org/whl/cpu\n",
+    "\n",
+    "# 2 · Import\n",
+    "import weightslab as wl\n",
+    "\n",
+    "# 3 · Serve to Weights Studio over a public bore tunnel.\n",
+    "#     Prints a `bore.pub:PORT` endpoint. Then, on your own machine (Docker running):\n",
+    "#         weightslab start                 # opens http://localhost:5173\n",
+    "#         weightslab tunnel bore.pub:PORT      # PORT = the port printed below\n",
+    "wl.serve(serving_grpc=True, serving_bore=True)\n",
+    "\n",
+    "# 4 · Register first the configuration then YOUR training objects here (uncomment and fill in):\n",
+    "# wl.watch_or_edit(config, flag=\"hyperparameters\", poll_interval=1.0)\n",
+    "# wl.watch_or_edit(model, optimizer, train_loader, test_loader, ...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Imports\n",
+    "\n",
+    "We reuse the segmentation example's helper modules (`utils/`) for the dataset, model, and the\n",
+    "per-sample / per-instance Dice + BCE criterions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch import optim\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "from weightslab.components.global_monitoring import (\n",
+    "    guard_training_context, guard_testing_context,\n",
+    ")\n",
+    "from utils.data import BDD100kSegDataset, seg_collate\n",
+    "from utils.model import SmallUNet\n",
+    "from utils.criterions import (\n",
+    "    PerSampleDice, PerInstanceDice, PerSampleBCE, PerInstanceBCE,\n",
+    ")\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(\"Using device:\", device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Configuration\n",
+    "\n",
+    "All tunables in one dict (like a `config.yaml`, with comments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = {\n",
+    "    # -- Experiment ----------------------------------------------------------\n",
+    "    \"experiment_name\": \"seg_loss_shapes\",   # name shown in Weights Studio\n",
+    "    \"root_log_dir\": None,                     # None -> a temp dir is created\n",
+    "\n",
+    "    # -- Data (bundled BDD_subset) -------------------------------------------\n",
+    "    \"data_root\": \"BDD_subset\",                # ships in the repo\n",
+    "    \"num_classes\": 6,                         # BDD label set\n",
+    "    \"class_names\": [\"Background\", \"Ego Road\", \"Driveable Area\",\n",
+    "                    \"Lane Line 1\", \"Lane Line 2\", \"Lane Line 3\"],\n",
+    "    \"ignore_index\": 255,                      # void pixels\n",
+    "    \"image_size\": 128,                        # images resized to image_size^2\n",
+    "    \"max_train_samples\": 256,                 # cap for a quick demo\n",
+    "    \"max_test_samples\": 100,\n",
+    "    \"train_batch_size\": 2,\n",
+    "    \"test_batch_size\": 2,\n",
+    "\n",
+    "    # -- Optimizer -----------------------------------------------------------\n",
+    "    \"learning_rate\": 1e-3,\n",
+    "\n",
+    "    # -- Training schedule ---------------------------------------------------\n",
+    "    \"training_steps_to_do\": 800,              # > min_step so the classifier fires\n",
+    "    \"eval_full_to_train_steps_ratio\": 100,    # full eval every N steps\n",
+    "    \"write_export_ratio\": 100,                # export history + dataframe every N steps\n",
+    "\n",
+    "    # -- Custom decorated signal --------------------------------------------\n",
+    "    \"shape_subscribe_to\": \"train_bce/sample\", # per-sample loss the classifier watches\n",
+    "    \"shape_compute_every_n_steps\": 25,        # throttle the classifier\n",
+    "    \"shape_min_step\": 505,                    # WARM-UP: don't classify before this step\n",
+    "\n",
+    "    # -- Services / live UI --------------------------------------------------\n",
+    "    \"serving_grpc\": True,\n",
+    "    \"expose_ui\": True,\n",
+    "    \"backend_port\": 50051,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Build data, model, optimizer, and the tracked seg signals\n",
+    "\n",
+    "Same wrapping pattern as the segmentation example: dataloaders, model, optimizer, and the\n",
+    "per-sample / per-instance Dice (metric) + BCE (loss) signals are all passed through\n",
+    "`wl.watch_or_edit(...)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_ds = BDD100kSegDataset(root=config[\"data_root\"], split=\"train\",\n",
+    "                             num_classes=config[\"num_classes\"], class_names=config[\"class_names\"],\n",
+    "                             ignore_index=config[\"ignore_index\"], image_size=config[\"image_size\"],\n",
+    "                             max_samples=config[\"max_train_samples\"])\n",
+    "val_ds = BDD100kSegDataset(root=config[\"data_root\"], split=\"val\",\n",
+    "                           num_classes=config[\"num_classes\"], class_names=config[\"class_names\"],\n",
+    "                           ignore_index=config[\"ignore_index\"], image_size=config[\"image_size\"],\n",
+    "                           max_samples=config[\"max_test_samples\"])\n",
+    "\n",
+    "wl.watch_or_edit(config, flag=\"hyperparameters\", name=config[\"experiment_name\"],\n",
+    "                 defaults=config, poll_interval=1.0)\n",
+    "\n",
+    "train_loader = wl.watch_or_edit(\n",
+    "    train_ds, flag=\"data\", loader_name=\"train_loader\",\n",
+    "    batch_size=config[\"train_batch_size\"], shuffle=True, is_training=True,\n",
+    "    compute_hash=False, array_autoload_arrays=False, array_return_proxies=True,\n",
+    "    array_use_cache=True, preload_labels=False, collate_fn=seg_collate)\n",
+    "test_loader = wl.watch_or_edit(\n",
+    "    val_ds, flag=\"data\", loader_name=\"test_loader\",\n",
+    "    batch_size=config[\"test_batch_size\"], shuffle=False, is_training=False,\n",
+    "    compute_hash=False, array_autoload_arrays=False, array_return_proxies=True,\n",
+    "    array_use_cache=True, preload_labels=True, collate_fn=seg_collate)\n",
+    "\n",
+    "model = wl.watch_or_edit(\n",
+    "    SmallUNet(in_channels=3, num_classes=config[\"num_classes\"],\n",
+    "              image_size=config[\"image_size\"]).to(device),\n",
+    "    flag=\"model\", device=device, compute_dependencies=True)\n",
+    "optimizer = wl.watch_or_edit(\n",
+    "    optim.Adam(model.parameters(), lr=config[\"learning_rate\"]), flag=\"optimizer\")\n",
+    "\n",
+    "\n",
+    "def make_seg_signals(split):\n",
+    "    return {\n",
+    "        \"dice_sample\": wl.watch_or_edit(PerSampleDice(), flag=\"metric\",\n",
+    "            name=f\"{split}_dice/sample\", per_sample=True, log=True),\n",
+    "        \"dice_instance\": wl.watch_or_edit(PerInstanceDice(), flag=\"metric\",\n",
+    "            name=f\"{split}_dice/instance\", per_instance=True, log=True),\n",
+    "        \"bce_sample\": wl.watch_or_edit(PerSampleBCE(), flag=\"loss\",\n",
+    "            name=f\"{split}_bce/sample\", per_sample=True, log=True),\n",
+    "        \"bce_instance\": wl.watch_or_edit(PerInstanceBCE(), flag=\"loss\",\n",
+    "            name=f\"{split}_bce/instance\", per_instance=True, log=True),\n",
+    "    }\n",
+    "\n",
+    "train_sig = make_seg_signals(\"train\")\n",
+    "test_sig = make_seg_signals(\"test\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. The custom, user-defined decorated signal\n",
+    "\n",
+    "This is the point of the usecase. `classify_loss_shape` is **your** function, decorated with\n",
+    "`@wl.signal`. Because it sets `subscribe_to=<per-sample loss>`, WeightsLab calls it **once per\n",
+    "sample** each time that loss is logged. It pulls the sample's full loss history, classifies the\n",
+    "**shape** of the curve, and tags the sample.\n",
+    "\n",
+    "Note `min_step=505`: the classifier is **skipped until training step 505**, so it only runs once\n",
+    "each sample has a meaningful trajectory. `compute_every_n_steps=25` then throttles it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Allowed categorical values (registered so the UI shows all choices up front).\n",
+    "LOSS_SHAPE_LABELS = [\"monotonic\", \"plateaued\", \"Flat_high\",\n",
+    "                     \"high_variance\", \"U_Shape\", \"Spiked\"]\n",
+    "LOSS_SHAPE_CODES = {label: i for i, label in enumerate(LOSS_SHAPE_LABELS)}\n",
+    "wl.register_categorical_tag(\"loss_shape\", LOSS_SHAPE_LABELS)\n",
+    "\n",
+    "_MIN_HISTORY = 5\n",
+    "\n",
+    "def _classify_loss_shape(values):\n",
+    "    \"\"\"Classify a per-sample loss trajectory (scale-invariant thresholds).\"\"\"\n",
+    "    y = np.asarray(values, dtype=float)\n",
+    "    if y.size < _MIN_HISTORY:\n",
+    "        return None\n",
+    "    n = y.size\n",
+    "    first, last = float(y[0]), float(y[-1])\n",
+    "    ymin, ymax = float(y.min()), float(y.max())\n",
+    "    rng = max(ymax - ymin, 1e-8)\n",
+    "    mean = float(y.mean())\n",
+    "    cv = float(y.std()) / (abs(mean) + 1e-8)\n",
+    "    drop = (first - last) / (abs(first) + 1e-8)\n",
+    "    argmin = int(np.argmin(y))\n",
+    "    rebound = (last - ymin) / rng\n",
+    "    max_up_jump = float(np.diff(y).max()) / rng\n",
+    "    tail = y[int(0.6 * n):]\n",
+    "    tail_flat = (float(tail.std()) / (abs(float(tail.mean())) + 1e-8)) < 0.1\n",
+    "    if max_up_jump > 0.5:\n",
+    "        return \"Spiked\"\n",
+    "    if cv > 0.5:\n",
+    "        return \"high_variance\"\n",
+    "    if 0.2 * n < argmin < 0.8 * n and rebound > 0.3:\n",
+    "        return \"U_Shape\"\n",
+    "    if drop > 0.4:\n",
+    "        return \"monotonic\"\n",
+    "    if drop > 0.15 and tail_flat:\n",
+    "        return \"plateaued\"\n",
+    "    return \"Flat_high\"\n",
+    "\n",
+    "\n",
+    "@wl.signal(\n",
+    "    name=\"seg_loss_shape_classifier\",\n",
+    "    subscribe_to=config[\"shape_subscribe_to\"],\n",
+    "    compute_every_n_steps=config[\"shape_compute_every_n_steps\"],\n",
+    "    min_step=config[\"shape_min_step\"],   # <-- warm-up gate: skip until this step\n",
+    "    log=False,                            # side-effecting: we tag samples, no aggregate curve\n",
+    ")\n",
+    "def classify_loss_shape(ctx: wl.SignalContext) -> int:\n",
+    "    \"\"\"Per-sample: classify the loss curve's shape and tag the sample.\"\"\"\n",
+    "    history = wl.query_sample_history(\n",
+    "        ctx.sample_id, signal_name=config[\"shape_subscribe_to\"],\n",
+    "        exp_hash=wl.get_current_experiment_hash())\n",
+    "    series = sorted(((step, val) for _, step, val, _ in history), key=lambda t: t[0])\n",
+    "    values = [v for _, v in series]\n",
+    "    label = _classify_loss_shape(values)\n",
+    "    if label is None:\n",
+    "        return -1\n",
+    "    wl.set_categorical_tag([ctx.sample_id], \"loss_shape\", label)\n",
+    "    return LOSS_SHAPE_CODES[label]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Train / eval steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _run_signals(sig, outputs, labels, ids, preds):\n",
+    "    bce_sample = sig[\"bce_sample\"](outputs, labels, batch_ids=ids, preds=preds)\n",
+    "    dice_sample = sig[\"dice_sample\"](outputs, labels, batch_ids=ids)\n",
+    "    sig[\"dice_instance\"](outputs, labels, batch_ids=ids)\n",
+    "    sig[\"bce_instance\"](outputs, labels, batch_ids=ids)\n",
+    "    avg = 0.5 * dice_sample + 0.5 * bce_sample\n",
+    "    wl.save_signals({\"combined_bce_dice_per_sample\": avg}, ids)\n",
+    "    return avg, dice_sample\n",
+    "\n",
+    "\n",
+    "def train_step(loader, model, optimizer, sig):\n",
+    "    with guard_training_context:\n",
+    "        inputs, ids, labels, _ = next(loader)\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = [[m.to(device) for m in insts] for insts in labels]\n",
+    "        optimizer.zero_grad()\n",
+    "        outputs = model(inputs)\n",
+    "        preds = outputs.argmax(dim=1)\n",
+    "        loss_per_sample, _ = _run_signals(sig, outputs, labels, ids, preds)\n",
+    "        loss = loss_per_sample.mean()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "    return float(loss.detach().cpu().item())\n",
+    "\n",
+    "\n",
+    "def evaluate(loader, model, sig, n_batches):\n",
+    "    losses = dices = 0.0\n",
+    "    with guard_testing_context, torch.no_grad():\n",
+    "        for inputs, ids, labels, _ in loader:\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = [[m.to(device) for m in insts] for insts in labels]\n",
+    "            outputs = model(inputs)\n",
+    "            preds = outputs.argmax(dim=1)\n",
+    "            loss_per_sample, dice_sample = _run_signals(sig, outputs, labels, ids, preds)\n",
+    "            losses += torch.mean(loss_per_sample)\n",
+    "            dices += torch.mean(dice_sample)\n",
+    "    return float((losses / n_batches)), float((dices / n_batches) * 100.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. (Optional) Expose the backend for the live UI\n",
+    "\n",
+    "Run this to open a raw-TCP [`bore`](https://github.com/ekzhang/bore) tunnel (`bore.pub`, no signup)\n",
+    "so a Weights Studio on your machine can connect. In Studio you'll see samples getting tagged with\n",
+    "their `loss_shape` **after step 505**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, re, tarfile, threading, urllib.request, subprocess\n",
+    "\n",
+    "endpoint = None\n",
+    "if config[\"expose_ui\"]:\n",
+    "    bore = os.path.join(os.getcwd(), \"bore\")\n",
+    "    if not os.path.exists(bore):\n",
+    "        BORE = \"v0.6.0\"\n",
+    "        urllib.request.urlretrieve(\n",
+    "            f\"https://github.com/ekzhang/bore/releases/download/{BORE}/bore-{BORE}-x86_64-unknown-linux-musl.tar.gz\",\n",
+    "            \"bore.tar.gz\")\n",
+    "        with tarfile.open(\"bore.tar.gz\") as t:\n",
+    "            t.extractall()\n",
+    "        os.chmod(bore, 0o755)\n",
+    "    proc = subprocess.Popen([bore, \"local\", str(config[\"backend_port\"]), \"--to\", \"bore.pub\"],\n",
+    "                            stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1)\n",
+    "    for line in proc.stdout:\n",
+    "        m = re.search(r\"bore\\.pub:(\\d+)\", line)\n",
+    "        if m:\n",
+    "            endpoint = f\"bore.pub:{m.group(1)}\"\n",
+    "            break\n",
+    "    threading.Thread(target=lambda: [None for _ in proc.stdout], daemon=True).start()\n",
+    "    print(\"=\" * 60)\n",
+    "    print(\"Backend exposed at:\", endpoint)\n",
+    "    print(\"On your machine: weightslab start  &&  weightslab tunnel\", endpoint)\n",
+    "    print(\"=\" * 60)\n",
+    "else:\n",
+    "    print(\"expose_ui is False - no tunnel.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Train\n",
+    "\n",
+    "Watch the progress bar: before step 505 the `seg_loss_shape_classifier` stays silent; once the\n",
+    "warm-up gate opens it starts tagging samples by loss shape (visible in Studio and in the exported\n",
+    "dataframe)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wl.start_training(timeout=3)\n",
+    "\n",
+    "steps = config[\"training_steps_to_do\"]\n",
+    "eval_ratio = config[\"eval_full_to_train_steps_ratio\"]\n",
+    "export_ratio = config[\"write_export_ratio\"]\n",
+    "\n",
+    "test_loss = test_dice = None\n",
+    "pbar = tqdm(range(steps), desc=\"Training\")\n",
+    "for step in pbar:\n",
+    "    age = model.get_age() if hasattr(model, \"get_age\") else step\n",
+    "    tr_loss = train_step(train_loader, model, optimizer, train_sig)\n",
+    "    if age > 0 and age % eval_ratio == 0:\n",
+    "        test_loss, test_dice = evaluate(test_loader, model, test_sig, len(test_loader))\n",
+    "    if age > 0 and age % export_ratio == 0:\n",
+    "        wl.write_history(); wl.write_dataframe()\n",
+    "    post = {\"loss\": f\"{tr_loss:.3f}\", \"shapes\": \"on\" if age >= config[\"shape_min_step\"] else \"warmup\"}\n",
+    "    if test_dice is not None:\n",
+    "        post[\"dice\"] = f\"{test_dice:.1f}%\"\n",
+    "    pbar.set_postfix(post)\n",
+    "\n",
+    "wl.write_history(); wl.write_dataframe()\n",
+    "print(\"Done.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Inspect the loss-shape tags\n",
+    "\n",
+    "After step 505 the classifier tags each sample. Export shows the `tag:loss_shape` column and the\n",
+    "numeric `seg_loss_shape_classifier` signal - group or sort by them in Studio to triage samples by\n",
+    "how their loss behaved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob, pandas as pd\n",
+    "lg = wl.write_dataframe()  # returns the written path\n",
+    "paths = sorted(glob.glob(os.path.join(os.path.dirname(lg), \"*dataframe*.json\")), key=os.path.getmtime)\n",
+    "if paths:\n",
+    "    df = pd.read_json(paths[-1])\n",
+    "    shape_cols = [c for c in df.columns if \"loss_shape\" in c.lower()]\n",
+    "    print(\"shape-related columns:\", shape_cols)\n",
+    "    display(df[shape_cols].head(30) if shape_cols else df.head())\n",
+    "else:\n",
+    "    print(\"No export found yet.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## See it live in Weights Studio\n",
+    "\n",
+    "**Colab has no Docker daemon**, so run Studio on your own machine and point it at this backend with\n",
+    "the `bore.pub:<port>` printed in Section 6:\n",
+    "\n",
+    "```bash\n",
+    "pip install weightslab\n",
+    "weightslab ui launch                       # Terminal 1 -> http://localhost:5173\n",
+    "weightslab tunnel bore.pub:12345           # Terminal 2 -> host:port from Section 6\n",
+    "```\n",
+    "\n",
+    "Group the sample grid by the `loss_shape` tag to see which samples plateaued, spiked, or never\n",
+    "learned - the decorated signal wrote those verdicts live, starting at step 505."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "<div align=\"center\">\n",
+    "Crafted by <a href=\"https://github.com/GrayboxTech/weightslab\">GrayboxTech</a> - if WeightsLab helps you catch a bad label, drop us a star on <a href=\"https://github.com/GrayboxTech/weightslab\">GitHub</a>.\n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "wl-segmentation-loss-shapes-classification.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/weightslab/examples/PyTorch/wl-ads-recommendation/README.md b/weightslab/examples/PyTorch/wl-ads-recommendation/README.md
new file mode 100644
index 00000000..2e8d9032
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-ads-recommendation/README.md
@@ -0,0 +1,102 @@
+# WeightsLab — Advertising CTR Recommendation (tabular, pure PyTorch)
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-ads-recommendation.ipynb)
+
+A fully-runnable **click-through-rate (CTR) prediction** example — the core of
+an advertising recommendation system. A **Wide & Deep** model learns to predict
+`P(click)` for `(user, ad, context)` impressions, streaming per-impression loss /
+prediction / accuracy to the WeightsLab UI. Being tabular, it's a natural fit
+for the **List Exploration (tabular) view** — sort by `loss` or `prediction` to
+inspect the impressions the model ranks best and worst.
+
+Everything is plain PyTorch + NumPy — no external download. The impressions are
+generated in-process so runs are reproducible and offline.
+
+## Quick start
+
+```bash
+cd weightslab/examples/PyTorch/wl-ads-recommendation
+pip install -r requirements.txt
+python main.py
+```
+
+Then open the UI (e.g. `http://localhost:5173`), switch the Data Exploration
+board to **List** view, press play, and sort columns to explore.
+
+## The data & model
+
+`make_synthetic_ctr(n, seed)` (in `utils/data.py`) generates reproducible ad
+impressions with:
+
+* **8 categorical fields** — `user_segment`, `ad_category`, `device_type`, `os`,
+  `publisher`, `placement`, `region`, `hour_bucket` (embedded by the model).
+* **8 numeric features** — `ad_position`, `bid_price`, `user_age`,
+  `session_depth`, `historical_ctr`, … (standardized).
+* a binary `clicked` label at a calibrated **~20% CTR**, driven by per-field
+  effects plus an `ad_category × user_segment` interaction.
+
+The 16 field values are packed into a `1x4x4` tensor per impression (heatmap
+thumbnail in the grid); the model unpacks them back into indices + numerics.
+
+**Model — Wide & Deep** (`utils/model.py`, after Cheng et al., 2016):
+* *Deep*: per-field embeddings ⊕ numeric → MLP (generalizes feature interactions).
+* *Wide*: first-order per-category effects + linear numeric term (memorizes
+  strong direct signals).
+The two logit heads are summed. Related architectures: DeepFM, Factorization
+Machines.
+
+### Using a real dataset
+
+Swap `AdsCTRDataset` for a loader over a real CTR log and set
+`CATEGORICAL_CARDINALITIES` to your vocab sizes:
+
+* **Criteo Display Advertising** — 13 numeric + 26 categorical fields.
+  <https://www.kaggle.com/c/criteo-display-ad-challenge>
+* **Avazu CTR** — <https://www.kaggle.com/c/avazu-ctr-prediction>
+* **MovieLens** (recommender variant) — <https://grouplens.org/datasets/movielens/>
+
+Keep the `__getitem__` contract `(packed_features_as_image, idx, label)`.
+
+## What "a sample" is here
+
+There are no images — **each sample is one ad impression (a row)**, and the
+model input **is** the packed 1-D field vector (not a reshaped image).
+WeightsLab carries that vector through gRPC as a `raw_data` stat of type
+`vector` (the actual values), so `inputs`, `labels`/`target` and `metadata` all
+reach the UI. The 8 categorical fields (as readable labels) and 8 numeric
+features are also exposed as **sortable columns** via the dataset's
+`get_items()` metadata contract (`preload_metadata=True`), so the List
+Exploration view shows real tabular columns (`ad_category`, `placement`,
+`bid_price`, …) alongside the tracked stats below. Sort, lock, histograms,
+discard/restore and neuron ops all work as on MNIST — they operate on the
+per-sample ledger, not on pixels.
+
+## What you'll see in the UI
+
+| Signal / column                          | Meaning                                       |
+| ---------------------------------------- | --------------------------------------------- |
+| field columns (`ad_category`, `placement`, `bid_price`, …) | The 16 impression fields, sortable |
+| `train-loss-CE`, `test-loss-CE`          | Weighted cross-entropy per split              |
+| `metric-ACC`                             | Overall accuracy                              |
+| `test_metric/PredictedCTR_per_sample`    | Model's predicted `P(click)` per impression   |
+| `test_metric/Accuracy_per_sample`        | Per-impression correctness (0/1)              |
+| `target`, `prediction` columns           | Per-sample truth/pred to sort/lock in List view |
+
+## Test it
+
+```bash
+# Fast, offline unit tests (pure PyTorch, no gRPC server):
+python -m pytest test_ads_recommendation.py -v
+
+# End-to-end integration check (needs weightslab installed): drives the tracked
+# loaders + watched loss + gRPC server, then asserts the ledger dataframe the UI
+# reads has per-sample rows, every field as a column, target/prediction/loss,
+# and a live gRPC endpoint.
+python verify_integration.py
+```
+
+The unit tests cover schema, reproducibility, calibrated CTR, pack/unpack
+roundtrip, `get_items` metadata columns, the model forward pass, and that
+training reduces loss and **ranks real clicks above non-clicks** on a held-out
+split (rank-AUC > 0.6). `verify_integration.py` proves WeightsLab is fully wired
+in tabular mode.
diff --git a/weightslab/examples/PyTorch/wl-ads-recommendation/config.yaml b/weightslab/examples/PyTorch/wl-ads-recommendation/config.yaml
new file mode 100644
index 00000000..856e6f1c
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-ads-recommendation/config.yaml
@@ -0,0 +1,52 @@
+# Global configuration
+experiment_name: ads_ctr_wide_deep
+device: auto
+training_steps_to_do: null  # null = train until manually stopped from the UI
+# root_log_dir: ...
+
+compute_natural_sort: false
+
+# Experiment parameters
+eval_full_to_train_steps_ratio: 100
+experiment_dump_to_train_steps_ratio: 250
+write_export_ratio: 100
+skip_checkpoint_load: false
+tqdm_display: true
+is_training: false  # Start paused; press play in the UI.
+
+# Global dataframe storage
+ledger_enable_flushing_threads: true
+ledger_enable_h5_persistence: true
+ledger_flush_max_rows: 15000
+ledger_flush_interval: 30.0
+
+# Clients
+serving_grpc: true
+
+# Model (Wide & Deep)
+model:
+  emb_dim: 8
+  hidden: 128
+
+# Optimizer
+optimizer:
+  lr: 0.005
+
+# Class weighting to counter ~20% click prevalence [no-click, click].
+class_weights: [1.0, 4.0]
+
+# Synthetic dataset generation (reproducible, no download).
+dataset:
+  seed: 0
+  n_train: 8000
+  n_test: 2000
+
+# DataLoader parameters
+data:
+  train_loader:
+    shuffle: true
+    batch_size: 64
+  test_loader:
+    shuffle: false
+    batch_size: 256
+    drop_last: false
diff --git a/weightslab/examples/PyTorch/wl-ads-recommendation/main.py b/weightslab/examples/PyTorch/wl-ads-recommendation/main.py
new file mode 100644
index 00000000..4b864c41
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-ads-recommendation/main.py
@@ -0,0 +1,284 @@
+"""WeightsLab example: advertising CTR recommendation (tabular, PyTorch).
+
+A Wide & Deep model predicts click-through-rate over synthetic ad impressions
+(8 categorical fields + 8 numeric features), wired into WeightsLab so the run
+streams per-impression stats (loss, prediction, target) to the UI. Being
+tabular, it pairs naturally with the List Exploration (tabular) view — sort by
+loss or prediction to inspect the impressions the model ranks best/worst.
+
+Run:
+    cd weightslab/examples/PyTorch/wl-ads-recommendation
+    python main.py
+
+The dataset/model live in ``utils/`` (pure PyTorch) so they can be unit tested
+without the gRPC backend — see ``test_ads_recommendation.py``.
+"""
+
+import itertools
+import os
+import time
+import logging
+import tempfile
+
+import yaml
+import tqdm
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+from torchmetrics.classification import Accuracy
+
+import weightslab as wl
+from weightslab.components.global_monitoring import (
+    guard_training_context,
+    guard_testing_context,
+)
+
+from utils.data import AdsCTRDataset, CATEGORICAL_CARDINALITIES, NUM_NUMERIC
+from utils.model import WideDeepCTR
+
+
+logging.basicConfig(level=logging.ERROR)
+logger = logging.getLogger(__name__)
+
+
+# -----------------------------------------------------------------------------
+# Train / Test steps
+# -----------------------------------------------------------------------------
+def train(loader, model, optimizer, criterion_mlt, device):
+    """Single training step using the tracked dataloader + watched loss."""
+    with guard_training_context:
+        (inputs, ids, labels) = next(loader)
+        inputs = inputs.to(device)
+        labels = labels.to(device)
+
+        optimizer.zero_grad()
+        preds_raw = model(inputs)
+        preds = preds_raw.argmax(dim=1, keepdim=True)
+
+        loss_batch_mlt = criterion_mlt(
+            preds_raw.float(),
+            labels.long(),
+            batch_ids=ids,
+            preds=preds,
+        )
+        total_loss = loss_batch_mlt.mean()
+
+        total_loss.backward()
+        optimizer.step()
+
+    return total_loss.detach().cpu().item()
+
+
+def test(loader, model, criterion_mlt, metric_mlt, device, test_loader_len):
+    """Full evaluation pass over the test loader, logging per-sample signals."""
+    losses = torch.tensor(0.0, device=device)
+
+    for (inputs, ids, labels) in loader:
+        with guard_testing_context:
+            inputs = inputs.to(device)
+            labels = labels.to(device)
+
+            outputs = model(inputs)
+            preds = outputs.argmax(dim=1, keepdim=True)
+
+            loss_batch = criterion_mlt(outputs, labels, batch_ids=ids, preds=preds)
+            losses += torch.mean(loss_batch)
+            metric_mlt.update(outputs, labels)
+
+            preds_flat = preds.view(-1)
+            labels_flat = labels.view(-1)
+            prob_click = torch.softmax(outputs, dim=1)[:, 1]
+            correct_per_sample = (preds_flat == labels_flat).float()
+
+            signals = {
+                "test_metric/Accuracy_per_sample": correct_per_sample,
+                "test_metric/PredictedCTR_per_sample": prob_click,
+            }
+            wl.save_signals(
+                preds_raw=outputs,
+                targets=labels,
+                batch_ids=ids,
+                signals=signals,
+                preds=preds,
+            )
+
+    loss = losses / max(1, test_loader_len)
+    metric = metric_mlt.compute() * 100
+
+    return loss.detach().cpu().item(), metric.detach().cpu().item()
+
+
+# -----------------------------------------------------------------------------
+# Main
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+    start_time = time.time()
+
+    parameters = {}
+    config_path = os.path.join(os.path.dirname(__file__), "config.yaml")
+    if os.path.exists(config_path):
+        with open(config_path, "r") as fh:
+            parameters = yaml.safe_load(fh) or {}
+    parameters = parameters or {}
+
+    # ---- sensible defaults / normalization ----
+    parameters.setdefault("experiment_name", "ads_ctr_wide_deep")
+    parameters.setdefault("device", "auto")
+    parameters.setdefault("training_steps_to_do", 1000000)
+    parameters.setdefault("eval_full_to_train_steps_ratio", 100)
+
+    exp_name = parameters["experiment_name"]
+
+    wl.watch_or_edit(parameters, flag="hyperparameters", poll_interval=1.0)
+
+    if parameters.get("device", "auto") == "auto":
+        parameters["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = parameters["device"]
+
+    if not parameters.get("root_log_dir"):
+        parameters["root_log_dir"] = tempfile.mkdtemp()
+        print(f"No root_log_dir specified, using temporary directory: {parameters['root_log_dir']}")
+    os.makedirs(parameters["root_log_dir"], exist_ok=True)
+
+    verbose = parameters.get("verbose", True)
+    log_dir = parameters["root_log_dir"]
+    tqdm_display = parameters.get("tqdm_display", True)
+    eval_full_to_train_steps_ratio = parameters.get("eval_full_to_train_steps_ratio", 100)
+    write_export_ratio = parameters.get("write_export_ratio", 100)
+    enable_h5_persistence = parameters.get("enable_h5_persistence", True)
+    training_steps_to_do = parameters.get("training_steps_to_do", 1000)
+
+    # Model (Wide & Deep CTR)
+    _model = WideDeepCTR(
+        cardinalities=CATEGORICAL_CARDINALITIES,
+        num_numeric=NUM_NUMERIC,
+        emb_dim=parameters.get("model", {}).get("emb_dim", 8),
+        hidden=parameters.get("model", {}).get("hidden", 128),
+        num_classes=2,
+    ).to(device)
+    model = wl.watch_or_edit(_model, flag="model", device=device)
+
+    # Optimizer
+    lr = parameters.get("optimizer", {}).get("lr", 0.005)
+    _optimizer = optim.Adam(model.parameters(), lr=lr)
+    optimizer = wl.watch_or_edit(_optimizer, flag="optimizer")
+
+    # Data (synthetic CTR impressions) — no download needed.
+    dataset_cfg = parameters.get("dataset", {})
+    seed = int(dataset_cfg.get("seed", 0))
+    n_train = int(dataset_cfg.get("n_train", 8000))
+    n_test = int(dataset_cfg.get("n_test", 2000))
+
+    train_cfg = parameters.get("data", {}).get("train_loader", {})
+    test_cfg = parameters.get("data", {}).get("test_loader", {})
+
+    _train_dataset = AdsCTRDataset(n_train, seed=seed, max_samples=train_cfg.get("max_samples"))
+    _test_dataset = AdsCTRDataset(n_test, seed=seed + 1, max_samples=test_cfg.get("max_samples"))
+
+    train_loader = wl.watch_or_edit(
+        _train_dataset,
+        flag="data",
+        loader_name="train_loader",
+        batch_size=train_cfg.get("batch_size", 64),
+        shuffle=train_cfg.get("shuffle", True),
+        is_training=True,
+        compute_hash=False,
+        preload_labels=True,
+        preload_metadata=True,
+        enable_h5_persistence=enable_h5_persistence,
+    )
+    test_loader = wl.watch_or_edit(
+        _test_dataset,
+        flag="data",
+        loader_name="test_loader",
+        batch_size=test_cfg.get("batch_size", 256),
+        shuffle=test_cfg.get("shuffle", False),
+        is_training=False,
+        compute_hash=False,
+        preload_labels=True,
+        preload_metadata=True,
+        enable_h5_persistence=enable_h5_persistence,
+    )
+
+    # Losses & metrics. Up-weight clicks (~20% prevalence) so the positive class
+    # drives the gradient.
+    class_weights = torch.tensor(
+        parameters.get("class_weights", [1.0, 4.0]), dtype=torch.float32, device=device
+    )
+    train_criterion = wl.watch_or_edit(
+        nn.CrossEntropyLoss(weight=class_weights, reduction="none"),
+        flag="loss", signal_name="train-loss-CE", log=True)
+    test_criterion = wl.watch_or_edit(
+        nn.CrossEntropyLoss(weight=class_weights, reduction="none"),
+        flag="loss", signal_name="test-loss-CE", log=True)
+
+    metric = wl.watch_or_edit(
+        Accuracy(task="multiclass", num_classes=2).to(device),
+        flag="metric", signal_name="metric-ACC", log=True)
+
+    wl.serve(serving_grpc=parameters.get("serving_grpc", False))
+
+    print("=" * 60)
+    print(" STARTING ADS-CTR TRAINING")
+    print(f" Evaluation every {eval_full_to_train_steps_ratio} steps")
+    print(f" Dataset splits: train={len(_train_dataset)}, test={len(_test_dataset)}")
+    print(f" Logs will be saved to: {log_dir}")
+    print("=" * 60 + "\n")
+
+    if tqdm_display:
+        train_range = tqdm.tqdm(
+            range(training_steps_to_do) if training_steps_to_do != None else itertools.count(),
+            desc="Training",
+            bar_format="{desc}: {n}/{total} [{elapsed}<{remaining}, {rate_fmt}] {bar} | {postfix}",
+            ncols=140,
+            position=0,
+            leave=True,
+        )
+    else:
+        train_range = range(training_steps_to_do) if training_steps_to_do != None else itertools.count()
+
+    # ================
+    # Training Loop
+    wl.start_training(timeout=3)
+
+    train_loss = None
+    test_loss, test_metric = None, None
+    test_loader_len = len(test_loader)
+    for train_step in train_range:
+        age = model.get_age() if hasattr(model, "get_age") else train_step
+
+        train_loss = train(train_loader, model, optimizer, train_criterion, device)
+
+        if age > 0 and age % eval_full_to_train_steps_ratio == 0:
+            test_loss, test_metric = test(
+                test_loader, model, test_criterion, metric, device, test_loader_len
+            )
+
+        if age > 0 and age % write_export_ratio == 0:
+            wl.write_history()
+            wl.write_dataframe()
+
+        if verbose and not tqdm_display:
+            import sys
+            msg = f"Step {train_step} (Age {age}): Loss={train_loss:.4f}"
+            if test_loss is not None:
+                msg += f" | Test={test_loss:.4f} ({test_metric:.1f}%)"
+            sys.stdout.write(f"\r{msg:<100}")
+            sys.stdout.flush()
+        elif tqdm_display:
+            postfix_parts = [f"train_loss={train_loss:.4f}"]
+            if test_loss is not None:
+                postfix_parts.append(f"test_loss={test_loss:.4f}")
+            if test_metric is not None:
+                postfix_parts.append(f"test_acc={test_metric:.1f}%")
+            train_range.set_postfix_str(" | ".join(postfix_parts))
+
+    print("\n" + "=" * 60)
+    print(f" Training completed in {time.time() - start_time:.2f} seconds")
+    print(f" Logs saved to: {log_dir}")
+    print("=" * 60)
+
+    wl.write_history()
+    wl.write_dataframe()
+    wl.keep_serving()
diff --git a/weightslab/examples/PyTorch/wl-ads-recommendation/test_ads_recommendation.py b/weightslab/examples/PyTorch/wl-ads-recommendation/test_ads_recommendation.py
new file mode 100644
index 00000000..a6e0c154
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-ads-recommendation/test_ads_recommendation.py
@@ -0,0 +1,185 @@
+"""Smoke tests for the ads CTR example (pure PyTorch, no weightslab).
+
+Run:  python -m pytest test_ads_recommendation.py
+  or:  python test_ads_recommendation.py
+"""
+
+import os
+import sys
+
+import numpy as np
+import torch
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from utils.data import (  # noqa: E402
+    CATEGORICAL_CARDINALITIES,
+    CATEGORICAL_FIELDS,
+    IMG_SIDE,
+    NUM_CATEGORICAL,
+    NUM_FIELDS,
+    NUM_NUMERIC,
+    NUMERIC_FIELDS,
+    AdsCTRDataset,
+    category_label,
+    make_synthetic_ctr,
+    unpack,
+)
+from utils.model import WideDeepCTR  # noqa: E402
+
+
+def test_schema():
+    assert NUM_FIELDS == 16 == IMG_SIDE * IMG_SIDE
+    assert NUM_CATEGORICAL == len(CATEGORICAL_FIELDS) == len(CATEGORICAL_CARDINALITIES) == 8
+    assert NUM_NUMERIC == 8
+
+
+def test_synthetic_shapes_and_ranges():
+    cat, num_std, num_raw, y = make_synthetic_ctr(500, seed=0)
+    assert cat.shape == (500, NUM_CATEGORICAL)
+    assert num_std.shape == (500, NUM_NUMERIC)
+    assert num_raw.shape == (500, NUM_NUMERIC)
+    assert y.shape == (500,)
+    assert str(cat.dtype) == "int64" and str(num_std.dtype) == "float32" and str(num_raw.dtype) == "float32"
+    for f, card in enumerate(CATEGORICAL_CARDINALITIES):
+        assert cat[:, f].min() >= 0 and cat[:, f].max() < card
+    assert set(int(v) for v in set(y.tolist())) <= {0, 1}
+
+
+def test_numeric_standardized_vs_raw():
+    """Model input (standardized) must differ in scale from the raw metadata values."""
+    _, num_std, num_raw, _ = make_synthetic_ctr(2000, seed=0)
+    # Standardized: ~zero mean, ~unit variance per feature.
+    assert np.allclose(num_std.mean(axis=0), 0.0, atol=0.15)
+    assert np.allclose(num_std.std(axis=0), 1.0, atol=0.15)
+    # Raw values live at their natural human-readable scale (not standardized),
+    # e.g. bid_price in dollars, user_age in years — nowhere near unit variance.
+    bid_idx = NUMERIC_FIELDS.index("bid_price")
+    age_idx = NUMERIC_FIELDS.index("user_age")
+    assert num_raw[:, bid_idx].mean() > 1.0  # dollars, not ~0
+    assert num_raw[:, age_idx].mean() > 18.0  # years, not ~0
+    assert not np.allclose(num_std, num_raw)
+
+
+def test_deterministic_for_seed():
+    a = make_synthetic_ctr(200, seed=5)
+    b = make_synthetic_ctr(200, seed=5)
+    assert all((x == y).all() for x, y in zip(a, b))
+    c = make_synthetic_ctr(200, seed=6)
+    assert not (a[0] == c[0]).all()
+
+
+def test_ctr_is_realistic():
+    _, _, _, y = make_synthetic_ctr(4000, seed=1)
+    assert 0.15 < float(y.mean()) < 0.26  # calibrated to ~20% CTR
+
+
+def test_dataset_item_contract():
+    ds = AdsCTRDataset(300, seed=2)
+    assert len(ds) == 300
+    x, idx, label = ds[0]
+    # Model input is the packed 1-D field vector (no fake image).
+    assert tuple(x.shape) == (NUM_FIELDS,)
+    assert x.dtype == torch.float32
+    assert idx == 0 and label in (0, 1)
+
+
+def test_unpack_roundtrip():
+    ds = AdsCTRDataset(64, seed=3)
+    image = torch.stack([ds[i][0] for i in range(len(ds))])  # [N,1,4,4]
+    cat, num = unpack(image)
+    assert cat.shape == (len(ds), NUM_CATEGORICAL)
+    assert num.shape == (len(ds), NUM_NUMERIC)
+    assert (cat == ds.cat).all(), "categorical indices must survive pack/unpack"
+    assert torch.allclose(num, ds.num, atol=1e-5)
+
+
+def test_get_items_exposes_field_metadata_columns():
+    """The ledger-init contract must expose every field as a metadata column."""
+    ds = AdsCTRDataset(50, seed=2)
+    image, uid, target, metadata = ds.get_items(
+        4, include_metadata=True, include_labels=True, include_images=False
+    )
+    assert image is None
+    assert uid == 4 and target in (0, 1)
+    assert set(metadata.keys()) == set(CATEGORICAL_FIELDS) | set(NUMERIC_FIELDS)
+    # Categorical fields are readable strings, numeric are floats.
+    assert isinstance(metadata["placement"], str)
+    assert metadata["device_type"] in ("mobile", "desktop", "tablet", "ctv")
+    assert all(isinstance(metadata[n], float) for n in NUMERIC_FIELDS)
+
+
+def test_metadata_numeric_values_are_raw_not_standardized():
+    """Metadata must carry the raw (human-scale) values, not the standardized
+    model input — the UI shows the model input in list mode and the raw
+    values as metadata, so the two must not collapse to the same numbers."""
+    ds = AdsCTRDataset(50, seed=2)
+    _, _, _, metadata = ds.get_items(4, include_metadata=True)
+    for j, name in enumerate(NUMERIC_FIELDS):
+        assert metadata[name] == round(float(ds.num_raw[4][j]), 4)
+        assert metadata[name] != round(float(ds.num[4][j]), 4)
+
+
+def test_category_label_fallback():
+    assert category_label("device_type", 0) == "mobile"
+    assert category_label("region", 3) == "region_3"  # no vocab -> fallback
+
+
+def test_model_forward_shape():
+    model = WideDeepCTR()
+    assert model(torch.randn(8, NUM_FIELDS)).shape == (8, 2)
+    assert model(torch.randn(8, 1, IMG_SIDE, IMG_SIDE)).shape == (8, 2)
+
+
+def test_training_learns_to_rank_clicks():
+    """Training must reduce loss and rank real clicks above non-clicks on a
+    held-out split (train/test share the same ground-truth CTR model)."""
+    torch.manual_seed(0)
+    train_ds = AdsCTRDataset(4000, seed=0)
+    test_ds = AdsCTRDataset(1000, seed=1)
+
+    train_x = torch.stack([train_ds[i][0] for i in range(len(train_ds))])
+    train_y = train_ds.labels
+    test_x = torch.stack([test_ds[i][0] for i in range(len(test_ds))])
+    test_y = test_ds.labels
+
+    model = WideDeepCTR()
+    optimizer = torch.optim.Adam(model.parameters(), lr=5e-3)
+    # Up-weight the ~20% positive class.
+    criterion = torch.nn.CrossEntropyLoss(weight=torch.tensor([1.0, 4.0]))
+
+    model.train()
+    initial_loss = criterion(model(train_x), train_y).item()
+    batch = 256
+    for _ in range(30):
+        perm = torch.randperm(len(train_ds))
+        for s in range(0, len(train_ds), batch):
+            idx = perm[s:s + batch]
+            optimizer.zero_grad()
+            loss = criterion(model(train_x[idx]), train_y[idx])
+            loss.backward()
+            optimizer.step()
+    final_loss = criterion(model(train_x), train_y).item()
+    assert final_loss < initial_loss, f"loss did not drop: {initial_loss:.4f} -> {final_loss:.4f}"
+
+    # Ranking quality on the held-out split: P(click) higher for real clicks.
+    model.eval()
+    with torch.no_grad():
+        prob_click = torch.softmax(model(test_x), dim=1)[:, 1]
+    pos = prob_click[test_y == 1].mean().item()
+    neg = prob_click[test_y == 0].mean().item()
+    assert pos > neg + 0.05, f"model does not separate clicks: pos={pos:.3f} neg={neg:.3f}"
+
+    # Rank-AUC (Mann-Whitney) should beat random (0.5).
+    with torch.no_grad():
+        p = prob_click
+        pos_p = p[test_y == 1]
+        neg_p = p[test_y == 0]
+        wins = (pos_p.unsqueeze(1) > neg_p.unsqueeze(0)).float().mean().item()
+    assert wins > 0.6, f"AUC too low: {wins:.3f}"
+
+
+if __name__ == "__main__":
+    import pytest
+
+    raise SystemExit(pytest.main([__file__, "-v"]))
diff --git a/weightslab/examples/PyTorch/ws-clustering/face/__init__.py b/weightslab/examples/PyTorch/wl-ads-recommendation/utils/__init__.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-clustering/face/__init__.py
rename to weightslab/examples/PyTorch/wl-ads-recommendation/utils/__init__.py
diff --git a/weightslab/examples/PyTorch/wl-ads-recommendation/utils/data.py b/weightslab/examples/PyTorch/wl-ads-recommendation/utils/data.py
new file mode 100644
index 00000000..b71b3823
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-ads-recommendation/utils/data.py
@@ -0,0 +1,269 @@
+"""Synthetic advertising click-through-rate (CTR) dataset (pure PyTorch).
+
+Import-light (only ``numpy`` + ``torch``) so it can be unit-tested without
+importing ``weightslab`` — see ``test_ads_recommendation.py``.
+
+CTR prediction is the core of an advertising recommendation system: given a
+(user, ad, context) triple, predict P(click). This module is a reproducible,
+offline stand-in for that task. Each impression has **8 categorical fields**
+(user segment, ad category, device, OS, publisher, placement, region, hour
+bucket) and **8 numeric features** (ad position, bid, user age, session depth,
+historical CTR, …), with a binary ``clicked`` label at a realistic ~20% CTR.
+
+Real-world analogues (drop-in replaceable):
+  * Criteo Display Advertising CTR — 13 numeric + 26 categorical fields.
+    <https://www.kaggle.com/c/criteo-display-ad-challenge>
+  * Avazu CTR — <https://www.kaggle.com/c/avazu-ctr-prediction>
+  * MovieLens (for the recommender variant) — <https://grouplens.org/datasets/movielens/>
+
+Canonical models for this task: Wide & Deep, DeepFM, Factorization Machines.
+``utils/model.py`` implements a compact Wide & Deep.
+
+The model input is the 16-field vector itself (categorical indices + per-feature
+standardized numerics; no fake image). WeightsLab transmits it through gRPC as a
+``vector`` raw_data stat carrying the actual values, and ``get_items`` exposes the
+raw, human-readable field values (dollars, years, page counts, …) as sortable
+metadata columns in the List Exploration (tabular) view.
+"""
+
+from __future__ import annotations
+
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+# --- Field schema ---------------------------------------------------------
+CATEGORICAL_FIELDS = [
+    "user_segment",
+    "ad_category",
+    "device_type",
+    "os",
+    "publisher",
+    "placement",
+    "region",
+    "hour_bucket",
+]
+CATEGORICAL_CARDINALITIES = [6, 10, 4, 4, 12, 5, 8, 6]
+
+NUMERIC_FIELDS = [
+    "ad_position",
+    "bid_price",
+    "user_age",
+    "session_depth",
+    "historical_ctr",
+    "days_since_last_click",
+    "num_ads_seen_today",
+    "creative_freshness",
+]
+
+NUM_CATEGORICAL = len(CATEGORICAL_FIELDS)   # 8
+NUM_NUMERIC = len(NUMERIC_FIELDS)           # 8
+NUM_FIELDS = NUM_CATEGORICAL + NUM_NUMERIC  # 16
+IMG_SIDE = 4
+assert IMG_SIDE * IMG_SIDE == NUM_FIELDS
+
+# Human-readable labels per categorical field (len == cardinality), used only to
+# make the UI metadata columns legible; the model still sees integer codes.
+CATEGORICAL_VOCABS = {
+    "device_type": ["mobile", "desktop", "tablet", "ctv"],
+    "os": ["android", "ios", "windows", "other"],
+    "placement": ["banner", "sidebar", "interstitial", "native", "video"],
+    "hour_bucket": ["night", "early", "morning", "midday", "afternoon", "evening"],
+}
+
+
+def category_label(field: str, code: int) -> str:
+    """Readable label for a categorical code, or ``"<field>_<code>"`` fallback."""
+    vocab = CATEGORICAL_VOCABS.get(field)
+    if vocab is not None and 0 <= code < len(vocab):
+        return vocab[code]
+    return f"{field}_{code}"
+
+TARGET_CTR = 0.20
+# Fixed seed for the *ground-truth* click model, independent of the data seed,
+# so train and test splits share the same underlying mapping.
+_TRUE_PARAM_SEED = 12345
+
+
+def _true_params():
+    """Stable 'ground-truth' click model parameters (same across all splits)."""
+    rng = np.random.default_rng(_TRUE_PARAM_SEED)
+    cat_weights = [rng.normal(0.0, 0.8, size=card) for card in CATEGORICAL_CARDINALITIES]
+    numeric_weights = rng.normal(0.0, 0.5, size=NUM_NUMERIC)
+    # One pairwise interaction: ad_category x user_segment (classic in ad CTR).
+    interaction = rng.normal(
+        0.0, 0.7, size=(CATEGORICAL_CARDINALITIES[1], CATEGORICAL_CARDINALITIES[0])
+    )
+    return cat_weights, numeric_weights, interaction
+
+
+def _draw_raw_numeric(rng: np.random.Generator, n: int) -> np.ndarray:
+    """Draw the 8 numeric fields at their natural, human-readable scale.
+
+    Order matches ``NUMERIC_FIELDS``. These are the values an ad-serving
+    pipeline would actually log (dollars, years, page counts, …) — this is
+    what the UI shows as metadata; the model never sees these directly.
+    """
+    ad_position = rng.integers(1, 9, size=n).astype(np.float64)  # slot 1-8
+    bid_price = rng.gamma(2.0, 1.25, size=n)  # dollars, mean ~$2.50 CPC
+    user_age = np.clip(rng.normal(35.0, 12.0, size=n), 18, 80)
+    session_depth = rng.poisson(3.0, size=n).astype(np.float64) + 1  # pages viewed
+    historical_ctr = rng.beta(2.0, 25.0, size=n)  # user's baseline CTR, mean ~7%
+    days_since_last_click = rng.exponential(10.0, size=n)
+    num_ads_seen_today = rng.poisson(12.0, size=n).astype(np.float64) + 1
+    creative_freshness = rng.exponential(20.0, size=n)  # days since creative was made
+
+    return np.stack(
+        [
+            ad_position, bid_price, user_age, session_depth,
+            historical_ctr, days_since_last_click, num_ads_seen_today,
+            creative_freshness,
+        ],
+        axis=1,
+    )
+
+
+def make_synthetic_ctr(
+    n_samples: int, seed: int = 0
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Generate deterministic synthetic ad impressions.
+
+    Returns ``(cat, num_std, num_raw, y)``:
+      * ``cat``: ``int64[n, NUM_CATEGORICAL]`` category indices
+      * ``num_std``: ``float32[n, NUM_NUMERIC]`` standardized numeric features
+        (model input)
+      * ``num_raw``: ``float32[n, NUM_NUMERIC]`` raw, human-readable numeric
+        values (surfaced as sortable metadata columns in the UI)
+      * ``y``:   ``int64[n]`` click label (1 = clicked), ~20% positive
+    """
+    if n_samples <= 0:
+        empty_num = np.zeros((0, NUM_NUMERIC), dtype=np.float32)
+        return (
+            np.zeros((0, NUM_CATEGORICAL), dtype=np.int64),
+            empty_num,
+            empty_num.copy(),
+            np.zeros((0,), dtype=np.int64),
+        )
+
+    rng = np.random.default_rng(seed)
+    cat_weights, numeric_weights, interaction = _true_params()
+
+    # Sample categorical indices (uniform over each field's cardinality).
+    cat = np.stack(
+        [rng.integers(0, card, size=n_samples) for card in CATEGORICAL_CARDINALITIES],
+        axis=1,
+    ).astype(np.int64)
+
+    # Raw, human-readable numeric values, then standardize per-feature
+    # (class-agnostic) for the model input — mirrors the fraud-detection example.
+    num_raw = _draw_raw_numeric(rng, n_samples)
+    mean = num_raw.mean(axis=0, keepdims=True)
+    std = num_raw.std(axis=0, keepdims=True)
+    std[std == 0] = 1.0
+    num_std = (num_raw - mean) / std
+
+    # Ground-truth click logit = sum of per-field effects + one interaction + numeric.
+    logit = np.zeros(n_samples, dtype=np.float64)
+    for f, w in enumerate(cat_weights):
+        logit += w[cat[:, f]]
+    logit += num_std @ numeric_weights
+    logit += interaction[cat[:, 1], cat[:, 0]]  # ad_category x user_segment
+
+    # Calibrate an additive bias so the *mean click probability* lands near
+    # TARGET_CTR. Centering the logit mean is not enough (mean(sigmoid) !=
+    # sigmoid(mean) by Jensen), so binary-search the bias on the actual mean prob.
+    logit -= logit.mean()
+    lo, hi = -20.0, 20.0
+    for _ in range(60):
+        mid = 0.5 * (lo + hi)
+        mean_prob = (1.0 / (1.0 + np.exp(-(logit + mid)))).mean()
+        if mean_prob < TARGET_CTR:
+            lo = mid
+        else:
+            hi = mid
+    logit += 0.5 * (lo + hi)
+
+    prob = 1.0 / (1.0 + np.exp(-logit))
+    y = (rng.uniform(0.0, 1.0, size=n_samples) < prob).astype(np.int64)
+
+    return cat, num_std.astype(np.float32), num_raw.astype(np.float32), y
+
+
+class AdsCTRDataset(Dataset):
+    """Tabular ads CTR dataset yielding ``(input, idx, label)``.
+
+    ``input`` is the 1-D packed field vector ``float32[NUM_FIELDS]`` fed straight
+    to the model — the 8 categorical indices (as floats) followed by the 8
+    per-feature standardized numerics; there is no image. ``idx`` is the tracked
+    sample id; ``label`` is 0 (no click) / 1 (click).
+    """
+
+    def __init__(self, n_samples: int, seed: int = 0, max_samples: Optional[int] = None):
+        cat, num_std, num_raw, y = make_synthetic_ctr(n_samples, seed=seed)
+        if max_samples is not None:
+            cat, num_std, num_raw, y = (
+                cat[:max_samples], num_std[:max_samples], num_raw[:max_samples], y[:max_samples],
+            )
+        packed = np.concatenate([cat.astype(np.float32), num_std], axis=1)  # [N, 16] (model input)
+        self.features = torch.from_numpy(packed)  # float32 [N, 16] (standardized, model input)
+        self.cat = torch.from_numpy(cat)          # int64 [N, 8]
+        self.num = torch.from_numpy(num_std)      # float32 [N, 8] (standardized)
+        self.num_raw = num_raw                    # float32 [N, 8] (raw, display values)
+        self.labels = torch.from_numpy(y)         # int64 [N]
+
+    def __len__(self) -> int:
+        return int(self.features.shape[0])
+
+    def _input(self, idx: int) -> torch.Tensor:
+        # Tabular: the model input IS the packed 1-D field vector (no fake image).
+        return self.features[idx]
+
+    def _metadata(self, idx: int) -> dict:
+        """Readable category labels + raw (non-standardized) numeric values -> sortable UI columns."""
+        meta = {}
+        cat_row = self.cat[idx]
+        for f, name in enumerate(CATEGORICAL_FIELDS):
+            meta[name] = category_label(name, int(cat_row[f]))
+        raw_row = self.num_raw[idx]
+        for j, name in enumerate(NUMERIC_FIELDS):
+            meta[name] = round(float(raw_row[j]), 4)
+        return meta
+
+    def __getitems__(self, idx: int):
+        # Training contract: (input, sample_id, label).
+        return self._input(idx), idx, int(self.labels[idx].item())
+
+    def __getitem__(self, idx: int):
+        # Training contract: (input, sample_id, label).
+        return self._input(idx), idx, int(self.labels[idx].item())
+
+    def get_items(self, idx: int, include_metadata: bool = False,
+                  include_labels: bool = False, include_images: bool = False):
+        """WeightsLab ledger-init contract: (image, uid, target, metadata).
+
+        The returned ``metadata`` dict (readable categorical labels + numeric
+        features) is flattened into per-sample columns, so each impression field
+        (``ad_category``, ``placement``, ``bid_price``, …) becomes a sortable
+        column in the List Exploration view.
+        """
+        image = self._input(idx) if include_images else None
+        target = int(self.labels[idx].item()) if include_labels else None
+        metadata = self._metadata(idx) if include_metadata else None
+        return image, idx, target, metadata
+
+
+def unpack(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Split a packed ``[N, 16]`` (or ``[N, 1, 4, 4]``) batch into (cat_idx, numeric).
+
+    ``cat_idx`` is ``long[N, 8]`` clamped to valid ranges; ``numeric`` is
+    ``float[N, 8]``. Shared by the model and the tests so packing lives in one
+    place.
+    """
+    flat = x.reshape(x.shape[0], -1)
+    cat = flat[:, :NUM_CATEGORICAL].round().long()
+    num = flat[:, NUM_CATEGORICAL:]
+    card = torch.tensor(CATEGORICAL_CARDINALITIES, device=x.device)
+    cat = cat.clamp(min=torch.zeros_like(card), max=card - 1)
+    return cat, num
diff --git a/weightslab/examples/PyTorch/wl-ads-recommendation/utils/model.py b/weightslab/examples/PyTorch/wl-ads-recommendation/utils/model.py
new file mode 100644
index 00000000..dfef847d
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-ads-recommendation/utils/model.py
@@ -0,0 +1,76 @@
+"""Wide & Deep CTR model for advertising recommendation (pure PyTorch).
+
+A compact take on the classic Wide & Deep architecture (Cheng et al., 2016):
+  * **Deep**: each categorical field -> embedding; embeddings are concatenated
+    with the numeric features and passed through an MLP (learns generalizable
+    feature interactions).
+  * **Wide**: first-order per-category effects + a linear numeric term (memorizes
+    strong direct signals).
+The two logits are summed. Outputs 2 logits (no-click / click) so it plugs into
+``CrossEntropyLoss`` and a 2-class accuracy metric like the other usecases.
+"""
+
+from __future__ import annotations
+
+from typing import List
+
+import torch
+import torch.nn as nn
+
+from .data import (
+    CATEGORICAL_CARDINALITIES,
+    NUM_CATEGORICAL,
+    NUM_NUMERIC,
+    IMG_SIDE,
+    unpack,
+)
+
+
+class WideDeepCTR(nn.Module):
+    def __init__(
+        self,
+        cardinalities: List[int] = CATEGORICAL_CARDINALITIES,
+        num_numeric: int = NUM_NUMERIC,
+        emb_dim: int = 8,
+        hidden: int = 128,
+        num_classes: int = 2,
+    ):
+        super().__init__()
+        self.input_shape = (1, len(cardinalities) + num_numeric)
+        self.cardinalities = list(cardinalities)
+        self.num_numeric = num_numeric
+
+        # Deep: one embedding table per categorical field.
+        self.embeddings = nn.ModuleList(
+            [nn.Embedding(card, emb_dim) for card in self.cardinalities]
+        )
+        deep_in = emb_dim * len(self.cardinalities) + num_numeric
+        self.deep = nn.Sequential(
+            nn.Linear(deep_in, hidden),
+            nn.ReLU(),
+            nn.Dropout(0.1),
+            nn.Linear(hidden, hidden // 2),
+            nn.ReLU(),
+            nn.Linear(hidden // 2, num_classes),
+        )
+
+        # Wide: first-order per-category effect (embedding dim 1) + linear numeric.
+        self.wide_cat = nn.ModuleList(
+            [nn.Embedding(card, num_classes) for card in self.cardinalities]
+        )
+        self.wide_num = nn.Linear(num_numeric, num_classes)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        cat_idx, numeric = unpack(x)  # [N, 8] long, [N, 8] float
+
+        # Deep branch.
+        embs = [emb(cat_idx[:, i]) for i, emb in enumerate(self.embeddings)]
+        deep_in = torch.cat(embs + [numeric], dim=1)
+        deep_logits = self.deep(deep_in)
+
+        # Wide branch.
+        wide_logits = self.wide_num(numeric)
+        for i, emb in enumerate(self.wide_cat):
+            wide_logits = wide_logits + emb(cat_idx[:, i])
+
+        return deep_logits + wide_logits
diff --git a/weightslab/examples/PyTorch/wl-ads-recommendation/verify_integration.py b/weightslab/examples/PyTorch/wl-ads-recommendation/verify_integration.py
new file mode 100644
index 00000000..b8b08d01
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-ads-recommendation/verify_integration.py
@@ -0,0 +1,219 @@
+"""Integration check: prove WeightsLab is fully wired for this tabular example.
+
+Unlike ``test_ads_recommendation.py`` (pure PyTorch), this drives the WeightsLab
+stack end-to-end — tracked dataloaders, watched loss/metric, the gRPC server,
+and the per-sample ledger — then asserts the sample dataframe the UI reads
+contains, for tabular mode:
+
+  * one row per impression (what you scroll/sort in the grid & List view),
+  * every ad/user/context field as its own column (from ``get_items`` metadata),
+  * ``target`` + ``prediction`` populated (so sort/filter/histograms work),
+  * a per-sample training/eval loss column,
+
+and that the gRPC server is actually listening (so Weights Studio can connect
+live during the run).
+
+Run:  python verify_integration.py
+Requires weightslab installed (not a pure-unit test).
+"""
+
+import glob
+import os
+import socket
+import sys
+import tempfile
+import time as _time
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchmetrics.classification import Accuracy
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+import weightslab as wl  # noqa: E402
+from weightslab.components.global_monitoring import (  # noqa: E402
+    guard_training_context,
+    guard_testing_context,
+)
+
+from utils.data import (  # noqa: E402
+    AdsCTRDataset,
+    CATEGORICAL_CARDINALITIES,
+    CATEGORICAL_FIELDS,
+    NUMERIC_FIELDS,
+    NUM_NUMERIC,
+)
+from utils.model import WideDeepCTR  # noqa: E402
+
+
+def _free_port() -> int:
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    s.bind(("127.0.0.1", 0))
+    port = s.getsockname()[1]
+    s.close()
+    return port
+
+
+def main() -> int:
+    device = torch.device("cpu")
+    log_dir = tempfile.mkdtemp(prefix="wl_ads_verify_")
+    grpc_port = _free_port()
+    os.environ["GRPC_BACKEND_PORT"] = str(grpc_port)
+
+    parameters = {
+        "experiment_name": "ads_verify",
+        "device": "cpu",
+        "root_log_dir": log_dir,
+        "serving_grpc": True,
+        "grpc_port": grpc_port,
+        "is_training": True,
+    }
+    wl.watch_or_edit(parameters, flag="hyperparameters", poll_interval=1.0)
+
+    model = wl.watch_or_edit(
+        WideDeepCTR(cardinalities=CATEGORICAL_CARDINALITIES, num_numeric=NUM_NUMERIC).to(device),
+        flag="model", device=device)
+    optimizer = wl.watch_or_edit(optim.Adam(model.parameters(), lr=0.005), flag="optimizer")
+
+    train_ds = AdsCTRDataset(800, seed=0)
+    test_ds = AdsCTRDataset(200, seed=1)
+
+    train_loader = wl.watch_or_edit(
+        train_ds, flag="data", loader_name="train_loader", batch_size=64, shuffle=True,
+        is_training=True, compute_hash=False, preload_labels=True, preload_metadata=True,
+        enable_h5_persistence=False)
+    test_loader = wl.watch_or_edit(
+        test_ds, flag="data", loader_name="test_loader", batch_size=128, shuffle=False,
+        is_training=False, compute_hash=False, preload_labels=True, preload_metadata=True,
+        enable_h5_persistence=False)
+
+    cw = torch.tensor([1.0, 4.0])
+    train_crit = wl.watch_or_edit(nn.CrossEntropyLoss(weight=cw, reduction="none"),
+                                  flag="loss", signal_name="train-loss-CE", log=True)
+    test_crit = wl.watch_or_edit(nn.CrossEntropyLoss(weight=cw, reduction="none"),
+                                 flag="loss", signal_name="test-loss-CE", log=True)
+    metric = wl.watch_or_edit(Accuracy(task="multiclass", num_classes=2),
+                              flag="metric", signal_name="metric-ACC", log=True)
+
+    wl.serve(serving_grpc=True, grpc_port=grpc_port)
+    wl.start_training()
+
+    for _ in range(120):
+        with guard_training_context:
+            inputs, ids, labels = next(train_loader)
+            optimizer.zero_grad()
+            out = model(inputs)
+            preds = out.argmax(dim=1, keepdim=True)
+            loss = train_crit(out, labels, batch_ids=ids, preds=preds).mean()
+            loss.backward()
+            optimizer.step()
+
+    for inputs, ids, labels in test_loader:
+        with guard_testing_context:
+            out = model(inputs)
+            preds = out.argmax(dim=1, keepdim=True)
+            test_crit(out, labels, batch_ids=ids, preds=preds)
+            metric.update(out, labels)
+
+    wl.drain_signals()
+
+    wl.write_dataframe(path=log_dir, format="csv")
+    csvs = glob.glob(os.path.join(log_dir, "*dataframe*.csv"))
+    assert csvs, f"no dataframe CSV written to {log_dir}"
+    with open(csvs[0], "r", encoding="utf-8") as fh:
+        header = fh.readline().strip().split(",")
+        rows = [ln for ln in fh.read().splitlines() if ln.strip()]
+
+    cols = set(c.strip().strip('"') for c in header)
+    n_rows = len(rows)
+
+    print("\n=== Ledger dataframe ===")
+    print(f"rows: {n_rows}")
+    print(f"columns ({len(cols)}): {sorted(cols)}")
+
+    problems = []
+    if n_rows < 900:
+        problems.append(f"expected ~1000 sample rows, got {n_rows}")
+
+    field_cols = set(CATEGORICAL_FIELDS) | set(NUMERIC_FIELDS)
+    missing_fields = [f for f in field_cols if f not in cols]
+    if missing_fields:
+        problems.append(f"missing field columns: {missing_fields}")
+
+    for required in ("target", "prediction"):
+        if required not in cols:
+            problems.append(f"missing '{required}' column")
+
+    if not any("loss" in c.lower() for c in cols):
+        problems.append("no per-sample loss column found")
+
+    grpc_up = False
+    for _ in range(20):
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        sock.settimeout(1)
+        if sock.connect_ex(("127.0.0.1", grpc_port)) == 0:
+            sock.close()
+            grpc_up = True
+            break
+        sock.close()
+        _time.sleep(0.5)
+    if not grpc_up:
+        problems.append(f"gRPC server not listening on {grpc_port}")
+    else:
+        print(f"gRPC server listening on 127.0.0.1:{grpc_port}  OK")
+
+        # Real gRPC round-trip: the packed field vector fed to the model must
+        # reach the UI as raw_data of type 'vector' carrying the values.
+        try:
+            import grpc
+            from weightslab.proto import experiment_service_pb2 as pb2
+            from weightslab.proto import experiment_service_pb2_grpc as pb2_grpc
+
+            n_fields = len(CATEGORICAL_CARDINALITIES) + NUM_NUMERIC
+            channel = grpc.insecure_channel(f"127.0.0.1:{grpc_port}")
+            grpc.channel_ready_future(channel).result(timeout=10)
+            stub = pb2_grpc.ExperimentServiceStub(channel)
+            resp = stub.GetDataSamples(pb2.DataSamplesRequest(
+                start_index=0, records_cnt=5, include_raw_data=True,
+                resize_width=0, resize_height=0), timeout=20)
+
+            raw = None
+            for rec in resp.data_records:
+                for st in rec.data_stats:
+                    if st.name == "raw_data":
+                        raw = st
+                        break
+                if raw is not None:
+                    break
+
+            if raw is None:
+                problems.append("gRPC GetDataSamples returned no raw_data stat")
+            elif raw.type != "vector":
+                problems.append(f"raw_data.type is '{raw.type}', expected 'vector'")
+            elif len(raw.value) != n_fields:
+                problems.append(
+                    f"raw_data carries {len(raw.value)} values, expected {n_fields}")
+            else:
+                print(f"gRPC GetDataSamples: raw_data type='vector', "
+                      f"{len(raw.value)} field values reached the UI  OK")
+            channel.close()
+        except Exception as e:
+            problems.append(f"gRPC GetDataSamples failed: {e}")
+
+    if problems:
+        print("\n VERIFY FAILED:")
+        for p in problems:
+            print(f"  - {p}")
+        return 1
+
+    print("\n VERIFY PASSED: WeightsLab is fully wired for tabular mode "
+          "(per-sample rows + field columns + target/prediction/loss + live gRPC).")
+    return 0
+
+
+if __name__ == "__main__":
+    code = main()
+    sys.stdout.flush()
+    sys.stderr.flush()
+    os._exit(code)
diff --git a/weightslab/examples/PyTorch/ws-classification/config.yaml b/weightslab/examples/PyTorch/wl-classification/config.yaml
similarity index 100%
rename from weightslab/examples/PyTorch/ws-classification/config.yaml
rename to weightslab/examples/PyTorch/wl-classification/config.yaml
diff --git a/weightslab/examples/PyTorch/ws-classification/main.py b/weightslab/examples/PyTorch/wl-classification/main.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-classification/main.py
rename to weightslab/examples/PyTorch/wl-classification/main.py
diff --git a/weightslab/examples/PyTorch/ws-clustering/config.yaml b/weightslab/examples/PyTorch/wl-clustering/config.yaml
similarity index 100%
rename from weightslab/examples/PyTorch/ws-clustering/config.yaml
rename to weightslab/examples/PyTorch/wl-clustering/config.yaml
diff --git a/weightslab/examples/PyTorch/wl-clustering/face/__init__.py b/weightslab/examples/PyTorch/wl-clustering/face/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/weightslab/examples/PyTorch/ws-clustering/face/data.py b/weightslab/examples/PyTorch/wl-clustering/face/data.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-clustering/face/data.py
rename to weightslab/examples/PyTorch/wl-clustering/face/data.py
diff --git a/weightslab/examples/PyTorch/ws-clustering/face/model.py b/weightslab/examples/PyTorch/wl-clustering/face/model.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-clustering/face/model.py
rename to weightslab/examples/PyTorch/wl-clustering/face/model.py
diff --git a/weightslab/examples/PyTorch/ws-clustering/face/signals.py b/weightslab/examples/PyTorch/wl-clustering/face/signals.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-clustering/face/signals.py
rename to weightslab/examples/PyTorch/wl-clustering/face/signals.py
diff --git a/weightslab/examples/PyTorch/ws-clustering/face/utils.py b/weightslab/examples/PyTorch/wl-clustering/face/utils.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-clustering/face/utils.py
rename to weightslab/examples/PyTorch/wl-clustering/face/utils.py
diff --git a/weightslab/examples/PyTorch/ws-clustering/main.py b/weightslab/examples/PyTorch/wl-clustering/main.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-clustering/main.py
rename to weightslab/examples/PyTorch/wl-clustering/main.py
diff --git a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/README.md b/weightslab/examples/PyTorch/wl-detection/README.md
similarity index 99%
rename from weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/README.md
rename to weightslab/examples/PyTorch/wl-detection/README.md
index ba8bddc6..3fee76a9 100644
--- a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/README.md
+++ b/weightslab/examples/PyTorch/wl-detection/README.md
@@ -20,7 +20,7 @@ weightslab start example --det
 Or run it directly:
 
 ```bash
-cd weightslab/examples/PyTorch/ws-detection
+cd weightslab/examples/PyTorch/wl-detection
 pip install -r requirements.txt
 python main.py
 ```
diff --git a/weightslab/examples/PyTorch/ws-detection/config.yaml b/weightslab/examples/PyTorch/wl-detection/config.yaml
similarity index 100%
rename from weightslab/examples/PyTorch/ws-detection/config.yaml
rename to weightslab/examples/PyTorch/wl-detection/config.yaml
diff --git a/weightslab/examples/PyTorch/ws-detection/main.py b/weightslab/examples/PyTorch/wl-detection/main.py
similarity index 99%
rename from weightslab/examples/PyTorch/ws-detection/main.py
rename to weightslab/examples/PyTorch/wl-detection/main.py
index ff4b2030..6cb4d627 100644
--- a/weightslab/examples/PyTorch/ws-detection/main.py
+++ b/weightslab/examples/PyTorch/wl-detection/main.py
@@ -119,6 +119,8 @@ def test(loader, model, sig, device, grid_size, conf_thresh, test_loader_len):
     parameters.setdefault("freeze_backbone", True)
     parameters.setdefault("compute_natural_sort", True)
 
+    exp_name = parameters["experiment_name"]
+
     # --- 2) Register hyperparameters ---
     exp_name = parameters["experiment_name"]
     wl.watch_or_edit(
@@ -128,7 +130,6 @@ def test(loader, model, sig, device, grid_size, conf_thresh, test_loader_len):
         defaults=parameters,
         poll_interval=1.0,
     )
-
     num_classes = int(parameters["num_classes"])
     image_size = int(parameters["image_size"])
     grid_size = int(parameters["grid_size"])
diff --git a/weightslab/examples/PyTorch/ws-detection/utils/criterions.py b/weightslab/examples/PyTorch/wl-detection/utils/criterions.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-detection/utils/criterions.py
rename to weightslab/examples/PyTorch/wl-detection/utils/criterions.py
diff --git a/weightslab/examples/PyTorch/ws-detection/utils/data.py b/weightslab/examples/PyTorch/wl-detection/utils/data.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-detection/utils/data.py
rename to weightslab/examples/PyTorch/wl-detection/utils/data.py
diff --git a/weightslab/examples/PyTorch/ws-detection/utils/model.py b/weightslab/examples/PyTorch/wl-detection/utils/model.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-detection/utils/model.py
rename to weightslab/examples/PyTorch/wl-detection/utils/model.py
diff --git a/weightslab/examples/PyTorch/wl-fraud-detection/README.md b/weightslab/examples/PyTorch/wl-fraud-detection/README.md
new file mode 100644
index 00000000..d32a00a3
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-fraud-detection/README.md
@@ -0,0 +1,92 @@
+# WeightsLab — Bank Fraud Detection (tabular, pure PyTorch)
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-fraud-detection.ipynb)
+
+A small, fully-runnable **tabular binary-classification** example: an MLP learns
+to flag fraudulent bank card transactions, streaming per-sample loss /
+prediction / accuracy to the WeightsLab UI. Being tabular, it's the natural
+companion to the **List Exploration (tabular) view** — sort by `loss` or
+`prediction` to triage the transactions the model finds hardest.
+
+Everything is plain PyTorch + NumPy — no external download. The dataset is
+generated in-process so runs are reproducible and offline.
+
+## Quick start
+
+```bash
+cd weightslab/examples/PyTorch/wl-fraud-detection
+pip install -r requirements.txt
+python main.py
+```
+
+Then open the UI (e.g. `http://localhost:5173`), switch the Data Exploration
+board to **List** view, press play, and sort columns to explore.
+
+## The data
+
+`make_synthetic_fraud(n, seed)` (in `utils/data.py`) generates a reproducible
+stream of transactions. Each row has **16 numeric features** (`amount`,
+`old_balance`, `merchant_risk`, `geo_distance_km`, `is_foreign`,
+`num_prior_disputes`, …) and a binary label (`0` legit, `1` fraud, ~12%
+prevalence). Fraud rows come from shifted distributions (larger amounts,
+off-hours activity, higher merchant risk, device changes, larger geo jumps).
+Features are standardized and reshaped to a `1x4x4` heatmap so the grid renders
+a thumbnail; the model flattens it back to 16.
+
+### Using a real dataset
+
+Swap `FraudDataset` for a loader over a real CSV to go from demo to reality:
+
+* **Kaggle Credit Card Fraud** (ULB) — `creditcard.csv`, 284k transactions with
+  anonymized `V1..V28` features + `Amount` + `Class`.
+  <https://www.kaggle.com/mlg-ulb/creditcardfraud>
+* **PaySim** mobile-money fraud simulator.
+  <https://www.kaggle.com/ealaxi/paysim1>
+
+Keep the `__getitem__` contract `(features_as_image, idx, label)` and adjust
+`NUM_FEATURES` / the reshape.
+
+## What "a sample" is here
+
+There are no images — **each sample is one transaction (a row)**, and the model
+input **is** the 1-D feature vector (not a reshaped image). WeightsLab carries
+that vector through gRPC as a `raw_data` stat of type `vector` (the actual
+values), so `inputs`, `labels`/`target` and `metadata` all reach the UI. The 16
+raw features are also exposed as **sortable columns** via the dataset's
+`get_items()` metadata contract (`preload_metadata=True`), so the List
+Exploration view shows real tabular columns (`amount`, `merchant_risk`,
+`geo_distance_km`, …) alongside the tracked stats below. Everything you do on
+MNIST — sort, lock, histograms, discard/restore, neuron ops — works the same
+way, because those operate on the per-sample ledger, not on pixels.
+
+## What you'll see in the UI
+
+| Signal / column                          | Meaning                                        |
+| ---------------------------------------- | ---------------------------------------------- |
+| feature columns (`amount`, `merchant_risk`, …) | The 16 raw transaction features, sortable |
+| `train-loss-CE`, `test-loss-CE`          | Weighted cross-entropy per split               |
+| `metric-ACC`                             | Overall accuracy                               |
+| `test_metric/Accuracy_per_sample`        | Per-transaction correctness (0/1)              |
+| `test_metric/Fraud_caught_per_sample`    | 1 when a true fraud was correctly flagged      |
+| `target`, `prediction` columns           | Per-sample truth/pred to sort/lock in List view |
+
+Class weights (`[1.0, 4.0]`, config) up-weight the fraud class against the ~12%
+prevalence so the minority class drives the gradient.
+
+## Test it
+
+```bash
+# Fast, offline unit tests (pure PyTorch, no gRPC server):
+python -m pytest test_fraud_detection.py -v
+
+# End-to-end integration check (needs weightslab installed): drives the tracked
+# loaders + watched loss + gRPC server, then asserts the ledger dataframe the UI
+# reads has per-sample rows, every feature as a column, target/prediction/loss,
+# and a live gRPC endpoint.
+python verify_integration.py
+```
+
+The unit tests cover the dataset contract, reproducibility, class balance, the
+model forward pass, `get_items` metadata columns, and that a few optimizer steps
+reduce the loss (final accuracy > 90%). `verify_integration.py` proves WeightsLab
+is fully wired in tabular mode.
diff --git a/weightslab/examples/PyTorch/wl-fraud-detection/config.yaml b/weightslab/examples/PyTorch/wl-fraud-detection/config.yaml
new file mode 100644
index 00000000..01b5a79c
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-fraud-detection/config.yaml
@@ -0,0 +1,48 @@
+# Global configuration
+experiment_name: fraud_detection_mlp
+device: auto
+training_steps_to_do: null  # null = train until manually stopped from the UI
+# root_log_dir: ...  # Path to save current experiment checkpoints and data
+
+# Initially compute natural sorting values (e.g. avg feature magnitude).
+compute_natural_sort: false
+
+# Experiment parameters
+eval_full_to_train_steps_ratio: 100
+experiment_dump_to_train_steps_ratio: 250
+write_export_ratio: 100
+skip_checkpoint_load: false
+tqdm_display: true
+is_training: false  # Start paused; press play in the UI.
+
+# Global dataframe storage
+ledger_enable_flushing_threads: true
+ledger_enable_h5_persistence: true
+ledger_flush_max_rows: 15000
+ledger_flush_interval: 30.0
+
+# Clients
+serving_grpc: true
+
+# Optimizer
+optimizer:
+  lr: 0.005
+
+# Class weighting to counter ~12% fraud prevalence [legit, fraud].
+class_weights: [1.0, 4.0]
+
+# Synthetic dataset generation (reproducible, no download).
+dataset:
+  seed: 0
+  n_train: 4000
+  n_test: 1000
+
+# DataLoader parameters
+data:
+  train_loader:
+    shuffle: true
+    batch_size: 32
+  test_loader:
+    shuffle: false
+    batch_size: 128
+    drop_last: false
diff --git a/weightslab/examples/PyTorch/wl-fraud-detection/main.py b/weightslab/examples/PyTorch/wl-fraud-detection/main.py
new file mode 100644
index 00000000..4001684b
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-fraud-detection/main.py
@@ -0,0 +1,284 @@
+"""WeightsLab example: bank card-transaction fraud detection (tabular, PyTorch).
+
+A small MLP binary classifier over synthetic transaction features, wired into
+WeightsLab so the run streams per-sample stats (loss, prediction, target,
+discard state) to the UI. Because the data is tabular, this is a natural fit for
+the List Exploration (tabular) view — sort by loss or prediction to triage the
+transactions the model finds hardest.
+
+Run:
+    cd weightslab/examples/PyTorch/wl-fraud-detection
+    python main.py
+
+The dataset/model live in ``utils/`` (pure PyTorch) so they can be unit tested
+without the gRPC backend — see ``test_fraud_detection.py``.
+"""
+
+import itertools
+import os
+import time
+import logging
+import tempfile
+
+import yaml
+import tqdm
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+from torchmetrics.classification import Accuracy
+
+import weightslab as wl
+from weightslab.components.global_monitoring import (
+    guard_training_context,
+    guard_testing_context,
+)
+
+from utils.data import FraudDataset, NUM_FEATURES
+from utils.model import FraudMLP
+
+
+logging.basicConfig(level=logging.ERROR)
+logger = logging.getLogger(__name__)
+
+
+# -----------------------------------------------------------------------------
+# Train / Test steps
+# -----------------------------------------------------------------------------
+def train(loader, model, optimizer, criterion_mlt, device):
+    """Single training step using the tracked dataloader + watched loss."""
+    with guard_training_context:
+        (inputs, ids, labels) = next(loader)
+        inputs = inputs.to(device)
+        labels = labels.to(device)
+
+        optimizer.zero_grad()
+        preds_raw = model(inputs)
+        preds = preds_raw.argmax(dim=1, keepdim=True)
+
+        loss_batch_mlt = criterion_mlt(
+            preds_raw.float(),
+            labels.long(),
+            batch_ids=ids,
+            preds=preds,
+        )
+        total_loss = loss_batch_mlt.mean()
+
+        total_loss.backward()
+        optimizer.step()
+
+    return total_loss.detach().cpu().item()
+
+
+def test(loader, model, criterion_mlt, metric_mlt, device, test_loader_len):
+    """Full evaluation pass over the test loader, logging per-sample signals."""
+    losses = torch.tensor(0.0, device=device)
+
+    for (inputs, ids, labels) in loader:
+        with guard_testing_context:
+            inputs = inputs.to(device)
+            labels = labels.to(device)
+
+            outputs = model(inputs)
+            preds = outputs.argmax(dim=1, keepdim=True)
+
+            loss_batch = criterion_mlt(outputs, labels, batch_ids=ids, preds=preds)
+            losses += torch.mean(loss_batch)
+            metric_mlt.update(outputs, labels)
+
+            preds_flat = preds.view(-1)
+            labels_flat = labels.view(-1)
+            acc_per_sample = (preds_flat == labels_flat).float()
+            fraud_caught_per_sample = ((preds_flat == 1) & (labels_flat == 1)).float()
+
+            signals = {
+                "test_metric/Accuracy_per_sample": acc_per_sample,
+                "test_metric/Fraud_caught_per_sample": fraud_caught_per_sample,
+            }
+            wl.save_signals(
+                preds_raw=outputs,
+                targets=labels,
+                batch_ids=ids,
+                signals=signals,
+                preds=preds,
+            )
+
+    loss = losses / max(1, test_loader_len)
+    metric = metric_mlt.compute() * 100
+
+    return loss.detach().cpu().item(), metric.detach().cpu().item()
+
+
+# -----------------------------------------------------------------------------
+# Main
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+    start_time = time.time()
+
+    # Load hyperparameters (from YAML if present).
+    parameters = {}
+    config_path = os.path.join(os.path.dirname(__file__), "config.yaml")
+    if os.path.exists(config_path):
+        with open(config_path, "r") as fh:
+            parameters = yaml.safe_load(fh) or {}
+    parameters = parameters or {}
+
+    # ---- sensible defaults / normalization ----
+    parameters.setdefault("experiment_name", "fraud_detection_mlp")
+    parameters.setdefault("device", "auto")
+    parameters.setdefault("training_steps_to_do", 1000000)
+    parameters.setdefault("eval_full_to_train_steps_ratio", 100)
+
+    exp_name = parameters["experiment_name"]
+
+    # Hyperparameters (must use 'hyperparameters' flag for trainer services / UI).
+    wl.watch_or_edit(parameters, flag="hyperparameters", poll_interval=1.0)
+
+    # Device selection
+    if parameters.get("device", "auto") == "auto":
+        parameters["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = parameters["device"]
+
+    # Logging dir
+    if not parameters.get("root_log_dir"):
+        parameters["root_log_dir"] = tempfile.mkdtemp()
+        print(f"No root_log_dir specified, using temporary directory: {parameters['root_log_dir']}")
+    os.makedirs(parameters["root_log_dir"], exist_ok=True)
+
+    verbose = parameters.get("verbose", True)
+    log_dir = parameters["root_log_dir"]
+    tqdm_display = parameters.get("tqdm_display", True)
+    eval_full_to_train_steps_ratio = parameters.get("eval_full_to_train_steps_ratio", 100)
+    write_export_ratio = parameters.get("write_export_ratio", 100)
+    enable_h5_persistence = parameters.get("enable_h5_persistence", True)
+    training_steps_to_do = parameters.get("training_steps_to_do", 1000)
+
+    # Model
+    _model = FraudMLP(in_features=NUM_FEATURES, num_classes=2).to(device)
+    model = wl.watch_or_edit(_model, flag="model", device=device)
+
+    # Optimizer
+    lr = parameters.get("optimizer", {}).get("lr", 0.005)
+    _optimizer = optim.Adam(model.parameters(), lr=lr)
+    optimizer = wl.watch_or_edit(_optimizer, flag="optimizer")
+
+    # Data (synthetic tabular fraud stream) — no download needed.
+    dataset_cfg = parameters.get("dataset", {})
+    seed = int(dataset_cfg.get("seed", 0))
+    n_train = int(dataset_cfg.get("n_train", 4000))
+    n_test = int(dataset_cfg.get("n_test", 1000))
+
+    train_cfg = parameters.get("data", {}).get("train_loader", {})
+    test_cfg = parameters.get("data", {}).get("test_loader", {})
+
+    _train_dataset = FraudDataset(n_train, seed=seed, max_samples=train_cfg.get("max_samples"))
+    _test_dataset = FraudDataset(n_test, seed=seed + 1, max_samples=test_cfg.get("max_samples"))
+
+    train_loader = wl.watch_or_edit(
+        _train_dataset,
+        flag="data",
+        loader_name="train_loader",
+        batch_size=train_cfg.get("batch_size", 32),
+        shuffle=train_cfg.get("shuffle", True),
+        is_training=True,
+        compute_hash=False,
+        preload_labels=True,
+        preload_metadata=True,
+        enable_h5_persistence=enable_h5_persistence,
+    )
+    test_loader = wl.watch_or_edit(
+        _test_dataset,
+        flag="data",
+        loader_name="test_loader",
+        batch_size=test_cfg.get("batch_size", 128),
+        shuffle=test_cfg.get("shuffle", False),
+        is_training=False,
+        compute_hash=False,
+        preload_labels=True,
+        preload_metadata=True,
+        enable_h5_persistence=enable_h5_persistence,
+    )
+
+    # Losses & metrics (watched objects – they log themselves).
+    # Class weighting counters the ~12% fraud prevalence so the minority class
+    # actually drives the gradient.
+    class_weights = torch.tensor(
+        parameters.get("class_weights", [1.0, 4.0]), dtype=torch.float32, device=device
+    )
+    train_criterion = wl.watch_or_edit(
+        nn.CrossEntropyLoss(weight=class_weights, reduction="none"),
+        flag="loss", signal_name="train-loss-CE", log=True)
+    test_criterion = wl.watch_or_edit(
+        nn.CrossEntropyLoss(weight=class_weights, reduction="none"),
+        flag="loss", signal_name="test-loss-CE", log=True)
+
+    metric = wl.watch_or_edit(
+        Accuracy(task="multiclass", num_classes=2).to(device),
+        flag="metric", signal_name="metric-ACC", log=True)
+
+    # Start WeightsLab services (gRPC only, no CLI).
+    wl.serve(serving_grpc=parameters.get("serving_grpc", False))
+
+    print("=" * 60)
+    print(" STARTING FRAUD-DETECTION TRAINING")
+    print(f" Evaluation every {eval_full_to_train_steps_ratio} steps")
+    print(f" Dataset splits: train={len(_train_dataset)}, test={len(_test_dataset)}")
+    print(f" Logs will be saved to: {log_dir}")
+    print("=" * 60 + "\n")
+
+    if tqdm_display:
+        train_range = tqdm.tqdm(
+            range(training_steps_to_do) if training_steps_to_do != None else itertools.count(),
+            desc="Training",
+            bar_format="{desc}: {n}/{total} [{elapsed}<{remaining}, {rate_fmt}] {bar} | {postfix}",
+            ncols=140,
+            position=0,
+            leave=True,
+        )
+    else:
+        train_range = range(training_steps_to_do) if training_steps_to_do != None else itertools.count()
+
+    # ================
+    # Training Loop
+    wl.start_training(timeout=3)
+
+    train_loss = None
+    test_loss, test_metric = None, None
+    test_loader_len = len(test_loader)
+    for train_step in train_range:
+        age = model.get_age() if hasattr(model, "get_age") else train_step
+
+        train_loss = train(train_loader, model, optimizer, train_criterion, device)
+
+        if age > 0 and age % eval_full_to_train_steps_ratio == 0:
+            test_loss, test_metric = test(
+                test_loader, model, test_criterion, metric, device, test_loader_len
+            )
+
+        if age > 0 and age % write_export_ratio == 0:
+            wl.write_history()
+            wl.write_dataframe()
+
+        if verbose and not tqdm_display:
+            import sys
+            msg = f"Step {train_step} (Age {age}): Loss={train_loss:.4f}"
+            if test_loss is not None:
+                msg += f" | Test={test_loss:.4f} ({test_metric:.1f}%)"
+            sys.stdout.write(f"\r{msg:<100}")
+            sys.stdout.flush()
+        elif tqdm_display:
+            postfix_parts = [f"train_loss={train_loss:.4f}"]
+            if test_loss is not None:
+                postfix_parts.append(f"test_loss={test_loss:.4f}")
+            if test_metric is not None:
+                postfix_parts.append(f"test_acc={test_metric:.1f}%")
+            train_range.set_postfix_str(" | ".join(postfix_parts))
+
+    print("\n" + "=" * 60)
+    print(f" Training completed in {time.time() - start_time:.2f} seconds")
+    print(f" Logs saved to: {log_dir}")
+    print("=" * 60)
+
+    wl.write_history()
+    wl.write_dataframe()
+    wl.keep_serving()
diff --git a/weightslab/examples/PyTorch/wl-fraud-detection/test_fraud_detection.py b/weightslab/examples/PyTorch/wl-fraud-detection/test_fraud_detection.py
new file mode 100644
index 00000000..21c4e21e
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-fraud-detection/test_fraud_detection.py
@@ -0,0 +1,118 @@
+"""Smoke tests for the fraud-detection example (pure PyTorch, no weightslab).
+
+Run:  python -m pytest test_fraud_detection.py
+  or:  python test_fraud_detection.py
+"""
+
+import os
+import sys
+
+import torch
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from utils.data import (  # noqa: E402
+    FEATURE_NAMES,
+    IMG_SIDE,
+    NUM_FEATURES,
+    FraudDataset,
+    make_synthetic_fraud,
+)
+from utils.model import FraudMLP  # noqa: E402
+
+
+def test_feature_layout_matches_image_grid():
+    assert NUM_FEATURES == len(FEATURE_NAMES) == 16
+    assert IMG_SIDE * IMG_SIDE == NUM_FEATURES
+
+
+def test_synthetic_shapes_and_labels():
+    x_std, x_raw, y = make_synthetic_fraud(500, seed=0)
+    assert x_std.shape == x_raw.shape == (500, NUM_FEATURES)
+    assert y.shape == (500,)
+    assert str(x_std.dtype) == "float32"
+    assert set(int(v) for v in set(y.tolist())) <= {0, 1}
+    # Standardized features are ~zero-mean, raw ones are not.
+    assert abs(float(x_std.mean())) < 0.1
+    assert abs(float(x_raw.mean())) > 0.1
+
+
+def test_synthetic_is_deterministic_for_a_seed():
+    x1, _, y1 = make_synthetic_fraud(200, seed=42)
+    x2, _, y2 = make_synthetic_fraud(200, seed=42)
+    assert (x1 == x2).all() and (y1 == y2).all()
+    x3, _, _ = make_synthetic_fraud(200, seed=7)
+    assert not (x1 == x3).all()
+
+
+def test_class_imbalance_is_realistic():
+    _, _, y = make_synthetic_fraud(1000, seed=1)
+    assert 0.05 < float(y.mean()) < 0.20
+
+
+def test_dataset_item_contract():
+    ds = FraudDataset(300, seed=3)
+    assert len(ds) == 300
+    x, idx, label = ds[0]
+    # Model input is the 1-D feature vector (no fake image).
+    assert tuple(x.shape) == (NUM_FEATURES,)
+    assert x.dtype == torch.float32
+    assert idx == 0 and label in (0, 1)
+
+
+def test_get_items_exposes_feature_metadata_columns():
+    """The ledger-init contract must return raw features as a metadata dict so
+    they become sortable columns in the WeightsLab UI."""
+    ds = FraudDataset(50, seed=0)
+    image, uid, target, metadata = ds.get_items(
+        3, include_metadata=True, include_labels=True, include_images=False
+    )
+    assert image is None  # init does not decode images
+    assert uid == 3 and target in (0, 1)
+    assert isinstance(metadata, dict)
+    assert set(metadata.keys()) == set(FEATURE_NAMES)
+    assert all(isinstance(v, float) for v in metadata.values())
+    # Raw values, not standardized (e.g. amount is a positive currency figure).
+    assert metadata["amount"] != 0.0
+
+
+def test_dataset_respects_max_samples():
+    assert len(FraudDataset(500, seed=0, max_samples=64)) == 64
+
+
+def test_model_forward_shape_flat_and_image():
+    model = FraudMLP()
+    assert model(torch.randn(8, NUM_FEATURES)).shape == (8, 2)
+    assert model(torch.randn(8, 1, IMG_SIDE, IMG_SIDE)).shape == (8, 2)
+
+
+def test_training_reduces_loss():
+    torch.manual_seed(0)
+    ds = FraudDataset(1000, seed=0)
+    features = ds.features  # [N, NUM_FEATURES]
+    labels = ds.labels
+
+    model = FraudMLP()
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
+    criterion = torch.nn.CrossEntropyLoss()
+
+    model.train()
+    initial_loss = criterion(model(features), labels).item()
+    for _ in range(50):
+        optimizer.zero_grad()
+        loss = criterion(model(features), labels)
+        loss.backward()
+        optimizer.step()
+    final_loss = criterion(model(features), labels).item()
+    assert final_loss < initial_loss * 0.85, f"{initial_loss:.4f} -> {final_loss:.4f}"
+
+    model.eval()
+    with torch.no_grad():
+        acc = float((model(features).argmax(dim=1) == labels).float().mean())
+    assert acc > 0.9, f"accuracy too low: {acc:.3f}"
+
+
+if __name__ == "__main__":
+    import pytest
+
+    raise SystemExit(pytest.main([__file__, "-v"]))
diff --git a/weightslab/examples/PyTorch/wl-fraud-detection/utils/__init__.py b/weightslab/examples/PyTorch/wl-fraud-detection/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/weightslab/examples/PyTorch/wl-fraud-detection/utils/data.py b/weightslab/examples/PyTorch/wl-fraud-detection/utils/data.py
new file mode 100644
index 00000000..d0d62873
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-fraud-detection/utils/data.py
@@ -0,0 +1,189 @@
+"""Synthetic bank card-transaction fraud dataset (pure PyTorch).
+
+Import-light (only ``numpy`` + ``torch``) so it can be unit-tested without
+importing ``weightslab`` or starting the gRPC backend — see
+``test_fraud_detection.py``.
+
+This is a reproducible, offline stand-in for a real card-fraud stream. Each row
+is a transaction described by 16 numeric features, with a binary label
+(0 = legitimate, 1 = fraud, ~12% prevalence).
+
+Real-world analogues (drop-in replaceable — just swap the dataset):
+  * Kaggle "Credit Card Fraud Detection" (ULB, ``creditcard.csv``, 284k txns,
+    anonymized V1..V28 PCA features) — https://www.kaggle.com/mlg-ulb/creditcardfraud
+  * PaySim mobile-money fraud simulator — https://www.kaggle.com/ealaxi/paysim1
+
+The model input is the 1-D feature vector itself (no fake image). WeightsLab
+transmits it through gRPC as a ``vector`` raw_data stat carrying the actual
+values, and ``get_items`` exposes the raw features as sortable metadata columns
+in the List Exploration (tabular) view.
+"""
+
+from __future__ import annotations
+
+from typing import Optional, Tuple
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+# 16 features -> a tidy 4x4 grid, no padding needed.
+FEATURE_NAMES = [
+    "amount",
+    "old_balance",
+    "new_balance",
+    "balance_delta",
+    "txn_hour",
+    "txn_day_of_week",
+    "txn_count_1h",
+    "txn_count_24h",
+    "avg_amount_7d",
+    "std_amount_7d",
+    "merchant_risk",
+    "device_change",
+    "geo_distance_km",
+    "is_foreign",
+    "account_age_days",
+    "num_prior_disputes",
+]
+NUM_FEATURES = len(FEATURE_NAMES)  # 16
+IMG_SIDE = 4
+assert IMG_SIDE * IMG_SIDE == NUM_FEATURES
+
+FRAUD_RATE = 0.12
+
+
+def make_synthetic_fraud(
+    n_samples: int, seed: int = 0
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Generate deterministic synthetic transactions.
+
+    Returns ``(X_std, X_raw, y)``:
+      * ``X_std``: ``float32[n, NUM_FEATURES]`` standardized features (model input)
+      * ``X_raw``: ``float32[n, NUM_FEATURES]`` raw, human-readable feature values
+        (surfaced as sortable metadata columns in the UI)
+      * ``y``: ``int64[n]`` label, 1 = fraud
+
+    Fraud rows are drawn from shifted distributions (larger amounts and balance
+    deltas, off-hours activity, higher merchant risk, more device changes, larger
+    geo jumps, more prior disputes). The signal is separable enough that a small
+    MLP learns it quickly, while class overlap keeps it non-trivial.
+    """
+    if n_samples <= 0:
+        empty = np.zeros((0, NUM_FEATURES), dtype=np.float32)
+        return (empty, empty.copy(), np.zeros((0,), dtype=np.int64))
+
+    rng = np.random.default_rng(seed)
+    n_fraud = max(1, int(round(n_samples * FRAUD_RATE)))
+    n_legit = max(1, n_samples - n_fraud)
+
+    def _draw(n: int, fraud: bool) -> list[dict]:
+        """One dict per row, keyed by FEATURE_NAMES — self-documenting, and the
+        final array assembly below reads the keys in FEATURE_NAMES order, so a
+        renamed/reordered feature can't silently desync from its column."""
+        amount = rng.gamma(2.0, 180.0 if fraud else 60.0, size=n)
+        old_balance = rng.gamma(2.0, 800.0, size=n)
+        spent = amount * (rng.uniform(0.6, 1.4, n) if fraud else rng.uniform(0.0, 0.6, n))
+        new_balance = np.clip(old_balance - spent, 0, None)
+        balance_delta = old_balance - new_balance
+        txn_hour = (rng.normal(2.5, 2.0, n) % 24) if fraud else rng.normal(13.0, 4.0, n)
+        txn_day_of_week = rng.integers(0, 7, n).astype(np.float64)
+        txn_count_1h = rng.poisson(4.0 if fraud else 1.0, n).astype(np.float64)
+        txn_count_24h = rng.poisson(18.0 if fraud else 6.0, n).astype(np.float64)
+        avg_amount_7d = rng.gamma(2.0, 90.0 if fraud else 70.0, size=n)
+        std_amount_7d = rng.gamma(2.0, 60.0 if fraud else 25.0, size=n)
+        merchant_risk = rng.beta(5.0, 2.0, n) if fraud else rng.beta(2.0, 6.0, n)
+        device_change = rng.binomial(1, 0.55 if fraud else 0.08, n).astype(np.float64)
+        geo_distance_km = rng.gamma(2.0, 400.0 if fraud else 25.0, size=n)
+        is_foreign = rng.binomial(1, 0.45 if fraud else 0.05, n).astype(np.float64)
+        account_age_days = rng.gamma(2.0, 120.0 if fraud else 500.0, size=n)
+        num_prior_disputes = rng.poisson(1.5 if fraud else 0.2, n).astype(np.float64)
+
+        return [
+            {'amount': amount[i],
+             'old_balance': old_balance[i],
+             'new_balance': new_balance[i],
+             'balance_delta': balance_delta[i],
+             'txn_hour': txn_hour[i],
+             'txn_day_of_week': txn_day_of_week[i],
+             'txn_count_1h': txn_count_1h[i],
+             'txn_count_24h': txn_count_24h[i],
+             'avg_amount_7d': avg_amount_7d[i],
+             'std_amount_7d': std_amount_7d[i],
+             'merchant_risk': merchant_risk[i],
+             'device_change': device_change[i],
+             'geo_distance_km': geo_distance_km[i],
+             'is_foreign': is_foreign[i],
+             'account_age_days': account_age_days[i],
+             'num_prior_disputes': num_prior_disputes[i]} for i in range(n)
+        ]
+
+    rows = _draw(n_legit, False) + _draw(n_fraud, True)
+    x_raw = np.array([[row[name] for name in FEATURE_NAMES] for row in rows], dtype=np.float64)
+    y = np.concatenate([np.zeros(n_legit, dtype=np.int64), np.ones(n_fraud, dtype=np.int64)])
+
+    # Standardize per-feature (class-agnostic) so the MLP trains stably.
+    mean = x_raw.mean(axis=0, keepdims=True)
+    std = x_raw.std(axis=0, keepdims=True)
+    std[std == 0] = 1.0
+    x_std = (x_raw - mean) / std
+
+    perm = rng.permutation(len(x_raw))  # interleave fraud/legit deterministically
+    return (
+        x_std[perm].astype(np.float32),
+        x_raw[perm].astype(np.float32),
+        y[perm],
+    )
+
+
+class FraudDataset(Dataset):
+    """Tabular fraud dataset yielding ``(input, idx, label)``.
+
+    ``input`` is the 1-D standardized feature vector ``float32[NUM_FEATURES]``
+    fed straight to the model — there is no image. WeightsLab transmits the
+    feature values through gRPC as a ``vector`` raw_data stat, and ``get_items``
+    exposes the raw values as sortable metadata columns. ``idx`` is the tracked
+    sample id; ``label`` is 0 (legit) / 1 (fraud).
+    """
+
+    def __init__(self, n_samples: int, seed: int = 0, max_samples: Optional[int] = None):
+        x_std, x_raw, y = make_synthetic_fraud(n_samples, seed=seed)
+        if max_samples is not None:
+            x_std, x_raw, y = x_std[:max_samples], x_raw[:max_samples], y[:max_samples]
+        self.features = torch.from_numpy(x_std)  # [N, 16] float32 (model input)
+        self.raw = x_raw                          # [N, 16] float32 (display values)
+        self.labels = torch.from_numpy(y)         # [N] int64
+
+    def __len__(self) -> int:
+        return int(self.features.shape[0])
+
+    def _input(self, idx: int) -> torch.Tensor:
+        # Tabular: the model input IS the 1-D feature vector (no fake image).
+        return self.features[idx]
+
+    def _metadata(self, idx: int) -> dict:
+        """Raw, human-readable feature values -> sortable UI columns."""
+        row = self.raw[idx]
+        return {name: round(float(row[i]), 4) for i, name in enumerate(FEATURE_NAMES)}
+
+    def __getitems__(self, idx: int):
+        # Training contract: (input, sample_id, label).
+        return self._input(idx), idx, int(self.labels[idx].item())
+
+    def __getitem__(self, idx: int):
+        # Training contract: (input, sample_id, label).
+        return self._input(idx), idx, int(self.labels[idx].item())
+
+    def get_items(self, idx: int, include_metadata: bool = False,
+                  include_labels: bool = False, include_images: bool = False):
+        """WeightsLab ledger-init contract: (image, uid, target, metadata).
+
+        Called once per sample at init with ``include_images=False``; the
+        returned ``metadata`` dict is flattened into per-sample columns, so each
+        transaction feature (``amount``, ``merchant_risk``, …) becomes a
+        sortable column in the List Exploration view.
+        """
+        image = self._input(idx) if include_images else None
+        target = int(self.labels[idx].item()) if include_labels else None
+        metadata = self._metadata(idx) if include_metadata else None
+        return image, idx, target, metadata
diff --git a/weightslab/examples/PyTorch/wl-fraud-detection/utils/model.py b/weightslab/examples/PyTorch/wl-fraud-detection/utils/model.py
new file mode 100644
index 00000000..61503096
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-fraud-detection/utils/model.py
@@ -0,0 +1,33 @@
+"""Small MLP binary classifier for tabular fraud detection (pure PyTorch)."""
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+
+from .data import NUM_FEATURES, IMG_SIDE
+
+
+class FraudMLP(nn.Module):
+    """MLP over the 16 transaction features.
+
+    Accepts either flat ``[N, 16]`` or image-shaped ``[N, 1, 4, 4]`` input and
+    outputs 2 logits (legit / fraud), so it plugs into ``CrossEntropyLoss`` and a
+    2-class accuracy metric like the other WeightsLab usecases.
+    """
+
+    def __init__(self, in_features: int = NUM_FEATURES, hidden: int = 64, num_classes: int = 2):
+        super().__init__()
+        self.input_shape = (1, NUM_FEATURES)
+        self.net = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(in_features, hidden),
+            nn.ReLU(),
+            nn.Dropout(0.1),
+            nn.Linear(hidden, hidden // 2),
+            nn.ReLU(),
+            nn.Linear(hidden // 2, num_classes),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.net(x)
diff --git a/weightslab/examples/PyTorch/wl-fraud-detection/verify_integration.py b/weightslab/examples/PyTorch/wl-fraud-detection/verify_integration.py
new file mode 100644
index 00000000..93de776f
--- /dev/null
+++ b/weightslab/examples/PyTorch/wl-fraud-detection/verify_integration.py
@@ -0,0 +1,221 @@
+"""Integration check: prove WeightsLab is fully wired for this tabular example.
+
+Unlike ``test_fraud_detection.py`` (pure PyTorch), this actually drives the
+WeightsLab stack end-to-end — tracked dataloaders, watched loss/metric, the
+gRPC server, and the per-sample ledger — then asserts the sample dataframe the
+UI reads contains, for tabular mode:
+
+  * one row per sample (what you scroll/sort in the grid & List view),
+  * every transaction feature as its own column (from ``get_items`` metadata),
+  * ``target`` + ``prediction`` populated (so sort/filter/histograms work),
+  * a per-sample training/eval loss column,
+
+and that the gRPC server is actually listening (so Weights Studio can connect
+live during the run).
+
+Run:  python verify_integration.py
+Requires weightslab installed (not a pure-unit test).
+"""
+
+import glob
+import os
+import socket
+import sys
+import tempfile
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchmetrics.classification import Accuracy
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+import weightslab as wl  # noqa: E402
+from weightslab.components.global_monitoring import (  # noqa: E402
+    guard_training_context,
+    guard_testing_context,
+)
+
+from utils.data import FraudDataset, FEATURE_NAMES, NUM_FEATURES  # noqa: E402
+from utils.model import FraudMLP  # noqa: E402
+
+
+def _free_port() -> int:
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    s.bind(("127.0.0.1", 0))
+    port = s.getsockname()[1]
+    s.close()
+    return port
+
+
+def main() -> int:
+    device = torch.device("cpu")
+    log_dir = tempfile.mkdtemp(prefix="wl_fraud_verify_")
+    grpc_port = _free_port()
+    # The gRPC server reads its port from GRPC_BACKEND_PORT (same knob the E2E
+    # harness uses); set it before serve() so we bind to a free ephemeral port.
+    os.environ["GRPC_BACKEND_PORT"] = str(grpc_port)
+
+    parameters = {
+        "experiment_name": "fraud_verify",
+        "device": "cpu",
+        "root_log_dir": log_dir,
+        "serving_grpc": True,
+        "grpc_port": grpc_port,
+        "is_training": True,
+    }
+    wl.watch_or_edit(parameters, flag="hyperparameters", poll_interval=1.0)
+
+    model = wl.watch_or_edit(FraudMLP(in_features=NUM_FEATURES, num_classes=2).to(device),
+                             flag="model", device=device)
+    optimizer = wl.watch_or_edit(optim.Adam(model.parameters(), lr=0.005), flag="optimizer")
+
+    train_ds = FraudDataset(600, seed=0)
+    test_ds = FraudDataset(200, seed=1)
+
+    train_loader = wl.watch_or_edit(
+        train_ds, flag="data", loader_name="train_loader", batch_size=32, shuffle=True,
+        is_training=True, compute_hash=False, preload_labels=True, preload_metadata=True,
+        enable_h5_persistence=False)
+    test_loader = wl.watch_or_edit(
+        test_ds, flag="data", loader_name="test_loader", batch_size=64, shuffle=False,
+        is_training=False, compute_hash=False, preload_labels=True, preload_metadata=True,
+        enable_h5_persistence=False)
+
+    cw = torch.tensor([1.0, 4.0])
+    train_crit = wl.watch_or_edit(nn.CrossEntropyLoss(weight=cw, reduction="none"),
+                                  flag="loss", signal_name="train-loss-CE", log=True)
+    test_crit = wl.watch_or_edit(nn.CrossEntropyLoss(weight=cw, reduction="none"),
+                                 flag="loss", signal_name="test-loss-CE", log=True)
+    metric = wl.watch_or_edit(Accuracy(task="multiclass", num_classes=2),
+                              flag="metric", signal_name="metric-ACC", log=True)
+
+    wl.serve(serving_grpc=True, grpc_port=grpc_port)
+    wl.start_training()
+
+    # ---- drive a few real training steps ----
+    for _ in range(120):
+        with guard_training_context:
+            inputs, ids, labels = next(train_loader)
+            optimizer.zero_grad()
+            out = model(inputs)
+            preds = out.argmax(dim=1, keepdim=True)
+            loss = train_crit(out, labels, batch_ids=ids, preds=preds).mean()
+            loss.backward()
+            optimizer.step()
+
+    # ---- one full eval pass (populates test loss/prediction per sample) ----
+    for inputs, ids, labels in test_loader:
+        with guard_testing_context:
+            out = model(inputs)
+            preds = out.argmax(dim=1, keepdim=True)
+            test_crit(out, labels, batch_ids=ids, preds=preds)
+            metric.update(out, labels)
+
+    wl.drain_signals()
+
+    # ---- dump the ledger dataframe the UI reads and inspect it ----
+    wl.write_dataframe(path=log_dir, format="csv")
+    csvs = glob.glob(os.path.join(log_dir, "*dataframe*.csv"))
+    assert csvs, f"no dataframe CSV written to {log_dir}"
+    with open(csvs[0], "r", encoding="utf-8") as fh:
+        header = fh.readline().strip().split(",")
+        rows = [ln for ln in fh.read().splitlines() if ln.strip()]
+
+    cols = set(c.strip().strip('"') for c in header)
+    n_rows = len(rows)
+
+    print("\n=== Ledger dataframe ===")
+    print(f"rows: {n_rows}")
+    print(f"columns ({len(cols)}): {sorted(cols)}")
+
+    # ---- assertions ----
+    problems = []
+    if n_rows < 700:  # 600 train + 200 test - some may be batched off; expect most
+        problems.append(f"expected ~800 sample rows, got {n_rows}")
+
+    missing_features = [f for f in FEATURE_NAMES if f not in cols]
+    if missing_features:
+        problems.append(f"missing feature columns: {missing_features}")
+
+    for required in ("target", "prediction"):
+        if required not in cols:
+            problems.append(f"missing '{required}' column")
+
+    if not any("loss" in c.lower() for c in cols):
+        problems.append("no per-sample loss column found")
+
+    # gRPC server must be reachable so Weights Studio can connect live.
+    # It starts on a background thread, so poll for a few seconds.
+    import time as _time
+    grpc_up = False
+    for _ in range(20):
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        sock.settimeout(1)
+        if sock.connect_ex(("127.0.0.1", grpc_port)) == 0:
+            sock.close()
+            grpc_up = True
+            break
+        sock.close()
+        _time.sleep(0.5)
+    if not grpc_up:
+        problems.append(f"gRPC server not listening on {grpc_port}")
+    else:
+        print(f"gRPC server listening on 127.0.0.1:{grpc_port}  ✓")
+
+        # Real gRPC round-trip: the input the model was fed (the feature vector)
+        # must reach the UI as raw_data of type 'vector' carrying the values.
+        try:
+            import grpc
+            from weightslab.proto import experiment_service_pb2 as pb2
+            from weightslab.proto import experiment_service_pb2_grpc as pb2_grpc
+
+            channel = grpc.insecure_channel(f"127.0.0.1:{grpc_port}")
+            grpc.channel_ready_future(channel).result(timeout=10)
+            stub = pb2_grpc.ExperimentServiceStub(channel)
+            resp = stub.GetDataSamples(pb2.DataSamplesRequest(
+                start_index=0, records_cnt=5, include_raw_data=True,
+                resize_width=0, resize_height=0), timeout=20)
+
+            records = list(resp.data_records)
+            raw = None
+            for rec in records:
+                for st in rec.data_stats:
+                    if st.name == "raw_data":
+                        raw = st
+                        break
+                if raw is not None:
+                    break
+
+            if raw is None:
+                problems.append("gRPC GetDataSamples returned no raw_data stat")
+            elif raw.type != "vector":
+                problems.append(f"raw_data.type is '{raw.type}', expected 'vector'")
+            elif len(raw.value) != NUM_FEATURES:
+                problems.append(
+                    f"raw_data carries {len(raw.value)} values, expected {NUM_FEATURES}")
+            else:
+                print(f"gRPC GetDataSamples: raw_data type='vector', "
+                      f"{len(raw.value)} feature values reached the UI  ✓")
+                print(f"  sample values: {[round(v, 3) for v in raw.value[:6]]} …")
+            channel.close()
+        except Exception as e:
+            problems.append(f"gRPC GetDataSamples failed: {e}")
+
+    if problems:
+        print("\n VERIFY FAILED:")
+        for p in problems:
+            print(f"  - {p}")
+        return 1
+
+    print("\n VERIFY PASSED: WeightsLab is fully wired for tabular mode "
+          "(per-sample rows + feature columns + target/prediction/loss + live gRPC).")
+    return 0
+
+
+if __name__ == "__main__":
+    code = main()
+    sys.stdout.flush()
+    sys.stderr.flush()
+    # Hard-exit so background serving threads don't keep the process alive.
+    os._exit(code)
diff --git a/weightslab/examples/PyTorch/ws-generation/config.yaml b/weightslab/examples/PyTorch/wl-generation/config.yaml
similarity index 100%
rename from weightslab/examples/PyTorch/ws-generation/config.yaml
rename to weightslab/examples/PyTorch/wl-generation/config.yaml
diff --git a/weightslab/examples/PyTorch/ws-generation/main.py b/weightslab/examples/PyTorch/wl-generation/main.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-generation/main.py
rename to weightslab/examples/PyTorch/wl-generation/main.py
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/0000f77c-6257be58.jpg b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/0000f77c-6257be58.jpg
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/0000f77c-6257be58.jpg
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/0000f77c-6257be58.jpg
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/000f8d37-d4c09a0f.jpg b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/000f8d37-d4c09a0f.jpg
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rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/000f8d37-d4c09a0f.jpg
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/000f8d37-d4c09a0f.jpg
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00a0f008-a315437f.jpg b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00a0f008-a315437f.jpg
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rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00a0f008-a315437f.jpg
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00a0f008-a315437f.jpg
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00c12bd0-bb46e479.jpg b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00c12bd0-bb46e479.jpg
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00c12bd0-bb46e479.jpg
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00c12bd0-bb46e479.jpg
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00c29c52-f9524f1e.jpg b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00c29c52-f9524f1e.jpg
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00c29c52-f9524f1e.jpg
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00c29c52-f9524f1e.jpg
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00ce6f6d-50bbee62.jpg b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00ce6f6d-50bbee62.jpg
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00ce6f6d-50bbee62.jpg
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00ce6f6d-50bbee62.jpg
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00d1bafa-1b47b41c.jpg b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00d1bafa-1b47b41c.jpg
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00d1bafa-1b47b41c.jpg
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00d1bafa-1b47b41c.jpg
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/images/train/00d7268f-fd4487be.jpg b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/train/00d7268f-fd4487be.jpg
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rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/images/val/b1ceb32e-3f481b43.jpg
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diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1dce572-c6a8cb5e.png b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1dce572-c6a8cb5e.png
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1dce572-c6a8cb5e.png
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1dce572-c6a8cb5e.png
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1e1a7b8-0aec80e8.png b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1e1a7b8-0aec80e8.png
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1e1a7b8-0aec80e8.png
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1e1a7b8-0aec80e8.png
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1e8ad72-c3c79240.png b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1e8ad72-c3c79240.png
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1e8ad72-c3c79240.png
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1e8ad72-c3c79240.png
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1ee702d-0ae1fc10.png b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1ee702d-0ae1fc10.png
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1ee702d-0ae1fc10.png
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1ee702d-0ae1fc10.png
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1f0efd9-37a14dda.png b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1f0efd9-37a14dda.png
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b1f0efd9-37a14dda.png
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b1f0efd9-37a14dda.png
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b2a0648b-d8e126bc.png b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b2a0648b-d8e126bc.png
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b2a0648b-d8e126bc.png
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b2a0648b-d8e126bc.png
diff --git a/weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b2b70230-bad4ff6e.png b/weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b2b70230-bad4ff6e.png
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/BDD_subset/labels/val/b2b70230-bad4ff6e.png
rename to weightslab/examples/PyTorch/wl-segmentation/BDD_subset/labels/val/b2b70230-bad4ff6e.png
diff --git a/weightslab/examples/PyTorch/ws-segmentation/config.yaml b/weightslab/examples/PyTorch/wl-segmentation/config.yaml
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/config.yaml
rename to weightslab/examples/PyTorch/wl-segmentation/config.yaml
diff --git a/weightslab/examples/PyTorch/ws-segmentation/main.py b/weightslab/examples/PyTorch/wl-segmentation/main.py
similarity index 98%
rename from weightslab/examples/PyTorch/ws-segmentation/main.py
rename to weightslab/examples/PyTorch/wl-segmentation/main.py
index f56b75e4..1f7b1b9d 100644
--- a/weightslab/examples/PyTorch/ws-segmentation/main.py
+++ b/weightslab/examples/PyTorch/wl-segmentation/main.py
@@ -34,14 +34,6 @@
 # Train / Test loops (segmentation, using watcher-wrapped loaders)
 # =============================================================================
 
-def _instance_batch_idx(labels):
-    """Flat instance→sample map (sample-major) matching the PerInstance* ordering."""
-    return torch.tensor(
-        [s for s, insts in enumerate(labels) for _ in insts],
-        dtype=torch.long,
-    )
-
-
 def _run_instance_signals(sig, outputs, labels, ids, preds, return_metric=False):
     """Compute + log/save the per-sample AND per-instance Dice (metric) and BCE (loss)."""
     bce_sample = sig["bce_sample"](outputs, labels, batch_ids=ids, preds=preds)
diff --git a/weightslab/examples/PyTorch/ws-segmentation/utils/criterions.py b/weightslab/examples/PyTorch/wl-segmentation/utils/criterions.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/utils/criterions.py
rename to weightslab/examples/PyTorch/wl-segmentation/utils/criterions.py
diff --git a/weightslab/examples/PyTorch/ws-segmentation/utils/data.py b/weightslab/examples/PyTorch/wl-segmentation/utils/data.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/utils/data.py
rename to weightslab/examples/PyTorch/wl-segmentation/utils/data.py
diff --git a/weightslab/examples/PyTorch/ws-segmentation/utils/model.py b/weightslab/examples/PyTorch/wl-segmentation/utils/model.py
similarity index 100%
rename from weightslab/examples/PyTorch/ws-segmentation/utils/model.py
rename to weightslab/examples/PyTorch/wl-segmentation/utils/model.py
diff --git a/weightslab/examples/PyTorch/ws-detection/README.md b/weightslab/examples/PyTorch/ws-detection/README.md
deleted file mode 100644
index ba8bddc6..00000000
--- a/weightslab/examples/PyTorch/ws-detection/README.md
+++ /dev/null
@@ -1,102 +0,0 @@
-# WeightsLab — Object Detection (pure PyTorch)
-
-A small, fully-runnable **object detection** example wired into WeightsLab. It
-trains a compact single-shot detector on the **Penn-Fudan Pedestrian** dataset
-(~170 real photos, one class: `person`) and streams per-sample / per-instance
-losses, IoU, and predicted bounding boxes to the WeightsLab UI.
-
-Everything here is plain PyTorch + torchvision — no detection framework
-(no Ultralytics/Detectron). The only pretrained piece is an ImageNet backbone.
-
-## Quick start
-
-From a WeightsLab install, the one-liner (installs this example's
-`requirements.txt`, then trains + serves until `Ctrl+C`):
-
-```bash
-weightslab start example --det
-```
-
-Or run it directly:
-
-```bash
-cd weightslab/examples/PyTorch/ws-detection
-pip install -r requirements.txt
-python main.py
-```
-
-The **first run downloads** the Penn-Fudan dataset (~50 MB, into `./data/`) and
-the MobileNetV3-Small ImageNet weights (~10 MB, cached by torch). Then open the
-UI (e.g. `http://localhost:5173`) to watch training.
-
-## What you'll see in the UI
-
-| Signal                  | Meaning                                              |
-| ----------------------- | ---------------------------------------------------- |
-| `train_loss/sample`     | Per-image training loss (the value being optimized)  |
-| `test_loss/sample`      | Per-image validation loss                            |
-| `train_iou/sample`      | Mean IoU per training image                          |
-| `test_iou/sample`       | Mean IoU per validation image                        |
-| `train_iou/instance`    | IoU per **ground-truth box** `(sample_id, annotation_id)` |
-| `test_iou/instance`     | Same, on validation                                  |
-
-Ground-truth and predicted **bounding boxes** are rendered as overlays on each
-sample (the dataset and model declare `task_type = "detection"`).
-
-## How it works
-
-```
-utils/data.py        PennFudanDetectionDataset — downloads Penn-Fudan, derives one
-                     bbox per pedestrian from the instance masks, returns the WL
-                     detection target [N, 6] = [x1, y1, x2, y2, class_id, conf]
-                     normalized to [0, 1]. ImageNet-normalized model inputs.
-                     `det_collate` keeps the variable box count as a per-sample list.
-
-utils/model.py       SmallDetector — ImageNet-pretrained MobileNetV3-Small backbone
-                     (frozen by default) + a small head that predicts ONE box per
-                     cell on an S x S grid: (objectness, tx, ty, tw, th, class...).
-                     `decode_grid` turns raw logits into xyxy boxes.
-
-utils/criterions.py  PerSampleDetectionLoss — YOLO-style objectness + coordinate +
-                     class loss, one differentiable scalar per sample (what WL
-                     backprops). PerSampleIoU / PerInstanceIoU — IoU metrics.
-                     decode_predictions — top-confidence boxes for the UI overlay.
-
-main.py              Wires it all to WeightsLab: watch_or_edit(...) for the logger,
-                     hyperparameters, data loaders, model, optimizer and the
-                     loss/metric signals; serve(); start_training(); train/test loop.
-```
-
-The detector is genuinely learnable: on a small subset, mean IoU rises from
-~0.39 to ~0.83 within ~60 steps.
-
-## Configuration (`config.yaml`)
-
-| Key                    | Default | Notes                                                       |
-| ---------------------- | ------- | ----------------------------------------------------------- |
-| `num_classes`          | `1`     | Penn-Fudan has one class (`person`).                        |
-| `image_size`           | `256`   | Square model input (UI shows the original image).           |
-| `grid_size`            | `8`     | Detector predicts on an `8 x 8` cell grid.                  |
-| `conf_thresh`          | `0.3`   | `objectness * class` threshold for displayed predictions.   |
-| `pretrained_backbone`  | `true`  | Load ImageNet weights for the MobileNetV3 backbone.         |
-| `freeze_backbone`      | `true`  | Train only the head (fast, less data-hungry). Set `false` to fine-tune the whole backbone once the head has warmed up. |
-| `data.*.batch_size`    | `8`     | Per-loader batch size.                                      |
-| `data.*.max_samples`   | `null`  | Cap a split for quick runs (`null` = full split).           |
-
-## Using your own dataset (e.g. traffic lights)
-
-The model, loss, metrics, `main.py`, and UI rendering are **dataset-agnostic** —
-only `utils/data.py` and a couple of config values change:
-
-1. Write a `Dataset` whose `get_items(idx, ...)` returns
-   `(image_tensor, uid, target, metadata)`, where `target` is an
-   `[N, 6]` float array `[x1, y1, x2, y2, class_id, confidence]` **normalized to
-   `[0, 1]`** (ground-truth confidence = `1.0`). Set `self.task_type = "detection"`,
-   `self.num_classes`, `self.class_names`, and expose `self.images` (a list of
-   image paths) so the UI can show the raw image.
-2. Reuse `det_collate` unchanged.
-3. In `config.yaml`, set `num_classes` to your class count (e.g. `3` for
-   `red / yellow / green`) and update `class_names` in the dataset / model.
-
-That's it — multi-class works out of the box (the classification head is already
-in the grid prediction; it's just trivial when `num_classes == 1`).
diff --git a/weightslab/examples/Ultralytics/ws-detection/config.yaml b/weightslab/examples/Ultralytics/wl-detection/config.yaml
similarity index 90%
rename from weightslab/examples/Ultralytics/ws-detection/config.yaml
rename to weightslab/examples/Ultralytics/wl-detection/config.yaml
index 50a4033e..512f60a4 100644
--- a/weightslab/examples/Ultralytics/ws-detection/config.yaml
+++ b/weightslab/examples/Ultralytics/wl-detection/config.yaml
@@ -38,7 +38,7 @@ ledger_flush_interval: 60.0
 # Data
 num_classes: 2
 image_size: 320
-data_root: C:\Users\GuillaumePELLUET\Documents\Codes\weightslab_kitchen\guillaume_playground\ws-ultralytics_yolo\data\data.yaml  # Uncomment and set the path to your data.yaml file. YOLO format.
+data_root: .\data\data.yaml  # Uncomment and set the path to your data.yaml file. YOLO format.
 data:
   train_loader:
     batch_size: 4
diff --git a/weightslab/examples/Ultralytics/ws-detection/main.py b/weightslab/examples/Ultralytics/wl-detection/main.py
similarity index 100%
rename from weightslab/examples/Ultralytics/ws-detection/main.py
rename to weightslab/examples/Ultralytics/wl-detection/main.py
diff --git a/weightslab/examples/Usecases/ws-2d-lidar-detection/README.md b/weightslab/examples/Usecases/wl-2d-lidar-detection/README.md
similarity index 94%
rename from weightslab/examples/Usecases/ws-2d-lidar-detection/README.md
rename to weightslab/examples/Usecases/wl-2d-lidar-detection/README.md
index 1925b9f4..46174c1a 100644
--- a/weightslab/examples/Usecases/ws-2d-lidar-detection/README.md
+++ b/weightslab/examples/Usecases/wl-2d-lidar-detection/README.md
@@ -1,12 +1,12 @@
 # 2D LiDAR (laser-scan) Object Detection (Pillars2D-lite)
 
-The 2D sibling of [`ws-3d-lidar-detection`](../ws-3d-lidar-detection/): object
+The 2D sibling of [`wl-3d-lidar-detection`](../wl-3d-lidar-detection/): object
 detection on a **2D point cloud** (a single-layer laser scan / bird's-eye
 occupancy slice) instead of a 3D LiDAR scene. Same WeightsLab wiring and
 per-sample / per-instance signals — just with `z` and `yaw` dropped.
 
 ```
-ws-2d-lidar-detection/
+wl-2d-lidar-detection/
   main.py             # WL wiring + train/eval loop
   config.yaml         # hyperparameters, plane range, loaders
   utils/
@@ -18,7 +18,7 @@ ws-2d-lidar-detection/
 ## Quick start
 
 ```bash
-cd weightslab/examples/Usecases/ws-2d-lidar-detection
+cd weightslab/examples/Usecases/wl-2d-lidar-detection
 python main.py
 # or, from anywhere:
 weightslab start example --2d_det
diff --git a/weightslab/examples/Usecases/ws-2d-lidar-detection/config.yaml b/weightslab/examples/Usecases/wl-2d-lidar-detection/config.yaml
similarity index 100%
rename from weightslab/examples/Usecases/ws-2d-lidar-detection/config.yaml
rename to weightslab/examples/Usecases/wl-2d-lidar-detection/config.yaml
diff --git a/weightslab/examples/Usecases/ws-2d-lidar-detection/main.py b/weightslab/examples/Usecases/wl-2d-lidar-detection/main.py
similarity index 100%
rename from weightslab/examples/Usecases/ws-2d-lidar-detection/main.py
rename to weightslab/examples/Usecases/wl-2d-lidar-detection/main.py
diff --git a/weightslab/examples/Usecases/ws-2d-lidar-detection/utils/criterions.py b/weightslab/examples/Usecases/wl-2d-lidar-detection/utils/criterions.py
similarity index 100%
rename from weightslab/examples/Usecases/ws-2d-lidar-detection/utils/criterions.py
rename to weightslab/examples/Usecases/wl-2d-lidar-detection/utils/criterions.py
diff --git a/weightslab/examples/Usecases/ws-2d-lidar-detection/utils/data.py b/weightslab/examples/Usecases/wl-2d-lidar-detection/utils/data.py
similarity index 100%
rename from weightslab/examples/Usecases/ws-2d-lidar-detection/utils/data.py
rename to weightslab/examples/Usecases/wl-2d-lidar-detection/utils/data.py
diff --git a/weightslab/examples/Usecases/ws-2d-lidar-detection/utils/model.py b/weightslab/examples/Usecases/wl-2d-lidar-detection/utils/model.py
similarity index 100%
rename from weightslab/examples/Usecases/ws-2d-lidar-detection/utils/model.py
rename to weightslab/examples/Usecases/wl-2d-lidar-detection/utils/model.py
diff --git a/weightslab/examples/Usecases/ws-3d-lidar-detection/README.md b/weightslab/examples/Usecases/wl-3d-lidar-detection/README.md
similarity index 98%
rename from weightslab/examples/Usecases/ws-3d-lidar-detection/README.md
rename to weightslab/examples/Usecases/wl-3d-lidar-detection/README.md
index 7694bfa8..4d266f14 100644
--- a/weightslab/examples/Usecases/ws-3d-lidar-detection/README.md
+++ b/weightslab/examples/Usecases/wl-3d-lidar-detection/README.md
@@ -6,8 +6,8 @@ with per-sample and per-instance signals flowing into the WeightsLab
 dashboards.
 
 ```
-ws-3d-lidar-detection/
-  main.py             # WL wiring + train/eval loop (mirrors PyTorch/ws-detection)
+wl-3d-lidar-detection/
+  main.py             # WL wiring + train/eval loop (mirrors PyTorch/wl-detection)
   config.yaml         # hyperparameters, ranges, loaders
   utils/
     data.py           # KITTI-format loader + synthetic scene fallback + collate
@@ -18,7 +18,7 @@ ws-3d-lidar-detection/
 ## Quick start
 
 ```bash
-cd weightslab/examples/Usecases/ws-3d-lidar-detection
+cd weightslab/examples/Usecases/wl-3d-lidar-detection
 python main.py
 ```
 
@@ -81,7 +81,7 @@ coordinates (`task_type = "detection_pointcloud"` — one task type covering 2D
 
 ## WeightsLab signals
 
-Same pattern as `PyTorch/ws-detection`:
+Same pattern as `PyTorch/wl-detection`:
 
 - `*_loss/sample` (`per_sample=True`) — the YOLO-in-BEV loss, one value per
   frame: localization (x, y, z) + log-size + sin/cos heading + class CE
diff --git a/weightslab/examples/Usecases/ws-3d-lidar-detection/config.yaml b/weightslab/examples/Usecases/wl-3d-lidar-detection/config.yaml
similarity index 100%
rename from weightslab/examples/Usecases/ws-3d-lidar-detection/config.yaml
rename to weightslab/examples/Usecases/wl-3d-lidar-detection/config.yaml
diff --git a/weightslab/examples/Usecases/ws-3d-lidar-detection/main.py b/weightslab/examples/Usecases/wl-3d-lidar-detection/main.py
similarity index 100%
rename from weightslab/examples/Usecases/ws-3d-lidar-detection/main.py
rename to weightslab/examples/Usecases/wl-3d-lidar-detection/main.py
diff --git a/weightslab/examples/Usecases/ws-3d-lidar-detection/utils/criterions.py b/weightslab/examples/Usecases/wl-3d-lidar-detection/utils/criterions.py
similarity index 100%
rename from weightslab/examples/Usecases/ws-3d-lidar-detection/utils/criterions.py
rename to weightslab/examples/Usecases/wl-3d-lidar-detection/utils/criterions.py
diff --git a/weightslab/examples/Usecases/ws-3d-lidar-detection/utils/data.py b/weightslab/examples/Usecases/wl-3d-lidar-detection/utils/data.py
similarity index 100%
rename from weightslab/examples/Usecases/ws-3d-lidar-detection/utils/data.py
rename to weightslab/examples/Usecases/wl-3d-lidar-detection/utils/data.py
diff --git a/weightslab/examples/Usecases/ws-3d-lidar-detection/utils/kitti_download.py b/weightslab/examples/Usecases/wl-3d-lidar-detection/utils/kitti_download.py
similarity index 100%
rename from weightslab/examples/Usecases/ws-3d-lidar-detection/utils/kitti_download.py
rename to weightslab/examples/Usecases/wl-3d-lidar-detection/utils/kitti_download.py
diff --git a/weightslab/examples/Usecases/ws-3d-lidar-detection/utils/model.py b/weightslab/examples/Usecases/wl-3d-lidar-detection/utils/model.py
similarity index 99%
rename from weightslab/examples/Usecases/ws-3d-lidar-detection/utils/model.py
rename to weightslab/examples/Usecases/wl-3d-lidar-detection/utils/model.py
index 0af99b0d..1fc71d27 100644
--- a/weightslab/examples/Usecases/ws-3d-lidar-detection/utils/model.py
+++ b/weightslab/examples/Usecases/wl-3d-lidar-detection/utils/model.py
@@ -14,7 +14,7 @@
 # (objectness, tx, ty, tz, log l, log w, log h, sin yaw, cos yaw,
 # class_logits...).
 #
-# Encoding (BEV cell-relative, mirrors the 2D ws-detection example):
+# Encoding (BEV cell-relative, mirrors the 2D wl-detection example):
 # * objectness = sigmoid(t_obj) -> P(box centered in cell)
 # * cx = x_min + (col + sigmoid(tx)) / S * range_x
 # * cy = y_min + (row + sigmoid(ty)) / S * range_y
diff --git a/weightslab/examples/Usecases/wl-classification-signals_shape_classification/config.yaml b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/config.yaml
new file mode 100644
index 00000000..97f6d760
--- /dev/null
+++ b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/config.yaml
@@ -0,0 +1,48 @@
+# =============================================================================
+# Configuration for the per-sample loss-shape classification example (MNIST)
+# =============================================================================
+# Every value below can be overridden by the matching WL_STRESS_* / WL_* env
+# var (see the env table below) so the example stays scriptable for stress runs.
+
+# Global
+experiment_name: signals-mnist
+device: auto                 # auto | cpu | cuda
+
+# Run parameters
+epochs: 10                   # env override: WL_STRESS_EPOCHS
+out: /tmp/wl_stress          # env override: WL_STRESS_OUT — wiped & recreated on start
+                             # (holds data/, wl_logs/, metrics.jsonl, report.csv)
+batch_size: 64
+
+# Signals
+loss_signal_name: loss_sample
+shape_every: 1               # env override: WL_SHAPE_EVERY — throttle for
+                             # sig/loss_shape (reads history, so it's costly).
+                             # 5 = every step (full coverage); higher = cheaper.
+min_step: 0                 # min step to start computing
+                             # sig/loss_shape (otherwise it will be NaN).
+
+# Serving
+serving_grpc: true
+serving_cli: true
+
+# Ledger / dataframe storage
+ledger_flush_max_rows: 8192
+ledger_enable_h5_persistence: false
+experiment_dump_to_train_steps_ratio: 10000000
+query_cache_maxsize: 2048    # env override: WL_QUERY_CACHE_MAXSIZE — LRU maxsize
+                             # of the per-sample query cache the ledger uses when
+                             # serving signals (larger = more cached, more memory).
+
+# Optimizer
+optimizer:
+  lr: 0.01
+
+# Data loaders
+data:
+  train_loader:
+    batch_size: 64
+    shuffle: true
+  test_loader:
+    batch_size: 256
+    shuffle: false
diff --git a/weightslab/examples/Usecases/wl-classification-signals_shape_classification/main.py b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/main.py
new file mode 100644
index 00000000..8c8de2c5
--- /dev/null
+++ b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/main.py
@@ -0,0 +1,252 @@
+"""Per-sample signals on MNIST — readable, zero save_signals, with train + eval.
+
+Parameters live in ``config.yaml`` (loaded at startup). A handful of knobs can
+also be overridden by environment variables for scripted stress runs.
+
+Per-step user code is just the watched loss. Everything else is a @wl.signal
+(defined in ``utils/signals.py``):
+  entropy    from ctx.logits when the loss fires
+  loss_norm  reactive, from the logged loss
+  hardness   reactive, from loss + entropy
+  loss_shape reactive, classifies each sample's loss trajectory (live signal,
+             not an end-of-run function). Reads history, so it's throttled by
+             shape_every (1 = every step, full coverage; higher = cheaper).
+
+Universal loss: the watched crit runs on the test split each epoch too, so test
+samples get a loss trajectory and a shape as well.
+
+Env overrides (else the config.yaml value is used):
+  WL_STRESS_EPOCHS       (int)  number of training epochs.
+  WL_STRESS_OUT          (str)  output dir; wiped and recreated on start. Holds
+                         data/, wl_logs/, metrics.jsonl and report.csv.
+  WL_SHAPE_EVERY         (int)  throttle for the sig/loss_shape signal, which
+                         reads history and is therefore costly. 1 = compute every
+                         step (full coverage); higher = every N steps (cheaper).
+  WL_QUERY_CACHE_MAXSIZE (int)  backend tuning knob (read in
+                         weightslab.backend.logger). Sets the LRU maxsize of the
+                         per-sample query cache the ledger uses when serving
+                         signals; larger values cache more distinct per-sample
+                         queries at the cost of memory.
+"""
+import os, time, gc, json, shutil
+import yaml
+import torch
+import numpy as np
+import pandas as pd
+import torch.nn as nn
+import torch.optim as optim
+
+import weightslab as wl
+
+from collections import Counter
+
+from weightslab import guard_training_context, guard_testing_context
+
+from utils.model import SmallCNN
+from utils.data import MNISTIdx
+from utils.criterions import SHAPES
+from utils.signals import register_signals
+
+
+# =============================================================================
+# Configuration (config.yaml + env overrides, see module docstring)
+# =============================================================================
+def load_config():
+    """Load config.yaml next to this file and apply defaults for missing keys."""
+    config_path = os.path.join(os.path.dirname(__file__), "config.yaml")
+    if os.path.exists(config_path):
+        with open(config_path, "r") as fh:
+            cfg = yaml.safe_load(fh) or {}
+    else:
+        cfg = {}
+
+    cfg.setdefault("experiment_name", "signals-mnist")
+    cfg.setdefault("device", "auto")
+    cfg.setdefault("epochs", 10)
+    cfg.setdefault("out", "/tmp/wl_stress")
+    cfg.setdefault("batch_size", 64)
+    cfg.setdefault("loss_signal_name", "loss_sample")
+    cfg.setdefault("shape_every", 1)
+    cfg.setdefault("serving_grpc", False)
+    cfg.setdefault("serving_cli", False)
+    cfg.setdefault("ledger_flush_max_rows", 8192)
+    cfg.setdefault("ledger_enable_h5_persistence", False)
+    cfg.setdefault("experiment_dump_to_train_steps_ratio", 10_000_000)
+    cfg.setdefault("query_cache_maxsize", 2048)
+    cfg.setdefault("optimizer", {}).setdefault("lr", 0.01)
+    data = cfg.setdefault("data", {})
+    data.setdefault("train_loader", {}).setdefault("batch_size", cfg["batch_size"])
+    cfg["data"]["train_loader"].setdefault("shuffle", True)
+    data.setdefault("test_loader", {}).setdefault("batch_size", 256)
+    cfg["data"]["test_loader"].setdefault("shuffle", False)
+
+    # Env overrides for scripted stress runs (env wins over the yaml value).
+    cfg["epochs"] = int(os.environ.get("WL_STRESS_EPOCHS", cfg["epochs"]))
+    cfg["out"] = os.environ.get("WL_STRESS_OUT", cfg["out"])
+    cfg["shape_every"] = int(os.environ.get("WL_SHAPE_EVERY", cfg["shape_every"]))
+    cfg["min_step"] = int(os.environ.get("WL_MIN_STEP", cfg["min_step"]))
+    cfg["query_cache_maxsize"] = int(
+        os.environ.get("WL_QUERY_CACHE_MAXSIZE", cfg["query_cache_maxsize"]))
+    return cfg
+
+
+def resolve_device(name):
+    if name == "auto":
+        return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    return torch.device(name)
+
+
+# =============================================================================
+# Helpers
+# =============================================================================
+def rss_gb():
+    try:
+        for ln in open("/proc/self/status"):
+            if ln.startswith("VmRSS:"):
+                return int(ln.split()[1]) / (1024 * 1024)
+    except Exception:
+        return -1.0
+
+
+def log(msg):
+    print(msg, flush=True)
+
+
+def vanilla_baseline(dev, batch, lr, sync):
+    """Plain-PyTorch step time (ms) for the WeightsLab overhead comparison."""
+    vb = SmallCNN().to(dev); vo = optim.Adam(vb.parameters(), lr=lr); vc = nn.CrossEntropyLoss()
+    xb = torch.randn(batch, 1, 28, 28, device=dev); yb = torch.randint(0, 10, (batch,), device=dev)
+    for _ in range(10):
+        vo.zero_grad(); vc(vb(xb), yb).backward(); vo.step()
+    sync(); t0 = time.perf_counter()
+    for _ in range(50):
+        vo.zero_grad(); vc(vb(xb), yb).backward(); vo.step()
+    sync(); vanilla_ms = 1000 * (time.perf_counter() - t0) / 50
+    del vb, vo; gc.collect()
+    return vanilla_ms
+
+
+# =============================================================================
+# Train / Test loops (classification, using watcher-wrapped loaders)
+# =============================================================================
+def train(loader, model, opt, crit, dev, sync):
+    """One training epoch. Returns the list of per-step wall times (ms).
+
+    The only per-step call is the watched loss: it logs loss_sample and fires
+    the @wl.signal chain. No save_signals.
+    """
+    ep_ms = []
+    for img, ids, lab in loader:
+        img, lab = img.to(dev), lab.to(dev)
+        sync(); ts = time.perf_counter()
+        with guard_training_context:
+            opt.zero_grad()
+            logits = model(img)
+            crit(logits, lab, batch_ids=ids, preds=logits.argmax(1, keepdim=True)).mean().backward()
+            opt.step()
+        sync(); ep_ms.append(1000 * (time.perf_counter() - ts))
+    return ep_ms
+
+
+def test(test_loader, model, crit, dev):
+    """Universal loss: run the watched crit over the whole test split (one pass).
+
+    Test samples get a loss trajectory and a shape too.
+    """
+    with torch.no_grad():
+        for tb in test_loader:
+            ti, tid, tl = tb[0].to(dev), tb[1], tb[2].to(dev)
+            with guard_testing_context:
+                tlg = model(ti)
+                crit(tlg, tl, batch_ids=tid, preds=tlg.argmax(1, keepdim=True))
+
+
+# =============================================================================
+# Main
+# =============================================================================
+def main():
+    # --- 1) Config: load, resolve device, prepare output dir ---
+    cfg = load_config()
+    LOSS = cfg["loss_signal_name"]
+    OUT = cfg["out"]
+    EPOCHS = cfg["epochs"]
+    SHAPE_EVERY = cfg["shape_every"]
+    SHAPE_MIN_STEP = cfg["min_step"]
+    BATCH = int(cfg["data"]["train_loader"]["batch_size"])
+    LR = float(cfg["optimizer"]["lr"])
+    # Backend reads this from the environment when it builds the query cache.
+    os.environ["WL_QUERY_CACHE_MAXSIZE"] = str(cfg["query_cache_maxsize"])
+
+    shutil.rmtree(OUT, ignore_errors=True)
+    os.makedirs(OUT + "/wl_logs", exist_ok=True)
+    dev = resolve_device(cfg["device"])
+    torch.manual_seed(0)
+    metrics = open(OUT + "/metrics.jsonl", "w")
+    sync = (lambda: torch.cuda.synchronize()) if dev.type == "cuda" else (lambda: None)
+
+    # --- 2) Vanilla baseline (plain PyTorch) for the overhead comparison ---
+    vanilla_ms = vanilla_baseline(dev, BATCH, LR, sync)
+    log(f"[run] vanilla baseline = {vanilla_ms:.2f} ms/step (dev {dev})")
+
+    # --- 3) WeightsLab setup: both splits tracked, watched loss ---
+    hp = {"experiment_name": cfg["experiment_name"], "device": str(dev),
+          "root_log_dir": OUT + "/wl_logs",
+          "serving_grpc": cfg["serving_grpc"], "serving_cli": cfg["serving_cli"],
+          "ledger_flush_max_rows": cfg["ledger_flush_max_rows"],
+          "ledger_enable_h5_persistence": cfg["ledger_enable_h5_persistence"],
+          "experiment_dump_to_train_steps_ratio": cfg["experiment_dump_to_train_steps_ratio"],
+          "data": {"train_loader": {"batch_size": BATCH,
+                                    "shuffle": cfg["data"]["train_loader"]["shuffle"]}}}
+    wl.watch_or_edit(hp, flag="hyperparameters", defaults=hp)
+    train_ds = MNISTIdx(OUT + "/data", train=True, base=0)
+    test_ds = MNISTIdx(OUT + "/data", train=False, base=1_000_000)
+    model = wl.watch_or_edit(SmallCNN().to(dev), flag="model", device=dev)
+    opt = wl.watch_or_edit(optim.Adam(model.parameters(), lr=LR), flag="optimizer")
+    loader = wl.watch_or_edit(train_ds, flag="data", loader_name="train_loader",
+                              batch_size=BATCH,
+                              shuffle=cfg["data"]["train_loader"]["shuffle"],
+                              is_training=True, preload_labels=True)
+    test_loader = wl.watch_or_edit(test_ds, flag="data", loader_name="test_loader",
+                                   batch_size=int(cfg["data"]["test_loader"]["batch_size"]),
+                                   shuffle=cfg["data"]["test_loader"]["shuffle"],
+                                   is_training=False, preload_labels=True)
+    crit = wl.watch_or_edit(nn.CrossEntropyLoss(reduction="none"),
+                            flag="loss", signal_name=LOSS, per_sample=True, log=True)
+
+    # --- 4) Per-sample signals (the @wl.signal chain, see utils/signals.py) ---
+    register_signals(LOSS, SHAPE_EVERY, SHAPE_MIN_STEP)
+
+    # --- 5) Serve + launch training ---
+    wl.serve(serving_grpc=cfg["serving_grpc"], serving_cli=cfg["serving_cli"])
+    wl.start_training(timeout=5)  # Let WeightsLab Initialize
+    log(f"[run] tracked train={len(train_ds)} test={len(test_ds)} | shape_every={SHAPE_EVERY}")
+
+    # --- 6) Train / eval loop ---
+    step_times, gstep, t_run = [], 0, time.perf_counter()
+    for ep in range(1, EPOCHS + 1):
+        ep_ms = train(loader, model, opt, crit, dev, sync)
+        gstep += len(ep_ms)
+        test(test_loader, model, crit, dev)
+        wl_ms = float(np.mean(ep_ms)); step_times += ep_ms
+        rec = {"epoch": ep, "gstep": gstep, "wl_ms": round(wl_ms, 2), "vanilla_ms": round(vanilla_ms, 2),
+               "rss_gb": round(rss_gb(), 2), "elapsed_s": round(time.perf_counter() - t_run, 1)}
+        metrics.write(json.dumps(rec) + "\n"); metrics.flush()
+        log(f"[run] ep {ep:3d}/{EPOCHS} | WL {wl_ms:6.2f} ms vs vanilla {vanilla_ms:.2f} "
+            f"= +{100*(wl_ms/vanilla_ms-1):.0f}% | RSS {rec['rss_gb']:.2f} GB | {rec['elapsed_s']:.0f}s")
+
+    # --- 7) Report + summary ---
+    path = wl.write_dataframe(OUT + "/report.csv", format="csv", columns="signals")
+    df = pd.read_csv(path)
+    sc = [c for c in df.columns if c.endswith("sig/loss_shape")][0]
+    dist = Counter(SHAPES[int(v)] for v in df[sc].dropna() if v >= 0)
+    med = float(np.median(step_times))
+    metrics.close()
+    log("\n[run] ===== SUMMARY =====")
+    log(f"[run] vanilla {vanilla_ms:.2f} ms | WL median {med:.2f} ms/step (+{100*(med/vanilla_ms-1):.0f}%)")
+    log(f"[run] report {len(df)} rows | loss_shape covered {df[sc].notna().sum()}/{len(df)}")
+    log(f"[run] shape distribution: {dict(dist)}")
+    log(f"[run] report -> {path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/criterions.py b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/criterions.py
new file mode 100644
index 00000000..0837832a
--- /dev/null
+++ b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/criterions.py
@@ -0,0 +1,36 @@
+# =============================================================================
+# Loss-shape classification
+# =============================================================================
+# The watched criterion is a stock ``nn.CrossEntropyLoss(reduction="none")``
+# wrapped per-sample in main.py. This module holds the loss-*trajectory*
+# classifier used by the ``sig/loss_shape`` signal: given each sample's loss
+# history it labels the curve as one of ``SHAPES``.
+import numpy as np
+
+SHAPES = ["monotonic", "plateaued", "Flat_high", "high_variance", "U_Shape", "Spiked"]
+
+
+def classify_shape(values):
+    """Loss trajectory -> shape index (or -1 with < 5 points)."""
+    y = np.asarray(values, dtype=float)
+    if y.size < 5:
+        return -1
+    n = y.size
+    rng = max(float(y.max() - y.min()), 1e-8)
+    drop = (float(y[0]) - float(y[-1])) / (abs(float(y[0])) + 1e-8)
+    cv = float(y.std()) / (abs(float(y.mean())) + 1e-8)
+    argmin = int(np.argmin(y))
+    rebound = (float(y[-1]) - float(y.min())) / rng
+    tail = y[int(0.6 * n):]
+    tail_flat = float(tail.std()) / (abs(float(tail.mean())) + 1e-8) < 0.1
+    if 0.2 * n < argmin < 0.8 * n and rebound > 0.3:
+        return SHAPES.index("U_Shape")
+    if drop > 0.4:
+        return SHAPES.index("monotonic")
+    if drop > 0.15 and tail_flat:
+        return SHAPES.index("plateaued")
+    if float(np.diff(y).max()) / rng > 0.5:
+        return SHAPES.index("Spiked")
+    if cv > 0.5:
+        return SHAPES.index("high_variance")
+    return SHAPES.index("Flat_high")
diff --git a/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/data.py b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/data.py
new file mode 100644
index 00000000..7f23157d
--- /dev/null
+++ b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/data.py
@@ -0,0 +1,23 @@
+# =============================================================================
+# MNIST dataset yielding per-sample uids for the shared WeightsLab ledger
+# =============================================================================
+from torch.utils.data import Dataset
+from torchvision import datasets, transforms
+
+
+class MNISTIdx(Dataset):
+    """Yields (image, uid, label). uid namespaced by split so train/test don't
+    collide in the shared ledger; fast_get_label skips decode at ledger init."""
+    def __init__(self, root, train, base):
+        self.m = datasets.MNIST(root, train=train, download=True, transform=None)
+        self.t = transforms.ToTensor(); self.base = base
+
+    def __len__(self):
+        return len(self.m)
+
+    def __getitem__(self, i):
+        img, lab = self.m[i]
+        return self.t(img), self.base + i, lab
+
+    def fast_get_label(self, i):
+        return int(self.m.targets[i])
diff --git a/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/model.py b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/model.py
new file mode 100644
index 00000000..311585e7
--- /dev/null
+++ b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/model.py
@@ -0,0 +1,19 @@
+# =============================================================================
+# Small CNN classifier for MNIST
+# =============================================================================
+# Two conv/pool blocks into a small MLP head producing 10 class logits. The
+# ``input_shape`` attribute lets WeightsLab introspect the model.
+import torch.nn as nn
+
+
+class SmallCNN(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.input_shape = (1, 1, 28, 28)
+        self.net = nn.Sequential(
+            nn.Conv2d(1, 8, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
+            nn.Conv2d(8, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
+            nn.Flatten(), nn.Linear(16 * 7 * 7, 64), nn.ReLU(), nn.Linear(64, 10))
+
+    def forward(self, x):
+        return self.net(x)
diff --git a/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/signals.py b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/signals.py
new file mode 100644
index 00000000..696ceb10
--- /dev/null
+++ b/weightslab/examples/Usecases/wl-classification-signals_shape_classification/utils/signals.py
@@ -0,0 +1,48 @@
+# =============================================================================
+# Per-sample @wl.signal chain
+# =============================================================================
+# Per-step user code is just the watched loss. Everything here is reactive:
+#   sig/entropy    from the logits when the watched loss fires
+#   sig/loss_norm  batch-normalized loss (loss / mean(loss))
+#   sig/hardness   loss * entropy
+#   sig/loss_shape classifies each sample's loss trajectory (live signal, not an
+#                  end-of-run function). Reads history, so it's throttled by
+#                  shape_every (1 = every step; higher = cheaper, sparser).
+import numpy as np
+import torch
+
+import weightslab as wl
+
+from utils.criterions import classify_shape
+
+
+def register_signals(loss_name, shape_every, min_step: int = 0):
+    """Define and register the per-sample signal chain on the watched loss.
+
+    Defining a ``@wl.signal`` registers it globally, so this must be called
+    before ``wl.serve`` / ``wl.start_training``.
+
+    Args:
+        loss_name: name of the watched per-sample loss signal to subscribe to.
+        shape_every: compute sig/loss_shape every N steps (throttle).
+        min_step: minimum step to start computing sig/loss_shape.
+    """
+
+    @wl.signal(name="sig/entropy", subscribe_to=loss_name, batched=True)
+    def entropy(b):
+        p = torch.softmax(b.logits, 1)
+        return (-(p * (p + 1e-12).log()).sum(1)).detach().cpu().numpy()
+
+    @wl.signal(name="sig/loss_norm", inputs=[loss_name], batched=True)
+    def loss_norm(b):
+        return b.inputs[loss_name] / (float(np.mean(b.inputs[loss_name])) + 1e-8)
+
+    @wl.signal(name="sig/hardness", inputs=[loss_name, "sig/entropy"], batched=True)
+    def hardness(b):
+        return b.inputs[loss_name] * b.inputs["sig/entropy"]
+
+    @wl.signal(name="sig/loss_shape", inputs=[loss_name], batched=True,
+               compute_every_n_steps=shape_every, min_step=min_step)
+    def loss_shape(b):
+        hist = b.history(loss_name)   # {uid: [loss values in step order]}
+        return np.array([classify_shape(hist[s]) for s in b.sample_ids], dtype=float)
diff --git a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/config.yaml b/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/config.yaml
deleted file mode 100644
index 26c6ae44..00000000
--- a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/config.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-# Global configuration
-device: auto
-experiment_name: detection_pennfudan_usecase
-training_steps_to_do: null  # null = infinite training until manually stopped (behavior set by the user in main script)
-# root_log_dir:  # Empty to write in tmp directory, or specify a path to store logs and checkpoints
-
-checkpoint_manager:
-  load_config: false  # Disable loading previous checkpoint configuration to allow for changes in the current config.yaml file between experiments. Otherwise changes will be replaced.
-
-# Initially compute natural sorting values, e.g., average intensity. More details in the README.md file.
-compute_natural_sort: true
-
-# Experiment parameters
-eval_full_to_train_steps_ratio: 5
-experiment_dump_to_train_steps_ratio: 5
-tqdm_display: true
-is_training: false  # Start training immediately or not
-
-# Serving
-serving_grpc: true
-serving_cli: true
-
-# Configure global dataframe storage
-ledger_enable_h5_persistence: true
-ledger_enable_flushing_threads: true
-ledger_flush_max_rows: 100
-ledger_flush_interval: 60.0
-
-# Model / task
-num_classes: 1            # Penn-Fudan: single class (person)
-image_size: 256
-grid_size: 8              # detector predicts on an 8x8 cell grid
-conf_thresh: 0.3          # objectness*class confidence threshold for displayed predictions
-pretrained_backbone: true # ImageNet-pretrained MobileNetV3-Small feature extractor
-freeze_backbone: true     # train only the detection head (faster, less data hungry)
-
-optimizer:
-  lr: 0.001
-
-# Data (Penn-Fudan pedestrians; ~170 images downloaded on first run under data_root)
-data_root: .\data
-data:
-  train_loader:
-    batch_size: 8
-    shuffle: true
-    max_samples: null     # null = use the full training split
-  test_loader:
-    batch_size: 8
-    shuffle: false
-    max_samples: null
diff --git a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/main.py b/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/main.py
deleted file mode 100644
index ff4b2030..00000000
--- a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/main.py
+++ /dev/null
@@ -1,340 +0,0 @@
-import os
-import time
-import numpy as np
-import tqdm
-import yaml
-import torch
-import logging
-import tempfile
-import itertools
-
-import weightslab as wl
-
-from torch import optim
-
-
-from weightslab.components.global_monitoring import (
-    guard_training_context,
-    guard_testing_context,
-)
-
-from utils.data import PennFudanDetectionDataset, det_collate
-from utils.model import SmallDetector
-from utils.criterions import (
-    PerSampleDetectionLoss,
-    PerSampleIoU,
-    PerInstanceIoU,
-    decode_predictions,
-)
-
-# Setup loggers
-logging.basicConfig(level=logging.ERROR)
-logging.getLogger("PIL").setLevel(logging.INFO)
-
-
-# =============================================================================
-# Train / Test loops (detection, using watcher-wrapped loaders)
-# =============================================================================
-
-def train(loader, model, optimizer, sig, device, grid_size, conf_thresh):
-    """Single training step using the tracked dataloader + watched loss.
-
-    loader yields (inputs, ids, targets, metadata) because of the
-    DataSampleTrackingWrapper. `targets` is per sample a [N, 6] tensor of boxes
-    ([x1, y1, x2, y2, class_id, confidence]); see utils/data.det_collate.
-    """
-    with guard_training_context:
-        (inputs, ids, targets, _) = next(loader)
-        inputs = inputs.to(device)
-        targets = [t.to(device) for t in targets]
-
-        optimizer.zero_grad()
-        outputs = model(inputs) # [B, S, S, 5 + num_classes]
-
-        # Decoded boxes for the UI overlay (detached — display only).
-        preds = decode_predictions(outputs.detach(), grid_size, conf_thresh=conf_thresh)
-
-        # Per-sample detection loss (tracked, saved, and the value we backprop).
-        # `preds=` rides along so WL stores the predicted boxes for this batch.
-        loss_per_sample = sig["loss"](outputs, targets, batch_ids=ids, preds=preds)
-
-        # Metrics: per-sample mean IoU + per-instance IoU (one value per box).
-        sig["iou_sample"](outputs, targets, batch_ids=ids)
-        sig["iou_instance"](outputs, targets, batch_ids=ids)
-
-        loss = loss_per_sample.mean()
-        loss.backward()
-        optimizer.step()
-
-    return float(loss.detach().cpu().item())
-
-
-def test(loader, model, sig, device, grid_size, conf_thresh, test_loader_len):
-    """Full evaluation pass over the val loader."""
-    losses = 0.0
-    ious = 0.0
-    with guard_testing_context, torch.no_grad():
-        for inputs, ids, targets, _ in loader:
-            inputs = inputs.to(device)
-            targets = [t.to(device) for t in targets]
-
-            outputs = model(inputs)
-            preds = decode_predictions(outputs, grid_size, conf_thresh=conf_thresh)
-
-            loss_per_sample = sig["loss"](outputs, targets, batch_ids=ids, preds=preds)
-            iou_per_sample = sig["iou_sample"](outputs, targets, batch_ids=ids)
-            sig["iou_instance"](outputs, targets, batch_ids=ids)
-
-            losses += torch.mean(loss_per_sample)
-            ious += torch.mean(iou_per_sample)
-
-    loss = float((losses / test_loader_len).detach().cpu().item())
-    iou = float((ious / test_loader_len).detach().cpu().item())
-    return loss, iou * 100.0 # Return mean IoU as percentage
-
-
-# =============================================================================
-# Main
-# =============================================================================
-if __name__ == "__main__":
-    # --- 1) Load hyperparameters from YAML (if present) ---
-    config_path = os.path.join(os.path.dirname(__file__), "config.yaml")
-    if os.path.exists(config_path):
-        with open(config_path, "r") as fh:
-            parameters = yaml.safe_load(fh) or {}
-    else:
-        parameters = {}
-
-    # Defaults
-    parameters.setdefault("experiment_name", "pennfudan_detection")
-    parameters.setdefault("device", "auto")
-    parameters.setdefault("training_steps_to_do", 500)
-    parameters.setdefault("eval_full_to_train_steps_ratio", 50)
-    parameters.setdefault("number_of_workers", 4)
-    parameters.setdefault("num_classes", 1) # Penn-Fudan: single class (person)
-    parameters.setdefault("image_size", 256)
-    parameters.setdefault("grid_size", 8)
-    parameters.setdefault("conf_thresh", 0.3)
-    parameters.setdefault("pretrained_backbone", True)
-    parameters.setdefault("freeze_backbone", True)
-    parameters.setdefault("compute_natural_sort", True)
-
-    # --- 2) Register hyperparameters ---
-    exp_name = parameters["experiment_name"]
-    wl.watch_or_edit(
-        parameters,
-        flag="hyperparameters",
-        name=exp_name,
-        defaults=parameters,
-        poll_interval=1.0,
-    )
-
-    num_classes = int(parameters["num_classes"])
-    image_size = int(parameters["image_size"])
-    grid_size = int(parameters["grid_size"])
-    conf_thresh = float(parameters["conf_thresh"])
-
-    # --- 3) Device selection ---
-    if parameters.get("device", "auto") == "auto":
-        parameters["device"] = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu"
-        )
-    device = parameters["device"]
-
-    # --- 4) Logging directory ---
-    if not parameters.get("root_log_dir"):
-        tmp_dir = tempfile.mkdtemp()
-        parameters["root_log_dir"] = tmp_dir
-        print(f"No root_log_dir specified, using temporary directory: {parameters['root_log_dir']}")
-    os.makedirs(parameters["root_log_dir"], exist_ok=True)
-    log_dir = parameters["root_log_dir"]
-    max_steps = parameters["training_steps_to_do"]
-    eval_full_to_train_steps_ratio = parameters["eval_full_to_train_steps_ratio"]
-    verbose = parameters.get("verbose", True)
-    tqdm_display = parameters.get("tqdm_display", True)
-
-
-    # --- 5) Data (Penn-Fudan pedestrians, downloaded on first run) ---
-    default_data_root = os.path.abspath(
-        os.path.join(os.path.dirname(__file__), "data")
-    )
-    data_root = parameters.get("data_root", default_data_root)
-
-    train_cfg = parameters.get("data", {}).get("train_loader", {})
-    test_cfg = parameters.get("data", {}).get("test_loader", {})
-
-    _train_dataset = PennFudanDetectionDataset(
-        root=data_root,
-        split="train",
-        num_classes=num_classes,
-        image_size=image_size,
-        max_samples=train_cfg.get("max_samples", None),
-    )
-    _val_dataset = PennFudanDetectionDataset(
-        root=data_root,
-        split="val",
-        num_classes=num_classes,
-        image_size=image_size,
-        max_samples=test_cfg.get("max_samples", None),
-    )
-
-    train_loader = wl.watch_or_edit(
-        _train_dataset,
-        flag="data",
-        loader_name="train_loader",
-        batch_size=train_cfg.get("batch_size", 8),
-        shuffle=train_cfg.get("shuffle", True),
-        compute_hash=False,
-        is_training=True,
-        array_autoload_arrays=False,
-        array_return_proxies=True,
-        array_use_cache=True,
-        preload_labels=False,
-        collate_fn=det_collate,
-    )
-    test_loader = wl.watch_or_edit(
-        _val_dataset,
-        flag="data",
-        loader_name="test_loader",
-        batch_size=test_cfg.get("batch_size", 8),
-        shuffle=test_cfg.get("shuffle", False),
-        compute_hash=False,
-        is_training=False,
-        array_autoload_arrays=False,
-        array_return_proxies=True,
-        array_use_cache=True,
-        preload_labels=True,
-        collate_fn=det_collate,
-    )
-
-    # --- 6) Model, optimizer, losses, metric ---
-    _model = SmallDetector(
-        in_channels=3, num_classes=num_classes,
-        image_size=image_size, grid_size=grid_size,
-        pretrained=bool(parameters["pretrained_backbone"]),
-        freeze_backbone=bool(parameters["freeze_backbone"]),
-    ).to(device)
-    model = wl.watch_or_edit(
-        _model,
-        flag="model",
-        device=device
-    )
-    lr = parameters.get("optimizer", {}).get("lr", 1e-3)
-    # Only optimize trainable params (the head; backbone may be frozen).
-    trainable = [p for p in model.parameters() if p.requires_grad]
-    _optimizer = optim.Adam(trainable, lr=lr)
-    optimizer = wl.watch_or_edit(
-        _optimizer,
-        flag="optimizer",
-    )
-
-    # --- Detection loss (per sample) + IoU (per sample & per instance) signals ---
-    # per_sample=True auto-saves one value per sample; per_instance=True auto-saves
-    # one IoU per (sample_id, annotation_id), i.e. one per ground-truth box.
-    def _make_det_signals(split: str, weights=None) -> dict:
-        return {
-            "loss": wl.watch_or_edit(
-                PerSampleDetectionLoss(num_classes, grid_size, weights=weights),
-                flag="loss",
-                name=f"{split}_loss/sample", per_sample=True, log=True,
-            ),
-            "iou_sample": wl.watch_or_edit(
-                PerSampleIoU(num_classes, grid_size), flag="metric",
-                name=f"{split}_iou/sample", per_sample=True, log=True,
-            ),
-            "iou_instance": wl.watch_or_edit(
-                PerInstanceIoU(num_classes, grid_size), flag="metric",
-                name=f"{split}_iou/instance", per_instance=True, log=True,
-            ),
-        }
-
-    # Class weights from ground-truth box counts (optional; balances rare shapes).
-    def compute_class_weights(dataset, num_classes, max_samples=200):
-        print("\n" + "=" * 60, flush=True)
-        print(f"Computing class weights for {num_classes} classes (max {max_samples} samples)...", flush=True)
-        class_counts = np.zeros(num_classes, dtype=np.float64)
-        num_samples = min(len(dataset), max_samples)
-
-        for idx in tqdm.tqdm(range(num_samples), desc=" Analyzing Distribution"):
-            _, _, target, _ = dataset.get_items(idx, include_labels=True)
-            if target is None or len(target) == 0:
-                continue
-            for c in target[:, 4].astype(np.int64):
-                if 0 <= c < num_classes:
-                    class_counts[c] += 1
-
-        class_counts = np.maximum(class_counts, 1) # Avoid div by zero
-        total = class_counts.sum()
-        class_weights = total / (num_classes * class_counts)
-        class_weights = class_weights / class_weights.mean() # Normalize
-
-        print("\nClass distribution and weights:", flush=True)
-        for c in range(num_classes):
-            pct = (class_counts[c] / total) * 100
-            print(f"Class {c} ({dataset.class_names[c]}): {pct:6.2f}% -> weight: {class_weights[c]:.3f}", flush=True)
-        print("=" * 60 + "\n", flush=True)
-        return torch.FloatTensor(class_weights).to(device)
-
-    weights = compute_class_weights(_train_dataset, num_classes)
-
-    train_sig = _make_det_signals("train", weights=weights)
-    test_sig = _make_det_signals("test", weights=weights)
-
-    # --- 7) Start WeightsLab services ---
-    wl.serve(
-        serving_grpc=parameters.get("serving_grpc", True),
-        serving_cli=parameters.get("serving_cli", True),
-    )
-
-    print("=" * 60)
-    print(" STARTING PENN-FUDAN PEDESTRIAN DETECTION TRAINING")
-    print(f" Total steps: {max_steps}")
-    print(f" Evaluation every {eval_full_to_train_steps_ratio} steps")
-    print(f" Logs will be saved to: {log_dir}")
-    print(f" Data root: {data_root}")
-    print("=" * 60 + "\n")
-
-    # ================
-    # Training Loop
-    wl.start_training(timeout=3) # Blocks and keeps the main thread alive while background services run. Optionally set a timeout (seconds) to auto-stop.
-
-    # ================
-    train_range = tqdm.tqdm(itertools.count(), desc="Training") if tqdm_display else itertools.count()
-    test_loss, test_metric = None, None
-    start_time = time.time()
-    for train_step in train_range:
-        age = model.get_age() if hasattr(model, "get_age") else train_step
-
-        # Train
-        train_loss = train(train_loader, model, optimizer, train_sig, device, grid_size, conf_thresh)
-
-        # Test
-        if age == 0 or age % eval_full_to_train_steps_ratio == 0:
-            test_loader_len = len(test_loader) # Store length before wrapping with tqdm
-            test_loader_it = tqdm.tqdm(test_loader, desc="Evaluating") if tqdm_display else test_loader
-            test_loss, test_metric = test(test_loader_it, model, test_sig, device, grid_size, conf_thresh, test_loader_len)
-
-        # Verbose
-        if verbose and not tqdm_display:
-            print(
-                "Training.. " +
-                f"Step {train_step} (Age {age}): " +
-                f"| Train Loss: {train_loss:.4f} " +
-                (f"| Test Loss: {test_loss:.4f} " if test_loss is not None else '') +
-                (f"| Test IoU: {test_metric:.2f}% " if test_metric is not None else '')
-            )
-        elif tqdm_display:
-            train_range.set_description("Step")
-            train_range.set_postfix(
-                train_loss=f"{train_loss:.4f}",
-                test_loss=f"{test_loss:.4f}" if test_loss is not None else "N/A",
-                iou=f"{test_metric:.2f}%" if test_metric is not None else "N/A"
-            )
-
-    print("\n" + "=" * 60)
-    print(f" Training completed in {time.time() - start_time:.2f} seconds")
-    print(f" Logs saved to: {log_dir}")
-    print("=" * 60)
-
-    # Keep the main thread alive to allow background serving threads to run
-    wl.keep_serving()
diff --git a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/utils/criterions.py b/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/utils/criterions.py
deleted file mode 100644
index 7cd6c9c9..00000000
--- a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/utils/criterions.py
+++ /dev/null
@@ -1,361 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .model import decode_grid
-
-import weightslab as wl
-import numpy as np
-
-
-# =============================================================================
-# Per-instance / per-sample detection criterions (YOLO-v1 style loss + IoU)
-# =============================================================================
-# The detection dataset yields, per sample, a [N, 6] target tensor
-# ``[x1, y1, x2, y2, class_id, confidence]`` (normalized to [0, 1]). Each GT box
-# is assigned to the grid cell containing its center; that cell is "responsible"
-# for predicting the box.
-#
-# * PerSampleDetectionLoss -> one differentiable loss scalar per sample ([B]),
-# wrapped with ``per_sample=True`` (the value WL backprops + dashboards).
-# * PerSampleIoU -> mean IoU over a sample's boxes ([B]), a metric.
-# * PerInstanceIoU -> flat tensor of one IoU per GT box (sample-major
-# order), wrapped with ``per_instance=True`` so WL auto-saves it at
-# (sample_id, annotation_id). The ordering matches the per-sample target
-# iteration, so the wrapper's auto ``batch_idx`` maps each value correctly.
-
-_EPS = 1e-6
-_LAMBDA_COORD = 5.0
-_LAMBDA_NOOBJ = 0.5
-
-
-def box_iou_xyxy(a, b):
-    """IoU between two aligned sets of xyxy boxes. a, b: [..., 4] -> [...]."""
-    x1 = torch.maximum(a[..., 0], b[..., 0])
-    y1 = torch.maximum(a[..., 1], b[..., 1])
-    x2 = torch.minimum(a[..., 2], b[..., 2])
-    y2 = torch.minimum(a[..., 3], b[..., 3])
-
-    inter = (x2 - x1).clamp(min=0) * (y2 - y1).clamp(min=0)
-    area_a = (a[..., 2] - a[..., 0]).clamp(min=0) * (a[..., 3] - a[..., 1]).clamp(min=0)
-    area_b = (b[..., 2] - b[..., 0]).clamp(min=0) * (b[..., 3] - b[..., 1]).clamp(min=0)
-    union = area_a + area_b - inter + _EPS
-    return inter / union
-
-
-def _responsible_cells(boxes, S):
-    """Map GT boxes -> their responsible (row, col) cell and center offsets.
-
-    Args:
-        boxes: [N, 4] xyxy in [0, 1].
-        S: grid size.
-
-    Returns:
-        rows, cols: [N] long, the responsible cell indices.
-        off_x, off_y: [N] center offset within the cell, in [0, 1).
-        w, h: [N] box size as a fraction of the image.
-    """
-    cx = (boxes[:, 0] + boxes[:, 2]) / 2
-    cy = (boxes[:, 1] + boxes[:, 3]) / 2
-    w = (boxes[:, 2] - boxes[:, 0]).clamp(_EPS, 1.0)
-    h = (boxes[:, 3] - boxes[:, 1]).clamp(_EPS, 1.0)
-
-    cols = (cx * S).long().clamp(0, S - 1)
-    rows = (cy * S).long().clamp(0, S - 1)
-    off_x = (cx * S - cols).clamp(0, 1)
-    off_y = (cy * S - rows).clamp(0, 1)
-    return rows, cols, off_x, off_y, w, h
-
-
-def _per_sample_loss(outputs, targets, num_classes, weights=None):
-    """YOLO-v1 style loss, returned as one scalar per sample ([B], with grad)."""
-    B, S = outputs.shape[0], outputs.shape[1]
-    device = outputs.device
-
-    obj_logit = outputs[..., 0] # [B, S, S]
-    tx = torch.sigmoid(outputs[..., 1])
-    ty = torch.sigmoid(outputs[..., 2])
-    w_pred = torch.sigmoid(outputs[..., 3])
-    h_pred = torch.sigmoid(outputs[..., 4])
-    cls_logits = outputs[..., 5:] # [B, S, S, C]
-
-    if weights is not None:
-        weights = torch.as_tensor(weights, device=device, dtype=outputs.dtype)
-
-    losses = []
-    for s in range(B):
-        tgt = targets[s]
-        tgt = torch.as_tensor(tgt, device=device, dtype=outputs.dtype)
-        if tgt.ndim == 1:
-            tgt = tgt.view(-1, 6) if tgt.numel() else tgt.view(0, 6)
-
-        obj_target = torch.zeros((S, S), device=device, dtype=outputs.dtype)
-
-        coord_loss = torch.zeros((), device=device)
-        class_loss = torch.zeros((), device=device)
-
-        if tgt.numel() > 0:
-            boxes = tgt[:, :4]
-            cls_ids = tgt[:, 4].long().clamp(0, num_classes - 1)
-            rows, cols, off_x, off_y, gw, gh = _responsible_cells(boxes, S)
-
-            obj_target[rows, cols] = 1.0
-
-            # Localization: center offset (linear) + size in sqrt space (YOLO trick
-            # so small-box errors weigh as much as large-box ones).
-            coord = (
-                (tx[s, rows, cols] - off_x) ** 2
-                + (ty[s, rows, cols] - off_y) ** 2
-                + (torch.sqrt(w_pred[s, rows, cols] + _EPS) - torch.sqrt(gw + _EPS)) ** 2
-                + (torch.sqrt(h_pred[s, rows, cols] + _EPS) - torch.sqrt(gh + _EPS)) ** 2
-            )
-            coord_loss = _LAMBDA_COORD * coord.sum()
-
-            ce = F.cross_entropy(
-                cls_logits[s, rows, cols], cls_ids, reduction="none"
-            )
-            if weights is not None:
-                ce = ce * weights[cls_ids]
-            class_loss = ce.sum()
-
-        # Objectness BCE over the whole grid; down-weight the (many) empty cells.
-        obj_weight = torch.where(
-            obj_target > 0,
-            torch.ones_like(obj_target),
-            torch.full_like(obj_target, _LAMBDA_NOOBJ),
-        )
-        obj_loss = (
-            F.binary_cross_entropy_with_logits(
-                obj_logit[s], obj_target, reduction="none"
-            ) * obj_weight
-        ).sum()
-
-        losses.append(coord_loss + class_loss + obj_loss)
-
-    return torch.stack(losses)
-
-
-def _per_box_iou(outputs, targets, grid_size):
-    """IoU of each GT box against its responsible cell's decoded prediction.
-
-    Returns a list[B] of 1-D tensors (one IoU per box for that sample, in
-    annotation order). Detached — this is a metric, not a loss.
-    """
-    boxes_grid, _, _ = decode_grid(outputs, grid_size) # [B, S, S, 4]
-    B = outputs.shape[0]
-    S = grid_size
-    device = outputs.device
-
-    per_sample = []
-    for s in range(B):
-        tgt = torch.as_tensor(targets[s], device=device, dtype=outputs.dtype)
-        if tgt.numel() == 0:
-            per_sample.append(torch.zeros(0, device=device))
-            continue
-        if tgt.ndim == 1:
-            tgt = tgt.view(-1, 6)
-
-        gt_boxes = tgt[:, :4]
-        rows, cols, _, _, _, _ = _responsible_cells(gt_boxes, S)
-        pred_boxes = boxes_grid[s, rows, cols] # [N, 4]
-        ious = box_iou_xyxy(pred_boxes, gt_boxes) # [N]
-        per_sample.append(ious.detach())
-
-    return per_sample
-
-
-class PerSampleDetectionLoss(nn.Module):
-    """Total detection loss aggregated to one value per sample ([B])."""
-
-    def __init__(self, num_classes, grid_size, weights=None):
-        super().__init__()
-        self.num_classes = num_classes
-        self.grid_size = grid_size
-        self.register_buffer(
-            "weights", torch.tensor(weights) if weights is not None else None
-        )
-
-    def forward(self, outputs, targets):
-        return _per_sample_loss(outputs, targets, self.num_classes, weights=self.weights)
-
-
-class PerSampleIoU(nn.Module):
-    """Mean IoU over a sample's boxes -> one value per sample ([B])."""
-
-    def __init__(self, num_classes, grid_size):
-        super().__init__()
-        self.num_classes = num_classes
-        self.grid_size = grid_size
-
-    def forward(self, outputs, targets):
-        per_sample = _per_box_iou(outputs, targets, self.grid_size)
-        out = [
-            v.mean() if v.numel() > 0 else torch.zeros((), device=outputs.device)
-            for v in per_sample
-        ]
-        return torch.stack(out).detach()
-
-
-class PerInstanceIoU(nn.Module):
-    """IoU per GT box -> flat tensor [total_boxes] (sample-major order)."""
-
-    def __init__(self, num_classes, grid_size):
-        super().__init__()
-        self.num_classes = num_classes
-        self.grid_size = grid_size
-
-    def forward(self, outputs, targets):
-        per_sample = _per_box_iou(outputs, targets, self.grid_size)
-        flat = [v for s in per_sample for v in s]
-        if not flat:
-            return torch.zeros(0, device=outputs.device)
-        return torch.stack(flat).detach()
-
-
-# =============================================================================
-# Inference-time decoding (for UI prediction overlays)
-# =============================================================================
-def decode_predictions(outputs, grid_size, conf_thresh=0.3, max_det=10):
-    """Turn raw grid logits into a per-sample list of detected boxes.
-
-    Returns list[B] of [M, 6] numpy-friendly tensors
-    ``[x1, y1, x2, y2, class_id, confidence]`` (kept on CPU, detached) — the
-    exact 6-column schema WL renders for detection predictions.
-    """
-    boxes_grid, obj, cls_probs = decode_grid(outputs, grid_size)
-    B, S = outputs.shape[0], grid_size
-
-    cls_conf, cls_id = cls_probs.max(dim=-1) # [B, S, S]
-    score = obj * cls_conf # combined confidence
-
-    flat_boxes = boxes_grid.view(B, S * S, 4)
-    flat_score = score.view(B, S * S)
-    flat_cls = cls_id.view(B, S * S)
-
-    results = []
-    for s in range(B):
-        keep = flat_score[s] >= conf_thresh
-        if keep.sum() == 0:
-            results.append(torch.zeros((0, 6)))
-            continue
-        sc = flat_score[s][keep]
-        bx = flat_boxes[s][keep]
-        cl = flat_cls[s][keep].to(bx.dtype)
-
-        # Keep the most confident detections (cheap top-k in place of full NMS).
-        order = torch.argsort(sc, descending=True)[:max_det]
-        det = torch.cat([bx[order], cl[order, None], sc[order, None]], dim=1)
-        results.append(det.detach().cpu())
-
-    return results
-
-
-
-
-# =========================================================================
-# Custom subscribed signal: per-sample loss-shape classification
-# =========================================================================
-# This is a *dynamic* WeightsLab signal. It subscribes to the per-sample
-# classification loss "train/clsf_sample" and, every 25 optimisation steps,
-# inspects each sample's full loss trajectory, classifies its *shape*, and
-# writes the verdict back onto the sample as the categorical tag "loss_shape".
-#
-# The six shapes describe how a sample's loss evolved over training:
-#   monotonic     -> loss steadily decreasing             (model is learning it)
-#   plateaued     -> dropped then leveled off still-high  (stuck / hard sample)
-#   Flat_high     -> never moved, stayed high             (mislabel / unlearnable)
-#   high_variance -> noisy oscillation                    (ambiguous label)
-#   U_Shape       -> learned then forgotten               (catastrophic interference)
-#   Spiked        -> sudden jump at some step             (data/aug/version change)
-
-# Allowed values for the categorical tag, in display order.
-LOSS_SHAPE_LABELS = [
-    "monotonic", "plateaued", "Flat_high",
-    "high_variance", "U_Shape", "Spiked",
-]
-
-# Numeric encoding returned by the signal so the verdict is also plottable
-# per-sample (a tag is a string; a signal must be numeric).
-LOSS_SHAPE_CODES = {label: i for i, label in enumerate(LOSS_SHAPE_LABELS)}
-
-# Minimum number of logged points before a trajectory can be classified.
-_MIN_HISTORY = 5
-
-# Get checkpoint manager
-checkpoint_manager = ledgers.get_checkpoint_manager()
-
-# Declare the categorical tag written by the loss-shape classifier signal,
-# so the UI shows all six choices even before any sample is tagged. Must be
-# called after the dataloader is registered (the dataframe now exists).
-wl.register_categorical_tag("loss_shape", LOSS_SHAPE_LABELS)
-
-
-def _classify_loss_shape(values):
-    """Classify a per-sample loss trajectory into one of LOSS_SHAPE_LABELS.
-
-    *values* is the loss series ordered by step. Returns a label string, or
-    ``None`` when there is not enough history yet to decide. All thresholds
-    are scale-invariant (expressed as fractions of the trajectory's own
-    range), so the same logic works regardless of the absolute loss scale.
-    These thresholds are illustrative — tune them for your own task.
-    """
-    y = np.asarray(values, dtype=float)
-    if y.size < _MIN_HISTORY:
-        return None
-
-    n = y.size
-    first, last = float(y[0]), float(y[-1])
-    ymin, ymax = float(y.min()), float(y.max())
-    rng = max(ymax - ymin, 1e-8)
-    mean = float(y.mean())
-
-    cv = float(y.std()) / (abs(mean) + 1e-8)        # noisiness (coeff. of variation)
-    drop = (first - last) / (abs(first) + 1e-8)     # net improvement, start -> end
-    argmin = int(np.argmin(y))
-    rebound = (last - ymin) / rng                    # how far it climbed back from the trough
-    max_up_jump = float(np.diff(y).max()) / rng      # largest single-step rise
-
-    # Flat, recent tail (last 40% of the trajectory) is used to detect plateaus.
-    tail = y[int(0.6 * n):]
-    tail_flat = (float(tail.std()) / (abs(float(tail.mean())) + 1e-8)) < 0.1
-
-    # Order matters: most specific / most alarming shapes are checked first.
-    if max_up_jump > 0.5:                            # one big isolated jump up
-        return "Spiked"
-    if cv > 0.5:                                     # globally noisy / oscillating
-        return "high_variance"
-    if 0.2 * n < argmin < 0.8 * n and rebound > 0.3:  # dipped mid-run, then rose
-        return "U_Shape"
-    if drop > 0.4:                                   # large, steady net decrease
-        return "monotonic"
-    if drop > 0.15 and tail_flat:                    # modest drop, then leveled high
-        return "plateaued"
-    return "Flat_high"                               # barely moved — never learned
-
-
-@wl.signal(
-    name="train_loss_sample_shape_classifier",
-    subscribe_to="train_loss/sample",
-    compute_every_n_steps=25,
-    log=False,  # side-effecting signal: we tag samples, no aggregate curve needed
-)
-def classify_loss_shape(ctx: wl.SignalContext) -> int:
-    """Dynamic signal fired per sample every 25 steps for "train_loss/sample".
-
-    Pulls the sample's full loss history, classifies its shape, and tags the
-    sample with the categorical tag ``loss_shape``. Returns the numeric shape
-    code (or -1 when there is not enough history yet) so the verdict is also
-    available as a per-sample signal column.
-    """
-    # Full per-sample trajectory of the subscribed metric, ordered by step.
-    history = wl.query_sample_history(ctx.sample_id, signal_name="train_loss/sample", exp_hash=checkpoint_manager.get_current_experiment_hash())
-    series = sorted(((step, val) for _, step, val, _ in history), key=lambda t: t[0])
-    values = [v for _, v in series]
-
-    label = _classify_loss_shape(values)
-    if label is None:
-        return -1
-
-    # Persist the verdict as a categorical tag on this sample.
-    wl.set_categorical_tag([ctx.sample_id], "loss_shape", label)
-    return LOSS_SHAPE_CODES[label]
diff --git a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/utils/data.py b/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/utils/data.py
deleted file mode 100644
index dbff4a02..00000000
--- a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/utils/data.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import ssl
-import zipfile
-import urllib.request
-
-import numpy as np
-import torch
-
-from torchvision import transforms
-
-from torch.utils.data import Dataset
-
-from PIL import Image
-
-
-# =============================================================================
-# Penn-Fudan Pedestrian detection dataset
-# =============================================================================
-# A small, real object-detection dataset (~170 photos, one class: "person").
-# It ships per-instance segmentation masks; we derive an axis-aligned bounding
-# box per pedestrian from each mask. Downloaded + extracted on first use.
-#
-# On-disk layout after extraction:
-# <root>/PennFudanPed/
-# PNGImages/FudanPed00001.png ...
-# PedMasks/FudanPed00001_mask.png ... # pixel value k = k-th pedestrian, 0 = bg
-#
-# WL renders detection targets/predictions from a per-sample [N, 6] array
-# ``[x1, y1, x2, y2, class_id, confidence]`` normalized to [0, 1] (GT conf = 1.0)
-# — see ``get_items`` below and ``data_service.py`` (task_type == "detection").
-
-CLASS_NAMES = ["person"]
-
-_URL = "https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip"
-
-# ImageNet statistics — the MobileNetV3 backbone is pretrained with these, so we
-# normalize model inputs the same way. (The UI still shows the original PNG.)
-IMAGENET_MEAN = [0.485, 0.456, 0.406]
-IMAGENET_STD = [0.229, 0.224, 0.225]
-
-
-def download_penn_fudan(root):
-    """Download + extract Penn-Fudan into <root>/PennFudanPed (idempotent)."""
-    base = os.path.join(root, "PennFudanPed")
-    if os.path.isdir(os.path.join(base, "PNGImages")):
-        return base
-
-    os.makedirs(root, exist_ok=True)
-    zip_path = os.path.join(root, "PennFudanPed.zip")
-
-    if not os.path.exists(zip_path):
-        print(f"[data] Downloading Penn-Fudan dataset to {zip_path} ...", flush=True)
-        try:
-            urllib.request.urlretrieve(_URL, zip_path)
-        except Exception as e:
-            # Some corporate environments break TLS verification - retry unverified.
-            print(f"[data] TLS verification failed ({e}); retrying without verification.", flush=True)
-            ctx = ssl._create_unverified_context()
-            with urllib.request.urlopen(_URL, context=ctx) as resp, open(zip_path, "wb") as fh:
-                fh.write(resp.read())
-
-    print("[data] Extracting Penn-Fudan ...", flush=True)
-    with zipfile.ZipFile(zip_path) as zf:
-        zf.extractall(root)
-    return base
-
-
-def _boxes_from_mask(mask_path):
-    """Derive one bbox per pedestrian from a Penn-Fudan instance mask.
-
-    Returns (boxes_px [N, 4] int xyxy, height, width). Background (0) skipped.
-    """
-    mask = np.array(Image.open(mask_path))
-    h, w = mask.shape[:2]
-    obj_ids = np.unique(mask)
-    obj_ids = obj_ids[obj_ids != 0] # drop background
-
-    boxes = []
-    for oid in obj_ids:
-        ys, xs = np.where(mask == oid)
-        if xs.size == 0:
-            continue
-        x1, x2 = int(xs.min()), int(xs.max())
-        y1, y2 = int(ys.min()), int(ys.max())
-        if x2 > x1 and y2 > y1:
-            boxes.append([x1, y1, x2, y2])
-    return np.asarray(boxes, dtype=np.float32).reshape(-1, 4), h, w
-
-
-class PennFudanDetectionDataset(Dataset):
-    """Pedestrian bounding-box detection over the Penn-Fudan images.
-
-    Args:
-        root: directory to download/extract the dataset into.
-        split: "train" or "val" (deterministic split of the 170 images).
-        image_size: square resize fed to the model.
-        val_fraction: fraction of images held out for validation.
-        max_samples: optional cap on the split size (for quick runs).
-    """
-
-    def __init__(
-        self,
-        root,
-        split="train",
-        num_classes=1,
-        image_size=256,
-        val_fraction=0.2,
-        max_samples=None,
-    ):
-        super().__init__()
-        self.root = root
-        self.split = split
-        self.num_classes = num_classes
-        self.image_size = image_size
-        # Explicit task type; bypasses WL's label-shape heuristic so bboxes are
-        # rendered as detection overlays (not mistaken for classification).
-        self.task_type = "detection"
-        self.class_names = CLASS_NAMES[:num_classes]
-
-        base = download_penn_fudan(root)
-        img_dir = os.path.join(base, "PNGImages")
-        mask_dir = os.path.join(base, "PedMasks")
-
-        all_imgs = sorted(f for f in os.listdir(img_dir) if f.lower().endswith(".png"))
-
-        # Deterministic train/val split: every k-th image goes to val.
-        k = max(2, int(round(1.0 / max(val_fraction, 1e-6))))
-        if split == "val":
-            selected = all_imgs[::k]
-        else:
-            val_set = set(all_imgs[::k])
-            selected = [f for f in all_imgs if f not in val_set]
-
-        selected = selected[:max_samples] if max_samples != None else selected
-
-        self.images = []
-        self.masks = []
-        for fname in selected:
-            base_name, _ = os.path.splitext(fname)
-            mask_path = os.path.join(mask_dir, base_name + "_mask.png")
-            if os.path.exists(mask_path):
-                self.images.append(os.path.join(img_dir, fname))
-                self.masks.append(mask_path)
-
-        if len(self.images) == 0:
-            raise RuntimeError(f"No image/mask pairs found under {base}")
-
-        self.image_transform = transforms.Compose(
-            [
-                transforms.Resize((image_size, image_size), interpolation=Image.BILINEAR),
-                transforms.ToTensor(),
-                transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
-            ]
-        )
-
-    def __len__(self):
-        return len(self.images)
-
-    def _load_boxes(self, mask_path):
-        """Read mask → [N, 6] float32 = [x1, y1, x2, y2, cls=0, conf=1.0] (normalized)."""
-        boxes_px, h, w = _boxes_from_mask(mask_path)
-        if boxes_px.shape[0] == 0:
-            return np.zeros((0, 6), dtype=np.float32)
-        norm = boxes_px.copy()
-        norm[:, [0, 2]] /= float(w)
-        norm[:, [1, 3]] /= float(h)
-        n = norm.shape[0]
-        cls = np.zeros((n, 1), dtype=np.float32) # single class: person
-        conf = np.ones((n, 1), dtype=np.float32)
-        return np.concatenate([norm, cls, conf], axis=1).astype(np.float32)
-
-    def __getitem__(self, idx):
-        """Returns (item, uid, target, metadata).
-
-        - item: normalized image tensor [C, H, W]
-        - uid: unique sample id (string)
-        - target: [N, 6] float32 = [x1, y1, x2, y2, class_id, confidence]
-        - metadata: dict with source paths
-        """
-        return self.get_items(idx, include_metadata=True, include_labels=True, include_images=True)
-
-    def get_items(self, idx, include_metadata=False, include_labels=False, include_images=False):
-        img_path = self.images[idx]
-        mask_path = self.masks[idx]
-        uid = os.path.splitext(os.path.basename(img_path))[0]
-
-        metadata = {
-            "img_path": img_path,
-            "mask_path": mask_path,
-        } if include_metadata else None
-
-        img_t = None
-        if include_images:
-            img = Image.open(img_path).convert("RGB")
-            img_t = self.image_transform(img)
-
-        target = None
-        if include_labels:
-            target = self._load_boxes(mask_path)
-
-        return img_t, uid, target, metadata
-
-
-def det_collate(batch):
-    """Collate WL per-sample tuples for object detection.
-
-    A sample owns a variable number of boxes, so targets cannot be stacked. We
-    keep them as a Python list (one [N_i, 6] tensor per sample), exactly the
-    layout WL's per-instance helpers expect (``targets[s]`` iterates that
-    sample's boxes in annotation order).
-
-    Returns:
-        images: FloatTensor [B, C, H, W]
-        ids: list[str] of length B
-        targets: list[B] of [N_i, 6] float tensors ([x1, y1, x2, y2, cls, conf])
-        metas: list[B] of metadata dicts
-    """
-    images = torch.stack([b[0] for b in batch], dim=0)
-    ids = [b[1] for b in batch]
-    targets = [
-        torch.as_tensor(b[2], dtype=torch.float32)
-        if not isinstance(b[2], torch.Tensor) else b[2].float()
-        for b in batch
-    ]
-    metas = [b[3] if len(b) > 3 else None for b in batch]
-    return images, ids, targets, metas
diff --git a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/utils/model.py b/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/utils/model.py
deleted file mode 100644
index ed204a66..00000000
--- a/weightslab/examples/Usecases/ws-loss_shapes_classification_per_sample/utils/model.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# =============================================================================
-# Small single-shot grid detector on a pretrained MobileNetV3-Small backbone
-# =============================================================================
-# A frozen/fine-tuned ImageNet-pretrained backbone extracts features; a tiny
-# detection head turns them into an S x S grid where each cell predicts ONE box:
-# (objectness, tx, ty, tw, th, class_logits...).
-#
-# Encoding (all coordinates normalized to the [0, 1] image frame):
-# * objectness = sigmoid(t_obj) -> P(box present in this cell)
-# * cx = (col + sigmoid(tx)) / S -> box center, x
-# * cy = (row + sigmoid(ty)) / S -> box center, y
-# * w = sigmoid(tw) -> box width (fraction of image)
-# * h = sigmoid(th) -> box height (fraction of image)
-# * class = softmax(class_logits)
-#
-# Raw forward output keeps logits (loss applies the activations); `decode`
-# turns logits into xyxy boxes for metrics and UI rendering.
-import torch
-import torch.nn as nn
-
-from torchvision.models import mobilenet_v3_small, MobileNet_V3_Small_Weights
-
-
-def decode_grid(outputs, grid_size):
-    """Decode raw grid logits -> per-cell xyxy boxes, objectness and class probs.
-
-    Shared by the model (``SmallDetector.decode``) and the criterions so the
-    encoding lives in exactly one place.
-
-    Args:
-        outputs: [B, S, S, 5 + num_classes] raw logits.
-        grid_size: S.
-
-    Returns:
-        boxes: [B, S, S, 4] xyxy in [0, 1]
-        obj: [B, S, S] objectness probability
-        cls_probs: [B, S, S, num_classes] class probabilities
-    """
-    B, S, _, _ = outputs.shape
-    device = outputs.device
-
-    obj = torch.sigmoid(outputs[..., 0])
-    tx = torch.sigmoid(outputs[..., 1])
-    ty = torch.sigmoid(outputs[..., 2])
-    w = torch.sigmoid(outputs[..., 3])
-    h = torch.sigmoid(outputs[..., 4])
-    cls_probs = torch.softmax(outputs[..., 5:], dim=-1)
-
-    # Cell-origin grid (col=x, row=y).
-    cols = torch.arange(S, device=device).view(1, 1, S).expand(B, S, S)
-    rows = torch.arange(S, device=device).view(1, S, 1).expand(B, S, S)
-
-    cx = (cols + tx) / S
-    cy = (rows + ty) / S
-    x1 = (cx - w / 2).clamp(0, 1)
-    y1 = (cy - h / 2).clamp(0, 1)
-    x2 = (cx + w / 2).clamp(0, 1)
-    y2 = (cy + h / 2).clamp(0, 1)
-
-    boxes = torch.stack([x1, y1, x2, y2], dim=-1)
-    return boxes, obj, cls_probs
-
-
-class SmallDetector(nn.Module):
-    def __init__(
-        self,
-        in_channels=3,
-        num_classes=1,
-        image_size=256,
-        grid_size=8,
-        pretrained=True,
-        freeze_backbone=True,
-    ):
-        super().__init__()
-        # For WeightsLab
-        self.task_type = "detection"
-        self.num_classes = num_classes
-        self.class_names = ["person"][:num_classes]
-        self.grid_size = grid_size
-        self.image_size = image_size
-        self.input_shape = (1, in_channels, image_size, image_size)
-
-        # Channels per cell: objectness(1) + box(4) + class logits(num_classes)
-        self.preds_per_cell = 5 + num_classes
-
-        # --- Pretrained backbone (ImageNet) ---
-        weights = MobileNet_V3_Small_Weights.IMAGENET1K_V1 if pretrained else None
-        backbone = mobilenet_v3_small(weights=weights)
-        self.backbone = backbone.features # [B, 576, H/32, W/32]
-        backbone_out_ch = 576
-
-        self.freeze_backbone = freeze_backbone
-        if freeze_backbone:
-            for p in self.backbone.parameters():
-                p.requires_grad = False
-
-        # --- Detection head (lightweight, trained from scratch) ---
-        self.neck = nn.Sequential(
-            nn.Conv2d(backbone_out_ch, 256, 3, padding=1, bias=False),
-            nn.BatchNorm2d(256),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(256, 128, 3, padding=1, bias=False),
-            nn.BatchNorm2d(128),
-            nn.ReLU(inplace=True),
-        )
-        self.head = nn.Conv2d(128, self.preds_per_cell, 1)
-
-    def train(self, mode=True):
-        """Keep a frozen backbone in eval mode (so its BatchNorm stats stay fixed)."""
-        super().train(mode)
-        if self.freeze_backbone:
-            self.backbone.eval()
-        return self
-
-    def forward(self, x):
-        """Returns raw logits [B, S, S, 5 + num_classes]."""
-        if self.freeze_backbone:
-            with torch.no_grad():
-                feat = self.backbone(x)
-        else:
-            feat = self.backbone(x)
-
-        feat = self.neck(feat)
-        out = self.head(feat) # [B, preds_per_cell, S', S']
-
-        # Resize feature grid to the configured grid_size.
-        if out.shape[-1] != self.grid_size or out.shape[-2] != self.grid_size:
-            out = nn.functional.adaptive_avg_pool2d(out, self.grid_size)
-        # [B, C, S, S] -> [B, S, S, C]
-        return out.permute(0, 2, 3, 1).contiguous()
-
-    def decode(self, outputs):
-        """Decode raw logits -> per-cell xyxy boxes, objectness, class probs."""
-        return decode_grid(outputs, self.grid_size)
diff --git a/weightslab/examples/Usecases/ws-signals-mnist/main.py b/weightslab/examples/Usecases/ws-signals-mnist/main.py
new file mode 100644
index 00000000..a2b32824
--- /dev/null
+++ b/weightslab/examples/Usecases/ws-signals-mnist/main.py
@@ -0,0 +1,180 @@
+"""Per-sample signals on MNIST — readable, zero save_signals, with train + eval.
+
+Per-step user code is just the watched loss. Everything else is a @wl.signal:
+  entropy    from ctx.logits when the loss fires
+  loss_norm  reactive, from the logged loss
+  hardness   reactive, from loss + entropy
+
+loss_shape comes from WeightsLab's built-in classifier, applied on report write
+(wl.write_dataframe(loss_shape_signal=...)) — no classifier code lives here.
+
+Universal loss: the watched crit runs on the test split each epoch too, so test
+samples get a loss trajectory and a shape as well.
+
+Env: WL_STRESS_EPOCHS, WL_STRESS_OUT.
+"""
+import os, time, gc, json, shutil
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset
+from torchvision import datasets, transforms
+
+import weightslab as wl
+from weightslab.components.global_monitoring import guard_training_context, guard_testing_context
+
+LOSS = "loss_sample"
+OUT = os.environ.get("WL_STRESS_OUT", "/tmp/wl_stress")
+EPOCHS = int(os.environ.get("WL_STRESS_EPOCHS", "10"))
+BATCH = 64
+
+
+def rss_gb():
+    try:
+        for ln in open("/proc/self/status"):
+            if ln.startswith("VmRSS:"):
+                return int(ln.split()[1]) / (1024 * 1024)
+    except Exception:
+        return -1.0
+
+
+class SmallCNN(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.input_shape = (1, 1, 28, 28)
+        self.net = nn.Sequential(
+            nn.Conv2d(1, 8, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
+            nn.Conv2d(8, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
+            nn.Flatten(), nn.Linear(16 * 7 * 7, 64), nn.ReLU(), nn.Linear(64, 10))
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class MNISTIdx(Dataset):
+    """Yields (image, uid, label). uid namespaced by split so train/test don't
+    collide in the shared ledger; fast_get_label skips decode at ledger init."""
+    def __init__(self, root, train, base):
+        self.m = datasets.MNIST(root, train=train, download=True, transform=None)
+        self.t = transforms.ToTensor(); self.base = base
+
+    def __len__(self):
+        return len(self.m)
+
+    def __getitem__(self, i):
+        img, lab = self.m[i]
+        return self.t(img), self.base + i, lab
+
+    def fast_get_label(self, i):
+        return int(self.m.targets[i])
+
+
+def log(msg):
+    print(msg, flush=True)
+
+
+def main():
+    shutil.rmtree(OUT, ignore_errors=True)
+    os.makedirs(OUT + "/wl_logs", exist_ok=True)
+    dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    torch.manual_seed(0)
+    metrics = open(OUT + "/metrics.jsonl", "w")
+
+    # vanilla baseline (plain PyTorch) for the overhead comparison
+    vb = SmallCNN().to(dev); vo = optim.Adam(vb.parameters(), lr=0.01); vc = nn.CrossEntropyLoss()
+    xb = torch.randn(BATCH, 1, 28, 28, device=dev); yb = torch.randint(0, 10, (BATCH,), device=dev)
+    sync = (lambda: torch.cuda.synchronize()) if dev.type == "cuda" else (lambda: None)
+    for _ in range(10):
+        vo.zero_grad(); vc(vb(xb), yb).backward(); vo.step()
+    sync(); t0 = time.perf_counter()
+    for _ in range(50):
+        vo.zero_grad(); vc(vb(xb), yb).backward(); vo.step()
+    sync(); vanilla_ms = 1000 * (time.perf_counter() - t0) / 50
+    del vb, vo; gc.collect()
+    log(f"[run] vanilla baseline = {vanilla_ms:.2f} ms/step (dev {dev})")
+
+    # WeightsLab setup: both splits tracked, watched loss, signals
+    hp = {"experiment_name": "signals-mnist", "device": str(dev), "root_log_dir": OUT + "/wl_logs",
+          "serving_grpc": False, "serving_cli": False, "ledger_flush_max_rows": 8192,
+          "ledger_enable_h5_persistence": False, "experiment_dump_to_train_steps_ratio": 10_000_000,
+          "data": {"train_loader": {"batch_size": BATCH, "shuffle": True}}}
+    wl.watch_or_edit(hp, flag="hyperparameters", defaults=hp)
+    train_ds = MNISTIdx(OUT + "/data", train=True, base=0)
+    test_ds = MNISTIdx(OUT + "/data", train=False, base=1_000_000)
+    model = wl.watch_or_edit(SmallCNN().to(dev), flag="model", device=dev)
+    opt = wl.watch_or_edit(optim.Adam(model.parameters(), lr=0.01), flag="optimizer")
+    loader = wl.watch_or_edit(train_ds, flag="data", loader_name="train_loader",
+                              batch_size=BATCH, shuffle=True, is_training=True, preload_labels=True)
+    test_loader = wl.watch_or_edit(test_ds, flag="data", loader_name="test_loader",
+                                   batch_size=256, shuffle=False, is_training=False, preload_labels=True)
+    crit = wl.watch_or_edit(nn.CrossEntropyLoss(reduction="none"),
+                            flag="loss", signal_name=LOSS, per_sample=True, log=True)
+
+    @wl.signal(name="sig/entropy", subscribe_to=LOSS, batched=True)
+    def entropy(b):
+        p = torch.softmax(b.logits, 1)
+        return (-(p * (p + 1e-12).log()).sum(1)).detach().cpu().numpy()
+
+    @wl.signal(name="sig/loss_norm", inputs=[LOSS], batched=True)
+    def loss_norm(b):
+        return b.inputs[LOSS] / (float(np.mean(b.inputs[LOSS])) + 1e-8)
+
+    @wl.signal(name="sig/hardness", inputs=[LOSS, "sig/entropy"], batched=True)
+    def hardness(b):
+        return b.inputs[LOSS] * b.inputs["sig/entropy"]
+
+    wl.serve(serving_grpc=False, serving_cli=False)
+    wl.start_training(timeout=0)
+    log(f"[run] tracked train={len(train_ds)} test={len(test_ds)}")
+
+    def test_eval():
+        """Universal loss: watched crit over the test split each epoch."""
+        with torch.no_grad():
+            for tb in test_loader:
+                ti, tid, tl = tb[0].to(dev), tb[1], tb[2].to(dev)
+                with guard_testing_context:
+                    tlg = model(ti)
+                    crit(tlg, tl, batch_ids=tid, preds=tlg.argmax(1, keepdim=True))
+
+    step_times, gstep, t_run = [], 0, time.perf_counter()
+    for ep in range(1, EPOCHS + 1):
+        ep_ms = []
+        for img, ids, lab in loader:
+            img, lab = img.to(dev), lab.to(dev)
+            sync(); ts = time.perf_counter()
+            with guard_training_context:
+                opt.zero_grad()
+                logits = model(img)
+                # only per-step call: the watched loss logs loss_sample and fires
+                # the @wl.signal chain. No save_signals.
+                crit(logits, lab, batch_ids=ids, preds=logits.argmax(1, keepdim=True)).mean().backward()
+                opt.step()
+            sync(); ep_ms.append(1000 * (time.perf_counter() - ts))
+            gstep += 1
+        test_eval()
+        wl_ms = float(np.mean(ep_ms)); step_times += ep_ms
+        rec = {"epoch": ep, "gstep": gstep, "wl_ms": round(wl_ms, 2), "vanilla_ms": round(vanilla_ms, 2),
+               "rss_gb": round(rss_gb(), 2), "elapsed_s": round(time.perf_counter() - t_run, 1)}
+        metrics.write(json.dumps(rec) + "\n"); metrics.flush()
+        log(f"[run] ep {ep:3d}/{EPOCHS} | WL {wl_ms:6.2f} ms vs vanilla {vanilla_ms:.2f} "
+            f"= +{100*(wl_ms/vanilla_ms-1):.0f}% | RSS {rec['rss_gb']:.2f} GB | {rec['elapsed_s']:.0f}s")
+
+    import pandas as pd
+    # loss_shape comes from WeightsLab's built-in classifier, run on report write.
+    path = wl.write_dataframe(OUT + "/report.csv", format="csv",
+                              columns=["signals", "tags"], loss_shape_signal=LOSS)
+    df = pd.read_csv(path)
+    sc = [c for c in df.columns if c.endswith("loss_shape")][0]
+    med = float(np.median(step_times))
+    metrics.close()
+    log("\n[run] ===== SUMMARY =====")
+    log(f"[run] vanilla {vanilla_ms:.2f} ms | WL median {med:.2f} ms/step (+{100*(med/vanilla_ms-1):.0f}%)")
+    log(f"[run] report {len(df)} rows | loss_shape covered {df[sc].notna().sum()}/{len(df)}")
+    log(f"[run] shape distribution: {df[sc].value_counts(dropna=True).to_dict()}")
+    log(f"[run] report -> {path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/weightslab/integrations/ultralytics/README.md b/weightslab/integrations/ultralytics/README.md
index 9b401a4b..ef9e464c 100644
--- a/weightslab/integrations/ultralytics/README.md
+++ b/weightslab/integrations/ultralytics/README.md
@@ -1,21 +1,26 @@
 # WeightsLab × Ultralytics
 
-Drop-in trainer that wires WeightsLab into UL's `YOLO.train()` — per-sample
+Drop-in trainers that wire WeightsLab into UL's `YOLO.train()` — per-sample
 signals, live studio overlay, and ledger-driven discard control with no
 changes to the model or to UL's training loop.
 
+| trainer                      | UL base             | model            | task     |
+|------------------------------|---------------------|------------------|----------|
+| `WLAwareTrainer`             | `DetectionTrainer`  | `yolo*n.pt`      | detect   |
+| `WLAwareSegmentationTrainer` | `SegmentationTrainer` | `yolo*n-seg.pt`  | segment  |
+
 ## Minimal use
 
 ```python
 import weightslab as wl
 from ultralytics import YOLO
-from weightslab.integrations.ultralytics import WLAwareTrainer
+from weightslab.integrations.ultralytics import WLAwareTrainer  # or WLAwareSegmentationTrainer
 
 wl.watch_or_edit(cfg, flag="hyperparameters", defaults=cfg)
 wl.serve()
 
-YOLO("yolov8n.pt").train(
-    trainer=WLAwareTrainer,
+YOLO("yolo11n.pt").train(          # yolo11n-seg.pt for segmentation
+    trainer=WLAwareTrainer,        # WLAwareSegmentationTrainer for segmentation
     data="my_dataset.yaml", imgsz=640, epochs=100, batch=16,
     project="./logs", name="exp",
     workers=0, amp=False,
@@ -23,6 +28,19 @@ YOLO("yolov8n.pt").train(
 wl.keep_serving()
 ```
 
+## Segmentation
+
+`WLAwareSegmentationTrainer` reuses the detection signal pack: `v8SegmentationLoss`
+routes through the same `get_assigned_targets_and_loss` sync point, `Segment`
+subclasses `Detect`, and `SegmentationValidator._process_batch(preds, batch)`
+matches the val-IoU tap — so per-sample **box/cls/dfl** losses and **box-IoU**
+flow unchanged, plus aggregate `train/seg` and mask `(M)` val metrics. Masks are
+trained on (they ride through the collate into UL's loss) but tracked/rendered at
+the **bbox** level in Studio for now (`task_type="detection"`); per-sample mask
+signals + mask overlays are a future addition. Any segmentation-only overlay
+incompatibility is absorbed by `_ship_round`'s best-effort per-signal guards, so
+it never crashes training.
+
 ## Required train kwargs
 
 | kwarg     | value     | why                                                       |
@@ -42,7 +60,7 @@ which becomes the WL logger's `log_dir/name`).
 
 | component   | tested              | notes                                          |
 |-------------|---------------------|------------------------------------------------|
-| Ultralytics | 8.3.x               | DetectionTrainer subclass; YOLOv8/v11 detect  |
+| Ultralytics | 8.3.x–8.4.x         | Detection + Segmentation trainers; YOLOv8/v11 |
 | Python      | 3.11                |                                                |
 | Linux       | ✅                  | CPU + CUDA                                     |
 | Windows     | ⚠️ caveats          | install `torchvision` CUDA wheels (else `torchvision::nms` CUDA backend missing); `dill` cannot pickle some module graphs — set `dump_model_architecture: false` in `config.yaml` |
@@ -53,8 +71,10 @@ which becomes the WL logger's `log_dir/name`).
 - **per-sample TRAIN signals:** `train/box_per_sample`,
   `train/cls_per_sample`, `train/dfl_per_sample`, live NMS predictions overlay
 - **per-sample VAL signals:** `val/iou_per_sample`, post-NMS predictions overlay
-- **aggregate train losses:** `train/{box,cls,dfl}` (from `trainer.loss_items`)
+- **aggregate train losses:** `train/{box,cls,dfl}` (+ `train/seg` for
+  segmentation) from `trainer.loss_items`
 - **aggregate val metrics:** `val/{precision,recall,mAP50,mAP50-95,fitness}`
+  (+ mask `_mask` variants for segmentation)
 
 ## Behavior under discard
 
diff --git a/weightslab/integrations/ultralytics/__init__.py b/weightslab/integrations/ultralytics/__init__.py
index 15f7c57e..3ec79271 100644
--- a/weightslab/integrations/ultralytics/__init__.py
+++ b/weightslab/integrations/ultralytics/__init__.py
@@ -1,13 +1,16 @@
 """WeightsLab ↔ Ultralytics integration.
 
-Two-name public surface:
+Public surface:
   * `WLAwareTrainer` — UL DetectionTrainer subclass that wires WL through
     the canonical pattern (both train/val loaders go through
     `wl.watch_or_edit(flag='data')`, per-sample TRAIN + VAL signals
     installed, live train + val NMS prediction overlays shipped to studio).
-  * `WLAwareDataset` — UL YOLODataset subclass that returns the WL
-    preview-protocol 4-tuple and provides a PIL fallback in
-    `fast_get_label` for ledger-init.
+  * `WLAwareSegmentationTrainer` — the same wiring on UL's SegmentationTrainer
+    for `*-seg.pt` models (masks train through the collate; per-sample signals
+    are box-level today — see trainer.py).
+  * `WLAwareDataset` / `WLAwareSegmentationDataset` — UL YOLODataset
+    subclasses that return the WL preview-protocol 4-tuple and provide a PIL
+    fallback in `fast_get_label` for ledger-init.
 
 Minimal user surface:
 
@@ -19,7 +22,7 @@
     wl.serve()
 
     YOLO(cfg["model"]).train(
-        trainer=WLAwareTrainer,
+        trainer=WLAwareTrainer,           # or WLAwareSegmentationTrainer
         data=cfg["data_root"], imgsz=640, epochs=1000, batch=4,
         project="./logs", name="exp", # → WL log_dir/name
         workers=0, # WL invariant (parent-process uid counter)
@@ -28,7 +31,12 @@
 
 See README.md for the supported-setup matrix.
 """
-from .dataset import WLAwareDataset
-from .trainer import WLAwareTrainer
+from .dataset import WLAwareDataset, WLAwareSegmentationDataset
+from .trainer import WLAwareSegmentationTrainer, WLAwareTrainer
 
-__all__ = ["WLAwareTrainer", "WLAwareDataset"]
+__all__ = [
+    "WLAwareTrainer",
+    "WLAwareSegmentationTrainer",
+    "WLAwareDataset",
+    "WLAwareSegmentationDataset",
+]
diff --git a/weightslab/integrations/ultralytics/dataset.py b/weightslab/integrations/ultralytics/dataset.py
index ff4be229..f3284a2a 100644
--- a/weightslab/integrations/ultralytics/dataset.py
+++ b/weightslab/integrations/ultralytics/dataset.py
@@ -89,3 +89,25 @@ def get_items(self, i, include_metadata=False, include_labels=False, include_ima
             )
             labels = _to_six_col(xyxy_np, np.asarray(data["cls"]))
         return image, str(i), labels, metadata
+
+
+class WLAwareSegmentationDataset(WLAwareDataset):
+    """YOLO segmentation dataset that speaks WL's preview protocol.
+
+    The parent `WLAwareDataset` already extracts the per-instance bboxes that
+    YOLO segmentation labels carry (`lab["bboxes"]` / `data["bboxes"]`), and
+    the full UL sample dict — masks included — rides through
+    `metadata["batch"]` untouched, so `wl_ul_dict_collate` -> UL's own
+    `collate_fn` still assembles the `masks` tensor the segmentation loss
+    needs. We therefore only need to (a) keep the segmentation dataset build
+    flags intact (this class is assigned onto an already-built segment dataset
+    via ``dataset.__class__ = WLAwareSegmentationDataset``, which preserves
+    ``use_segments`` / ``task``), and (b) advertise the task.
+
+    Studio tracks and renders these runs at the **bbox** level (the per-sample
+    signals and overlays are box-based), so `task_type` stays ``"detection"``
+    — masks are trained on but not yet streamed to the grid. When per-sample
+    mask signals/overlays land, flip this to ``"segmentation"``.
+    """
+
+    task_type = "detection"
diff --git a/weightslab/integrations/ultralytics/signals.py b/weightslab/integrations/ultralytics/signals.py
index e6a733f7..c58650aa 100644
--- a/weightslab/integrations/ultralytics/signals.py
+++ b/weightslab/integrations/ultralytics/signals.py
@@ -23,6 +23,7 @@
 """
 from __future__ import annotations
 
+import logging
 from dataclasses import dataclass
 from typing import Any, Callable, Optional
 
@@ -41,6 +42,9 @@
 from ._utils import normalize_post_nms_preds
 
 
+logger = logging.getLogger(__name__)
+
+
 # ─── overlay fallback tunables ─────────────────────────────────────────
 # Train overlay's NMS pulls thresholds from `model.args.{conf,iou}` (the
 # values you'd pass through `YOLO.train(conf=..., iou=...)`). These
@@ -174,19 +178,35 @@ def _ship_round(signals, channels, batch):
     """Iterate the signal list, ship anything the reducers produce. Wrapped
     in `no_grad` because signal capture is observational — running e.g.
     `Detect._inference` + NMS inside the train autograd graph would keep
-    those tensors alive until backward and compound across steps."""
+    those tensors alive until backward and compound across steps.
+
+    Signal capture is best-effort: every reducer / preds / channel call is
+    guarded so a signal that can't run on a given model or task (e.g. a
+    detection-style bbox overlay attempted on a `Segment` head whose
+    `_inference` output NMS can't parse) is skipped for that round instead
+    of crashing UL's training loop. Failures are logged at debug level."""
     ids = batch["ids"]
     with th.no_grad():
         for s in signals:
-            v = s.reduce(batch)
+            try:
+                v = s.reduce(batch)
+            except Exception as e:
+                logger.debug("Signal %s reduce failed; skipping round: %s", s.name, e)
+                continue
             if v is None:
                 continue
             kw = {"batch_ids": ids}
             if s.preds is not None:
-                p = s.preds(batch)
-                if p is not None:
-                    kw["preds"] = p
-            channels[s.name](v, **kw)
+                try:
+                    p = s.preds(batch)
+                    if p is not None:
+                        kw["preds"] = p
+                except Exception as e:
+                    logger.debug("Signal %s preds failed; shipping scalar only: %s", s.name, e)
+            try:
+                channels[s.name](v, **kw)
+            except Exception as e:
+                logger.debug("Signal %s channel emit failed: %s", s.name, e)
 
 
 def install_train_pipeline(model, signals: list[Signal]):
diff --git a/weightslab/integrations/ultralytics/trainer.py b/weightslab/integrations/ultralytics/trainer.py
index 6efc7aaa..2fac84cc 100644
--- a/weightslab/integrations/ultralytics/trainer.py
+++ b/weightslab/integrations/ultralytics/trainer.py
@@ -1,24 +1,36 @@
-"""WLAwareTrainer — UL DetectionTrainer subclass that wires WL through the
-canonical pattern.
+"""WL-aware UL trainers — DetectionTrainer / SegmentationTrainer subclasses
+that wire WL through the canonical pattern.
 
-What this trainer does for you:
-  * Builds both `train_loader` and `test_loader` through
+What these trainers do for you:
+  * Build both `train_loader` and `test_loader` through
     `wl.watch_or_edit(flag='data', loader_name=...)` so each split has its
     own `DataSampleTrackingWrapper` with a disjoint uid range — no
     cross-origin collisions in the shared ledger.
-  * Registers the optimizer and model with WL on `on_train_start`.
-  * Installs per-sample TRAIN signals (cls/box/dfl per image + live NMS
+  * Register the optimizer and model with WL on `on_train_start`.
+  * Install per-sample TRAIN signals (cls/box/dfl per image + live NMS
     predictions overlay).
-  * Installs per-sample VAL signals (per-image IoU + AP@0.5 + post-NMS
+  * Install per-sample VAL signals (per-image IoU + AP@0.5 + post-NMS
     predictions overlay).
-  * Ships aggregate train losses + val metrics (P/R/mAP50/mAP50-95/fitness)
+  * Ship aggregate train losses + val metrics (P/R/mAP50/mAP50-95/fitness)
     as curves through WL channels.
-  * Wraps each train/val batch in WL's training/testing guard contexts.
+  * Wrap each train/val batch in WL's training/testing guard contexts.
+
+Two public trainers, one shared wiring mixin:
+  * `WLAwareTrainer`            — `detect` task (YOLO*n.pt).
+  * `WLAwareSegmentationTrainer` — `segment` task (YOLO*n-seg.pt). Reuses the
+    detection signal pack: `v8SegmentationLoss` calls the same
+    `get_assigned_targets_and_loss` sync point, `Segment` subclasses `Detect`,
+    and `SegmentationValidator._process_batch(preds, batch)` matches the
+    val-IoU tap — so per-sample box/cls/dfl + box-IoU flow unchanged. Masks are
+    trained on (they ride through the collate) but tracked at the bbox level;
+    per-sample mask signals/overlays are a future addition. Segmentation-only
+    overlay quirks are absorbed by `_ship_round`'s best-effort guards.
 
 Aggregate curves shipped via UL callbacks:
-  * `train/{box,cls,dfl}` from `trainer.loss_items`
-  * `val/{precision,recall,mAP50,mAP50-95,fitness}` from
-    `validator.metrics.results_dict`
+  * `train/{box,cls,dfl}` (+ `train/seg` for segmentation) from
+    `trainer.loss_items`
+  * `val/{precision,recall,mAP50,mAP50-95,fitness}` (+ mask `(M)` variants for
+    segmentation) from `validator.metrics.results_dict`
 """
 from __future__ import annotations
 
@@ -26,29 +38,78 @@
 from torch.nn import Identity
 
 from ultralytics.models.yolo.detect import DetectionTrainer
+from ultralytics.models.yolo.segment import SegmentationTrainer
 
 import weightslab as wl
 from weightslab.backend import ledgers
 
 from .collate import wl_ul_dict_collate
-from .dataset import WLAwareDataset
+from .dataset import WLAwareDataset, WLAwareSegmentationDataset
 from .signals import install_per_sample_signals, install_per_sample_val_signals
 
 
-class WLAwareTrainer(DetectionTrainer):
+# ─── per-task wiring config ─────────────────────────────────────────────
+# `loss_items` order per task (drives both channel names and index mapping):
+#   detect : (box, cls, dfl)
+#   segment: (box, seg, cls, dfl, sem)  ← we ship the first four; sem is 0 for
+#            models without a semantic head.
+_WL_TRAIN_LOSS_NAMES = {
+    "detect": ("box", "cls", "dfl"),
+    "segment": ("box", "seg", "cls", "dfl"),
+}
+
+# (results_dict key -> WL channel key). Segmentation adds the mask `(M)` metrics
+# alongside the box `(B)` ones.
+_WL_VAL_METRICS = {
+    "detect": (
+        ("metrics/precision(B)", "val/precision"),
+        ("metrics/recall(B)", "val/recall"),
+        ("metrics/mAP50(B)", "val/mAP50"),
+        ("metrics/mAP50-95(B)", "val/mAP50-95"),
+        ("fitness", "val/fitness"),
+    ),
+    "segment": (
+        ("metrics/precision(B)", "val/precision"),
+        ("metrics/recall(B)", "val/recall"),
+        ("metrics/mAP50(B)", "val/mAP50"),
+        ("metrics/mAP50-95(B)", "val/mAP50-95"),
+        ("metrics/precision(M)", "val/precision_mask"),
+        ("metrics/recall(M)", "val/recall_mask"),
+        ("metrics/mAP50(M)", "val/mAP50_mask"),
+        ("metrics/mAP50-95(M)", "val/mAP50-95_mask"),
+        ("fitness", "val/fitness"),
+    ),
+}
+
+
+class _WLTrainerMixin:
+    """Shared WL callback wiring + dataloader construction for UL trainers.
+
+    Concrete subclasses set two class attributes:
+      * `_WL_TASK`        — "detect" | "segment"
+      * `_WL_DATASET_CLS` — the `WLAwareDataset` subclass to re-class the
+                            UL-built dataset onto.
+    """
+
+    _WL_TASK = "detect"
+    _WL_DATASET_CLS = WLAwareDataset
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
+        task = self._WL_TASK
+        train_loss_names = _WL_TRAIN_LOSS_NAMES[task]
+        val_metrics = _WL_VAL_METRICS[task]
         state = {"channels": {}}
 
         def _register_channels():
             ch = state["channels"]
-            for n in ("box", "cls", "dfl"):
+            for n in train_loss_names:
                 ch[f"train/{n}"] = wl.watch_or_edit(
                     Identity(), flag="loss", name=f"train/{n}", log=True,
                 )
-            for key in ("precision", "recall", "mAP50", "mAP50-95", "fitness"):
-                ch[f"val/{key}"] = wl.watch_or_edit(
-                    Identity(), flag="metric", name=f"val/{key}", log=True,
+            for _ul_key, wl_key in val_metrics:
+                ch[wl_key] = wl.watch_or_edit(
+                    Identity(), flag="metric", name=wl_key, log=True,
                 )
 
         def _on_train_start(trainer):
@@ -102,8 +163,9 @@ def _on_train_batch_end(trainer):
             ch = state["channels"]
             li = getattr(trainer, "loss_items", None)
             if li is not None and ch:
-                for i, n in enumerate(("box", "cls", "dfl")):
-                    ch[f"train/{n}"](li[i:i+1].detach())
+                for i, n in enumerate(train_loss_names):
+                    if i < li.numel():
+                        ch[f"train/{n}"](li[i:i+1].detach())
             wl.guard_training_context.__exit__(None, None, None)
 
         def _on_val_batch_start(validator):
@@ -118,13 +180,7 @@ def _on_val_end(validator):
             rd = getattr(md, "results_dict", None) if md is not None else None
             if not rd or not ch:
                 return
-            for ul_key, wl_key in (
-                ("metrics/precision(B)", "val/precision"),
-                ("metrics/recall(B)", "val/recall"),
-                ("metrics/mAP50(B)", "val/mAP50"),
-                ("metrics/mAP50-95(B)", "val/mAP50-95"),
-                ("fitness", "val/fitness"),
-            ):
+            for ul_key, wl_key in val_metrics:
                 if ul_key in rd and wl_key in ch:
                     ch[wl_key](torch.tensor([float(rd[ul_key])]))
 
@@ -141,14 +197,14 @@ def validate(self):
         # so loader len reflects the active set. ({}, 0.0) unpacks cleanly into
         # UL's `{**self.metrics, ...}` which (None, None) does not.
         if len(self.test_loader) == 0:
-            print("[WLAwareTrainer] val skipped: all val samples are discarded",
-                  flush=True)
+            print("[%s] val skipped: all val samples are discarded"
+                  % type(self).__name__, flush=True)
             return {}, 0.0
         return super().validate()
 
     def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode="train"):
         dataset = self.build_dataset(dataset_path, mode, batch_size)
-        dataset.__class__ = WLAwareDataset
+        dataset.__class__ = self._WL_DATASET_CLS
         is_train = (mode == "train")
 
         # Get data configuration from ledger
@@ -176,6 +232,26 @@ def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode="train"):
             preload_metadata=cfg.get('preload_metadata', True),
         )
 
-        if not hasattr(loader, "reset"):
-            loader.reset = lambda *a, **k: None
+        # NOTE: no `reset` shim needed here. `loader` is a ledger Proxy whose
+        # __getattr__ raises AttributeError for missing attrs;
+        # DataLoaderInterface.reset() provides the real Ultralytics-compatible
+        # implementation that the proxy forwards to.
         return loader
+
+
+class WLAwareTrainer(_WLTrainerMixin, DetectionTrainer):
+    """WL-aware YOLO **detection** trainer. Drop-in for `YOLO(...).train(trainer=...)`."""
+
+    _WL_TASK = "detect"
+    _WL_DATASET_CLS = WLAwareDataset
+
+
+class WLAwareSegmentationTrainer(_WLTrainerMixin, SegmentationTrainer):
+    """WL-aware YOLO **segmentation** trainer. Drop-in for `YOLO(...-seg.pt).train(trainer=...)`.
+
+    See the module docstring: reuses the detection signal pack; masks train but
+    are tracked at the bbox level for now.
+    """
+
+    _WL_TASK = "segment"
+    _WL_DATASET_CLS = WLAwareSegmentationDataset
diff --git a/weightslab/proto/experiment_service.proto b/weightslab/proto/experiment_service.proto
index 40961ff4..e4d394f0 100644
--- a/weightslab/proto/experiment_service.proto
+++ b/weightslab/proto/experiment_service.proto
@@ -69,6 +69,7 @@ message LoggerDataPoint {
 
 message GetLatestLoggerDataResponse {
   repeated LoggerDataPoint points = 1;  // All points from all metrics
+  string weightslab_version = 2;        // backend weightslab package version (shown in UI logo tooltip)
 }
 
 message Empty {}
@@ -176,6 +177,13 @@ message SaveCheckpointOperation {
   bool save_optimizer = 2;     // also persist optimizer state
 }
 
+// Restarts the backend Python process. The process relies on the container's
+// restart policy (e.g. `restart: unless-stopped`) to come back up, at which
+// point it reloads its last saved checkpoint the same way it does on a fresh
+// container start.
+message RestartInstanceOperation {
+}
+
 message TrainerCommand {
   bool get_hyper_parameters = 4;
   bool get_interactive_layers = 5;
@@ -189,6 +197,7 @@ message TrainerCommand {
   optional DenySamplesOperation remove_eval_from_denylist_operation = 12;
   optional PlotNoteOperation plot_note_operation = 13;
   optional SaveCheckpointOperation save_checkpoint_operation = 14;
+  optional RestartInstanceOperation restart_operation = 15;
 }
 
 message HyperParameterDesc {
diff --git a/weightslab/proto/experiment_service_pb2.py b/weightslab/proto/experiment_service_pb2.py
index 7c109dfa..dd74b19b 100644
--- a/weightslab/proto/experiment_service_pb2.py
+++ b/weightslab/proto/experiment_service_pb2.py
@@ -2,7 +2,7 @@
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # NO CHECKED-IN PROTOBUF GENCODE
 # source: weightslab/proto/experiment_service.proto
-# Protobuf Python Version: 6.31.1
+# Protobuf Python Version: 5.28.1
 """Generated protocol buffer code."""
 from google.protobuf import descriptor as _descriptor
 from google.protobuf import descriptor_pool as _descriptor_pool
@@ -11,8 +11,8 @@
 from google.protobuf.internal import builder as _builder
 _runtime_version.ValidateProtobufRuntimeVersion(
     _runtime_version.Domain.PUBLIC,
-    6,
-    31,
+    5,
+    28,
     1,
     '',
     'weightslab/proto/experiment_service.proto'
@@ -24,7 +24,7 @@
 
 
 
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
 _globals = globals()
 _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
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-  _globals['_AGENTPROVIDERTYPE']._serialized_start=10103
-  _globals['_AGENTPROVIDERTYPE']._serialized_end=10147
+  _globals['_WEIGHTOPERATIONTYPE']._serialized_start=9871
+  _globals['_WEIGHTOPERATIONTYPE']._serialized_end=9971
+  _globals['_ZEROFYPREDICATE']._serialized_start=9973
+  _globals['_ZEROFYPREDICATE']._serialized_end=10084
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+  _globals['_AGENTINTENTTYPE']._serialized_end=10163
+  _globals['_SAMPLEEDITTYPE']._serialized_start=10165
+  _globals['_SAMPLEEDITTYPE']._serialized_end=10238
+  _globals['_AGENTPROVIDERTYPE']._serialized_start=10240
+  _globals['_AGENTPROVIDERTYPE']._serialized_end=10284
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   _globals['_GETLATESTLOGGERDATAREQUEST']._serialized_end=183
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   _globals['_GETLATESTLOGGERDATARESPONSE']._serialized_start=445
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-  _globals['_CANCELEVALUATIONRESPONSE']._serialized_end=9732
-  _globals['_EXPERIMENTSERVICE']._serialized_start=10150
-  _globals['_EXPERIMENTSERVICE']._serialized_end=11547
+  _globals['_GETLATESTLOGGERDATARESPONSE']._serialized_end=536
+  _globals['_EMPTY']._serialized_start=538
+  _globals['_EMPTY']._serialized_end=545
+  _globals['_NEURONID']._serialized_start=547
+  _globals['_NEURONID']._serialized_end=594
+  _globals['_WEIGHTOPERATION']._serialized_start=597
+  _globals['_WEIGHTOPERATION']._serialized_end=870
+  _globals['_WEIGHTSOPERATIONREQUEST']._serialized_start=872
+  _globals['_WEIGHTSOPERATIONREQUEST']._serialized_end=967
+  _globals['_WEIGHTSOPERATIONRESPONSE']._serialized_start=969
+  _globals['_WEIGHTSOPERATIONRESPONSE']._serialized_end=1029
+  _globals['_HYPERPARAMETERS']._serialized_start=1032
+  _globals['_HYPERPARAMETERS']._serialized_end=1737
+  _globals['_METRICSSTATUS']._serialized_start=1739
+  _globals['_METRICSSTATUS']._serialized_end=1783
+  _globals['_ANNOTATSTATUS']._serialized_start=1785
+  _globals['_ANNOTATSTATUS']._serialized_end=1911
+  _globals['_ANNOTATSTATUS_METADATAENTRY']._serialized_start=1864
+  _globals['_ANNOTATSTATUS_METADATAENTRY']._serialized_end=1911
+  _globals['_TRAININGSTATUSEX']._serialized_start=1914
+  _globals['_TRAININGSTATUSEX']._serialized_end=2186
+  _globals['_HYPERPARAMETERCOMMAND']._serialized_start=2188
+  _globals['_HYPERPARAMETERCOMMAND']._serialized_end=2281
+  _globals['_DENYSAMPLESOPERATION']._serialized_start=2283
+  _globals['_DENYSAMPLESOPERATION']._serialized_end=2345
+  _globals['_LOADCHECKPOINTOPERATION']._serialized_start=2347
+  _globals['_LOADCHECKPOINTOPERATION']._serialized_end=2395
+  _globals['_PLOTNOTEOPERATION']._serialized_start=2397
+  _globals['_PLOTNOTEOPERATION']._serialized_end=2495
+  _globals['_SAVECHECKPOINTOPERATION']._serialized_start=2497
+  _globals['_SAVECHECKPOINTOPERATION']._serialized_end=2573
+  _globals['_RESTARTINSTANCEOPERATION']._serialized_start=2575
+  _globals['_RESTARTINSTANCEOPERATION']._serialized_end=2601
+  _globals['_TRAINERCOMMAND']._serialized_start=2604
+  _globals['_TRAINERCOMMAND']._serialized_end=3641
+  _globals['_HYPERPARAMETERDESC']._serialized_start=3644
+  _globals['_HYPERPARAMETERDESC']._serialized_end=3801
+  _globals['_NEURONSTATISTICS']._serialized_start=3804
+  _globals['_NEURONSTATISTICS']._serialized_end=4174
+  _globals['_NEURONSTATISTICS_INCOMINGLRENTRY']._serialized_start=4033
+  _globals['_NEURONSTATISTICS_INCOMINGLRENTRY']._serialized_end=4082
+  _globals['_LAYERREPRESENTATION']._serialized_start=4177
+  _globals['_LAYERREPRESENTATION']._serialized_end=4545
+  _globals['_ACTIVATIONREQUEST']._serialized_start=4547
+  _globals['_ACTIVATIONREQUEST']._serialized_end=4619
+  _globals['_ACTIVATIONMAP']._serialized_start=4621
+  _globals['_ACTIVATIONMAP']._serialized_end=4693
+  _globals['_ACTIVATIONRESPONSE']._serialized_start=4695
+  _globals['_ACTIVATIONRESPONSE']._serialized_end=4795
+  _globals['_TASKFIELD']._serialized_start=4798
+  _globals['_TASKFIELD']._serialized_end=4945
+  _globals['_RECORDMETADATA']._serialized_start=4948
+  _globals['_RECORDMETADATA']._serialized_end=5280
+  _globals['_RECORDMETADATA_SAMPLELASTLOSSENTRY']._serialized_start=5227
+  _globals['_RECORDMETADATA_SAMPLELASTLOSSENTRY']._serialized_end=5280
+  _globals['_SAMPLESTATISTICS']._serialized_start=5283
+  _globals['_SAMPLESTATISTICS']._serialized_end=5430
+  _globals['_COMMANDRESPONSE']._serialized_start=5433
+  _globals['_COMMANDRESPONSE']._serialized_end=5663
+  _globals['_SAMPLEREQUEST']._serialized_start=5665
+  _globals['_SAMPLEREQUEST']._serialized_end=5750
+  _globals['_SAMPLEREQUESTRESPONSE']._serialized_start=5753
+  _globals['_SAMPLEREQUESTRESPONSE']._serialized_end=6054
+  _globals['_BATCHSAMPLEREQUEST']._serialized_start=6057
+  _globals['_BATCHSAMPLEREQUEST']._serialized_end=6203
+  _globals['_BATCHSAMPLERESPONSE']._serialized_start=6205
+  _globals['_BATCHSAMPLERESPONSE']._serialized_end=6267
+  _globals['_WEIGHTSREQUEST']._serialized_start=6269
+  _globals['_WEIGHTSREQUEST']._serialized_end=6315
+  _globals['_WEIGHTSRESPONSE']._serialized_start=6318
+  _globals['_WEIGHTSRESPONSE']._serialized_end=6603
+  _globals['_DATAQUERYREQUEST']._serialized_start=6605
+  _globals['_DATAQUERYREQUEST']._serialized_end=6687
+  _globals['_CATEGORICALTAGDEF']._serialized_start=6689
+  _globals['_CATEGORICALTAGDEF']._serialized_end=6742
+  _globals['_DATAQUERYRESPONSE']._serialized_start=6745
+  _globals['_DATAQUERYRESPONSE']._serialized_end=7042
+  _globals['_DATASAMPLESREQUEST']._serialized_start=7045
+  _globals['_DATASAMPLESREQUEST']._serialized_end=7239
+  _globals['_DATASTAT']._serialized_start=7241
+  _globals['_DATASTAT']._serialized_end=7350
+  _globals['_DATARECORD']._serialized_start=7352
+  _globals['_DATARECORD']._serialized_end=7414
+  _globals['_DATASAMPLESRESPONSE']._serialized_start=7416
+  _globals['_DATASAMPLESRESPONSE']._serialized_end=7506
+  _globals['_HISTOGRAMSUBBAR']._serialized_start=7508
+  _globals['_HISTOGRAMSUBBAR']._serialized_end=7575
+  _globals['_HISTOGRAMBIN']._serialized_start=7577
+  _globals['_HISTOGRAMBIN']._serialized_end=7681
+  _globals['_CATEGORICALHISTOGRAMBAR']._serialized_start=7683
+  _globals['_CATEGORICALHISTOGRAMBAR']._serialized_end=7774
+  _globals['_HISTOGRAMREQUEST']._serialized_start=7776
+  _globals['_HISTOGRAMREQUEST']._serialized_end=7828
+  _globals['_HISTOGRAMRESPONSE']._serialized_start=7831
+  _globals['_HISTOGRAMRESPONSE']._serialized_end=8009
+  _globals['_GETMETADATAREQUEST']._serialized_start=8011
+  _globals['_GETMETADATAREQUEST']._serialized_end=8098
+  _globals['_GETMETADATARESPONSE']._serialized_start=8101
+  _globals['_GETMETADATARESPONSE']._serialized_end=8254
+  _globals['_POINTCLOUDREQUEST']._serialized_start=8256
+  _globals['_POINTCLOUDREQUEST']._serialized_end=8330
+  _globals['_POINTCLOUDCHUNK']._serialized_start=8333
+  _globals['_POINTCLOUDCHUNK']._serialized_end=8524
+  _globals['_DATAEDITSREQUEST']._serialized_start=8527
+  _globals['_DATAEDITSREQUEST']._serialized_end=8747
+  _globals['_DATAEDITSRESPONSE']._serialized_start=8749
+  _globals['_DATAEDITSRESPONSE']._serialized_end=8802
+  _globals['_DATASPLITSRESPONSE']._serialized_start=8804
+  _globals['_DATASPLITSRESPONSE']._serialized_end=8862
+  _globals['_AGENTHEALTHRESPONSE']._serialized_start=8864
+  _globals['_AGENTHEALTHRESPONSE']._serialized_end=8921
+  _globals['_INITIALIZEAGENTREQUEST']._serialized_start=8923
+  _globals['_INITIALIZEAGENTREQUEST']._serialized_end=9017
+  _globals['_INITIALIZEAGENTRESPONSE']._serialized_start=9019
+  _globals['_INITIALIZEAGENTRESPONSE']._serialized_end=9078
+  _globals['_CHANGEAGENTMODELREQUEST']._serialized_start=9080
+  _globals['_CHANGEAGENTMODELREQUEST']._serialized_end=9120
+  _globals['_CHANGEAGENTMODELRESPONSE']._serialized_start=9122
+  _globals['_CHANGEAGENTMODELRESPONSE']._serialized_end=9182
+  _globals['_GETAGENTMODELSREQUEST']._serialized_start=9184
+  _globals['_GETAGENTMODELSREQUEST']._serialized_end=9207
+  _globals['_GETAGENTMODELSRESPONSE']._serialized_start=9209
+  _globals['_GETAGENTMODELSRESPONSE']._serialized_end=9283
+  _globals['_RESETAGENTRESPONSE']._serialized_start=9285
+  _globals['_RESETAGENTRESPONSE']._serialized_end=9339
+  _globals['_RESTORECHECKPOINTREQUEST']._serialized_start=9341
+  _globals['_RESTORECHECKPOINTREQUEST']._serialized_end=9392
+  _globals['_RESTORECHECKPOINTRESPONSE']._serialized_start=9394
+  _globals['_RESTORECHECKPOINTRESPONSE']._serialized_end=9455
+  _globals['_TRIGGEREVALUATIONREQUEST']._serialized_start=9457
+  _globals['_TRIGGEREVALUATIONREQUEST']._serialized_end=9539
+  _globals['_TRIGGEREVALUATIONRESPONSE']._serialized_start=9541
+  _globals['_TRIGGEREVALUATIONRESPONSE']._serialized_end=9602
+  _globals['_GETEVALUATIONSTATUSREQUEST']._serialized_start=9604
+  _globals['_GETEVALUATIONSTATUSREQUEST']._serialized_end=9632
+  _globals['_GETEVALUATIONSTATUSRESPONSE']._serialized_start=9635
+  _globals['_GETEVALUATIONSTATUSRESPONSE']._serialized_end=9764
+  _globals['_CANCELEVALUATIONREQUEST']._serialized_start=9766
+  _globals['_CANCELEVALUATIONREQUEST']._serialized_end=9807
+  _globals['_CANCELEVALUATIONRESPONSE']._serialized_start=9809
+  _globals['_CANCELEVALUATIONRESPONSE']._serialized_end=9869
+  _globals['_EXPERIMENTSERVICE']._serialized_start=10287
+  _globals['_EXPERIMENTSERVICE']._serialized_end=11684
 # @@protoc_insertion_point(module_scope)
diff --git a/weightslab/proto/experiment_service_pb2_grpc.py b/weightslab/proto/experiment_service_pb2_grpc.py
index e75d391c..440a5a51 100644
--- a/weightslab/proto/experiment_service_pb2_grpc.py
+++ b/weightslab/proto/experiment_service_pb2_grpc.py
@@ -5,7 +5,7 @@
 
 from weightslab.proto import experiment_service_pb2 as weightslab_dot_proto_dot_experiment__service__pb2
 
-GRPC_GENERATED_VERSION = '1.76.0'
+GRPC_GENERATED_VERSION = '1.68.1'
 GRPC_VERSION = grpc.__version__
 _version_not_supported = False
 
@@ -18,7 +18,7 @@
 if _version_not_supported:
     raise RuntimeError(
         f'The grpc package installed is at version {GRPC_VERSION},'
-        + ' but the generated code in weightslab/proto/experiment_service_pb2_grpc.py depends on'
+        + f' but the generated code in weightslab/proto/experiment_service_pb2_grpc.py depends on'
         + f' grpcio>={GRPC_GENERATED_VERSION}.'
         + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}'
         + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.'
diff --git a/weightslab/security/cert_auth_manager.py b/weightslab/security/cert_auth_manager.py
index 85c63b34..168072c4 100644
--- a/weightslab/security/cert_auth_manager.py
+++ b/weightslab/security/cert_auth_manager.py
@@ -123,24 +123,34 @@ def generate_certs(self, force: bool = False) -> Tuple[bool, str]:
             logger.info(f"Certificates already exist in {self.certs_dir}")
             return True, f"Certificates already exist in {self.certs_dir}"
 
-        # Try to use PowerShell to generate certs
+        # Use the bundled cert-generation scripts under weightslab/ui/utils.
         try:
-            script_dir = Path(__file__).parent.parent.parent / 'docker' / 'docker' / 'utils'
-            generate_script = script_dir / 'generate-certs.ps1'
-
-            if not generate_script.exists():
-                return False, f"Certificate generation script not found at {generate_script}"
-
-            cmd = ['powershell', '-NoProfile', '-ExecutionPolicy', 'Bypass', '-File', str(generate_script)]
-            if force:
-                cmd.append('-force_create_certs')
+            script_dir = Path(__file__).parent.parent / 'ui' / 'utils'
+            env = os.environ.copy()
+            env['WEIGHTSLAB_CERTS_DIR'] = str(self.certs_dir)
+
+            if os.name == 'nt':
+                generate_script = script_dir / 'generate-certs-auth-token.ps1'
+                if not generate_script.exists():
+                    return False, f"Certificate generation script not found at {generate_script}"
+                cmd = ['powershell', '-NoProfile', '-ExecutionPolicy', 'Bypass', '-File', str(generate_script)]
+                if force:
+                    cmd.append('-ForceCreateCerts')
+            else:
+                generate_script = script_dir / 'generate-certs-auth-token.sh'
+                if not generate_script.exists():
+                    return False, f"Certificate generation script not found at {generate_script}"
+                cmd = ['bash', str(generate_script)]
+                if force:
+                    cmd.append('--force-create-certs')
 
             result = subprocess.run(
                 cmd,
                 capture_output=True,
                 text=True,
                 check=False,
-                cwd=str(script_dir)
+                cwd=str(script_dir),
+                env=env,
             )
 
             if result.returncode != 0:
@@ -214,10 +224,6 @@ def setup_tls_environment(self) -> dict:
             'GRPC_TLS_KEY_FILE': str(self.key_file),
             'GRPC_TLS_CA_FILE': str(self.ca_file),
             'WEIGHTSLAB_CERTS_DIR': str(self.certs_dir),
-            'ENVOY_DOWNSTREAM_TLS': 'on',
-            'ENVOY_UPSTREAM_TLS': 'on',
-            'WS_SERVER_PROTOCOL': 'https',
-            'VITE_SERVER_PROTOCOL': 'https',
         }
 
         return env_vars
@@ -234,12 +240,9 @@ def setup_auth_environment(self) -> dict:
         if self.enable_auth:
             token = self.get_or_create_auth_token()
             env_vars['WL_ENABLE_GRPC_AUTH_TOKEN'] = '1'
-            env_vars['VITE_WL_ENABLE_GRPC_AUTH_TOKEN'] = '1'
             env_vars['GRPC_AUTH_TOKEN'] = token
-            env_vars['VITE_GRPC_AUTH_TOKEN'] = token
         else:
             env_vars['WL_ENABLE_GRPC_AUTH_TOKEN'] = '0'
-            env_vars['VITE_WL_ENABLE_GRPC_AUTH_TOKEN'] = '0'
 
         return env_vars
 
@@ -252,7 +255,7 @@ def check_and_apply(self) -> Tuple[bool, str]:
             Tuple of (success, message)
         """
         if not self.has_valid_certs():
-            return False, f"No certs found in {self.certs_dir}. Run: weightslab ui se"
+            return False, f"No certs found in {self.certs_dir}. Run: weightslab se"
 
         env_vars = self.setup_tls_environment()
         env_vars.update(self.setup_auth_environment())
@@ -266,7 +269,7 @@ def check_and_apply(self) -> Tuple[bool, str]:
     def setup_secure_environment(self, force_certs: bool = False) -> Tuple[bool, str]:
         """
         Generate TLS certificates and auth token.
-        Called explicitly by 'weightslab ui se' command.
+        Called explicitly by 'weightslab se' command.
 
         Args:
             force_certs: Force regenerate certificates
diff --git a/weightslab/src.py b/weightslab/src.py
index aa5a39b0..5601c867 100644
--- a/weightslab/src.py
+++ b/weightslab/src.py
@@ -14,6 +14,7 @@
 import tempfile
 import functools
 import threading
+import queue as _queue
 import traceback
 import numpy as np
 import torch as th
@@ -24,12 +25,12 @@
 from weightslab.backend.dataloader_interface import DataLoaderInterface
 from weightslab.components.checkpoint_manager import CheckpointManager
 from weightslab.backend.optimizer_interface import OptimizerInterface
-from weightslab.backend.ledgers import DEFAULT_NAME, get_checkpoint_manager, get_model, get_dataloader, get_dataframe, get_optimizer, register_hyperparams, watch_hyperparams_file, get_hyperparams, register_logger, get_logger, register_signal, get_signal, list_models
+from weightslab.backend.ledgers import DEFAULT_NAME, get_checkpoint_manager, get_model, get_dataloader, get_dataframe, get_optimizer, register_hyperparams, watch_hyperparams_file, get_hyperparams, register_logger, get_logger, register_signal, get_signal, list_models, has_serving_config
 from weightslab.backend.model_interface import ModelInterface
 from weightslab.trainer.trainer_services import grpc_serve
 from weightslab.data.sample_stats import SampleStatsEx
 from weightslab.utils.logs import set_log_directory
-from weightslab.utils.tools import detach_to_cpu
+from weightslab.utils.tools import detach_to_cpu, _running_in_notebook
 from weightslab.backend.logger import LoggerQueue
 from weightslab.backend.cli import cli_serve
 from weightslab.backend import ledgers
@@ -71,6 +72,231 @@ def _rebind_caller_local(original_obj: Any, new_obj: Any) -> None:
 DATAFRAME_M = None
 # Global registry for custom signals
 _REGISTERED_SIGNALS = {}
+_REACTIVE_FIRED = {}   # signal_name -> step it last fired at (per-step dedup)
+
+
+def _gather_inputs_fresh(lg, inputs, ids, step, fresh_cache=None):
+    """``{name: (B,) array}`` if every input has a value at *step* for every id,
+    else ``None`` (value AT step, not latest — a lagging worker still gathers it).
+    Reads via ``query_per_sample_at_step`` (O(batch), cached).
+
+    *fresh_cache*: in-memory values fired this pass — read them so the pass
+    persists once at the end, not per signal."""
+    cols = {}
+    for inp in inputs:
+        if fresh_cache is not None and inp in fresh_cache:
+            cols[inp] = fresh_cache[inp]
+            continue
+        # A reactive-derived input lives only in fresh_cache this pass (it's not
+        # in the ledger at this step until the end-of-pass persist). If it's not
+        # there yet, skip — don't query the ledger (a guaranteed-miss flush).
+        _m = getattr(_REGISTERED_SIGNALS.get(inp), '_wl_signal_meta', {})
+        if _m.get('inputs'):
+            return None
+        at = {int(sid): val for sid, val in lg.query_per_sample_at_step(inp, ids, step)}
+        for sid in ids:
+            if sid not in at:
+                return None
+        cols[inp] = np.array([at[sid] for sid in ids], dtype=float)
+    return cols
+
+
+def _react_dependents(seed_names, batch_ids, step, origin='train'):
+    """Reactive signal firing. When the signals in *seed_names* were just logged,
+    fire any ``@wl.signal(inputs=[...])`` whose inputs are now ALL present at
+    *step* — order-independently, regardless of which input arrived last. Each
+    fired signal is itself a seed (chaining) and fires at most once per step.
+    Inputs are read from the logger, so ingested signals must be logged."""
+    _warn_on_signal_cycles()  # lazy, once/process — fires even with no wl.serve
+    if batch_ids is None:
+        return
+    try:
+        ids = [int(u) for u in (batch_ids.detach().cpu().numpy()
+                                if hasattr(batch_ids, 'detach') else batch_ids)]
+    except Exception:
+        return
+    lg = get_logger()
+    if lg is None:
+        return
+    try:
+        df_proxy = get_dataframe()
+    except Exception:
+        df_proxy = None
+
+    def dependents_of(nm):
+        for name, func in list(_REGISTERED_SIGNALS.items()):
+            meta = getattr(func, '_wl_signal_meta', {})
+            inputs = meta.get('inputs')
+            if inputs and nm in inputs:
+                yield name, func, meta, inputs
+
+    # Fired this pass; kept in-memory so chains read without a ledger round-trip
+    # and the whole pass persists in ONE save_signals (not one write per signal).
+    fired = {}          # {name: (B,) array}
+    fired_log = {}      # {name: log flag}
+    queue = list(seed_names)
+    while queue:
+        logged = queue.pop()
+        for name, func, meta, inputs in dependents_of(logged):
+            if _REACTIVE_FIRED.get(name) == step:
+                continue
+            every = meta.get('compute_every_n_steps', 1) or 1
+            if step % every != 0:
+                continue
+            cols = _gather_inputs_fresh(lg, inputs, ids, step, fresh_cache=fired)
+            if cols is None:
+                continue  # inputs not all fresh yet — a later log will re-trigger
+            _REACTIVE_FIRED[name] = step
+            try:
+                if meta.get('batched'):
+                    bctx = BatchSignalContext(sample_ids=ids, subscribed_values=cols[inputs[0]],
+                                              logger=lg, dataframe=df_proxy, origin=origin, step=step,
+                                              inputs=cols)
+                    res = func(bctx)
+                    vals = res.tolist() if hasattr(res, 'tolist') else list(res)
+                else:
+                    vals = []
+                    for i, sid in enumerate(ids):
+                        ctx = SignalContext(sample_id=sid, subscribed_value=float(cols[inputs[0]][i]),
+                                            logger=lg, dataframe=df_proxy, origin=origin, step=step,
+                                            inputs={k: float(cols[k][i]) for k in cols})
+                        vals.append(func(ctx))
+                vals = [float(x) for x in vals]
+            except StaleSignalError:
+                raise
+            except Exception as e:
+                logger.debug(f"reactive signal '{name}' failed: {e}")
+                continue
+            fired[name] = np.asarray(vals, dtype=float)
+            fired_log[name] = bool(meta.get('log', True))
+            queue.append(name)
+
+    # One persist for all signals fired this pass. step=step attributes values to
+    # the firing step (matters when the worker lags). _react=False: no re-dispatch.
+    if fired:
+        try:
+            _logged = {n: v.tolist() for n, v in fired.items() if fired_log.get(n, True)}
+            _unlogged = {n: v.tolist() for n, v in fired.items() if not fired_log.get(n, True)}
+            if _logged:
+                save_signals(signals=_logged, batch_ids=ids, step=step, log=True, _react=False)
+            if _unlogged:
+                save_signals(signals=_unlogged, batch_ids=ids, step=step, log=False, _react=False)
+        except Exception as e:
+            logger.debug(f"reactive batched persist failed: {e}")
+
+
+def _detect_signal_cycles():
+    """Cycles in the reactive ``inputs`` graph, each as a name list ending where
+    it began. Only edges to other registered signals count (base metrics are
+    leaves). A signal in a cycle never fires — surfacing it beats silent no-fire."""
+    graph = {}
+    for name, func in _REGISTERED_SIGNALS.items():
+        meta = getattr(func, '_wl_signal_meta', {})
+        inputs = meta.get('inputs') or []
+        graph[name] = [dep for dep in inputs if dep in _REGISTERED_SIGNALS]  # depends-on edges
+    WHITE, GREY, BLACK = 0, 1, 2
+    color = {n: WHITE for n in graph}
+    stack, cycles, seen = [], [], set()
+
+    def dfs(n):
+        color[n] = GREY
+        stack.append(n)
+        for dep in graph.get(n, ()):
+            if color.get(dep) == GREY:                 # back-edge → cycle
+                cyc = stack[stack.index(dep):] + [dep]
+                key = frozenset(cyc)
+                if key not in seen:
+                    seen.add(key)
+                    cycles.append(cyc)
+            elif color.get(dep) == WHITE:
+                dfs(dep)
+        stack.pop()
+        color[n] = BLACK
+
+    for n in graph:
+        if color[n] == WHITE:
+            dfs(n)
+    return cycles
+
+
+_SIGNAL_CYCLE_CHECKED = False
+
+
+def _warn_on_signal_cycles(force=False):
+    """Warn once per process for each reactive cycle. Fired from start_training
+    and the first dispatch (so it works headless). Warns, doesn't raise."""
+    global _SIGNAL_CYCLE_CHECKED
+    if _SIGNAL_CYCLE_CHECKED and not force:
+        return
+    _SIGNAL_CYCLE_CHECKED = True
+    for cyc in _detect_signal_cycles():
+        logger.warning(
+            "WeightsLab reactive signals: circular dependency %s — these signals "
+            "will NEVER fire (each waits on the other's value, which never becomes "
+            "fresh). Break the cycle in their @wl.signal(inputs=[...]).",
+            " -> ".join(cyc))
+
+
+# --- optional signal-worker thread: run reactive dispatch off the train thread -
+_SIGNAL_WORKER = {"q": None, "thread": None}
+
+
+def _signal_worker_enabled():
+    try:
+        return bool(get_hyperparams().get("ledger_signal_worker", False))
+    except Exception:
+        return False
+
+
+def _ensure_signal_worker():
+    if _SIGNAL_WORKER["thread"] is not None and _SIGNAL_WORKER["thread"].is_alive():
+        return _SIGNAL_WORKER["q"]
+    q = _queue.Queue()
+
+    def _worker():
+        while True:
+            job = q.get()
+            if job is None:
+                q.task_done()
+                break
+            seed_names, ids, step, origin = job
+            try:
+                _react_dependents(seed_names, ids, step, origin)
+            except Exception as e:
+                logger.debug(f"signal worker job failed: {e}")
+            finally:
+                q.task_done()
+
+    t = threading.Thread(target=_worker, name="WL-Signal-Worker", daemon=True)
+    t.start()
+    _SIGNAL_WORKER["q"] = q
+    _SIGNAL_WORKER["thread"] = t
+    return q
+
+
+def _dispatch_or_enqueue(seed_names, batch_ids, step, origin):
+    """Reactive dispatch: inline on the train thread, or handed to the signal
+    worker thread when ``ledger_signal_worker`` is set. The seed signals are
+    already logged synchronously, so the worker only defers the derived compute
+    + persistence — the inputs it reads are already in the ledger."""
+    if not _signal_worker_enabled():
+        _react_dependents(seed_names, batch_ids, step, origin)
+        return
+    try:
+        ids = [int(u) for u in (batch_ids.detach().cpu().numpy()
+                                if hasattr(batch_ids, 'detach') else batch_ids)]
+    except Exception:
+        return
+    _ensure_signal_worker().put((list(seed_names), ids, step, origin))
+
+
+def drain_signals():
+    """Block until the signal-worker thread has processed all queued reactive
+    jobs. Called automatically by :func:`write_dataframe`; call it manually
+    before reading signals mid-run when ``ledger_signal_worker`` is enabled."""
+    q = _SIGNAL_WORKER["q"]
+    if q is not None:
+        q.join()
 
 # Evaluation function registered via the @wl.eval_fn decorator.
 _REGISTERED_EVAL_FN: Optional[Any] = None
@@ -78,18 +304,36 @@ def _rebind_caller_local(original_obj: Any, new_obj: Any) -> None:
 _EVAL_WORKER_THREAD: Optional[threading.Thread] = None
 
 
+class StaleSignalError(RuntimeError):
+    """Raised when a signal ingests another signal that is not *fresh* — i.e. the
+    ingested signal has no value at the current step (it was not logged, or was
+    written after the trigger this step). See ``SignalContext.latest`` /
+    ``BatchSignalContext.latest`` (``require_fresh=True``) and
+    ``@wl.signal(ingests=[...])``."""
+    pass
+
+
 class SignalContext:
     """
     Unified context object for WeightsLab signals.
     Carries all available metadata for a single sample during computation.
     """
-    def __init__(self, sample_id, dataframe, data=None, subscribed_value=None, logger=None, origin=None):
+    def __init__(self, sample_id, dataframe, data=None, subscribed_value=None, logger=None, origin=None,
+                 step=None, logits=None, preds=None, targets=None, inputs=None):
         self.sample_id = sample_id
         self.dataframe = dataframe
         self.data = data
         self.subscribed_value = subscribed_value
         self.logger = logger
         self.origin = origin
+        self.step = step          # step the trigger fired at (for freshness checks)
+        self.logits = logits      # this sample's raw model output row (subscribe_to path)
+        self.preds = preds
+        self.targets = targets
+        # Current-step value of each declared @wl.signal(inputs=[...]) input for
+        # THIS sample, keyed by signal name: ``ctx.inputs["sig/entropy"]``. Empty
+        # for signals that don't declare inputs (e.g. the subscribe_to path).
+        self.inputs = inputs if inputs is not None else {}
 
     @property
     def image(self) -> Optional[np.ndarray]:
@@ -163,6 +407,91 @@ def is_dynamic(self) -> bool:
         """True if running during training (triggered by a metric)."""
         return self.subscribed_value is not None
 
+    def latest(self, signal_name, default=float("nan"), require_fresh=False):
+        """Most recent value of ANOTHER signal for THIS sample. Lets a signal
+        ingest several other signals: read each with ``ctx.latest(name)`` and
+        combine. Returns *default* if the signal has no value yet.
+
+        With ``require_fresh=True`` this raises if the ingested signal has no
+        value at the current step (``self.step``) — i.e. it was not logged, or
+        was written after (not before) the trigger this step.
+        """
+        if self.logger is None:
+            return default
+        rows = self.logger.query_per_sample(signal_name, sample_ids=[self.sample_id])
+        if require_fresh and (not rows or self.step is None or rows[-1][1] != self.step):
+            raise StaleSignalError(
+                f"ingested signal '{signal_name}' is not fresh at step {self.step} for "
+                f"sample {self.sample_id} (log it with log=True and write it BEFORE the "
+                f"subscribing signal fires).")
+        return rows[-1][2] if rows else default   # rows ordered by seq -> last is newest
+
+
+class BatchSignalContext:
+    """Batched context for a vectorized ``@wl.signal(batched=True)``.
+
+    Instead of one :class:`SignalContext` per sample, a batched signal receives
+    the whole batch at once: ``sample_ids`` and ``subscribed_values`` are arrays
+    of length B, so the signal computes over all samples with vector ops and
+    returns one array of length B. It also exposes **batched** ledger reads
+    (:meth:`history` runs a single query for the whole batch instead of one query
+    per sample), which is where the large speed-ups come from.
+    """
+    def __init__(self, sample_ids, subscribed_values, logger=None, dataframe=None, origin=None,
+                 step=None, logits=None, preds=None, targets=None, inputs=None):
+        self.sample_ids = [int(s) for s in sample_ids]          # length B
+        self.subscribed_values = np.asarray(subscribed_values, dtype=float)  # (B,)
+        self.logger = logger
+        self.dataframe = dataframe
+        self.origin = origin
+        self.step = step          # step the trigger fired at (for freshness checks)
+        # Raw model output for this batch (subscribe_to path): logit-derived
+        # signals compute from these, no save_signals. None on the inputs=[...] path.
+        self.logits = logits      # (B, C)
+        self.preds = preds
+        self.targets = targets
+        # Current-step values of each declared @wl.signal(inputs=[...]) input for
+        # the whole batch, keyed by signal name: ``ctx.inputs["sig/entropy"]`` is a
+        # ``(B,)`` array aligned to ``sample_ids``. Populated by the reactive
+        # dispatch; empty for signals that don't declare inputs.
+        self.inputs = inputs if inputs is not None else {}
+
+    def history(self, signal_name):
+        """Per-sample history of *signal_name* for every sample in the batch, in
+        a SINGLE ledger query. Returns ``{sample_id: [values in step order]}``
+        (empty list for samples with no history yet)."""
+        out = {s: [] for s in self.sample_ids}
+        if self.logger is None:
+            return out
+        # query_per_sample accepts a list of ids -> one scan for the whole batch.
+        for sid, step, val, _ in self.logger.query_per_sample(signal_name, sample_ids=self.sample_ids):
+            out.setdefault(int(sid), []).append(val)   # rows already ordered by seq (= step order)
+        return out
+
+    def latest(self, signal_name, default=float("nan"), require_fresh=False):
+        """Most recent value of ANOTHER signal for each sample in the batch, in a
+        single batched query. ``(B,)`` array aligned to ``self.sample_ids``. Use
+        this to build a signal that ingests several other signals — call it once
+        per input signal, then combine the arrays with vector ops.
+
+        With ``require_fresh=True`` this raises unless EVERY sample has a value of
+        *signal_name* at the current step (``self.step``) — catching a stale
+        ingest (input not logged, or written after the trigger this step).
+        """
+        last = {}
+        if self.logger is not None:
+            for sid, step, val, _ in self.logger.query_per_sample(signal_name, sample_ids=self.sample_ids):
+                last[int(sid)] = (step, val)   # rows ordered by seq -> last assignment wins
+        if require_fresh:
+            stale = [s for s in self.sample_ids
+                     if s not in last or self.step is None or last[s][0] != self.step]
+            if stale:
+                raise StaleSignalError(
+                    f"ingested signal '{signal_name}' is not fresh at step {self.step} for "
+                    f"{len(stale)}/{len(self.sample_ids)} sample(s) (e.g. {stale[:3]}). Log it "
+                    f"with log=True and write it BEFORE the subscribing signal fires.")
+        return np.array([last.get(s, (None, default))[1] for s in self.sample_ids], dtype=float)
+
 
 # #####################################################################################################################
 # WEIGHTSLAB INTERNAL FUNCTIONS FOR LOGGING, SIGNAL EXTRACTION, WRAPPING, ETC. (not typically called directly by users)
@@ -273,9 +602,21 @@ def _extract_scalar_from_tensor(batch_scalar: th.Tensor | np.ndarray, out: th.Te
                     batch_scalar = _tmp
                 except Exception:
                     pass
-            # Merged batch scalar with ids
+            # Merged batch scalar with ids. Vectorize the tensor->python
+            # conversion: one .tolist() (a single device sync) instead of a
+            # per-element .item() in a comprehension (B device syncs + B python
+            # calls). This is on the per-step hot path — profiling showed the
+            # per-element version was a top self-time cost.
             if isinstance(batch_scalar, (th.Tensor, np.ndarray)) and ids is not None and len(batch_scalar) == len(ids):
-                batch_scalar = {ids[i].item() if isinstance(ids, th.Tensor) else ids[i]: batch_scalar[i].item() for i in range(len(batch_scalar))}
+                _vals = (batch_scalar.detach().cpu().tolist() if isinstance(batch_scalar, th.Tensor)
+                         else np.asarray(batch_scalar).tolist())
+                if isinstance(ids, th.Tensor):
+                    _keys = ids.detach().cpu().tolist()
+                elif isinstance(ids, np.ndarray):
+                    _keys = ids.tolist()
+                else:
+                    _keys = list(ids)
+                batch_scalar = dict(zip(_keys, _vals))
         # 2. Otherwise fall back to extracting from 'out'
         elif out is not None:
             if isinstance(out, th.Tensor):
@@ -466,6 +807,8 @@ def wrappered_fwd(original_forward, kwargs, reg_name, *a, **kw):
 
     # Extract torch function parameters
     _ = wl_kw.get('flag')
+    # Kept in-memory for ctx.logits (logit-derived signals); NOT persisted unless
+    # ledger_store_preds_raw is set (see the gated save_signals below).
     preds_raw = a[0] if len(a) > 0 else None
 
     # User parameters
@@ -620,39 +963,87 @@ def wrappered_fwd(original_forward, kwargs, reg_name, *a, **kw):
                  for name, func in subscribers:
                      meta = getattr(func, '_wl_signal_meta', {})
                      compute_every = meta.get('compute_every_n_steps', 1)
+                     min_step = meta.get('min_step', 0)
+
+                     # Warm-up gate: don't fire until we've reached min_step
+                     # (e.g. a signal that needs enough per-sample history).
+                     if current_step < min_step:
+                         continue
 
                      # Frequency Check
                      if current_step % compute_every != 0:
                          continue
 
                      try:
-                         batch_res = {}
-                         for i, uid in enumerate(ids_np):
-                             # Generic 'value' argument
-                             val = float(val_vec[i])
-
-                             # Unified Context Pattern
-                             ctx = SignalContext(
-                                 sample_id=int(uid),
-                                 subscribed_value=val,
-                                 logger=ledgers.get_logger(),
+                         _lg = ledgers.get_logger()
+                         ingests = meta.get('ingests') or []
+                         if meta.get('batched'):
+                             # Vectorized path: build ONE context for the whole
+                             # batch and call the signal once. It returns a length-B
+                             # array. Avoids B Python calls + B SignalContext allocs,
+                             # and lets the signal do batched ledger reads.
+                             bctx = BatchSignalContext(
+                                 sample_ids=[int(u) for u in ids_np],
+                                 subscribed_values=[float(v) for v in val_vec],
+                                 logger=_lg,
                                  dataframe=df_proxy,
-                                 origin=kwargs.get('origin', 'train')
+                                 origin=kwargs.get('origin', 'train'),
+                                 step=step,
+                                 logits=preds_raw.detach() if hasattr(preds_raw, 'detach') else preds_raw,
+                                 preds=preds.detach() if hasattr(preds, 'detach') else preds,
+                                 targets=targets.detach() if hasattr(targets, 'detach') else targets,
                              )
-                             try:
-                                 res = func(ctx) # Compute per sample result with unified context
-                             except TypeError:
-                                 # Fallback for legacy subscriber functions
-                                 res = func(sample_id=int(uid), value=val, dataframe=df_proxy)
-
-                             batch_res[uid] = res
-                         signal_value = list(batch_res.values())
+                             # Enforce that declared ingested signals are fresh.
+                             for dep in ingests:
+                                 bctx.latest(dep, require_fresh=True)
+                             res = func(bctx)
+                             res = res.tolist() if hasattr(res, 'tolist') else list(res)
+                             signal_value = [float(x) for x in res]
+                         else:
+                             # Validate declared ingests once for the whole batch.
+                             if ingests:
+                                 _vctx = BatchSignalContext(
+                                     sample_ids=[int(u) for u in ids_np],
+                                     subscribed_values=[float(v) for v in val_vec],
+                                     logger=_lg, step=step)
+                                 for dep in ingests:
+                                     _vctx.latest(dep, require_fresh=True)
+                             batch_res = {}
+                             for i, uid in enumerate(ids_np):
+                                 # Generic 'value' argument
+                                 val = float(val_vec[i])
+
+                                 # Unified Context Pattern
+                                 _lrow = preds_raw[i] if hasattr(preds_raw, '__getitem__') and preds_raw is not None else None
+                                 ctx = SignalContext(
+                                     sample_id=int(uid),
+                                     subscribed_value=val,
+                                     logger=_lg,
+                                     dataframe=df_proxy,
+                                     origin=kwargs.get('origin', 'train'),
+                                     step=step,
+                                     logits=_lrow.detach() if hasattr(_lrow, 'detach') else _lrow,
+                                 )
+                                 try:
+                                     res = func(ctx) # Compute per sample result with unified context
+                                 except TypeError:
+                                     # Fallback for legacy subscriber functions
+                                     res = func(sample_id=int(uid), value=val, dataframe=df_proxy)
+
+                                 batch_res[uid] = res
+                             signal_value = list(batch_res.values())
                          dynamic_updates[name] = signal_value
                          if dynamic_updates and meta.get('log', True):
                              logger.debug(f"Dynamic updates computed for signal '{reg_name}': {list(dynamic_updates.keys())}")
-                             _log_signal(sum(signal_value)/len(signal_value), signal_value, name, step=step, **kwargs) # Log custom subscribed signals
+                             # Log per-sample keyed by id (dict), not a bare list —
+                             # else it reaches the dataframe but not the logger's
+                             # per_sample table, and reactive dependents can't read it.
+                             _sv = {int(ids_np[i]): float(signal_value[i]) for i in range(len(ids_np))}
+                             _log_signal(sum(signal_value)/len(signal_value), _sv, name, step=step, **kwargs) # Log custom subscribed signals
+                     except StaleSignalError:
+                         raise  # correctness enforcement: surface, don't swallow
                      except Exception as e:
-                         logger.debug(f"Dynamic signal {name} failed: {e}")
+                         logger.error(f"Dynamic signal {name} failed: {e}")
                          pass # User function error, skip
 
     # Save statistics if requested and applicable.
@@ -667,18 +1058,37 @@ def wrappered_fwd(original_forward, kwargs, reg_name, *a, **kw):
             signals.update(dynamic_updates) # Merge dynamic signals
 
         preds = detach_to_cpu(preds)
-        preds_raw = detach_to_cpu(preds_raw)
+        # preds_raw = detach_to_cpu(preds_raw)
+
+        # Storing preds_raw (the (B,C) logits) every step is costly and only
+        # needed for FiftyOne/report drill-down — signals already read them via
+        # ctx.logits. Default off (dev disabled it); set the hyperparam to re-enable.
+        try:
+            _store_preds = bool(get_hyperparams().get('ledger_store_preds_raw', False))
+        except Exception:
+            _store_preds = False
 
         # Enqueue signals and data
         save_signals(
             signals=signals,
             batch_ids=batch_ids,
-            preds_raw=preds_raw,
+            preds_raw=preds_raw if _store_preds else None,
             preds=preds,
             targets=targets,
             log=False # Already logged above, no need to log again in save_signals; set to False to avoid duplicate logging if save_signals is called separately without logging
         )
 
+    # Seed reactive dispatch with the watched metric AND subscribe_to results, so
+    # a chain like conf_r<-entropy<-logits fires without any user save_signals.
+    if not per_instance and batch_ids is not None:
+        try:
+            _seed = [reg_name] + [n for n in dynamic_updates.keys() if n != reg_name]
+            _dispatch_or_enqueue(_seed, batch_ids, step, origin or 'train')
+        except StaleSignalError:
+            raise
+        except Exception as e:
+            logger.debug(f"reactive dispatch after {reg_name} failed: {e}")
+
     # Return the original output (dict for per-instance losses so caller can
     # use out['batch'] for backward, tensor for standard per-sample losses).
     return out_original if isinstance(out_original, dict) else out
@@ -869,6 +1279,14 @@ def new_forward(*a, **kw):
             except Exception:
                 pass
 
+            # Auto-detect for background loss-shape classification: any signal
+            # registered specifically via flag="loss" is, by convention, a
+            # reduction="none" per-sample criterion — exactly what the loss-shape
+            # classifier expects. No opt-in call needed; see
+            # auto_loss_shape_signal_names / LoggerQueue._autotag_loss_shapes.
+            if reg_name and 'loss' in flag.lower():
+                _AUTO_LOSS_SHAPE_SIGNALS.add(reg_name)
+
             # rebind caller, i.e., in-place update
             _loss = get_signal(reg_name)
             _rebind_caller_local(obj, _loss)
@@ -1068,38 +1486,132 @@ def start_training(timeout: int = None) -> None:
         timeout: Maximum number of seconds to keep running. If ``None``, runs
             until interrupted.
     """
+    _warn_on_signal_cycles()  # surface circular reactive-signal deps (warn, don't raise)
     if timeout is not None and isinstance(timeout, int) and timeout > 0:
         logger.info(f"Starting WeightsLab training mode with a timeout of {timeout} seconds.")
         time.sleep(timeout)
     pause_ctrl.resume() # Ensure we're not paused if start_training is called after serve
 
+
 def serve(serving_cli: bool = True, serving_grpc: bool = False,
-          spawn_cli_client: bool = False, **kwargs) -> None:
+          spawn_cli_client: bool = False, serving_bore: bool = False,
+          bore_port: int = None, allow_unconfigured: bool = True, **kwargs):
     """Start WeightsLab services.
 
     Args:
         serving_cli: Start the interactive CLI server.
-        serving_grpc: Start the gRPC server.
+        serving_grpc: Start the gRPC server. In a notebook/Colab, if this is on
+            but ``serving_bore`` is off, a warning suggests ``serving_bore=True``
+            (the UI runs on your own machine and needs a tunnel to reach here).
         spawn_cli_client: When ``serving_cli`` is True, also open the interactive
             REPL in a new console window (the default, backward-compatible
             behavior). Set to ``False`` to start the CLI server *headless* — it
             still advertises its port, so you can attach on demand from any
             terminal with ``weightslab cli`` (or ``weightslab cli --port <port>``).
+        serving_bore: Expose the gRPC backend to the internet via a zero-signup
+            `bore <https://github.com/ekzhang/bore>`_ raw-TCP relay, so a Weights
+            Studio running on another machine can reach it (e.g. training in
+            Google Colab, UI on your laptop). Prints the public endpoint and the
+            two commands to run locally (``weightslab start`` +
+            ``weightslab tunnel <endpoint>``). Implies a gRPC backend. Uses the
+            shared public relay ``bore.pub`` — the random port is the only thing
+            guarding it, so keep it to non-sensitive demos.
+        bore_port: Port to expose when ``serving_bore`` is set. Defaults to the
+            gRPC port (``grpc_port`` kwarg, else ``$GRPC_BACKEND_PORT``, else
+            50051).
+        allow_unconfigured: Escape hatch for the serving-config guard. ``serve``
+            reads hyperparameters once, here, for TLS, ports and
+            ``root_log_dir``; if none were wrapped
+            (``wl.watch_or_edit(params, flag="hyperparameters")``) those settings
+            silently fall back to env vars / defaults. Serving over the network
+            (``serving_grpc`` / ``serving_bore``) without config therefore
+            raises, because the backend may come up without TLS/auth. Pass
+            ``True`` (or set ``WL_ALLOW_UNCONFIGURED_SERVE=1``) to downgrade the
+            error to a warning and serve anyway. CLI-only serving always warns
+            (never raises), regardless of this flag.
         **kwargs: Extra server options passed to underlying backends
             (e.g. ``cli_host``, ``cli_port``, ``grpc_port``).
+
+    Returns:
+        The public ``host:port`` bore endpoint string when ``serving_bore`` is
+        set and the tunnel came up, otherwise ``None``.
+
+    Raises:
+        RuntimeError: If ``serving_grpc`` or ``serving_bore`` is requested before
+            any serving config was wrapped and the escape hatch is not set.
     """
 
+    # ------------------------------------------------------------------
+    # Serving-config guard. grpc_serve reads hyperparameters ONCE, at this
+    # call, for TLS, ports and root_log_dir. Serving over the network before
+    # config is wrapped can bring the backend up UNSECURED (TLS falls back to
+    # GRPC_TLS_ENABLED, default off), so hard-fail on gRPC/bore; CLI-only is
+    # local, so warn. Escape hatch: allow_unconfigured / WL_ALLOW_UNCONFIGURED_SERVE.
+    # ------------------------------------------------------------------
+    if not has_serving_config():
+        network_serving = serving_grpc or serving_bore
+        allow = allow_unconfigured or os.environ.get(
+            "WL_ALLOW_UNCONFIGURED_SERVE", "0"
+        ).lower() in ("1", "true", "yes")
+        base_msg = (
+            "wl.serve() was called before any serving config was wrapped. Wrap "
+            'it first with wl.watch_or_edit(parameters, flag="hyperparameters") '
+            "so TLS, ports and root_log_dir come from your config instead of "
+            "falling back to environment variables / defaults."
+        )
+        if network_serving and not allow:
+            raise RuntimeError(
+                base_msg + " Refusing to expose an unconfigured backend over "
+                "gRPC/bore — it may come up without TLS/auth. Pass "
+                "allow_unconfigured=True (or set WL_ALLOW_UNCONFIGURED_SERVE=1) "
+                "to override."
+            )
+        logger.warning(base_msg)
+
     if serving_grpc:
         grpc_serve(**kwargs)
 
+    # In a notebook/Colab the backend has no local Weights Studio to talk to
+    # (the UI runs on the user's own machine). Nudge them to open a tunnel.
+    if serving_grpc and not serving_bore and _running_in_notebook():
+        logger.warning(
+            "Running in a notebook/Colab with serving_grpc=True but "
+            "serving_bore=False. Weights Studio runs on your OWN machine and "
+            "cannot reach this backend directly. Pass serving_bore=True to open "
+            "a tunnel and sync with the UI: wl.serve(serving_grpc=True, "
+            "serving_bore=True)."
+        )
+
+    bore_endpoint = None
+    if serving_bore:
+        from weightslab.tunnel import serve_bore
+        port = (bore_port
+                or kwargs.get("grpc_port")
+                or int(os.getenv("GRPC_BACKEND_PORT", 50051)))
+        if not serving_grpc:
+            logger.warning(
+                "serving_bore=True but serving_grpc=False — the tunnel will have "
+                "no gRPC backend to expose. Set serving_grpc=True."
+            )
+        bore_endpoint = serve_bore(port=port)
+        if bore_endpoint:
+            print("=" * 60)
+            print(f" Backend exposed via bore at: {bore_endpoint}")
+            print(" On your local machine, run:")
+            print("     weightslab start")
+            print(f"     weightslab tunnel {bore_endpoint}")
+            print("=" * 60)
+
     if serving_cli:
         # The explicit parameter is the source of truth; drop any duplicate
         # spawn_client passed through kwargs so we don't pass it twice.
         kwargs.pop("spawn_client", None)
         cli_serve(spawn_client=spawn_cli_client, **kwargs)
 
+    return bore_endpoint
 
-def keep_serving(timeout: int = None, release_gpu: bool = True) -> None:
+
+def keep_serving(timeout: int = None, release_gpu: bool = False) -> None:
     """Keep process alive while background WeightsLab services are running.
 
     Args:
@@ -1123,7 +1635,8 @@ def keep_serving(timeout: int = None, release_gpu: bool = True) -> None:
         logger.info("Shutting down WeightsLab services.")
 
 
-def signal(name: str, subscribe_to: str = None, compute_every_n_steps: int = 1, **kwargs):
+def signal(name: str, subscribe_to: str = None, compute_every_n_steps: int = 1,
+           min_step: int = 0, **kwargs):
     """
     Decorator that registers a custom signal function.
 
@@ -1135,6 +1648,12 @@ def signal(name: str, subscribe_to: str = None, compute_every_n_steps: int = 1,
         name: Public signal name. Defaults to decorated function name.
         subscribe_to: Optional signal/metric name this signal reacts to.
         compute_every_n_steps: Frequency for dynamic computation.
+        min_step: Minimum training step before a subscribed (dynamic) signal
+            starts firing. The signal is skipped while ``current_step <
+            min_step``. Defaults to ``0`` (fire from the start). Useful when a
+            signal needs enough history to be meaningful — e.g. a loss-shape
+            classifier that should only run once each sample has a trajectory
+            (``min_step=505``).
         **kwargs: Additional signal metadata stored in the ledger.
 
     Returns:
@@ -1144,7 +1663,8 @@ def signal(name: str, subscribe_to: str = None, compute_every_n_steps: int = 1,
         @wl.signal(name="blue_pixels")
         def compute_blue(sample, **kwargs): ...
 
-        @wl.signal(name="weighted_loss", subscribe_to="train_loss", compute_every_n_steps=10)
+        @wl.signal(name="weighted_loss", subscribe_to="train_loss",
+                   compute_every_n_steps=10, min_step=505)
         def compute_weighted(value, sample_id, **kwargs): ...
     """
     def decorator(func):
@@ -1157,6 +1677,14 @@ def decorator(func):
         func._wl_signal_meta = kwargs
         func._wl_signal_meta['subscribe_to'] = subscribe_to
         func._wl_signal_meta['compute_every_n_steps'] = compute_every_n_steps
+        # Reactive dependency set. ``inputs=[...]`` is the unified form: the signal
+        # fires when ALL its inputs are present at a step, order-independently
+        # (subsumes subscribe_to + ingests). A single-input signal is the alias
+        # ``inputs=["x"]`` == ``subscribe_to="x"``. Signals that use the legacy
+        # ``subscribe_to`` keyword instead stay on the legacy dispatch.
+        _inp = kwargs.get('inputs')
+        func._wl_signal_meta['inputs'] = list(_inp) if _inp else None
+        func._wl_signal_meta['min_step'] = min_step
         func._wl_signal_name = reg_name
 
         return func
@@ -1602,7 +2130,8 @@ def save_signals(
     targets: th.Tensor | np.ndarray | dict = None,
     preds: th.Tensor | np.ndarray | dict = None,
     step: int | None = None,
-    log: bool = False
+    log: bool = False,
+    _react: bool = True,
 ):
     """Save **per-sample** statistics to the tracked dataset.
 
@@ -1666,8 +2195,10 @@ def save_signals(
     if DATAFRAME_M is None:
         DATAFRAME_M = get_dataframe()
 
-    # Get current model step
-    step = _get_step(step=step)
+    # Honor an explicit step (as documented); only infer from the model when it
+    # is omitted. Reactive backfill on the signal-worker thread depends on this —
+    # it logs derived values at the job's step, not the live (ahead) model step.
+    step = step if step is not None else _get_step()
 
     # Log if requested for each signals
     if log:
@@ -1699,7 +2230,17 @@ def normalize(x):
         if x is None:
             return None
         if isinstance(x, list) and isinstance(x[0], list):
-            return [np.max(np.array([to_numpy(t) for t in row]), axis=0) for row in x]
+            rows = [[to_numpy(t) for t in row] for row in x]
+            # A sample can legitimately have zero instances (e.g. no tracked
+            # class visible in frame); fall back to an all-background mask
+            # shaped like its neighbors instead of reducing an empty array.
+            shape = next((r[0].shape for r in rows if r), None)
+            dtype = next((r[0].dtype for r in rows if r), np.uint16)
+            return [
+                np.max(np.array(row), axis=0) if row
+                else np.zeros(shape, dtype=dtype) if shape is not None else np.zeros(0, dtype=dtype)
+                for row in rows
+            ]
         elif isinstance(x, list):
             return [to_numpy(t) for t in x]
         if isinstance(x, th.Tensor):
@@ -1750,6 +2291,18 @@ def expand_dim(x):
         step=step
     )
 
+    # Reactive signals: these just-logged signals may satisfy an inputs=[...]
+    # signal. Only logged (queryable) signals can be inputs. _react=False on the
+    # reactive persist path prevents re-entrant dispatch.
+    if _react and log and isinstance(signals, dict):
+        try:
+            _dispatch_or_enqueue(list(signals.keys()), batch_ids, step,
+                                 get_active_origin() or 'train')
+        except StaleSignalError:
+            raise
+        except Exception as e:
+            logger.debug(f"reactive dispatch after save_signals failed: {e}")
+
 
 def save_instance_signals(
     signals: dict,
@@ -3189,7 +3742,9 @@ def write_history(
     graph_name=None,
     experiment_hash: str | None = None,
     sample_id=None,
+    orient: str = "columns",
     instance_id=None,
+    verbose: bool = False,
 ) -> str:
     """Dump signal history to *path* as JSON or CSV.
 
@@ -3229,6 +3784,14 @@ def write_history(
     instance_id : int or list of int, optional
         Restrict per-instance rows to one or more annotation IDs.
         Has no effect on global or per-sample history.
+    orient : str, optional
+        JSON layout for each section (``"global"``/``"sample"``/``"instance"``),
+        forwarded to ``pandas.DataFrame.to_json``. Default ``"columns"``
+        (``{column: {row_index: value}}``) — compact, since each column name
+        is written once per section instead of once per row. Pass
+        ``"records"`` for the row-list-of-dicts shape
+        (``[{"graph_name": ..., "step": ..., ...}, ...]``) if that's more
+        convenient downstream. Ignored for ``format="csv"``.
 
     Returns
     -------
@@ -3253,6 +3816,10 @@ def write_history(
             type_of_history="sample",
             experiment_hash="abc123",
         )
+
+    Get the row-list-of-dicts JSON shape instead of the compact default::
+
+        wl.write_history("history.json", orient="records")
     """
     import csv as _csv
     import json as _json
@@ -3266,6 +3833,10 @@ def write_history(
         sample_id, instance_id,
     )
 
+    # Progress lines are INFO only when verbose; otherwise DEBUG (quiet by
+    # default so periodic exports don't spam a training log).
+    _log = logger.info if verbose else logger.debug
+
     _lg = get_logger()
     if _lg is None:
         logger.warning(
@@ -3287,7 +3858,7 @@ def write_history(
             logger.debug("write_history: failed to resolve root_log_dir (%s); "
                          "falling back to current directory.", _e)
             path = "."
-        logger.info("write_history: no path given, using output directory %r.", path)
+        _log("write_history: no path given, using output directory %r.", path)
 
     fmt = format.lower().strip()
 
@@ -3332,7 +3903,7 @@ def write_history(
     write_sample = _type in ("all", "sample")
     write_instance = _type in ("all", "instance")
 
-    logger.info(
+    _log(
         "write_history: resolved filters → type=%r, experiment_hash=%s, "
         "graph_name=%s, sample_id=%s, instance_id=%s",
         _type,
@@ -3357,10 +3928,10 @@ def write_history(
         )
         _phash = _hashlib.md5(str(_params_key).encode()).hexdigest()[:8]
         path = _os.path.join(path, f"{_phash}_history.{fmt}")
-        logger.info("write_history: auto-generated filename %r (params hash "
-                    "%s).", _os.path.basename(path), _phash)
+        _log("write_history: auto-generated filename %r (params hash "
+             "%s).", _os.path.basename(path), _phash)
     _os.makedirs(_os.path.dirname(_os.path.abspath(path)), exist_ok=True)
-    logger.info("write_history: output file → %s", _os.path.abspath(path))
+    _log("write_history: output file → %s", _os.path.abspath(path))
 
     global_rows: list = []
     sample_rows: list = []
@@ -3448,8 +4019,20 @@ def write_history(
             payload["sample"] = sample_rows
         if write_instance:
             payload["instance"] = instance_rows
+
+        # Each section is a list of row dicts sharing the same keys; route it
+        # through pandas so `orient` is genuinely honored. Default "columns"
+        # ({column: {row_index: value}}) writes each column name once per
+        # section instead of once per row — much smaller for many rows.
+        # orient="records" reproduces the original row-list-of-dicts shape.
+        import pandas as _pd
+        json_payload = {
+            section: _json.loads(_pd.DataFrame(rows).to_json(orient=orient, default_handler=str))
+            for section, rows in payload.items()
+        }
+
         with open(path, "w", encoding="utf-8") as fh:
-            _json.dump(payload, fh, indent=2)
+            _json.dump(json_payload, fh, indent=2)
 
     elif fmt == "csv":
         _CSV_FIELDS = [
@@ -3474,7 +4057,7 @@ def write_history(
         )
 
     _total = len(global_rows) + len(sample_rows) + len(instance_rows)
-    logger.info(
+    _log(
         "write_history: wrote %d row(s) (global=%d, sample=%d, instance=%d) "
         "as %s to %s",
         _total, len(global_rows), len(sample_rows), len(instance_rows),
@@ -3491,15 +4074,180 @@ def write_history(
     return path
 
 
+# Signal names registered via watch_or_edit(..., flag="loss") — populated
+# automatically (see the loss branch above), so the background flush thread
+# can auto-classify every per-sample loss without the user calling anything.
+_AUTO_LOSS_SHAPE_SIGNALS: set = set()
+
+
+def auto_loss_shape_signal_names() -> list:
+    """Every signal name currently registered via ``flag="loss"`` — the
+    automatic loss-shape classification target set. Used internally by
+    :class:`weightslab.backend.logger.LoggerQueue`'s background flush thread;
+    read it yourself only for introspection/debugging."""
+    return sorted(_AUTO_LOSS_SHAPE_SIGNALS)
+
+
+LOSS_SHAPES = ["monotonic", "plateaued", "Flat_high", "high_variance", "U_Shape", "Spiked"]
+
+
+def trajectory_stats(values):
+    """Scale-invariant summary stats of one sample's trajectory — the reusable
+    feature layer. Returns a dict (or ``None`` with < 2 points) so you can build
+    a custom classifier on top without re-deriving the features::
+
+        def my_clf(v):
+            s = wl.trajectory_stats(v)
+            return None if s is None else ("fast" if s["drop"] > 0.6 else "slow")
+    """
+    y = np.asarray(values, dtype=float)
+    if y.size < 2:
+        return None
+    n = y.size
+    rng = max(float(y.max() - y.min()), 1e-8)
+    tail = y[int(0.6 * n):]
+    return {
+        "n": n,
+        "first": float(y[0]), "last": float(y[-1]),
+        "drop": (float(y[0]) - float(y[-1])) / (abs(float(y[0])) + 1e-8),
+        "cv": float(y.std()) / (abs(float(y.mean())) + 1e-8),
+        "argmin_frac": float(np.argmin(y)) / n,
+        "rebound": (float(y[-1]) - float(y.min())) / rng,
+        "max_up_jump": float(np.diff(y).max()) / rng,
+        "tail_cv": float(tail.std()) / (abs(float(tail.mean())) + 1e-8),
+    }
+
+
+def classify_loss_shape(values, min_points=5, drop_learned=0.4, drop_plateau=0.15,
+                        tail_flat_cv=0.1, spike_jump=0.5, noisy_cv=0.5, u_rebound=0.3):
+    """Default classifier -> one of ``LOSS_SHAPES`` (or ``None`` with <
+    *min_points*). Built on :func:`trajectory_stats` (reuse those features
+    for your own rules) with every threshold a named, tunable param. Override the
+    whole thing by passing your own ``classifier`` to :func:`write_signal_shapes`."""
+    s = trajectory_stats(values)
+    if s is None or s["n"] < min_points:
+        return None
+    if 0.2 < s["argmin_frac"] < 0.8 and s["rebound"] > u_rebound:
+        return "U_Shape"
+    if s["drop"] > drop_learned:
+        return "monotonic"
+    if s["drop"] > drop_plateau and s["tail_cv"] < tail_flat_cv:
+        return "plateaued"
+    if s["max_up_jump"] > spike_jump:
+        return "Spiked"
+    if s["cv"] > noisy_cv:
+        return "high_variance"
+    return "Flat_high"
+
+
+def write_signal_shapes(signal_name, tag_name=None, classifier=None):
+    """Reusable engine: classify every sample's trajectory of *signal_name* into
+    a categorical tag and return the ``{label: count}`` distribution. Works for
+    ANY per-sample signal — loss, accuracy, a second loss, any metric. Reads the
+    full history once (cheap end-of-report reduction). *classifier* (trajectory
+    -> label|None) defaults to the loss-shaped one (for a decreasing metric);
+    pass your own for e.g. accuracy (increasing). *tag_name* defaults to
+    ``'<signal>_shape'``."""
+    clf = classifier or classify_loss_shape
+    if tag_name is None:
+        tag_name = signal_name + "_shape" if signal_name.endswith('_loss') else signal_name + "_loss_shape"
+    series = {}
+    for sid, step, val, _ in query_signal_history(signal_name):
+        series.setdefault(sid, []).append((step, val))
+    by_label = {}
+    for sid, pts in series.items():
+        label = clf([v for _, v in sorted(pts)])
+        if label is not None:
+            by_label.setdefault(label, []).append(sid)
+    for label, sids in by_label.items():
+        set_categorical_tag(sids, tag_name, label)
+    return {k: len(v) for k, v in by_label.items()}
+
+
+def write_loss_shapes(loss_signal="loss_sample", classifier=None):
+    """Convenience wrapper over :func:`write_signal_shapes` for the loss signal
+    (tag ``loss_shape``, default loss classifier)."""
+    return write_signal_shapes(loss_signal, tag_name="loss_shape", classifier=classifier)
+
+
+def enable_loss_shape_autotag(loss_signal=None, tag_name=None, classifier=None):
+    """Customize automatic loss-shape tagging — **not required to turn it on**.
+
+    Every signal registered via ``wl.watch_or_edit(criterion, flag="loss", ...)``
+    is already auto-classified in the background with zero setup: the logger's
+    periodic flush thread (``WL_LOGGER_FLUSH_INTERVAL_SECONDS`` env var, default
+    2s) discovers it automatically and re-tags it as ``'<signal>_shape'`` every
+    tick, once it has enough per-sample history to classify (see
+    :func:`classify_loss_shape`'s ``min_points``) — no call needed, and no
+    ``write_dataframe(loss_shape_signal=...)`` required either.
+
+    Call this only to override the tag name or classifier used for one specific
+    *loss_signal* (e.g. it isn't a decreasing loss, so the default classifier is
+    wrong for it). Re-enables that signal if it was previously disabled via
+    :func:`disable_loss_shape_autotag`.
+    """
+    if loss_signal is None:
+        raise ValueError(
+            "enable_loss_shape_autotag: loss_signal is required — every "
+            "flag='loss' signal is already auto-classified without calling "
+            "this; only call it to override the tag_name/classifier for one."
+        )
+    lg = get_logger()
+    if lg is None:
+        raise RuntimeError(
+            "enable_loss_shape_autotag: no logger registered yet — call this "
+            "after wl.watch_or_edit(criterion, flag='loss', log=True)."
+        )
+    lg.set_loss_shape_override(loss_signal, tag_name=tag_name, classifier=classifier)
+
+
+def disable_loss_shape_autotag(loss_signal=None):
+    """Stop automatic loss-shape tagging for *loss_signal*, or for every
+    signal (including ones registered later) if *loss_signal* is ``None``."""
+    lg = get_logger()
+    if lg is not None:
+        lg.disable_loss_shape_autotag(loss_signal)
+
+
+def enable_loss_shape_signal(loss_signal="loss_sample", name="sig/loss_shape",
+                             every=1, classifier=None):
+    """Register a LIVE per-step ``@wl.signal`` that classifies each sample's
+    loss-trajectory-so-far into an int-coded shape (index into ``LOSS_SHAPES``,
+    ``-1`` before there's enough history), updated every *every* steps.
+
+    This is the live counterpart to :func:`write_loss_shapes` (report-time). It's
+    heavier — it reads history on every fire — so throttle with *every* or prefer
+    the report-time path for a definitive, full-coverage tag."""
+    clf = classifier or classify_loss_shape
+
+    @signal(name=name, inputs=[loss_signal], batched=True, compute_every_n_steps=every)
+    def _live_shape(b):
+        hist = b.history(loss_signal)
+        return np.array([(LOSS_SHAPES.index(lbl) if (lbl := clf(hist[s])) is not None else -1)
+                         for s in b.sample_ids], dtype=float)
+    return _live_shape
+
+
 def write_dataframe(
     path: str | None = None,
     format: str = "json",
     columns=None,
     sample_id=None,
     instance_id=None,
+    orient: str = "columns",
+    verbose: bool = False,
+    loss_shape_signal: str | None = None,
 ) -> str:
     """Dump the WeightsLab sample dataframe to *path* as JSON or CSV.
 
+    Every ``flag="loss"`` signal is already auto-classified into a
+    ``'<signal>_shape'`` tag in the background with zero setup (see
+    :func:`enable_loss_shape_autotag`'s docstring), so you normally don't need
+    *loss_shape_signal* at all. It remains as a one-off: when set (e.g.
+    ``"loss_sample"``), the classifier runs synchronously first so this
+    specific report is guaranteed fresh at the moment it's written, rather than
+    whatever the background thread last computed.
+
     Parameters
     ----------
     path : str, optional
@@ -3567,12 +4315,27 @@ def write_dataframe(
     import os as _os
     import hashlib as _hashlib
 
+    # If reactive signals run on the worker thread, process every queued job
+    # before reading, so the report includes the latest steps' derived signals.
+    drain_signals()
+
+    # Default report summary: tag each sample's loss shape + log the distribution.
+    if loss_shape_signal is not None:
+        try:
+            dist = write_loss_shapes(loss_shape_signal)
+            logger.info("[WeightsLab] loss-shape summary (%s): %s", loss_shape_signal, dist)
+        except Exception as e:
+            logger.debug("loss-shape summary skipped: %s", e)
+
     logger.debug(
         "write_dataframe called: path=%r, format=%r, columns=%r, "
         "sample_id=%r, instance_id=%r",
         path, format, columns, sample_id, instance_id,
     )
 
+    # INFO only when verbose; DEBUG otherwise (quiet periodic exports).
+    _log = logger.info if verbose else logger.debug
+
     _dm = get_dataframe()
     if _dm is None:
         logger.warning(
@@ -3592,7 +4355,7 @@ def write_dataframe(
             logger.debug("write_dataframe: failed to resolve root_log_dir (%s); "
                          "falling back to current directory.", _e)
             path = "."
-        logger.info("write_dataframe: no path given, using output directory %r.", path)
+        _log("write_dataframe: no path given, using output directory %r.", path)
 
     fmt = format.lower().strip()
 
@@ -3611,7 +4374,7 @@ def write_dataframe(
     if columns is not None and not (isinstance(columns, str) and columns.lower() == "all"):
         _col_filter = [columns] if isinstance(columns, str) else list(columns)
 
-    logger.info(
+    _log(
         "write_dataframe: resolved filters → columns=%s, sample_id=%s, instance_id=%s",
         _col_filter if _col_filter is not None else "<all>",
         _sid_filter if _sid_filter is not None else "<all>",
@@ -3629,10 +4392,10 @@ def write_dataframe(
         )
         _phash = _hashlib.md5(str(_params_key).encode()).hexdigest()[:8]
         path = _os.path.join(path, f"{_phash}_dataframe.{fmt}")
-        logger.info("write_dataframe: auto-generated filename %r (params hash %s).",
-                    _os.path.basename(path), _phash)
+        _log("write_dataframe: auto-generated filename %r (params hash %s).",
+             _os.path.basename(path), _phash)
     _os.makedirs(_os.path.dirname(_os.path.abspath(path)), exist_ok=True)
-    logger.info("write_dataframe: output file → %s", _os.path.abspath(path))
+    _log("write_dataframe: output file → %s", _os.path.abspath(path))
 
     # Flush pending buffer to H5 before reading
     try:
@@ -3703,7 +4466,7 @@ def write_dataframe(
     df_out = df_out.reset_index()
 
     if fmt == "json":
-        _json_str = df_out.to_json(orient="records", default_handler=str)
+        _json_str = df_out.to_json(orient=orient, default_handler=str)
         with open(path, "w", encoding="utf-8") as fh:
             _json.dump(_json.loads(_json_str), fh, indent=2)
 
diff --git a/weightslab/trainer/experiment_context.py b/weightslab/trainer/experiment_context.py
index 36dccc86..082d65a4 100644
--- a/weightslab/trainer/experiment_context.py
+++ b/weightslab/trainer/experiment_context.py
@@ -184,7 +184,7 @@ def _get_last_hash():
             last_hash = "000000000000000000000000"
             try:
                 chkpt_m = self._components.get("checkpoint_manager")
-                if chkpt_m and hasattr(chkpt_m, "get_last_checkpoint_hash"):
+                if chkpt_m and hasattr(chkpt_m, "get_current_experiment_hash"):
                     resolved = chkpt_m.get_current_experiment_hash()
                     if resolved:
                         last_hash = resolved
diff --git a/weightslab/trainer/services/agent/agent.py b/weightslab/trainer/services/agent/agent.py
index dcede2c7..08aaa572 100644
--- a/weightslab/trainer/services/agent/agent.py
+++ b/weightslab/trainer/services/agent/agent.py
@@ -731,6 +731,13 @@ def _load_config(self):
         self.openrouter_base_url = os.environ.get("OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1")
         self.openrouter_api_key = os.environ.get("OPENROUTER_API_KEY", None)
         self.openrouter_request_timeout = float(os.environ.get("OPENROUTER_REQUEST_TIMEOUT", "15.0"))
+        # Cap the completion length. OpenRouter pre-authorizes
+        # `max_tokens * completion_price` against the key's remaining budget
+        # BEFORE generating; with no cap the client requests the model's full
+        # output window (tens of thousands of tokens), which 402s on a
+        # credit/weekly-limited key even though the actual intent-planning
+        # response is only a few hundred tokens. Keep this modest.
+        self.openrouter_max_tokens = int(os.environ.get("OPENROUTER_MAX_TOKENS", "2048"))
         # Bias OpenRouter's upstream provider selection ("throughput"/"latency"/
         # "price"); empty string lets OpenRouter choose freely (see Phase B.2).
         self.openrouter_provider_sort = os.environ.get("OPENROUTER_PROVIDER_SORT", "throughput")
@@ -783,6 +790,7 @@ def _load_config(self):
                 self.openrouter_base_url = a_cfg.get("openrouter_base_url", self.openrouter_base_url)
                 self.openrouter_api_key = a_cfg.get("openrouter_api_key", self.openrouter_api_key)
                 self.openrouter_request_timeout = float(a_cfg.get("openrouter_request_timeout", self.openrouter_request_timeout))
+                self.openrouter_max_tokens = int(a_cfg.get("openrouter_max_tokens", self.openrouter_max_tokens))
                 self.openrouter_provider_sort = a_cfg.get("openrouter_provider_sort", self.openrouter_provider_sort)
                 self.openrouter_structured_output = bool(a_cfg.get("openrouter_structured_output", self.openrouter_structured_output))
 
@@ -872,6 +880,7 @@ def _setup_providers(self):
                         api_key=self.openrouter_api_key,
                         base_url=openrouter_base_url,
                         streaming=False, max_retries=1, request_timeout=self.openrouter_request_timeout,
+                        max_tokens=self.openrouter_max_tokens,
                         extra_body=extra_body,
                     )
                     self.chain_openrouter = llm
@@ -930,7 +939,17 @@ def _check_chat_provider(self, provider: str) -> "tuple[bool, str]":
             return False, f"{provider} client was not initialized."
 
         try:
-            response = chain.invoke("Reply with OK.")
+            # Keep the probe's reservation tiny. OpenRouter pre-authorizes
+            # `max_tokens * price` before generating, so a large (or unset)
+            # cap makes the health check 402 on a budget-limited key even
+            # though the model is perfectly usable for real (short) requests.
+            probe = chain
+            if provider == "openrouter" and hasattr(chain, "bind"):
+                try:
+                    probe = chain.bind(max_tokens=16)
+                except Exception:
+                    probe = chain
+            response = probe.invoke("Reply with OK.")
         except Exception as e:
             _LOGGER.warning(f"[{provider}] connectivity check failed: {e}")
             return False, f"{provider} connectivity check failed: {e}"
diff --git a/weightslab/trainer/services/agent/intent_prompt.py b/weightslab/trainer/services/agent/intent_prompt.py
index a9fe7bba..663a91d3 100644
--- a/weightslab/trainer/services/agent/intent_prompt.py
+++ b/weightslab/trainer/services/agent/intent_prompt.py
@@ -74,13 +74,10 @@
    - "average train loss over training below 0.2" → `signal_history('train_loss','mean') < 0.2`
    Use it exactly like a normal column expression (create a tag, discard, or keep on it — see Ex39). A sample with no recorded history yields NaN (comparisons are False), so it's safely excluded.
 11. **Hyperparameter tuning (`action_name="set_hyperparam"`)**: to change a training hyperparameter, emit a `kind="action"` step with `action_name="set_hyperparam"` and `action_params={{"param": <name>, "op": <"set"|"scale">, "value": <number>}}`.
-   - `param`: prefer a semantic name — `"batch_size"`, `"learning_rate"`, `"dump_ratio"` (the model-dump/checkpoint ratio), `"eval_ratio"` (the evaluation ratio) — or an exact dotted config path (e.g. `"data.train_loader.batch_size"`). The backend resolves the semantic name to the real config key.
-   - Absolute change ("set X to N", "change X to N") → `op="set"`, `value=N`.
    - Relative change ("increase X by 10%", "decrease by 20%") → `op="scale"`, `value` = the multiplier (10% increase → `1.1`; 20% decrease → `0.8`). Compute the multiplier yourself.
    - See Ex45–Ex48.
 
 ---
-## 4. SCHEMA RULES (STRICT)
 - **Primary Goal**: `ui_manipulation` (grid changes), `data_analysis` (answers), `action` (external), `model_management` (model_info/model_action), `out_of_scope`.
 - **Atomic Operations**:
   - `conditions`: List of dicts with keys "column", "op", "value". Operators: `==, !=, >, <, >=, <=, contains, in, max, min`.
diff --git a/weightslab/trainer/services/data_image_utils.py b/weightslab/trainer/services/data_image_utils.py
index 81358b32..30695b83 100644
--- a/weightslab/trainer/services/data_image_utils.py
+++ b/weightslab/trainer/services/data_image_utils.py
@@ -165,6 +165,63 @@ def encode_image_webp(pil_image, quality=70, method=2):
         return b""
 
 
+# =============================================================================
+# Tabular inputs (1-D feature vectors)
+# =============================================================================
+# Tabular samples have no image — the model input IS a 1-D feature vector. We
+# transmit the actual feature values losslessly in the DataStat ``value`` field
+# (type ``"vector"``), and attach a small heatmap so image-only grids still show
+# a cell. The List Exploration view reads the per-feature metadata columns; a
+# tabular-aware grid can read ``value`` directly.
+
+def looks_like_tabular(np_img) -> bool:
+    """True when the sample input is a 1-D feature vector (no spatial dims)."""
+    return np_img is not None and getattr(np_img, "ndim", None) == 1
+
+
+def render_tabular_heatmap(vec: np.ndarray, cell: int = 16, quality: int = 60):
+    """Render a 1-D feature vector as a small square grayscale heatmap.
+
+    Returns ``(webp_bytes, [h, w, c])``. Values are min-max normalised for
+    display only; the exact values travel in the DataStat ``value`` field.
+    """
+    v = np.asarray(vec, dtype=np.float32).ravel()
+    n = int(v.size)
+    if n == 0:
+        return b"", [0, 0, 0]
+    side = int(np.ceil(np.sqrt(n)))
+    padded = np.zeros(side * side, dtype=np.float32)
+    padded[:n] = v
+    grid = padded.reshape(side, side)
+    mn, mx = float(np.nanmin(grid)), float(np.nanmax(grid))
+    norm = (grid - mn) / (mx - mn + 1e-8)
+    img8 = np.clip(norm * 255.0, 0, 255).astype(np.uint8)
+    try:
+        pil = Image.fromarray(img8, mode="L").resize(
+            (side * cell, side * cell), Image.Resampling.NEAREST)
+        buf = io.BytesIO()
+        pil.convert("RGB").save(buf, format="WEBP", quality=quality, method=1)
+        data = buf.getvalue()
+        buf.close()
+        return data, [side * cell, side * cell, 3]
+    except Exception as e:  # pragma: no cover - display-only fallback
+        logger.warning(f"Failed to render tabular heatmap: {e}")
+        return b"", [side, side, 1]
+
+
+def build_tabular_raw_data_stat(vec: np.ndarray):
+    """Build the ``raw_data`` DataStat for a tabular (1-D) input.
+
+    The feature values are carried losslessly in ``value`` (type ``"vector"``,
+    ``shape=[N]``); a heatmap rides along in ``thumbnail`` for display only.
+    """
+    v = np.asarray(vec, dtype=np.float32).ravel()
+    values = [float(x) for x in v]
+    thumb, _shape = render_tabular_heatmap(v)
+    return create_data_stat(
+        "raw_data", "vector", shape=[len(values)], value=values, thumbnail=thumb)
+
+
 def resize_mask_nearest(mask_arr: np.ndarray, target_w: int, target_h: int) -> np.ndarray:
     """Resize a 2D+ mask array using nearest-neighbour (preserves class IDs).
 
diff --git a/weightslab/trainer/services/data_service.py b/weightslab/trainer/services/data_service.py
index 454e2a12..a5b3923e 100755
--- a/weightslab/trainer/services/data_service.py
+++ b/weightslab/trainer/services/data_service.py
@@ -46,6 +46,8 @@
     rle_encode_mask,
     create_data_stat,
     resize_mask_nearest,
+    looks_like_tabular,
+    build_tabular_raw_data_stat,
 )
 
 
@@ -81,6 +83,29 @@ def _point_cloud_chunk_bytes() -> int:
     return val
 
 
+def peek_is_tabular_sample(dataset, sample_id) -> bool:
+    """True when ``sample_id``'s raw model input is a 1-D feature vector.
+
+    Used as the last step of task_type auto-detection: image/detection/
+    segmentation datasets are identified by their *label* shape, but a tabular
+    dataset's label is a plain scalar, so the only way to tell it apart is to
+    peek at the *input* it feeds the model. Any resolution failure (missing
+    sample, dataset without the expected lookup methods, …) is treated as
+    "not tabular" rather than raised, since this is a best-effort heuristic.
+    """
+    try:
+        if hasattr(dataset, "get_physical_location"):
+            idx, rank = dataset.get_physical_location(sample_id)
+        else:
+            idx, rank = dataset.get_index_from_sample_id(sample_id), 0
+        if idx is None:
+            return False
+        img, _, _, _ = load_raw_image_array(dataset, idx, rank=rank)
+        return looks_like_tabular(img)
+    except (KeyError, ValueError, AttributeError, IndexError):
+        return False
+
+
 def normalize_metadata_copy_source_name(source_name: str, experiment_hash: str = None) -> str:
     """Normalize a source metadata name for deterministic copied-column naming."""
     name = str(source_name or "").strip()
@@ -493,7 +518,12 @@ def _build_preview_cache(self) -> None:
                         ds_idx = dataset.get_index_from_sample_id(sample_id)
                         member_rank = 0
 
-                    _, _, _, pil_img = load_raw_image_array(dataset, ds_idx, rank=member_rank)
+                    _pv_np_img, _, _, pil_img = load_raw_image_array(dataset, ds_idx, rank=member_rank)
+                    if looks_like_tabular(_pv_np_img):
+                        # Tabular sample: cache the feature values (lossless) as a
+                        # 'vector' raw_data stat + heatmap thumbnail, not an image.
+                        stats.append(build_tabular_raw_data_stat(_pv_np_img))
+                        pil_img = None  # skip the image-thumbnail path below
                     if pil_img is not None:
                         ar = pil_img.size[0] / max(pil_img.size[1], 1)
                         if PREVIEW_SIZE >= max(pil_img.size):
@@ -595,6 +625,10 @@ def _build_preview_cache(self) -> None:
                     task_type = str(db_task).strip().lower() if db_task and str(db_task).strip().lower() not in ("none", "nan", "unknown", "") else "unknown"
                     if task_type == "unknown" and _early_task:
                         task_type = _early_task
+                    if task_type == "unknown" and looks_like_tabular(_pv_np_img):
+                        # Tabular: the model INPUT (not the label) is a 1-D feature
+                        # vector — no spatial dims to key a heuristic off of.
+                        task_type = "tabular"
                     if task_type == "unknown" and label is not None:
                         l_arr = to_numpy_safe(label)
                         if l_arr is not None and l_arr.ndim >= 2 and l_arr.shape[-2] >= 16 and l_arr.shape[-1] >= 16:
@@ -860,8 +894,10 @@ def _pull_into_all_data_view_df(self):
 
             # The manager now expands samples into one row per (sample_id, annotation_id)
             # instance. Collapse back to one row per sample for the sample-centric UI/agent
-            # view, nesting per-instance signals into a dict column.
-            df = self._df_manager.get_collapse_annotations_to_samples_df()
+            # view, nesting per-instance signals into a dict column. Reuse the frame we just
+            # pulled — otherwise collapse would re-run get_combined_df() (full copy + buffer
+            # merge + proxy conversion) a second time over the whole dataset every refresh.
+            df = self._df_manager.get_collapse_annotations_to_samples_df(df)
 
             # Ensure sample_id is a column if it was the index
             df = safe_reset_index(df)
@@ -1143,10 +1179,20 @@ def process_sample(args):
                 if "origin" in df_update.columns:
                      del df_update["origin"]
 
-                self._df_manager.upsert_df(df_update, origin=origin, force_flush=True)
+                try:
+                    self._df_manager.upsert_df(df_update, origin=origin, force_flush=True)
+                except Exception as e:
+                    logger.warning(
+                        "[DataService] Skipping natural sort stats write for origin=%s: %s",
+                        origin,
+                        e,
+                    )
 
         # Force refresh internal view
-        self._slowUpdateInternals(force=True)
+        try:
+            self._slowUpdateInternals(force=True)
+        except Exception as e:
+            logger.warning("[DataService] Natural sort refresh failed, continuing startup: %s", e)
 
         logger.info(f"[DataService] Completed stats computation for {processed_count} samples")
         print(f"\n\nNatural sort computation finished for {processed_count} samples\n\n")
@@ -1285,6 +1331,13 @@ def _process_sample_row(self, args):
                             except Exception:
                                 pass
 
+                # Tabular: the model INPUT (not the label) is a 1-D feature vector.
+                # Only peek at it when nothing else already matched, so this is a
+                # cheap no-op for image/detection/segmentation datasets.
+                if task_type == "classification" and dataset is not None:
+                    if peek_is_tabular_sample(dataset, sample_id):
+                        task_type = "tabular"
+
             # ====== Step 5a: Metadata stats — moved to GetMetaData ======
             # Generic dataframe metadata columns (signals, tags, custom fields, etc.)
             # are no longer returned by GetDataSamples; the dedicated GetMetaData RPC
@@ -1323,7 +1376,7 @@ def _process_sample_row(self, args):
 
             # ====== Step 7: Process labels ======
             t_label_convert = 0.0
-            if not skip_label_for_request and task_type != "classification":
+            if not skip_label_for_request and task_type not in ("classification", "tabular"):
                 t0_gt_conv = time.time()
                 label_raw = row.get(SampleStatsEx.TARGET.value) if label is None else label
                 if label_raw is None and dataset is not None:
@@ -1581,7 +1634,7 @@ def _json_default(o):
             if skip_prediction_for_request:
                 pred = None
 
-            if task_type != "classification" and pred is not None:
+            if task_type not in ("classification", "tabular") and pred is not None:
                 t0_pmask = time.time()
 
                 # 3D (point cloud) detection predictions: same dual payload as
@@ -1748,7 +1801,12 @@ def _json_default(o):
                 else:
                     np_img, is_volumetric, original_shape, middle_pil = None, False, [], None
 
-                if middle_pil is not None:
+                # Tabular input: the model input is a 1-D feature vector, not an
+                # image. Transmit the actual values (lossless) as a 'vector'
+                # raw_data stat instead of a lossy WebP image.
+                if looks_like_tabular(np_img):
+                    data_stats.append(build_tabular_raw_data_stat(np_img))
+                elif middle_pil is not None:
                     original_size = middle_pil.size
                     target_width = original_size[0]
                     target_height = original_size[1]
@@ -2653,6 +2711,45 @@ def _agent_show_config(self, param=None) -> str:
             text = text[:max_len] + "\n... (truncated; ask for a specific key, e.g. 'show the root log dir')"
         return f"Configuration ({hp_name}):\n{text}"
 
+    @staticmethod
+    def _mask_from_coerced_query(df, expr: str):
+        """Best-effort boolean mask for `expr` against `df`, tolerating two
+        shapes of "numeric in practice, awkward in dtype" columns that break a
+        plain comparison in df.query()/df.eval():
+
+        1. A column referenced by `expr` is index-level-only (e.g. ``sample_id``
+           on a (sample_id, annotation_id) MultiIndex) — promoted to a real
+           column via the same ``safe_reset_index`` GetHistogram already uses,
+           so pandas' query engine can reference it at all instead of raising
+           obscure internal errors (e.g. "'UnaryOp' object has no attribute
+           'evaluate'").
+        2. An object-dtype column holds numeric values mixed with a few
+           non-numeric placeholders (e.g. a per-sample signal not yet computed
+           for every row) — cast via ``pd.to_numeric`` (errors="coerce"),
+           matching GetHistogram's own is_numeric classification, so a filter
+           the UI offered as numeric doesn't crash with e.g. "'<' not
+           supported between instances of 'str' and 'int'".
+
+        Returns a boolean array positionally aligned to `df` (safe to apply as
+        ``df[mask]`` regardless of `df`'s actual index), or None if `expr`
+        doesn't parse/evaluate even against this coerced view.
+        """
+        reset = safe_reset_index(df)
+        candidate_names = set(re.findall(r'`([^`]+)`', expr)) | set(re.findall(r'\b[A-Za-z_]\w*\b', expr))
+        to_coerce = {}
+        for col in candidate_names:
+            if col not in reset.columns or reset[col].dtype != object:
+                continue
+            numeric_vals = pd.to_numeric(reset[col], errors="coerce")
+            if numeric_vals.notna().any():
+                to_coerce[col] = numeric_vals
+        coerced = reset.assign(**to_coerce) if to_coerce else reset
+        try:
+            mask = coerced.eval(expr, local_dict={"df": coerced, "np": np, "pd": pd})
+        except Exception:
+            return None
+        return np.asarray(mask, dtype=bool)
+
     def _apply_agent_operation(self, df, func: str, params: dict) -> str:
         """
         Apply an agent-described operation to df in-place.
@@ -2762,8 +2859,23 @@ def _apply_agent_operation(self, df, func: str, params: dict) -> str:
                     df.drop(index=df.index.difference(kept.index), inplace=True)
                     return f"Applied query (eval): {expr}"
                 except Exception as eval_e:
-                    logger.error(f"Query failed: {e} | Eval failed: {eval_e}")
-                    return f"Failed to filter: {expr}"
+                    # Both a plain df.query and df.eval can fail on a column that
+                    # is "numeric enough" by GetHistogram's own classification but
+                    # awkward in practice — either index-level-only (e.g.
+                    # sample_id on a MultiIndex) or object-dtype with a stray
+                    # non-numeric placeholder among otherwise-numeric values (e.g.
+                    # a per-sample signal not yet computed for every row). Retry
+                    # once against a coerced view (see _mask_from_coerced_query)
+                    # so a filter the UI offered as numeric doesn't fail here with
+                    # e.g. "'<' not supported between instances of 'str' and
+                    # 'int'".
+                    mask = self._mask_from_coerced_query(df, expr)
+                    if mask is None:
+                        logger.error(f"Query failed: {e} | Eval failed: {eval_e}")
+                        return f"Failed to filter: {expr}"
+                    kept = df[mask]
+                    df.drop(index=df.index.difference(kept.index), inplace=True)
+                    return f"Applied query: {expr}"
 
         # C) Column Modification (Transform)
         if func == "df.modify":
@@ -3429,6 +3541,48 @@ def _is_metadata_only_request(self, request) -> bool:
         except Exception:
             return False
 
+    @staticmethod
+    def _downsample_curve(vals, max_points=32):
+        """Uniformly downsample a series to <= max_points floats (endpoints kept)."""
+        if len(vals) <= max_points:
+            return [float(v) for v in vals]
+        idx = np.linspace(0, len(vals) - 1, max_points).round().astype(int)
+        return [float(vals[j]) for j in idx]
+
+    def _loss_trajectory_curves(self, sample_ids):
+        """Downsampled per-sample loss curve for the on-cell sparkline.
+
+        Returns ``{str(sample_id): [float, ...]}`` for samples with history; an
+        empty dict when there is no logger or no per-sample loss signal. Read-only —
+        shape classification lives on the write path (enable_loss_shape_signal),
+        not here. Signal name overridable via WL_LOSS_TRAJ_SIGNAL (default "loss").
+        """
+        from weightslab.backend import ledgers
+
+        logger_q = ledgers.get_logger()
+        if logger_q is None:
+            return {}
+
+        signal = os.environ.get("WL_LOSS_TRAJ_SIGNAL", "loss")
+        try:
+            rows = logger_q.query_per_sample(signal, sample_ids=sample_ids)
+        except Exception:
+            logger.debug("loss_trajectory query skipped", exc_info=True)
+            return {}
+
+        by_sid = {}
+        for sid, step, val, _ in rows:
+            by_sid.setdefault(str(sid), []).append((int(step), float(val)))
+
+        curves = {
+            sid: self._downsample_curve([v for _, v in sorted(pts)])
+            for sid, pts in by_sid.items()
+        }
+        if curves:
+            logger.info("[loss_traj] page_ids=%d with_history=%d signal=%s",
+                        len(sample_ids), len(curves), signal)
+        return curves
+
     def _build_metadata_only_response(self, df_slice: pd.DataFrame, requested_cols=None):
         """Build a DataSamplesResponse of metadata DataRecords from dataframe columns only.
 
@@ -3547,6 +3701,20 @@ def _build_metadata_only_response(self, df_slice: pd.DataFrame, requested_cols=N
                             _DataStat(name=col, type="string", shape=[1], value_string="1")
                         )
 
+        # -- Per-sample loss trajectory (the on-cell sparkline; read-only) -----
+        # Ship each visible sample's loss curve as an 'array' DataStat for the grid
+        # sparkline. The loss-SHAPE haze is NOT computed here: classification runs on
+        # the write/train path (enable_loss_shape_signal / write_loss_shapes) which
+        # maintains the tag:loss_shape column, so this path does no per-request work.
+        loss_curves = self._loss_trajectory_curves(sample_ids)
+        for i, sid in enumerate(sample_ids):
+            curve = loss_curves.get(str(sid))
+            if not curve:
+                continue
+            row_stats[i].append(
+                _DataStat(name="loss_trajectory", type="array",
+                          shape=[len(curve)], value=curve))
+
         # -- Build DataRecord list in one comprehension -----------------------
         _DataRecord = pb2.DataRecord
         data_records = [
@@ -4213,6 +4381,12 @@ def status_cb(msg: str):
                     if isinstance(operations, dict): operations = [operations] # Backwards compat
                     if not operations: operations = []
 
+                    # Cooperative cancel: if the client aborted during the (possibly
+                    # long) LLM call, skip executing the operations entirely.
+                    if abort_event.is_set():
+                        logger.info("[ApplyDataQuery] Canceled by client before execution")
+                        return pb2.DataQueryResponse(success=False, message="Query canceled.")
+
                     with self._lock:
                         self._slowUpdateInternals(force=True)
 
@@ -4228,6 +4402,11 @@ def status_cb(msg: str):
                         _SCHEMA_MUTATING_FUNCS = {"df.modify", "df.drop_column"}
 
                         for op in operations:
+                            # Cooperative cancel between operations (frees the lock
+                            # promptly when the client aborts a multi-step query).
+                            if abort_event.is_set():
+                                logger.info("[ApplyDataQuery] Canceled by client mid-execution")
+                                return pb2.DataQueryResponse(success=False, message="Query canceled.")
                             func = op.get("function")
                             params = op.get("params", {}) or {}
 
diff --git a/weightslab/trainer/services/experiment_service.py b/weightslab/trainer/services/experiment_service.py
index 2a4a66d7..b7313c4e 100644
--- a/weightslab/trainer/services/experiment_service.py
+++ b/weightslab/trainer/services/experiment_service.py
@@ -727,9 +727,28 @@ def _handle_save_checkpoint(self, save_op, components, context):
         self._log_audit("checkpoint_save", "success", audit_details)
         return pb2.CommandResponse(success=True, message=msg)
 
+    def _handle_restart_instance(self):
+        self._log_audit("restart_instance", "success", {})
+        logger.warning(
+            "[ExperimentCommand] Restart requested via UI; exiting process so the "
+            "container's restart policy brings it back up and reloads the last checkpoint."
+        )
+
+        def _delayed_exit():
+            # Give the gRPC response a moment to flush to the client before the
+            # process dies.
+            time.sleep(0.5)
+            os._exit(0)
+
+        threading.Thread(target=_delayed_exit, daemon=True).start()
+        return pb2.CommandResponse(success=True, message="Restart requested. The instance will restart shortly.")
+
     # Training & hyperparameter commands
     # -------------------------------------------------------------------------
     def ExperimentCommand(self, request, context):
+        if request.HasField("restart_operation"):
+            return self._handle_restart_instance()
+
         self._ctx.ensure_components()
         components = self._ctx.components
 
@@ -773,11 +792,11 @@ def ExperimentCommand(self, request, context):
                     checkpoint_manager = ledgers.get_checkpoint_manager()
                 except Exception:
                     checkpoint_manager = None
-            if checkpoint_manager is not None and hasattr(checkpoint_manager, "save_logger_snapshot"):
+            if checkpoint_manager is not None and hasattr(checkpoint_manager, "flush_logger_to_disk"):
                 try:
-                    checkpoint_manager.save_logger_snapshot()
+                    checkpoint_manager.flush_logger_to_disk()
                 except Exception:
-                    logger.debug("Could not persist logger snapshot after note update", exc_info=True)
+                    logger.debug("Could not persist logger history after note update", exc_info=True)
 
             # Log to audit trail
             self._log_audit(
diff --git a/weightslab/trainer/trainer_services.py b/weightslab/trainer/trainer_services.py
index 0400c434..030b31e7 100644
--- a/weightslab/trainer/trainer_services.py
+++ b/weightslab/trainer/trainer_services.py
@@ -32,6 +32,25 @@ def _is_truthy(value: str | None) -> bool:
     return str(value or "").strip().lower() in {"1", "true", "yes", "on"}
 
 
+_WEIGHTSLAB_VERSION: str | None = None
+
+
+def _weightslab_version() -> str:
+    """Backend weightslab package version, resolved once and cached.
+
+    Sent to the UI (via GetLatestLoggerDataResponse) so the studio can show the
+    backend version alongside the UI version in the logo tooltip.
+    """
+    global _WEIGHTSLAB_VERSION
+    if _WEIGHTSLAB_VERSION is None:
+        try:
+            from weightslab import __version__ as v
+            _WEIGHTSLAB_VERSION = str(v or "")
+        except Exception:
+            _WEIGHTSLAB_VERSION = ""
+    return _WEIGHTSLAB_VERSION
+
+
 def _load_auth_tokens() -> set[str]:
     """Load accepted bearer/API-key tokens from env vars."""
     raw_values: list[str] = []
@@ -408,7 +427,14 @@ def ResetAgent(self, request, context):
     # -------------------------------------------------------------------------
     def GetLatestLoggerData(self, request, context):
         logger.debug(f"ExperimentServiceServicer.GetLatestLoggerData({request})")
-        return self._exp_service.GetLatestLoggerData(request, context)
+        response = self._exp_service.GetLatestLoggerData(request, context)
+        # Advertise the backend version to the UI (shown in the logo tooltip
+        # next to the UI version). Best-effort: never fail the poll over this.
+        try:
+            response.weightslab_version = _weightslab_version()
+        except Exception:
+            pass
+        return response
 
     # -------------------------------------------------------------------------
     # Training & hyperparameter commands
diff --git a/weightslab/tunnel.py b/weightslab/tunnel.py
new file mode 100644
index 00000000..ba10313c
--- /dev/null
+++ b/weightslab/tunnel.py
@@ -0,0 +1,350 @@
+"""Raw-TCP tunnel client for Weights Studio.
+
+Exposes a *remote* gRPC training backend — e.g. a Google Colab run sitting behind
+a raw-TCP tunnel — on a **local** port, so a local ``weightslab start`` session
+can proxy to it as if it were local.
+
+Why a *raw* byte forwarder (no protocol parsing): the browser speaks gRPC-Web to
+Envoy, and Envoy speaks native HTTP/2 gRPC to its upstream. Those HTTP/2 frames
+must pass through byte-for-byte — anything that re-frames them (an HTTP/1 proxy,
+a gRPC-Web tunnel) breaks the connection. So this shuttles bytes both ways and
+leaves the protocol untouched. The matching remote tunnel must likewise be raw
+TCP — ``bore local 50051 --to bore.pub`` (zero-signup) or ``ngrok tcp 50051``
+(needs a card on the free tier) — and the backend must run **plaintext** so no
+TLS terminates mid-path.
+"""
+
+import logging
+import os
+import re
+import socket
+import subprocess
+import sys
+import threading
+
+logger = logging.getLogger(__name__)
+
+# Env var read as the default ENDPOINT so a bare ``weightslab tunnel`` works
+# once it is exported (e.g. the notebook can print `export WEIGHTSLAB_TUNNEL_ENDPOINT=...`).
+_ENDPOINT_ENV_VAR = "WEIGHTSLAB_TUNNEL_ENDPOINT"
+
+# `bore` (https://github.com/ekzhang/bore) — the zero-signup raw-TCP relay used
+# by the ``serving_bore`` path of ``wl.serve`` to expose a training backend
+# (e.g. from Colab) to the internet so a local Weights Studio can reach it.
+_BORE_VERSION = "v0.6.0"
+_BORE_RELAY = "bore.pub"
+# Keep spawned bore processes referenced so they are not garbage-collected
+# (which would close their pipes and drop the tunnel) while the kernel lives.
+_BORE_PROCS = []
+
+# The default local port a local UI proxy dials (GRPC_BACKEND_PORT default).
+# Binding here makes a remote backend look local to the UI server.
+DEFAULT_LISTEN_PORT = 50051
+
+# Size of the per-read buffer for the byte pump.
+_BUF = 65536
+
+# Scheme prefixes we tolerate on an endpoint string (ngrok prints ``tcp://...``).
+_STRIP_SCHEMES = ("tcp://", "grpc://", "grpcs://", "http://", "https://")
+
+
+def _default_listen_host() -> str:
+    """Interface to bind so the UI's Envoy container can reach the tunnel.
+
+    Docker Desktop (Windows/macOS) routes ``host.docker.internal`` to host
+    loopback, so ``127.0.0.1`` is reachable *and* stays private to this machine.
+    On Linux the compose ``host-gateway`` resolves to the docker bridge IP, which
+    cannot reach a loopback-only listener — so bind all interfaces there.
+    """
+    if sys.platform in ("win32", "darwin"):
+        return "127.0.0.1"
+    return "0.0.0.0"
+
+
+def _parse_endpoint(endpoint: str, default_port=None):
+    """Parse ``[scheme://]host[:port]`` into ``(host, port)``.
+
+    ``default_port`` is used when the endpoint has no ``:port`` (e.g. the user
+    passed ``--remote-port`` separately). Raises ``ValueError`` if neither is
+    available.
+    """
+    ep = endpoint.strip()
+    for scheme in _STRIP_SCHEMES:
+        if ep.lower().startswith(scheme):
+            ep = ep[len(scheme):]
+            break
+    ep = ep.rstrip("/")
+    if ":" in ep:
+        host, _, port = ep.rpartition(":")
+        if not host:
+            raise ValueError(f"Endpoint '{endpoint}' is missing a host")
+        try:
+            return host, int(port)
+        except ValueError:
+            raise ValueError(f"Endpoint '{endpoint}' has a non-numeric port '{port}'")
+    if default_port is None:
+        raise ValueError(
+            f"No port in endpoint '{endpoint}' and no --remote-port given"
+        )
+    return ep, int(default_port)
+
+
+def _pipe(src: socket.socket, dst: socket.socket) -> None:
+    """Copy bytes from ``src`` to ``dst`` until either side closes."""
+    try:
+        while True:
+            data = src.recv(_BUF)
+            if not data:
+                break
+            dst.sendall(data)
+    except OSError:
+        # Peer reset / half-open close — normal on connection teardown.
+        pass
+    finally:
+        # Unblock the paired pump so both sockets tear down together.
+        for s in (src, dst):
+            try:
+                s.shutdown(socket.SHUT_RDWR)
+            except OSError:
+                pass
+
+
+def _handle(client: socket.socket, remote_addr) -> None:
+    """Dial the remote for one accepted client and pump both directions.
+
+    The remote is re-resolved per connection (via ``create_connection``) so a
+    tunnel endpoint whose DNS/IP changes is picked up without a restart.
+    """
+    try:
+        remote = socket.create_connection(remote_addr, timeout=10)
+    except OSError as exc:
+        logger.warning(
+            f"Could not reach remote {remote_addr[0]}:{remote_addr[1]}: {exc}"
+        )
+        try:
+            client.close()
+        except OSError:
+            pass
+        return
+    remote.settimeout(None)
+    client.settimeout(None)
+    threading.Thread(target=_pipe, args=(client, remote), daemon=True).start()
+    threading.Thread(target=_pipe, args=(remote, client), daemon=True).start()
+
+
+def run_tunnel(remote_host: str, remote_port: int,
+               listen_host: str = None,
+               listen_port: int = DEFAULT_LISTEN_PORT) -> int:
+    """Forward ``listen_host:listen_port`` (local) to ``remote_host:remote_port``.
+
+    Blocks until Ctrl+C. Returns a process exit code (0 on clean stop, 1 if the
+    local port could not be bound).
+    """
+    listen_host = listen_host or _default_listen_host()
+    remote_addr = (remote_host, remote_port)
+
+    # Best-effort reachability probe so the user gets immediate feedback instead
+    # of a silent hang when the Colab cell / ngrok tunnel isn't up yet.
+    try:
+        probe = socket.create_connection(remote_addr, timeout=8)
+        probe.close()
+        logger.info(f" Remote backend reachable at {remote_host}:{remote_port}")
+    except OSError as exc:
+        logger.warning(
+            f"Remote {remote_host}:{remote_port} not reachable yet ({exc}). "
+            "Starting anyway — is the Colab training cell running with the tunnel "
+            "(e.g. bore) up?"
+        )
+
+    srv = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    srv.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+    try:
+        srv.bind((listen_host, listen_port))
+    except OSError as exc:
+        logger.error(
+            f"Cannot bind {listen_host}:{listen_port}: {exc}. "
+            "Another process (a local backend, or a previous tunnel) may already "
+            "hold it — stop it or pass a different --listen-port."
+        )
+        srv.close()
+        return 1
+    srv.listen(128)
+
+    logger.info("=" * 60)
+    logger.info(f" Tunnel up: {listen_host}:{listen_port}  ->  {remote_host}:{remote_port}")
+    logger.info(" If the UI isn't running yet, in another terminal: weightslab start")
+    logger.info(" Then open the URL printed by the start command")
+    logger.info(" Ctrl+C to stop the tunnel.")
+    logger.info("=" * 60)
+
+    try:
+        while True:
+            client, peer = srv.accept()
+            logger.debug(f"New connection from {peer[0]}:{peer[1]}")
+            _handle(client, remote_addr)
+    except KeyboardInterrupt:
+        logger.info("Tunnel stopped.")
+        return 0
+    finally:
+        srv.close()
+
+
+def tunnel_connect(args) -> None:
+    """`weightslab tunnel [ENDPOINT] [opts]` handler. Exits the process.
+
+    ENDPOINT is optional: when omitted it falls back to the
+    ``WEIGHTSLAB_TUNNEL_ENDPOINT`` env var, so a bare ``weightslab tunnel``
+    works once that is set. Everything else (local port 50051, auto listen
+    host) already defaults.
+    """
+    endpoint = getattr(args, "endpoint", None) or os.environ.get(_ENDPOINT_ENV_VAR)
+    if not endpoint:
+        logger.error(
+            "No endpoint given. Pass it as an argument or set "
+            f"{_ENDPOINT_ENV_VAR}.\n"
+            "  e.g. weightslab tunnel bore.pub:12345\n"
+            f"  or   export {_ENDPOINT_ENV_VAR}=bore.pub:12345 && weightslab tunnel"
+        )
+        sys.exit(2)
+
+    remote_port_arg = getattr(args, "remote_port", None)
+    try:
+        host, port = _parse_endpoint(endpoint, default_port=remote_port_arg)
+    except ValueError as exc:
+        logger.error(str(exc))
+        sys.exit(2)
+
+    listen_host = getattr(args, "listen_host", None)
+    listen_port = getattr(args, "listen_port", None) or DEFAULT_LISTEN_PORT
+    rc = run_tunnel(host, port, listen_host=listen_host, listen_port=listen_port)
+    sys.exit(rc or 0)
+
+
+# ---------------------------------------------------------------------------
+# Server side: expose a LOCAL backend port to the internet via a bore relay.
+# This is the counterpart of the client above — it runs on the training host
+# (e.g. Colab) and is what ``wl.serve(serving_bore=True)`` drives.
+# ---------------------------------------------------------------------------
+
+def _bore_asset(version: str):
+    """Return ``(asset_filename, archive_kind)`` for the running platform."""
+    import platform
+
+    system = platform.system().lower()
+    machine = platform.machine().lower()
+    arch = {"x86_64": "x86_64", "amd64": "x86_64",
+            "aarch64": "aarch64", "arm64": "aarch64"}.get(machine)
+    if system == "linux" and arch:
+        return f"bore-{version}-{arch}-unknown-linux-musl.tar.gz", "tar"
+    if system == "darwin" and arch:
+        return f"bore-{version}-{arch}-apple-darwin.tar.gz", "tar"
+    if system == "windows":
+        return f"bore-{version}-x86_64-pc-windows-msvc.zip", "zip"
+    raise RuntimeError(
+        f"No prebuilt bore binary for {system}/{machine}. "
+        "Install bore manually (https://github.com/ekzhang/bore) and put it on PATH."
+    )
+
+
+def _ensure_bore(version: str = _BORE_VERSION) -> str:
+    """Return a path to a runnable ``bore`` binary, downloading it if needed.
+
+    Prefers a ``bore`` already on PATH; otherwise downloads the release asset
+    for this platform into ``~/.weightslab/bin`` and caches it there.
+    """
+    import shutil
+    from pathlib import Path
+
+    on_path = shutil.which("bore")
+    if on_path:
+        return on_path
+
+    cache = Path.home() / ".weightslab" / "bin"
+    cache.mkdir(parents=True, exist_ok=True)
+    exe = cache / ("bore.exe" if sys.platform == "win32" else "bore")
+    if exe.exists():
+        return str(exe)
+
+    import tarfile
+    import tempfile
+    import urllib.request
+    import zipfile
+
+    asset, kind = _bore_asset(version)
+    url = f"https://github.com/ekzhang/bore/releases/download/{version}/{asset}"
+    logger.info(f"Downloading bore {version} ({asset})...")
+    tmp = Path(tempfile.mkdtemp())
+    archive = tmp / asset
+    urllib.request.urlretrieve(url, archive)
+    if kind == "tar":
+        with tarfile.open(archive) as t:
+            t.extractall(tmp)
+    else:
+        with zipfile.ZipFile(archive) as z:
+            z.extractall(tmp)
+
+    found = None
+    for p in tmp.rglob("bore*"):
+        if p.is_file() and p.suffix in ("", ".exe"):
+            found = p
+            break
+    if found is None:
+        raise RuntimeError(f"bore binary not found inside {asset}")
+    shutil.copy2(found, exe)
+    if sys.platform != "win32":
+        os.chmod(exe, 0o755)
+    return str(exe)
+
+
+def serve_bore(port: int = DEFAULT_LISTEN_PORT, relay: str = _BORE_RELAY,
+               version: str = _BORE_VERSION, timeout: float = 30.0):
+    """Expose local ``port`` to the internet over a raw-TCP bore relay.
+
+    Downloads the bore client if needed, starts ``bore local <port> --to
+    <relay>`` in the background, and returns the public endpoint string (e.g.
+    ``"bore.pub:53984"``), or ``None`` if no endpoint was reported within
+    ``timeout`` seconds. Non-blocking beyond that initial handshake — the tunnel
+    keeps running in the background for the life of the process.
+
+    Security note: the public relay (``bore.pub``) is shared; the random remote
+    port is the only thing guarding the endpoint. Fine for a demo, not for
+    sensitive data.
+    """
+    try:
+        bore = _ensure_bore(version)
+    except Exception as exc:  # download / platform failure — non-fatal for serve()
+        logger.warning(f"Could not set up bore tunnel: {exc}")
+        return None
+
+    # Imported lazily: utils.tools pulls in torch, and keeping it out of this
+    # module's import path lets the torch-free `weightslab` CLI stay fast.
+    from weightslab.utils.tools import _running_in_notebook
+
+    proc = subprocess.Popen(
+        [bore, "local", str(port), "--to", relay],
+        stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1, start_new_session=True if _running_in_notebook() else False
+    )  # Bore independent of the parent process (so it keeps running if the notebook kernel restarts)
+    _BORE_PROCS.append(proc)
+
+    result = {"endpoint": None}
+    found = threading.Event()
+    relay_re = re.compile(re.escape(relay) + r":(\d+)")
+
+    def _read():
+        # One reader thread both captures the endpoint and keeps draining the
+        # pipe afterwards, so it never fills and stalls the tunnel.
+        for line in proc.stdout:
+            if result["endpoint"] is None:
+                m = relay_re.search(line)
+                if m:
+                    result["endpoint"] = f"{relay}:{m.group(1)}"
+                    found.set()
+
+    threading.Thread(target=_read, daemon=True).start()
+    found.wait(timeout)
+    if result["endpoint"] is None:
+        logger.warning(
+            f"bore did not report an endpoint within {timeout:.0f}s "
+            "(is the relay reachable?)."
+        )
+    return result["endpoint"]
diff --git a/weightslab/ui/__init__.py b/weightslab/ui/__init__.py
new file mode 100644
index 00000000..aaaff4df
--- /dev/null
+++ b/weightslab/ui/__init__.py
@@ -0,0 +1,10 @@
+"""Pure-Python WeightsLab UI server.
+
+This package bundles the pre-built Weights Studio single-page app under
+``weightslab/ui/static`` and serves it together with a gRPC-Web proxy from a
+single stdlib HTTP server -- no Docker, no Envoy, no nginx.
+
+Public entry point: :func:`weightslab.ui.server.serve_ui`.
+"""
+
+from weightslab.ui.server import serve_ui, static_dir  # noqa: F401
diff --git a/weightslab/ui/server.py b/weightslab/ui/server.py
new file mode 100644
index 00000000..945162a9
--- /dev/null
+++ b/weightslab/ui/server.py
@@ -0,0 +1,561 @@
+"""Docker-free WeightsLab UI server.
+
+A single stdlib HTTP server that does everything the old Docker stack
+(Envoy + nginx frontend image) used to do:
+
+* serves the pre-built Weights Studio SPA vendored under ``weightslab/ui/static``,
+* injects a tiny runtime config so the SPA talks to *this* same origin,
+* proxies gRPC-Web (both ``application/grpc-web-text`` and
+  ``application/grpc-web+proto``) to the running backend gRPC server -- the
+  exact translation Envoy performed, re-implemented generically in Python.
+
+The proxy is fully generic: it forwards raw protobuf bytes and never needs the
+message definitions, so it keeps working when the proto changes.  Every RPC is
+dialed as a server-streaming call (a unary response is just a stream of one
+message on the wire), which means one code path handles unary and streaming
+RPCs alike.
+
+Only the Python stdlib and ``grpcio`` (already a hard dependency) are used --
+no new runtime dependencies.
+"""
+
+from __future__ import annotations
+
+import base64
+import os
+import posixpath
+import socket
+import ssl
+import struct
+import sys
+import threading
+import webbrowser
+from http import HTTPStatus
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from typing import Iterable, Optional, Tuple
+from urllib.parse import unquote
+
+import grpc
+
+# --------------------------------------------------------------------------- #
+# Constants
+# --------------------------------------------------------------------------- #
+
+# gRPC-Web frame flags (first byte of every 5-byte frame prefix).
+_FLAG_DATA = 0x00
+_FLAG_TRAILER = 0x80
+
+# 256 MiB, matching the backend's grpc.max_*_message_length options.
+_MAX_MESSAGE_LENGTH = 256 * 1024 * 1024
+
+# Request headers we must never forward as gRPC metadata.
+_HOP_BY_HOP = {
+    "connection", "keep-alive", "proxy-authenticate", "proxy-authorization",
+    "te", "trailers", "transfer-encoding", "upgrade", "host", "content-length",
+    "content-type", "accept", "accept-encoding", "origin", "referer",
+}
+
+
+def static_dir() -> str:
+    """Absolute path to the bundled SPA directory (``weightslab/ui/static``)."""
+    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "static")
+
+
+def has_static_assets() -> bool:
+    """True when a real built SPA (index.html) is bundled."""
+    return os.path.isfile(os.path.join(static_dir(), "index.html"))
+
+
+# --------------------------------------------------------------------------- #
+# gRPC-Web framing helpers
+# --------------------------------------------------------------------------- #
+
+def _iter_frames(payload: bytes) -> Iterable[Tuple[int, bytes]]:
+    """Yield ``(flag, message)`` tuples from a gRPC-Web framed byte string."""
+    offset = 0
+    n = len(payload)
+    while offset + 5 <= n:
+        flag = payload[offset]
+        (length,) = struct.unpack(">I", payload[offset + 1:offset + 5])
+        offset += 5
+        message = payload[offset:offset + length]
+        offset += length
+        yield flag, message
+
+
+def _first_message(payload: bytes) -> bytes:
+    """Extract the first data message from a gRPC-Web request body."""
+    for flag, message in _iter_frames(payload):
+        if not (flag & _FLAG_TRAILER):
+            return message
+    return b""
+
+
+def _data_frame(message: bytes) -> bytes:
+    return bytes([_FLAG_DATA]) + struct.pack(">I", len(message)) + message
+
+
+def _trailer_frame(status: int, message: str) -> bytes:
+    # gRPC-Web trailers are HTTP/1.1-style header lines in the frame payload.
+    safe = message.replace("\r", " ").replace("\n", " ")
+    text = f"grpc-status:{status}\r\ngrpc-message:{safe}\r\n".encode("utf-8")
+    return bytes([_FLAG_TRAILER]) + struct.pack(">I", len(text)) + text
+
+
+# --------------------------------------------------------------------------- #
+# Request handler
+# --------------------------------------------------------------------------- #
+
+class _UIRequestHandler(BaseHTTPRequestHandler):
+    """Serves the SPA and proxies gRPC-Web to the backend gRPC server."""
+
+    protocol_version = "HTTP/1.1"
+    server_version = "WeightsLabUI"
+
+    # Injected by the factory in :func:`serve_ui`.
+    api_prefix: str = "/api"
+    static_root: str = ""
+    channel: "grpc.Channel" = None  # type: ignore[assignment]
+    grpc_auth_token: Optional[str] = None
+    rpc_timeout: float = 300.0
+
+    # -- logging: quiet by default, honour WEIGHTSLAB_UI_VERBOSE ------------- #
+    def log_message(self, fmt, *args):  # noqa: D401
+        if os.getenv("WEIGHTSLAB_UI_VERBOSE"):
+            sys.stderr.write("[weightslab-ui] %s - %s\n"
+                             % (self.address_string(), fmt % args))
+
+    # -- CORS (harmless same-origin; also enables `vite dev` against us) ----- #
+    def _send_cors(self):
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.send_header("Access-Control-Allow-Methods", "POST, GET, OPTIONS")
+        self.send_header(
+            "Access-Control-Allow-Headers",
+            "content-type,x-grpc-web,x-user-agent,grpc-timeout,authorization,"
+            "x-grpc-token",
+        )
+        self.send_header(
+            "Access-Control-Expose-Headers",
+            "grpc-status,grpc-message,grpc-status-details-bin",
+        )
+
+    def do_OPTIONS(self):  # noqa: N802
+        self.send_response(HTTPStatus.NO_CONTENT)
+        self._send_cors()
+        self.send_header("Content-Length", "0")
+        self.end_headers()
+
+    def do_GET(self):  # noqa: N802
+        self._serve_static()
+
+    def do_HEAD(self):  # noqa: N802
+        self._serve_static(head_only=True)
+
+    def do_POST(self):  # noqa: N802
+        path = self.path.split("?", 1)[0]
+        if path.startswith(self.api_prefix + "/") or path == self.api_prefix:
+            self._proxy_grpc_web(path)
+        else:
+            self._send_simple(HTTPStatus.NOT_FOUND, "Not found")
+
+    # ------------------------------------------------------------------ #
+    # gRPC-Web proxy
+    # ------------------------------------------------------------------ #
+    def _proxy_grpc_web(self, path: str):
+        # Strip the API prefix to recover the gRPC method path, e.g.
+        # /api/ExperimentService/GetWeights -> /ExperimentService/GetWeights
+        method_path = path[len(self.api_prefix):]
+        if not method_path.startswith("/"):
+            method_path = "/" + method_path
+
+        content_type = (self.headers.get("Content-Type") or "").lower()
+        is_text = "grpc-web-text" in content_type
+
+        try:
+            length = int(self.headers.get("Content-Length", "0"))
+        except ValueError:
+            length = 0
+        raw = self.rfile.read(length) if length else b""
+        body = base64.b64decode(raw) if (is_text and raw) else raw
+        request_message = _first_message(body)
+
+        metadata = self._collect_metadata()
+
+        stub = self.channel.unary_stream(
+            method_path,
+            request_serializer=lambda b: b,
+            response_deserializer=lambda b: b,
+        )
+
+        out = bytearray()
+        status_code = 0
+        status_message = ""
+        try:
+            call = stub(request_message, metadata=metadata,
+                        timeout=self.rpc_timeout)
+            for message in call:
+                out += _data_frame(message)
+        except grpc.RpcError as err:
+            status_code = int(err.code().value[0]) if err.code() else 2
+            status_message = err.details() or str(err)
+        except Exception as err:  # pragma: no cover - defensive
+            status_code = 2  # UNKNOWN
+            status_message = str(err)
+
+        out += _trailer_frame(status_code, status_message)
+        payload = base64.b64encode(bytes(out)) if is_text else bytes(out)
+
+        resp_content_type = (
+            "application/grpc-web-text" if is_text
+            else "application/grpc-web+proto"
+        )
+        self.send_response(HTTPStatus.OK)
+        self._send_cors()
+        self.send_header("Content-Type", resp_content_type)
+        self.send_header("Content-Length", str(len(payload)))
+        # Mirror status in HTTP headers too (belt and suspenders).
+        self.send_header("grpc-status", str(status_code))
+        if status_message:
+            self.send_header("grpc-message", status_message)
+        self.end_headers()
+        if payload:
+            self.wfile.write(payload)
+
+    def _collect_metadata(self):
+        metadata = []
+        for key, value in self.headers.items():
+            lkey = key.lower()
+            if lkey in _HOP_BY_HOP or lkey.startswith("access-control"):
+                continue
+            # grpc metadata keys must be ascii lowercase; skip pseudo headers.
+            if lkey.startswith(":"):
+                continue
+            metadata.append((lkey, value))
+        if self.grpc_auth_token:
+            metadata.append(("x-grpc-token", self.grpc_auth_token))
+            metadata.append(("authorization", f"Bearer {self.grpc_auth_token}"))
+        return metadata
+
+    # ------------------------------------------------------------------ #
+    # Static SPA serving (with SPA fallback to index.html)
+    # ------------------------------------------------------------------ #
+    def _resolve_static_path(self, url_path: str) -> Optional[str]:
+        url_path = unquote(url_path.split("?", 1)[0].split("#", 1)[0])
+        # Normalise and prevent path traversal.
+        norm = posixpath.normpath(url_path)
+        parts = [p for p in norm.split("/") if p not in ("", ".", "..")]
+        candidate = os.path.join(self.static_root, *parts)
+        if os.path.isdir(candidate):
+            candidate = os.path.join(candidate, "index.html")
+        return candidate
+
+    def _serve_static(self, head_only: bool = False):
+        if not self.static_root or not os.path.isdir(self.static_root):
+            self._send_simple(
+                HTTPStatus.SERVICE_UNAVAILABLE,
+                "Weights Studio UI assets are not bundled in this install.",
+            )
+            return
+
+        url_path = self.path.split("?", 1)[0]
+        candidate = self._resolve_static_path(url_path)
+        index_path = os.path.join(self.static_root, "index.html")
+
+        if candidate and os.path.isfile(candidate):
+            target = candidate
+        else:
+            # SPA fallback: any unknown route serves index.html.
+            target = index_path
+
+        if not os.path.isfile(target):
+            self._send_simple(HTTPStatus.NOT_FOUND, "Not found")
+            return
+
+        is_index = os.path.abspath(target) == os.path.abspath(index_path)
+        if is_index:
+            data = self._render_index(target)
+            ctype = "text/html; charset=utf-8"
+        else:
+            with open(target, "rb") as fh:
+                data = fh.read()
+            ctype = _guess_type(target)
+
+        self.send_response(HTTPStatus.OK)
+        self._send_cors()
+        self.send_header("Content-Type", ctype)
+        self.send_header("Content-Length", str(len(data)))
+        if is_index:
+            self.send_header("Cache-Control", "no-cache")
+        self.end_headers()
+        if not head_only:
+            self.wfile.write(data)
+
+    def _render_index(self, index_path: str) -> bytes:
+        with open(index_path, "r", encoding="utf-8") as fh:
+            html = fh.read()
+        token_js = (
+            f"window.GRPC_AUTH_TOKEN={_js_str(self.grpc_auth_token)};"
+            "window.WS_ENABLE_GRPC_AUTH_TOKEN='1';"
+            if self.grpc_auth_token
+            else "window.WS_ENABLE_GRPC_AUTH_TOKEN='0';"
+        )
+        # Self-configuring: point the SPA at *this* origin's /api, whatever the
+        # host/port/scheme happens to be (no rebuild). Plus the feature/cache
+        # runtime globals from env vars — the faithful replacement for the old
+        # nginx config.js, so the UI stays tunable without a rebuild or Docker.
+        config = (
+            "<script>(function(){try{"
+            "window.WS_SERVER_HOST=window.location.host+'"
+            f"{self.api_prefix}';"
+            "window.WS_SERVER_PROTOCOL=window.location.protocol.replace(':','');"
+            f"{token_js}"
+            f"{_ui_env_globals_js()}"
+            "}catch(e){console.error('[weightslab-ui] config error',e);}})();"
+            "</script>"
+        )
+        if "</head>" in html:
+            html = html.replace("</head>", config + "</head>", 1)
+        else:
+            html = config + html
+        return html.encode("utf-8")
+
+    def _send_simple(self, status: HTTPStatus, message: str):
+        data = message.encode("utf-8")
+        self.send_response(status)
+        self._send_cors()
+        self.send_header("Content-Type", "text/plain; charset=utf-8")
+        self.send_header("Content-Length", str(len(data)))
+        self.end_headers()
+        self.wfile.write(data)
+
+
+def _js_str(value: Optional[str]) -> str:
+    if value is None:
+        return "''"
+    escaped = value.replace("\\", "\\\\").replace("'", "\\'")
+    return f"'{escaped}'"
+
+
+# Runtime UI-config globals mirrored from environment variables — the faithful,
+# Docker-free replacement for the old nginx entrypoint's config.js. Each
+# ``window.WS_*`` global is set from the FIRST environment variable present in
+# its candidate list, so a deployer can tune the UI at launch time without
+# rebuilding. When none are set the global is omitted and the SPA falls back to
+# its own built-in default.
+_UI_ENV_GLOBALS = [
+    ("WS_HISTOGRAM_MAX_BINS", ("WS_HISTOGRAM_MAX_BINS", "VITE_HISTOGRAM_MAX_BINS")),
+    ("WS_BB_THUMB_RENDER", ("BB_THUMB_RENDER", "WS_BB_THUMB_RENDER", "VITE_BB_THUMB_RENDER")),
+    ("WS_BB_MODAL_RENDER", ("BB_MODAL_RENDER", "WS_BB_MODAL_RENDER", "VITE_BB_MODAL_RENDER")),
+    ("WS_GRID_WINDOW_SIZE", ("GRID_WINDOW_SIZE", "VITE_GRID_WINDOW_SIZE")),
+    ("WS_MAX_IMAGE_CACHE_SIZE", ("GRID_MAX_IMAGE_CACHE_SIZE", "VITE_WS_MAX_IMAGE_CACHE_SIZE")),
+    ("WS_GRID_CACHE_MAX_MB", ("GRID_CACHE_MAX_MB", "VITE_WS_GRID_CACHE_MAX_MB")),
+    ("WS_MODAL_CACHE_MAX_MB", ("MODAL_CACHE_MAX_MB", "VITE_WS_MODAL_CACHE_MAX_MB")),
+    ("WS_WL_PC_MAX_POINTS", ("PC_MAX_POINTS", "VITE_WL_PC_MAX_POINTS")),
+    ("WS_WL_DISABLE_GPU_RENDERING", ("DISABLE_GPU_RENDERING", "VITE_WL_DISABLE_GPU_RENDERING")),
+    ("WS_ENABLE_PLOTS", ("ENABLE_PLOTS", "WS_ENABLE_PLOTS", "VITE_ENABLE_PLOTS")),
+    ("WS_ENABLE_DATA_EXPLORATION",
+     ("ENABLE_DATA_EXPLORATION", "WS_ENABLE_DATA_EXPLORATION", "VITE_ENABLE_DATA_EXPLORATION")),
+    ("WS_ENABLE_HYPERPARAMETERS_OPTIMIZATION",
+     ("ENABLE_HYPERPARAMETERS_OPTIMIZATION", "WS_ENABLE_HYPERPARAMETERS_OPTIMIZATION",
+      "VITE_ENABLE_HYPERPARAMETERS_OPTIMIZATION")),
+    ("WS_ENABLE_AGENT", ("ENABLE_AGENT", "WS_ENABLE_AGENT", "VITE_ENABLE_AGENT")),
+]
+
+
+def _ui_env_globals_js() -> str:
+    """Build ``window.WS_*=...;`` assignments for any configured env vars (else empty)."""
+    parts = []
+    for window_key, candidates in _UI_ENV_GLOBALS:
+        for env_name in candidates:
+            val = os.environ.get(env_name)
+            if val:
+                parts.append(f"window.{window_key}={_js_str(val)};")
+                break
+    return "".join(parts)
+
+
+def _guess_type(path: str) -> str:
+    import mimetypes
+    ctype, _ = mimetypes.guess_type(path)
+    if ctype is None:
+        # Common web types mimetypes sometimes misses.
+        ext = os.path.splitext(path)[1].lower()
+        ctype = {
+            ".js": "text/javascript",
+            ".mjs": "text/javascript",
+            ".wasm": "application/wasm",
+            ".map": "application/json",
+        }.get(ext, "application/octet-stream")
+    if ctype.startswith("text/") and "charset" not in ctype:
+        ctype += "; charset=utf-8"
+    return ctype
+
+
+# --------------------------------------------------------------------------- #
+# gRPC upstream channel
+# --------------------------------------------------------------------------- #
+
+def _build_channel(backend_host: str, backend_port: int,
+                   certs_dir: Optional[str]) -> "grpc.Channel":
+    target = f"{backend_host}:{backend_port}"
+    options = [
+        ("grpc.max_send_message_length", _MAX_MESSAGE_LENGTH),
+        ("grpc.max_receive_message_length", _MAX_MESSAGE_LENGTH),
+    ]
+    if certs_dir:
+        ca = os.path.join(certs_dir, "ca.crt")
+        client_crt = os.path.join(certs_dir, "ui-client.crt")
+        client_key = os.path.join(certs_dir, "ui-client.key")
+        if os.path.isfile(ca):
+            root = _read(ca)
+            key = _read(client_key) if os.path.isfile(client_key) else None
+            crt = _read(client_crt) if os.path.isfile(client_crt) else None
+            creds = grpc.ssl_channel_credentials(
+                root_certificates=root,
+                private_key=key,
+                certificate_chain=crt,
+            )
+            return grpc.secure_channel(target, creds, options=options)
+    return grpc.insecure_channel(target, options=options)
+
+
+def _read(path: str) -> bytes:
+    with open(path, "rb") as fh:
+        return fh.read()
+
+
+# --------------------------------------------------------------------------- #
+# Public entry point
+# --------------------------------------------------------------------------- #
+
+def serve_ui(
+    ui_host: str = "0.0.0.0",
+    ui_port: int = 8080,
+    backend_host: str = "localhost",
+    backend_port: int = 50051,
+    open_browser: bool = True,
+    certs_dir: Optional[str] = None,
+    grpc_auth_token: Optional[str] = None,
+    block: bool = True,
+) -> ThreadingHTTPServer:
+    """Start the Docker-free WeightsLab UI server.
+
+    Serves the bundled SPA and proxies gRPC-Web to ``backend_host:backend_port``.
+
+    Parameters
+    ----------
+    ui_host, ui_port:
+        Interface / port the HTTP server binds.  Open ``http://<host>:<port>``.
+    backend_host, backend_port:
+        The running WeightsLab gRPC backend (``wl.serve``) to proxy to.
+    open_browser:
+        Open the default web browser at the UI URL once the server is up.
+    certs_dir:
+        Optional ``WEIGHTSLAB_CERTS_DIR``.  When it contains TLS material the
+        server serves HTTPS downstream and dials the backend with mTLS.
+    grpc_auth_token:
+        Optional gRPC auth token forwarded to the backend and exposed to the SPA.
+    block:
+        When True (default) serve forever; otherwise return immediately with the
+        server running in a daemon thread.
+    """
+    root = static_dir()
+    if not has_static_assets():
+        sys.stderr.write(
+            "\n[weightslab] WARNING: no built UI assets found at\n"
+            f"    {root}\n"
+            "  The server will still proxy gRPC-Web, but the web page will be\n"
+            "  unavailable.  Build the frontend and vendor it into this package\n"
+            "  (see weights_studio: `npm run build:embed`).\n\n"
+        )
+
+    channel = _build_channel(backend_host, backend_port, certs_dir)
+
+    # Determine downstream TLS from cert presence.
+    ssl_ctx = None
+    scheme = "http"
+    if certs_dir:
+        server_crt = os.path.join(certs_dir, "ui-server.crt")
+        server_key = os.path.join(certs_dir, "ui-server.key")
+        if os.path.isfile(server_crt) and os.path.isfile(server_key):
+            ssl_ctx = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
+            ssl_ctx.load_cert_chain(server_crt, server_key)
+            scheme = "https"
+
+    handler_cls = type(
+        "_BoundUIRequestHandler",
+        (_UIRequestHandler,),
+        {
+            "static_root": root,
+            "channel": channel,
+            "api_prefix": "/api",
+            "grpc_auth_token": grpc_auth_token,
+        },
+    )
+
+    httpd = ThreadingHTTPServer((ui_host, ui_port), handler_cls)
+    httpd.daemon_threads = True
+    if ssl_ctx is not None:
+        httpd.socket = ssl_ctx.wrap_socket(httpd.socket, server_side=True)
+
+    # Use the port actually bound (handles ui_port=0 and any late fallback).
+    ui_port = httpd.server_address[1]
+    display_host = "localhost" if ui_host in ("0.0.0.0", "::", "") else ui_host
+    url = f"{scheme}://{display_host}:{ui_port}"
+
+    sys.stdout.write(
+        "\n"
+        "  WeightsLab UI is running:\n"
+        f"      {url}\n"
+        f"    proxying gRPC-Web  ->  {backend_host}:{backend_port}\n"
+        "    Press Ctrl+C to stop.\n\n"
+    )
+    sys.stdout.flush()
+
+    if open_browser:
+        threading.Timer(0.6, lambda: _safe_open(url)).start()
+
+    if not block:
+        thread = threading.Thread(target=httpd.serve_forever, daemon=True)
+        thread.start()
+        return httpd
+
+    try:
+        httpd.serve_forever()
+    except KeyboardInterrupt:
+        sys.stdout.write("\n  Shutting down WeightsLab UI...\n")
+    finally:
+        httpd.shutdown()
+        httpd.server_close()
+    return httpd
+
+
+def _safe_open(url: str):
+    try:
+        webbrowser.open(url)
+    except Exception:
+        pass
+
+
+def find_free_port(preferred: int, host: str = "0.0.0.0") -> int:
+    """Return ``preferred`` if bindable, else an OS-assigned free port."""
+    if preferred <= 0:
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        try:
+            sock.bind((host, 0))
+            return sock.getsockname()[1]
+        finally:
+            sock.close()
+    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    try:
+        sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        sock.bind((host, preferred))
+        return preferred
+    except OSError:
+        sock.close()
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        sock.bind((host, 0))
+        port = sock.getsockname()[1]
+        return port
+    finally:
+        sock.close()
diff --git a/weightslab/docker/docker/utils/generate-certs-auth-token.ps1 b/weightslab/ui/utils/generate-certs-auth-token.ps1
similarity index 66%
rename from weightslab/docker/docker/utils/generate-certs-auth-token.ps1
rename to weightslab/ui/utils/generate-certs-auth-token.ps1
index caa7bded..dea6737f 100644
--- a/weightslab/docker/docker/utils/generate-certs-auth-token.ps1
+++ b/weightslab/ui/utils/generate-certs-auth-token.ps1
@@ -24,7 +24,7 @@ New-Item -ItemType Directory -Force -Path $userCertDir | Out-Null
 
 # Check if certs already exist in user directory
 $certsExist = (Test-Path (Join-Path $userCertDir "ca.crt")) -and `
-              (Test-Path (Join-Path $userCertDir "envoy-server.crt")) -and `
+              (Test-Path (Join-Path $userCertDir "ui-server.crt")) -and `
               (Test-Path (Join-Path $userCertDir "backend-server.crt"))
 
 # If certs exist and not forcing recreation, skip generation
@@ -50,40 +50,40 @@ try {
     @"
     subjectAltName = DNS:localhost,IP:127.0.0.1,IP:0:0:0:0:0:0:0:1
 extendedKeyUsage = serverAuth
-"@ | Set-Content -Path (Join-Path $tmpDir "envoy-server.ext") -Encoding ASCII
+"@ | Set-Content -Path (Join-Path $tmpDir "ui-server.ext") -Encoding ASCII
 
     @"
-    subjectAltName = DNS:localhost,DNS:host.docker.internal,IP:127.0.0.1,IP:0:0:0:0:0:0:0:1
+    subjectAltName = DNS:localhost,IP:127.0.0.1,IP:0:0:0:0:0:0:0:1
 extendedKeyUsage = serverAuth
 "@ | Set-Content -Path (Join-Path $tmpDir "backend-server.ext") -Encoding ASCII
 
     @"
 extendedKeyUsage = clientAuth
-"@ | Set-Content -Path (Join-Path $tmpDir "envoy-client.ext") -Encoding ASCII
+"@ | Set-Content -Path (Join-Path $tmpDir "ui-client.ext") -Encoding ASCII
 
-    Write-Host "Generating Envoy HTTPS server cert..."
-    & openssl genrsa -out (Join-Path $tmpDir "envoy-server.key") 2048
-    & openssl req -new -key (Join-Path $tmpDir "envoy-server.key") -subj "/CN=localhost" -out (Join-Path $tmpDir "envoy-server.csr")
-    & openssl x509 -req -in (Join-Path $tmpDir "envoy-server.csr") -CA (Join-Path $tmpDir "ca.crt") -CAkey (Join-Path $tmpDir "ca.key") `
-        -CAcreateserial -out (Join-Path $tmpDir "envoy-server.crt") -days 825 -sha256 -extfile (Join-Path $tmpDir "envoy-server.ext")
+    Write-Host "Generating UI HTTPS server cert..."
+    & openssl genrsa -out (Join-Path $tmpDir "ui-server.key") 2048
+    & openssl req -new -key (Join-Path $tmpDir "ui-server.key") -subj "/CN=localhost" -out (Join-Path $tmpDir "ui-server.csr")
+    & openssl x509 -req -in (Join-Path $tmpDir "ui-server.csr") -CA (Join-Path $tmpDir "ca.crt") -CAkey (Join-Path $tmpDir "ca.key") `
+        -CAcreateserial -out (Join-Path $tmpDir "ui-server.crt") -days 825 -sha256 -extfile (Join-Path $tmpDir "ui-server.ext")
 
     Write-Host "Generating backend gRPC server cert..."
     & openssl genrsa -out (Join-Path $tmpDir "backend-server.key") 2048
-    & openssl req -new -key (Join-Path $tmpDir "backend-server.key") -subj "/CN=host.docker.internal" -out (Join-Path $tmpDir "backend-server.csr")
+    & openssl req -new -key (Join-Path $tmpDir "backend-server.key") -subj "/CN=localhost" -out (Join-Path $tmpDir "backend-server.csr")
     & openssl x509 -req -in (Join-Path $tmpDir "backend-server.csr") -CA (Join-Path $tmpDir "ca.crt") -CAkey (Join-Path $tmpDir "ca.key") `
         -CAcreateserial -out (Join-Path $tmpDir "backend-server.crt") -days 825 -sha256 -extfile (Join-Path $tmpDir "backend-server.ext")
 
-    Write-Host "Generating Envoy mTLS client cert..."
-    & openssl genrsa -out (Join-Path $tmpDir "envoy-client.key") 2048
-    & openssl req -new -key (Join-Path $tmpDir "envoy-client.key") -subj "/CN=envoy-client" -out (Join-Path $tmpDir "envoy-client.csr")
-    & openssl x509 -req -in (Join-Path $tmpDir "envoy-client.csr") -CA (Join-Path $tmpDir "ca.crt") -CAkey (Join-Path $tmpDir "ca.key") `
-        -CAcreateserial -out (Join-Path $tmpDir "envoy-client.crt") -days 825 -sha256 -extfile (Join-Path $tmpDir "envoy-client.ext")
+    Write-Host "Generating UI mTLS client cert..."
+    & openssl genrsa -out (Join-Path $tmpDir "ui-client.key") 2048
+    & openssl req -new -key (Join-Path $tmpDir "ui-client.key") -subj "/CN=ui-client" -out (Join-Path $tmpDir "ui-client.csr")
+    & openssl x509 -req -in (Join-Path $tmpDir "ui-client.csr") -CA (Join-Path $tmpDir "ca.crt") -CAkey (Join-Path $tmpDir "ca.key") `
+        -CAcreateserial -out (Join-Path $tmpDir "ui-client.crt") -days 825 -sha256 -extfile (Join-Path $tmpDir "ui-client.ext")
 
     Copy-Item -Force (Join-Path $tmpDir "ca.crt") (Join-Path $userCertDir "ca.crt")
-    Copy-Item -Force (Join-Path $tmpDir "envoy-server.crt") (Join-Path $userCertDir "envoy-server.crt")
-    Copy-Item -Force (Join-Path $tmpDir "envoy-server.key") (Join-Path $userCertDir "envoy-server.key")
-    Copy-Item -Force (Join-Path $tmpDir "envoy-client.crt") (Join-Path $userCertDir "envoy-client.crt")
-    Copy-Item -Force (Join-Path $tmpDir "envoy-client.key") (Join-Path $userCertDir "envoy-client.key")
+    Copy-Item -Force (Join-Path $tmpDir "ui-server.crt") (Join-Path $userCertDir "ui-server.crt")
+    Copy-Item -Force (Join-Path $tmpDir "ui-server.key") (Join-Path $userCertDir "ui-server.key")
+    Copy-Item -Force (Join-Path $tmpDir "ui-client.crt") (Join-Path $userCertDir "ui-client.crt")
+    Copy-Item -Force (Join-Path $tmpDir "ui-client.key") (Join-Path $userCertDir "ui-client.key")
     Copy-Item -Force (Join-Path $tmpDir "backend-server.crt") (Join-Path $userCertDir "backend-server.crt")
     Copy-Item -Force (Join-Path $tmpDir "backend-server.key") (Join-Path $userCertDir "backend-server.key")
 
diff --git a/weightslab/docker/docker/utils/generate-certs-auth-token.sh b/weightslab/ui/utils/generate-certs-auth-token.sh
similarity index 68%
rename from weightslab/docker/docker/utils/generate-certs-auth-token.sh
rename to weightslab/ui/utils/generate-certs-auth-token.sh
index c45a8d68..6c8f2f2a 100644
--- a/weightslab/docker/docker/utils/generate-certs-auth-token.sh
+++ b/weightslab/ui/utils/generate-certs-auth-token.sh
@@ -44,7 +44,7 @@ mkdir -p "$USER_CERT_DIR"
 
 CERTS_EXIST=false
 if [ -f "$USER_CERT_DIR/ca.crt" ] && \
-   [ -f "$USER_CERT_DIR/envoy-server.crt" ] && \
+   [ -f "$USER_CERT_DIR/ui-server.crt" ] && \
    [ -f "$USER_CERT_DIR/backend-server.crt" ]; then
     CERTS_EXIST=true
 fi
@@ -74,43 +74,43 @@ openssl req -x509 -new -nodes -key "$TMP_DIR/ca.key" -sha256 -days 825 \
     -subj "/CN=weightslab-dev-ca" \
     -out "$TMP_DIR/ca.crt"
 
-cat > "$TMP_DIR/envoy-server.ext" << 'EOF'
+cat > "$TMP_DIR/ui-server.ext" << 'EOF'
 subjectAltName = DNS:localhost,IP:127.0.0.1,IP:0:0:0:0:0:0:0:1
 extendedKeyUsage = serverAuth
 EOF
 
 cat > "$TMP_DIR/backend-server.ext" << 'EOF'
-subjectAltName = DNS:localhost,DNS:host.docker.internal,IP:127.0.0.1,IP:0:0:0:0:0:0:0:1
+subjectAltName = DNS:localhost,IP:127.0.0.1,IP:0:0:0:0:0:0:0:1
 extendedKeyUsage = serverAuth
 EOF
 
-cat > "$TMP_DIR/envoy-client.ext" << 'EOF'
+cat > "$TMP_DIR/ui-client.ext" << 'EOF'
 extendedKeyUsage = clientAuth
 EOF
 
-echo "Generating Envoy HTTPS server cert..."
-openssl genrsa -out "$TMP_DIR/envoy-server.key" 2048
-openssl req -new -key "$TMP_DIR/envoy-server.key" -subj "/CN=localhost" -out "$TMP_DIR/envoy-server.csr"
-openssl x509 -req -in "$TMP_DIR/envoy-server.csr" -CA "$TMP_DIR/ca.crt" -CAkey "$TMP_DIR/ca.key" \
-    -CAcreateserial -out "$TMP_DIR/envoy-server.crt" -days 825 -sha256 -extfile "$TMP_DIR/envoy-server.ext"
+echo "Generating UI HTTPS server cert..."
+openssl genrsa -out "$TMP_DIR/ui-server.key" 2048
+openssl req -new -key "$TMP_DIR/ui-server.key" -subj "/CN=localhost" -out "$TMP_DIR/ui-server.csr"
+openssl x509 -req -in "$TMP_DIR/ui-server.csr" -CA "$TMP_DIR/ca.crt" -CAkey "$TMP_DIR/ca.key" \
+    -CAcreateserial -out "$TMP_DIR/ui-server.crt" -days 825 -sha256 -extfile "$TMP_DIR/ui-server.ext"
 
 echo "Generating backend gRPC server cert..."
 openssl genrsa -out "$TMP_DIR/backend-server.key" 2048
-openssl req -new -key "$TMP_DIR/backend-server.key" -subj "/CN=host.docker.internal" -out "$TMP_DIR/backend-server.csr"
+openssl req -new -key "$TMP_DIR/backend-server.key" -subj "/CN=localhost" -out "$TMP_DIR/backend-server.csr"
 openssl x509 -req -in "$TMP_DIR/backend-server.csr" -CA "$TMP_DIR/ca.crt" -CAkey "$TMP_DIR/ca.key" \
     -CAcreateserial -out "$TMP_DIR/backend-server.crt" -days 825 -sha256 -extfile "$TMP_DIR/backend-server.ext"
 
-echo "Generating Envoy mTLS client cert..."
-openssl genrsa -out "$TMP_DIR/envoy-client.key" 2048
-openssl req -new -key "$TMP_DIR/envoy-client.key" -subj "/CN=envoy-client" -out "$TMP_DIR/envoy-client.csr"
-openssl x509 -req -in "$TMP_DIR/envoy-client.csr" -CA "$TMP_DIR/ca.crt" -CAkey "$TMP_DIR/ca.key" \
-    -CAcreateserial -out "$TMP_DIR/envoy-client.crt" -days 825 -sha256 -extfile "$TMP_DIR/envoy-client.ext"
+echo "Generating UI mTLS client cert..."
+openssl genrsa -out "$TMP_DIR/ui-client.key" 2048
+openssl req -new -key "$TMP_DIR/ui-client.key" -subj "/CN=ui-client" -out "$TMP_DIR/ui-client.csr"
+openssl x509 -req -in "$TMP_DIR/ui-client.csr" -CA "$TMP_DIR/ca.crt" -CAkey "$TMP_DIR/ca.key" \
+    -CAcreateserial -out "$TMP_DIR/ui-client.crt" -days 825 -sha256 -extfile "$TMP_DIR/ui-client.ext"
 
 cp -f "$TMP_DIR/ca.crt" "$USER_CERT_DIR/ca.crt"
-cp -f "$TMP_DIR/envoy-server.crt" "$USER_CERT_DIR/envoy-server.crt"
-cp -f "$TMP_DIR/envoy-server.key" "$USER_CERT_DIR/envoy-server.key"
-cp -f "$TMP_DIR/envoy-client.crt" "$USER_CERT_DIR/envoy-client.crt"
-cp -f "$TMP_DIR/envoy-client.key" "$USER_CERT_DIR/envoy-client.key"
+cp -f "$TMP_DIR/ui-server.crt" "$USER_CERT_DIR/ui-server.crt"
+cp -f "$TMP_DIR/ui-server.key" "$USER_CERT_DIR/ui-server.key"
+cp -f "$TMP_DIR/ui-client.crt" "$USER_CERT_DIR/ui-client.crt"
+cp -f "$TMP_DIR/ui-client.key" "$USER_CERT_DIR/ui-client.key"
 cp -f "$TMP_DIR/backend-server.crt" "$USER_CERT_DIR/backend-server.crt"
 cp -f "$TMP_DIR/backend-server.key" "$USER_CERT_DIR/backend-server.key"
 
diff --git a/weightslab/ui/utils/sync-frontend.sh b/weightslab/ui/utils/sync-frontend.sh
new file mode 100644
index 00000000..6db41053
--- /dev/null
+++ b/weightslab/ui/utils/sync-frontend.sh
@@ -0,0 +1,49 @@
+#!/bin/sh
+# Vendor the built Weights Studio SPA into this package so `weightslab start`
+# can serve it (and so it ships inside the wheel). Docker-free.
+#
+# The frontend build lives in the weights_studio repo (its ./dist, gitignored).
+# This script builds it there (unless SKIP_BUILD=1) and copies dist/ into
+# weightslab/ui/static/ here.
+#
+# Usage:
+#   weightslab/ui/utils/sync-frontend.sh
+#   WEIGHTS_STUDIO_DIR=../weights_studio SKIP_BUILD=1 weightslab/ui/utils/sync-frontend.sh
+#
+# Env:
+#   WEIGHTS_STUDIO_DIR  Path to the weights_studio repo (default: ../weights_studio
+#                       relative to the weightslab repo root).
+#   SKIP_BUILD=1        Reuse the existing dist/ instead of rebuilding.
+
+set -e
+
+# weightslab repo root = three levels up from this script (weightslab/ui/utils/).
+PKG_STATIC_DIR="$(cd "$(dirname "$0")/.." && pwd)/static"
+WL_REPO_ROOT="$(cd "$(dirname "$0")/../../.." && pwd)"
+STUDIO_DIR="${WEIGHTS_STUDIO_DIR:-$WL_REPO_ROOT/../weights_studio}"
+
+if [ ! -d "$STUDIO_DIR" ]; then
+    echo "ERROR: weights_studio repo not found at: $STUDIO_DIR" >&2
+    echo "  Set WEIGHTS_STUDIO_DIR to the weights_studio repo root." >&2
+    exit 1
+fi
+STUDIO_DIR="$(cd "$STUDIO_DIR" && pwd)"
+
+if [ "${SKIP_BUILD:-0}" = "1" ]; then
+    echo "[sync-frontend] SKIP_BUILD=1 -> reusing existing dist/"
+else
+    echo "[sync-frontend] building SPA in $STUDIO_DIR ..."
+    ( cd "$STUDIO_DIR" && npm run build )
+fi
+
+if [ ! -d "$STUDIO_DIR/dist" ]; then
+    echo "ERROR: no dist/ at $STUDIO_DIR/dist (build first)" >&2
+    exit 1
+fi
+
+echo "[sync-frontend] vendoring dist/ -> $PKG_STATIC_DIR"
+rm -rf "$PKG_STATIC_DIR"
+mkdir -p "$PKG_STATIC_DIR"
+cp -R "$STUDIO_DIR/dist/." "$PKG_STATIC_DIR/"
+
+echo "[sync-frontend] done. 'weightslab start' will now serve the updated UI."
diff --git a/weightslab/ui_docker_bridge.py b/weightslab/ui_docker_bridge.py
deleted file mode 100644
index 5fa795e0..00000000
--- a/weightslab/ui_docker_bridge.py
+++ /dev/null
@@ -1,1225 +0,0 @@
-"""Weights Studio Docker management with secure TLS."""
-
-import argparse
-import os
-import re
-import shutil
-import stat
-import subprocess
-import sys
-import socket
-import logging
-from pathlib import Path
-
-from weightslab.security import CertAuthManager
-
-logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
-logger = logging.getLogger(__name__)
-
-# Capture whether WEIGHTSLAB_CERTS_DIR was already in the shell env when this
-# process started (set by the user) vs. injected later by our own code.
-_CERTS_DIR_IN_ORIGINAL_ENV: bool = "WEIGHTSLAB_CERTS_DIR" in os.environ
-
-# Docker resources owned by the bundled stack. Cleanup is scoped strictly to
-# these names — we never run a global `docker system prune`.
-_FRONTEND_IMAGE = "graybx/weightslab"
-_STACK_CONTAINERS = ("weights_studio_envoy", "weights_studio_frontend")
-
-# Default frontend image repo (no tag) and tag. Overridable per-launch via the
-# `--image/-i` and `--version/-v` flags on `weightslab ui launch`, which are
-# passed to docker compose through the WS_FRONTEND_IMAGE env var (see the
-# `${WS_FRONTEND_IMAGE:-...}` substitution in docker-compose.yml).
-_DEFAULT_FRONTEND_REPO = "graybx/weightslab"
-_DEFAULT_FRONTEND_TAG = "latest"
-
-
-def _resolve_frontend_image(image_arg=None, version_arg=None) -> str:
-    """Return the full ``repo:tag`` frontend image ref from the CLI flags.
-
-    ``image_arg`` is the repo (e.g. ``guillaumep2705/weightslab``) and may itself
-    carry a tag; ``version_arg`` is the tag (e.g. ``latest`` or ``v1.2.3``). An
-    explicit ``--version`` always wins over any tag embedded in ``--image``.
-    Falls back to ``graybx/weightslab:latest`` when neither is given.
-    """
-    image = image_arg or _DEFAULT_FRONTEND_REPO
-    # A ':' in the last path segment is a tag (not a registry port like host:5000/img).
-    last_segment = image.rsplit("/", 1)[-1]
-    has_tag = ":" in last_segment
-    if version_arg:
-        repo = image.rsplit(":", 1)[0] if has_tag else image
-        return f"{repo}:{version_arg}"
-    if has_tag:
-        return image
-    return f"{image}:{_DEFAULT_FRONTEND_TAG}"
-
-# Cached Docker Compose base command. Resolved once per process to either the
-# v2 plugin (``["docker", "compose"]``) or the legacy v1 standalone binary
-# (``["docker-compose"]``) — see _detect_compose_cmd(). None until first probe.
-_COMPOSE_BASE_CMD = None
-
-# TLS/auth env vars that are *derived* from cert-file presence in
-# WEIGHTSLAB_CERTS_DIR. WEIGHTSLAB_CERTS_DIR is the single source of truth; these
-# are computed by the deploy pipeline (build-and-deploy.sh + the compose `auto`
-# logic) from the actual files. They are stripped before launching so a stale or
-# pre-set value can never override the file-based decision.
-_DERIVED_DEPLOY_ENV_VARS = (
-    "GRPC_TLS_ENABLED",
-    "GRPC_TLS_REQUIRE_CLIENT_AUTH",
-    "GRPC_TLS_CERT_FILE",
-    "GRPC_TLS_KEY_FILE",
-    "GRPC_TLS_CA_FILE",
-    "ENVOY_UPSTREAM_TLS",
-    "ENVOY_DOWNSTREAM_TLS",
-    "WS_SERVER_PROTOCOL",
-    "VITE_SERVER_PROTOCOL",
-    "VITE_DEV_SERVER_HTTPS",
-    "WL_ENABLE_GRPC_AUTH_TOKEN",
-    "VITE_WL_ENABLE_GRPC_AUTH_TOKEN",
-    "GRPC_AUTH_TOKEN",
-    "VITE_GRPC_AUTH_TOKEN",
-)
-
-
-def _persist_certs_dir(certs_dir_str: str) -> None:
-    """Persist WEIGHTSLAB_CERTS_DIR so future terminals and the training backend find it.
-
-    Windows — runs `setx` (permanent user env) and prints the PS one-liner for
-               the current session.
-    Linux/macOS — appends an export line to ~/.bashrc (idempotent) and prints the
-               source command for the current session.
-    """
-    export_line = f'export WEIGHTSLAB_CERTS_DIR="{certs_dir_str}"'
-    if _is_windows():
-        result = subprocess.run(
-            ["setx", "WEIGHTSLAB_CERTS_DIR", certs_dir_str],
-            stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-        )
-        if result.returncode == 0:
-            logger.info(" WEIGHTSLAB_CERTS_DIR saved permanently via setx (new terminals will have it)")
-        else:
-            logger.warning(f"setx failed — set it manually: setx WEIGHTSLAB_CERTS_DIR \"{certs_dir_str}\"")
-        logger.info(f" Current terminal (PowerShell): $env:WEIGHTSLAB_CERTS_DIR = \"{certs_dir_str}\"")
-    else:
-        bashrc = Path.home() / ".bashrc"
-        try:
-            existing = bashrc.read_text(encoding="utf-8") if bashrc.exists() else ""
-            if export_line not in existing:
-                with open(bashrc, "a", encoding="utf-8") as f:
-                    f.write(f"\n# Added by weightslab\n{export_line}\n")
-                logger.info(f" WEIGHTSLAB_CERTS_DIR appended to {bashrc} (new terminals will have it)")
-            else:
-                logger.info(f" WEIGHTSLAB_CERTS_DIR already in {bashrc}")
-        except OSError as e:
-            logger.warning(f"Could not write to {bashrc}: {e}")
-            logger.info(f" Add manually: {export_line}")
-        logger.info(f" Current terminal: source ~/.bashrc (or open a new terminal)")
-
-
-def _strip_derived_deploy_env() -> None:
-    """Drop derived TLS/auth env vars so the deploy pipeline decides solely from
-    cert-file presence in WEIGHTSLAB_CERTS_DIR (the single source of truth)."""
-    for key in _DERIVED_DEPLOY_ENV_VARS:
-        os.environ.pop(key, None)
-
-
-def _banner() -> str:
-    """Return the WeightsLab ASCII banner, or a plain title if art is unavailable."""
-    try:
-        from weightslab.art import _BANNER
-        return _BANNER
-    except Exception:
-        return "WeightsLab"
-
-
-_DESCRIPTION = (
-    _banner()
-    + "\nWeightsLab — Inspect, Edit, and Evolve Neural Networks\n"
-    + "Manage the Weights Studio UI, its Docker stack, and the secure "
-    + "(TLS + gRPC auth) environment."
-)
-
-_EPILOG = """\
-commands:
-  se Set up the secure environment: generate TLS
-                           certificates + a gRPC auth token in
-                           ~/.weightslab-certs. Then set WEIGHTSLAB_CERTS_DIR
-                           (the single source of truth) so the backend + new
-                           shells find them.
-                             --force-certs regenerate even if certs exist
-
-  ui launch Purge stale weightslab/weights_studio Docker
-                           resources, then build & start the UI stack.
-                           UNSECURED (HTTP) by default — no certs generated.
-                             --certs generate (if missing) + use TLS
-                                             certs + gRPC auth (HTTPS)
-
-  start example Run a bundled PyTorch example (foreground; stop with
-                           Ctrl+C). Installs the example's requirements first,
-                           without prompting. Defaults to classification:
-                             --cls classification example (default)
-                             --seg segmentation example
-                             --det detection example
-                             --clus clustering example
-                             --gen generation example
-                             --3d_det 3D LiDAR point-cloud detection example
-                             --2d_det 2D LiDAR point-cloud detection example
-
-  cli Open an interactive terminal connected to a
-                           currently-running experiment (pause/resume, status,
-                           evaluate, agent query, etc.). Auto-discovers the
-                           running experiment; the experiment must be serving
-                           the CLI (e.g. wl.serve(serving_cli=True)).
-                             --port PORT connect to a specific CLI port
-                             --host HOST connect to a specific host (default: localhost)
-
-examples:
-  weightslab se # one-time secure setup (then export WEIGHTSLAB_CERTS_DIR)
-  weightslab se --force-certs # regenerate the certs
-  weightslab ui launch # clean + launch (unsecured HTTP, default)
-  weightslab ui launch --certs # secured launch (HTTPS + gRPC auth)
-  weightslab ui launch -i guillaumep2705/weightslab # pull frontend from a custom repo (latest)
-  weightslab ui launch -i guillaumep2705/weightslab -v v1.2.3 # pin a specific version/tag
-  weightslab start example # run the classification demo (default)
-  weightslab start example --seg # run the segmentation demo
-  weightslab start example --det # run the detection demo
-  weightslab start example --3d_det # run the 3D LiDAR detection demo
-  weightslab start example --2d_det # run the 2D LiDAR detection demo
-  weightslab cli # connect a terminal to the running experiment
-  weightslab cli --port 60000 # connect to a specific CLI port
-"""
-
-
-def _get_compose_file():
-    """Return the path to the bundled docker-compose.yml."""
-    # return files("weightslab.docker.docker") / "docker-compose.yml"
-    return Path(__file__).parent / 'docker' / 'docker' / 'docker-compose.yml'
-
-
-def _get_envoy_config():
-    """Return the path to the bundled envoy.yaml."""
-    # return files("weightslab.docker.envoy") / "envoy.yaml"
-    return Path(__file__).parent / 'docker' / 'docker' / 'envoy.yaml'
-
-
-def _get_bootstrap_script() -> Path:
-    """Get the bootstrap-secure.ps1 script path."""
-    return Path(__file__).parent / 'docker' / 'docker' / 'utils' / 'build-and-deploy.sh'
-
-
-def _get_cert_script() -> Path:
-    """Get the generate-certs-auth-token.sh script path."""
-    return Path(__file__).parent / 'docker' / 'docker' / 'utils' / 'generate-certs-auth-token.sh'
-
-
-def _get_cert_script_ps1() -> Path:
-    """Get the generate-certs-auth-token.ps1 script path."""
-    return Path(__file__).parent / 'docker' / 'docker' / 'utils' / 'generate-certs-auth-token.ps1'
-
-
-def _is_windows() -> bool:
-    """Check if running on Windows."""
-    return sys.platform == 'win32'
-
-
-def _make_executable(path) -> None:
-    """Add the execute bit to a file so it can be run directly (POSIX only).
-
-    pip installs the bundled ``.sh`` scripts as package data *without* the execute
-    bit, and they are committed/shipped non-executable. ``_run_shell_script`` runs
-    them as a command (``bash -c "VAR=... '/path/script.sh'"``), which requires
-    the execute bit — otherwise bash fails with "Permission denied" until the user
-    ``chmod +x`` by hand. This grants u+x,g+x,o+x (e.g. 0644 -> 0755) and is a
-    best-effort no-op on Windows, where the POSIX execute bit is not used.
-    """
-    if _is_windows():
-        return
-    try:
-        mode = os.stat(path).st_mode
-        os.chmod(path, mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
-    except OSError as exc:
-        # Non-fatal: e.g. a root-owned system install the user can't chmod. They
-        # would not have been able to chmod it manually either; surface as debug.
-        logger.debug(f"Could not set execute bit on {path}: {exc}")
-
-
-def _ensure_scripts_executable() -> None:
-    """Make every bundled shell script executable (POSIX only).
-
-    Covers build-and-deploy.sh, generate-certs-auth-token.sh and the other ``.sh``
-    files under ``weightslab/ui`` so a freshly pip-installed package can run them
-    without the user having to ``chmod +x`` first. No-op on Windows.
-    """
-    if _is_windows():
-        return
-    ui_dir = Path(__file__).parent / 'docker'
-    try:
-        scripts = list(ui_dir.rglob('*.sh'))
-    except OSError as exc:
-        logger.debug(f"Could not enumerate bundled scripts under {ui_dir}: {exc}")
-        return
-    for script in scripts:
-        _make_executable(script)
-
-
-def _detect_compose_cmd():
-    """Return the base command for Docker Compose, preferring v2 over v1.
-
-    Docker Compose ships in two forms:
-      * v2 — the ``docker compose`` CLI plugin (bundled with Docker Desktop and
-        the recommended install). Invoked as two words: ``docker compose``.
-      * v1 — the legacy standalone ``docker-compose`` binary (one word, hyphen).
-
-    We probe v2 first (``docker compose version``) then fall back to v1
-    (``docker-compose version``) so ``weightslab ui launch`` works with either.
-    Returns ``["docker", "compose"]``, ``["docker-compose"]``, or None if neither
-    is available.
-    """
-    # v2: the `docker compose` plugin.
-    try:
-        result = subprocess.run(
-            ["docker", "compose", "version"],
-            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
-        )
-        if result.returncode == 0:
-            return ["docker", "compose"]
-    except FileNotFoundError:
-        # docker itself is missing; _check_docker() surfaces the user-facing error.
-        pass
-
-    # v1: the legacy standalone `docker-compose` binary.
-    if shutil.which("docker-compose") is not None:
-        try:
-            result = subprocess.run(
-                ["docker-compose", "version"],
-                stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
-            )
-            if result.returncode == 0:
-                return ["docker-compose"]
-        except FileNotFoundError:
-            pass
-
-    return None
-
-
-def _compose_base_cmd():
-    """Cached Docker Compose base command (v2 ``docker compose``, else v1 ``docker-compose``)."""
-    global _COMPOSE_BASE_CMD
-    if _COMPOSE_BASE_CMD is None:
-        _COMPOSE_BASE_CMD = _detect_compose_cmd()
-    return _COMPOSE_BASE_CMD
-
-
-def _check_docker():
-    """Verify that docker is installed and the daemon is running."""
-    if shutil.which("docker") is None:
-        logger.error(
-            "Docker is required but not found on your PATH.\n"
-            "Install it from: https://docs.docker.com/get-docker/"
-        )
-        sys.exit(1)
-
-    try:
-        subprocess.run(
-            ["docker", "info"],
-            check=True,
-            stdout=subprocess.DEVNULL,
-            stderr=subprocess.DEVNULL,
-        )
-    except subprocess.CalledProcessError:
-        logger.error(
-            "\n"
-            "============================================================="
-            "============================================================="
-            "Docker is installed but the daemon is not running.\n"
-            "Start it with: Docker Desktop or 'sudo systemctl start docker'."
-            "============================================================="
-            "\n"
-        )
-        sys.exit(1)
-
-
-def _compose_cmd(compose_file, envoy_config, action):
-    """Build and run a docker compose command."""
-    env = os.environ.copy()
-    env["WS_ENVOY_CONFIG"] = str(envoy_config)
-
-    # If WEIGHTSLAB_CERTS_DIR isn't set, fall back to the default certs dir when
-    # it actually holds a full cert set + gRPC token. This gives docker compose a
-    # valid host-native bind-mount source (so Envoy gets its certs) even when the
-    # user never exported the var — and covers compose calls that run before
-    # ui_launch sets it (e.g. the stale-resource cleanup `compose down`).
-    if not env.get("WEIGHTSLAB_CERTS_DIR"):
-        default_mgr = CertAuthManager()
-        if default_mgr.has_valid_certs() and default_mgr.token_file.exists():
-            host_path = _to_docker_host_path(default_mgr.certs_dir)
-            env["WEIGHTSLAB_CERTS_DIR"] = host_path
-            logger.info(
-                f"WEIGHTSLAB_CERTS_DIR not set — using default certs dir for docker: {host_path}"
-            )
-
-    base = _compose_base_cmd()
-    if base is None:
-        logger.error(
-            "Docker Compose is required but was not found.\n"
-            "Install Compose v2 (recommended) — the `docker compose` CLI plugin, "
-            "bundled with Docker Desktop: https://docs.docker.com/compose/install/\n"
-            "The legacy v1 `docker-compose` binary also works if it is on your PATH."
-        )
-        sys.exit(1)
-
-    action = list(action)
-    # Compose v1 (`docker-compose`) has no `up --pull <policy>` flag (it is
-    # v2-only). Emulate it: `pull` first (best-effort — some services only build
-    # locally), then `up` without the flag. v2 supports it inline, so leave it.
-    if base == ["docker-compose"] and action and action[0] == "up" and "--pull" in action:
-        i = action.index("--pull")
-        del action[i:i + 2] # drop '--pull' and its policy value (e.g. 'always')
-        logger.info("Docker Compose v1 detected — pulling images before 'up'...")
-        pull_result = subprocess.run(
-            base + ["-f", str(compose_file), "pull"],
-            env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True,
-        )
-        if pull_result.stdout:
-            for line in pull_result.stdout.splitlines():
-                logger.info(line)
-
-    cmd = base + ["-f", str(compose_file)] + action
-    result = subprocess.run(cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
-
-    if result.stdout:
-        for line in result.stdout.splitlines():
-            logger.info(line)
-
-    if result.returncode != 0:
-        raise subprocess.CalledProcessError(result.returncode, cmd)
-
-
-def _test_backend_connection(host: str = '127.0.0.1', port: int = 50051, timeout: float = 5.0) -> bool:
-    """Test if backend gRPC server is reachable."""
-    try:
-        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        sock.settimeout(timeout)
-        result = sock.connect_ex((host, port))
-        sock.close()
-        return result == 0
-    except Exception as e:
-        logger.debug(f"Backend connection test failed: {e}")
-        return False
-
-
-def _run_powershell_script(script_path: str, args: list = None, env_vars: dict = None) -> int:
-    """Run a PowerShell script and return exit code."""
-    if not _is_windows():
-        logger.error("Secure launch requires Windows with PowerShell")
-        return 1
-
-    cmd = [
-        'powershell',
-        '-NoProfile',
-        '-ExecutionPolicy', 'Bypass',
-        '-File', script_path
-    ]
-
-    if args:
-        cmd.extend(args)
-
-    try:
-        env = os.environ.copy()
-        if env_vars:
-            env.update(env_vars)
-        result = subprocess.run(cmd, env=env)
-        return result.returncode
-    except Exception as e:
-        logger.error(f"Failed to run script: {e}")
-        return 1
-
-
-def _convert_to_git_bash_path(win_path: str) -> str:
-    """Convert Windows path to Git Bash compatible format."""
-    # Normalize backslashes to forward slashes explicitly: Path(...).as_posix()
-    # does NOT convert '\' on POSIX hosts (e.g. Linux CI runners), so a Windows
-    # input path would keep its separators there.
-    p = str(win_path).replace("\\", "/")
-    # Convert C:/Users/... to /mnt/c/Users/... for Git Bash
-    if len(p) > 1 and p[1] == ':':
-        drive = p[0].lower()
-        rest = p[2:]
-        return f"/mnt/{drive}{rest}"
-    return p
-
-
-def _to_docker_host_path(path) -> str:
-    """Return a path that Docker Desktop can bind-mount.
-
-    On Windows, normalize WSL (/mnt/c/...) and Git Bash (/c/...) forms — and
-    native paths that Path() mangled into \\mnt\\c\\... — to C:/... form.
-    Docker Desktop cannot resolve /mnt-style sources and silently mounts an
-    empty directory, which crashes Envoy on missing certs. On Linux/macOS the
-    path is returned unchanged.
-    """
-    p = str(path).replace('\\', '/')
-    if _is_windows():
-        m = re.match(r'^/mnt/([a-zA-Z])/(.*)$', p) or re.match(r'^/([a-zA-Z])/(.*)$', p)
-        if m:
-            return f"{m.group(1).upper()}:/{m.group(2)}"
-    return p
-
-
-def _run_shell_script(script_path: str, args: list = None, env_vars: dict = None) -> int:
-    """Run a shell script using bash with proper environment variable passing."""
-
-    try:
-        # Fix line endings in the file before running
-        with open(script_path, 'rb') as f:
-            script_bytes = f.read()
-
-        # Ensure Unix line endings
-        fixed_bytes = script_bytes.replace(b'\r\n', b'\n').replace(b'\r', b'\n')
-
-        # Write back if needed
-        if fixed_bytes != script_bytes:
-            with open(script_path, 'wb') as f:
-                f.write(fixed_bytes)
-
-        # We invoke the script as a command below (bash -c "VAR=... 'script'"),
-        # which needs the execute bit. pip installs it without one, so add it here
-        # (no-op on Windows). Uses the on-disk path before any Git Bash conversion.
-        _make_executable(script_path)
-
-        env = os.environ.copy()
-        if env_vars:
-            env.update(env_vars)
-            # Debug: log all environment variables being passed
-            for key, value in env_vars.items():
-                logger.info(f"Passing env var: {key}={value}")
-
-        # Build bash command - pass Windows path directly, script will handle conversion
-        # # Process path to ensure it's compatible with bash, especially on Windows
-        if _is_windows() and '\\' in script_path:
-            script_path = script_path.replace("\\", "/") # Ensure path is Unix-style for bash
-            script_path = _convert_to_git_bash_path(script_path)
-            logger.info(f"Converted script path for bash: {script_path}")
-        logger.info(f"Running shell script: {script_path} with args: {args} and env_vars: {env_vars}")
-
-        # Build environment variable assignments for bash command
-        env_assignments = ' '.join([f"{k}='{v}'" for k, v in env_vars.items()]) if env_vars else ""
-
-        if env_assignments:
-            # Pass env vars directly in bash command using -c flag
-            # This works on Windows (Git Bash), Linux, and macOS
-            logger.info('Using env assignments')
-            bash_script_cmd = f"{env_assignments} '{script_path}'"
-            if args:
-                bash_script_cmd += " " + " ".join(f"'{arg}'" for arg in args)
-            bash_cmd = ['bash', '-c', bash_script_cmd]
-        else:
-            logger.info('Not using env assignments')
-            bash_cmd = ['bash', '-x', str(script_path)]
-            if args:
-                bash_cmd.extend(args)
-
-        result = subprocess.run(bash_cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
-        if result.stdout:
-            for line in result.stdout.splitlines():
-                logger.info(line)
-        return result.returncode
-    except FileNotFoundError:
-        logger.error(f"Script file not found: {script_path}")
-        return 1
-    except Exception as e:
-        logger.error(f"Failed to run script: {e}")
-        return 1
-
-
-def _generate_certs_with_fallback(force_certs: bool = False, certs_dir=None) -> int:
-    """Try shell script first, fall back to PowerShell on Windows if it fails.
-
-    ``certs_dir`` is forwarded to the generation scripts as ``WEIGHTSLAB_CERTS_DIR``
-    so certs land in the single source-of-truth directory (the scripts default to
-    ``~/.weightslab-certs`` when it is not provided). The shell script receives a
-    POSIX absolute path (``/mnt/c/...`` on Windows/WSL); PowerShell receives the
-    host-native path (``C:/...``).
-    """
-    env_vars = None
-    if certs_dir is not None:
-        # Shell scripts (bash/WSL) need a POSIX-style absolute path.
-        # _to_docker_host_path gives C:/... which WSL bash treats as a relative
-        # path, creating the certs dir under cwd instead of ~/.weightslab-certs.
-        # Use the /mnt/c/... form so the path is absolute inside WSL.
-        if _is_windows():
-            bash_certs_dir = _convert_to_git_bash_path(str(certs_dir))
-        else:
-            bash_certs_dir = str(certs_dir)
-        env_vars = {'WEIGHTSLAB_CERTS_DIR': bash_certs_dir}
-
-    cert_script = str(_get_cert_script())
-    if not Path(cert_script).exists():
-        logger.warning(f"Shell script not found: {cert_script}")
-    else:
-        script_args = []
-        if force_certs:
-            script_args.append('--force-create-certs')
-
-        logger.info("Attempting certificate generation with shell script...")
-        exit_code = _run_shell_script(cert_script, script_args, env_vars)
-        if exit_code == 0:
-            return 0
-        logger.warning(f"Shell script failed (exit code {exit_code})")
-
-    # Fallback to PowerShell on Windows
-    if _is_windows():
-        logger.info("Falling back to PowerShell for certificate generation...")
-        cert_script_ps1 = str(_get_cert_script_ps1())
-        if not Path(cert_script_ps1).exists():
-            logger.error(f"PowerShell script not found: {cert_script_ps1}")
-            return 1
-
-        script_args = []
-        if force_certs:
-            script_args.append('-ForceCreateCerts')
-
-        exit_code = _run_powershell_script(cert_script_ps1, script_args, env_vars)
-        return exit_code
-    else:
-        logger.error("Neither shell nor PowerShell script could generate certificates")
-        return 1
-
-
-_DEV_CA_SUBJECT = "weightslab-dev-ca"
-
-
-def _install_ca_trust(ca_file: Path) -> None:
-    """Install the dev CA into the OS trust store so browsers trust the HTTPS UI.
-
-    Idempotent and safe to call on every launch. Platform behavior:
-      * Windows — adds to the CurrentUser\\Root store via the .NET X509Store API
-        (silent, no prompt).
-      * macOS — adds to the login keychain (may show a one-time auth prompt).
-      * Linux — installs into the system trust store via sudo (one-time prompt)
-        and, best-effort, the user's NSS DB so Chrome/Firefox trust it too.
-
-    A failure here is non-fatal: TLS still works, the browser just shows a
-    self-signed warning until the CA is trusted manually.
-    """
-    if not ca_file.exists():
-        logger.warning(f"CA file not found, skipping trust install: {ca_file}")
-        return
-
-    if _is_windows():
-        ps = (
-            "$ErrorActionPreference='Stop';"
-            f"$caPath='{ca_file}';"
-            "$certObj=New-Object System.Security.Cryptography.X509Certificates.X509Certificate2($caPath);"
-            "$store=New-Object System.Security.Cryptography.X509Certificates.X509Store('Root','CurrentUser');"
-            "$store.Open('ReadWrite');"
-            "try{"
-            # Already trusted (same thumbprint)? Nothing to do — avoids a fragile Remove.
-            "$match=$store.Certificates|Where-Object{$_.Thumbprint -eq $certObj.Thumbprint};"
-            "if(-not $match){"
-            # Drop stale same-subject CAs from a previous rotation (best-effort).
-            f"$stale=$store.Certificates|Where-Object{{$_.Subject -eq 'CN={_DEV_CA_SUBJECT}'}};"
-            "foreach($c in $stale){try{$store.Remove($c)}catch{}};"
-            "$store.Add($certObj);"
-            "}"
-            "}finally{$store.Close()}"
-        )
-        result = subprocess.run(
-            ["powershell", "-NoProfile", "-Command", ps],
-            stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True,
-        )
-        if result.returncode == 0:
-            logger.info(" Dev CA trusted in Windows CurrentUser\\Root store (restart browser to apply)")
-        else:
-            logger.warning(f"Could not auto-trust dev CA: {result.stderr.strip()}")
-        return
-
-    if sys.platform == "darwin":
-        check = subprocess.run(
-            ["security", "find-certificate", "-c", _DEV_CA_SUBJECT],
-            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
-        )
-        if check.returncode == 0:
-            logger.info(" Dev CA already trusted (macOS keychain)")
-            return
-        logger.info("Installing dev CA into macOS login keychain (may prompt)...")
-        subprocess.run(
-            ["security", "add-trusted-cert", "-r", "trustRoot",
-             "-k", os.path.expanduser("~/Library/Keychains/login.keychain-db"), str(ca_file)],
-        )
-        return
-
-    # Linux: system trust store (curl/openssl) + best-effort NSS (browsers).
-    system_ca = Path("/usr/local/share/ca-certificates/weightslab-dev-ca.crt")
-    try:
-        already = system_ca.exists() and system_ca.read_bytes() == ca_file.read_bytes()
-    except OSError:
-        already = False
-    if not already:
-        logger.info("Installing dev CA into the Linux system trust store (may prompt for sudo)...")
-        subprocess.run(["sudo", "cp", str(ca_file), str(system_ca)])
-        subprocess.run(["sudo", "update-ca-certificates"])
-    else:
-        logger.info(" Dev CA already in Linux system trust store")
-
-    # Browsers use their own NSS DB; add it there too if certutil is available.
-    if shutil.which("certutil"):
-        nssdb = Path.home() / ".pki" / "nssdb"
-        nssdb.mkdir(parents=True, exist_ok=True)
-        subprocess.run(
-            ["certutil", "-A", "-n", _DEV_CA_SUBJECT, "-t", "C,,",
-             "-i", str(ca_file), "-d", f"sql:{nssdb}"],
-            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
-        )
-
-
-def _ensure_certificates(manager: CertAuthManager, force_certs: bool = False) -> bool:
-    """Generate certs + auth token in ``manager.certs_dir`` if missing (or forced).
-
-    Does NOT export any TLS/auth env vars: the launch pipeline derives TLS purely
-    from cert-file presence in ``WEIGHTSLAB_CERTS_DIR`` (the single source of
-    truth). Returns True if certs are present afterwards, False otherwise.
-    """
-    if manager.has_any_credentials() and not force_certs:
-        logger.info(f" Using existing credentials in {manager.certs_dir}")
-        manager.get_or_create_auth_token()
-        # Ensure the CA is trusted even when reusing certs from a prior run that
-        # was generated via bash (which does not install OS trust).
-        _install_ca_trust(manager.ca_file)
-        return manager.has_valid_certs()
-
-    logger.info(
-        "Regenerating certificates (--force-certs)..."
-        if force_certs
-        else "No certificates found — generating them now..."
-    )
-    Path(manager.certs_dir).mkdir(parents=True, exist_ok=True)
-    exit_code = _generate_certs_with_fallback(force_certs=force_certs, certs_dir=manager.certs_dir)
-    if exit_code != 0:
-        logger.warning("Certificate generation failed — continuing in unsecured mode")
-        return False
-
-    manager.get_or_create_auth_token()
-    _install_ca_trust(manager.ca_file)
-    logger.info(f" Certificates ready in {manager.certs_dir}")
-    return manager.has_valid_certs()
-
-
-def _remove_docker_image(image: str) -> None:
-    """Remove a Docker image (all tags) if present; no-op when absent."""
-    result = subprocess.run(
-        ["docker", "images", "-q", image],
-        stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True,
-    )
-    image_ids = sorted(set(result.stdout.split()))
-    if not image_ids:
-        return
-    logger.info(f"Removing cached image '{image}' ({len(image_ids)} ref(s))...")
-    subprocess.run(
-        ["docker", "rmi", "-f", *image_ids],
-        stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
-    )
-
-
-def _clean_stale_docker_resources(frontend_image: str = _FRONTEND_IMAGE) -> None:
-    """Remove leftover weightslab / weights_studio Docker state before a launch.
-
-    Stale containers, the compose network, anonymous volumes, a cached frontend
-    image, and a leftover generated ``.env`` can each silently break a fresh
-    launch (an old image served instead of a rebuild, or an empty cert mount
-    that crashes Envoy). This is scoped STRICTLY to weightslab/weights_studio
-    resources — it never runs a global ``docker system prune``.
-
-    ``frontend_image`` is the repo (optionally ``repo:tag``) whose cached copy is
-    dropped so ``up --pull always`` fetches a fresh one — pass the resolved value
-    when ``--image/--version`` point at a non-default repo.
-    """
-    logger.info("Cleaning stale Docker resources (weightslab/weights_studio only)...")
-    compose_file = _get_compose_file()
-    envoy_config = _get_envoy_config()
-
-    # 1. Tear down any existing stack: containers + default network + anon volumes.
-    try:
-        _compose_cmd(compose_file, envoy_config, ["down", "--remove-orphans", "--volumes"])
-    except subprocess.CalledProcessError as exc:
-        logger.debug(f"'compose down' returned non-zero (nothing to remove?): {exc}")
-
-    # 2. Force-remove leftover named containers started outside this compose project.
-    for container in _STACK_CONTAINERS:
-        subprocess.run(
-            ["docker", "rm", "-f", container],
-            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
-        )
-
-    # 3. Drop the cached frontend image so a fresh one is built/pulled.
-    _remove_docker_image(frontend_image)
-
-    # 4. Remove the generated .env so stale values don't leak into compose.
-    env_file = Path(compose_file).parent / ".env"
-    if env_file.exists():
-        try:
-            env_file.unlink()
-            logger.info(f"Removed stale env file: {env_file}")
-        except OSError as exc:
-            logger.debug(f"Could not remove {env_file}: {exc}")
-
-
-def ui_launch(args):
-    """Purge stale Docker state, then build & start the UI.
-
-    By default the UI launches UNSECURED (HTTP, no gRPC auth) — no certificates
-    are generated. Pass ``--certs`` to generate (if missing) and use TLS certs +
-    a gRPC auth token. Existing certs in WEIGHTSLAB_CERTS_DIR are always honored
-    (file presence is the single source of truth) and are never deleted here.
-
-    Flags (all optional, read defensively so legacy callers still work):
-      --certs generate (if missing) and use TLS certs + gRPC auth (HTTPS)
-      --force-certs with --certs, regenerate certificates even if they exist
-      --no-clean skip the stale Docker resource cleanup step
-      --dev use the dev compose overlay
-      -i/--image frontend image repo to run/pull (default: graybx/weightslab)
-      -v/--version frontend image tag/version to pull (default: latest)
-    """
-    try:
-        from weightslab.utils.telemetry import ping_ui_launch
-        from weightslab import __version__ as _wl_version
-        ping_ui_launch(_wl_version)
-    except Exception:
-        pass
-    _check_docker()
-    # pip installs the bundled .sh scripts without the execute bit; make them
-    # runnable so the user never has to `chmod +x` before `weightslab ui launch`.
-    _ensure_scripts_executable()
-
-    use_certs = getattr(args, "certs", False)
-    no_auth = getattr(args, "no_auth", False)
-    force_certs = getattr(args, "force_certs", False)
-    no_clean = getattr(args, "no_clean", False)
-    certs_dir_arg = getattr(args, "certs_dir", None)
-
-    # Resolve which frontend image to run. docker compose reads it via the
-    # WS_FRONTEND_IMAGE env var (see `${WS_FRONTEND_IMAGE:-...}` in the compose
-    # file); export it so both `up --pull always` below and the stale-image
-    # cleanup target the same repo/tag.
-    frontend_image = _resolve_frontend_image(
-        getattr(args, "image", None), getattr(args, "version", None)
-    )
-    os.environ["WS_FRONTEND_IMAGE"] = frontend_image
-    frontend_repo = frontend_image.rsplit(":", 1)[0] if ":" in frontend_image.rsplit("/", 1)[-1] else frontend_image
-    logger.info(f"Using frontend image: {frontend_image}")
-
-    # A custom certs dir (positional arg) takes precedence over the env var /
-    # default. Otherwise fall back to $WEIGHTSLAB_CERTS_DIR or ~/.weightslab-certs.
-    if certs_dir_arg:
-        # Resolve to an absolute path so Windows Python, WSL bash and docker all
-        # agree on the location (a relative path resolves differently per shell).
-        certs_dir_arg = str(Path(certs_dir_arg).resolve())
-        manager = CertAuthManager(certs_dir=certs_dir_arg, enable_auth=not no_auth)
-        logger.info(f"Using custom certs directory: {manager.certs_dir}")
-    else:
-        manager = CertAuthManager.from_env_or_default(enable_auth=not no_auth)
-
-    if use_certs:
-        _ensure_certificates(manager, force_certs=force_certs)
-    else:
-        # Default: do NOT generate certs. Existing certs in WEIGHTSLAB_CERTS_DIR
-        # are still honored (file-presence is the single source of truth); this
-        # never deletes anything. Ensure the dir exists for the compose bind-mount.
-        logger.info("Launching WITHOUT cert generation (default; HTTP). "
-                    "Pass --certs for secured HTTPS + gRPC auth.")
-        try:
-            manager.certs_dir.mkdir(parents=True, exist_ok=True)
-        except (OSError, AttributeError):
-            # AttributeError: certs_dir is a plain str (e.g. in unit tests).
-            try:
-                os.makedirs(str(manager.certs_dir), exist_ok=True)
-            except OSError:
-                logger.warning('Fail to create weightslab-certs directory!')
-
-    # Single source of truth: TLS is on iff cert files exist in the dir.
-    secured = manager.has_valid_certs()
-
-    # Set the correct certs path in the process env NOW so that _compose_cmd
-    # passes it to docker compose — this overrides any stale/malformed value
-    # in a leftover .env file and prevents the "too many colons" bind-mount error.
-    os.environ["WEIGHTSLAB_CERTS_DIR"] = _to_docker_host_path(manager.certs_dir)
-
-    # Remove stale weightslab/weights_studio Docker resources that could prevent
-    # a clean launch. Scoped strictly to our own resources (see helper docstring).
-    if no_clean:
-        logger.info("Skipping stale Docker resource cleanup (--no-clean)")
-    else:
-        _clean_stale_docker_resources(frontend_repo)
-
-    # Single source of truth: the deploy pipeline (build-and-deploy.sh + the
-    # compose `auto` logic) derives TLS/auth solely from cert-file presence in
-    # WEIGHTSLAB_CERTS_DIR. Strip any pre-set/derived TLS env so it cannot
-    # override that file-based decision.
-    _strip_derived_deploy_env()
-
-    # Run bootstrap script to setup environment
-    bootstrap_script = str(_get_bootstrap_script())
-    if Path(bootstrap_script).exists():
-        logger.info("Running bootstrap script...")
-        logger.info(f"Bootstrap script path: {bootstrap_script}")
-        # Convert Windows path to Unix-style for bash
-        certs_dir_str = str(manager.certs_dir)
-        if _is_windows() and '\\' in certs_dir_str:
-            certs_dir_str = _convert_to_git_bash_path(certs_dir_str)
-            logger.info(f"Converted path to Unix-style: {certs_dir_str}")
-
-        # Calculate WEIGHTSLAB_ROOT (parent of weightslab package)
-        weightslab_root = str(Path(__file__).parent)
-        if _is_windows() and '\\' in weightslab_root:
-            weightslab_root = _convert_to_git_bash_path(weightslab_root)
-
-        # Docker Desktop (Windows) bind mounts need a host-native path
-        # (e.g. C:/Users/...), NOT the /mnt/c Unix path used for the bash
-        # script's own file checks. as_posix() yields C:/... on Windows and a
-        # normal /abs/path on Linux/macOS — correct for docker compose on every
-        # platform. The bash script writes this into .env for the compose mount.
-        certs_dir_host = _to_docker_host_path(manager.certs_dir)
-
-        # Always pass the real certs dir so the compose bind-mount has a valid
-        # source. When there are no certs we add --unsecure, which forces
-        # Envoy/nginx to plaintext (mounted files, if any, are ignored) without
-        # leaving the mount source empty.
-        bootstrap_env_vars = {
-            'WEIGHTSLAB_CERTS_DIR': certs_dir_str,
-            'WEIGHTSLAB_CERTS_DIR_HOST': certs_dir_host,
-            'WEIGHTSLAB_ROOT': weightslab_root
-        }
-        # On Windows the bootstrap runs in WSL/Git-Bash where `docker` usually
-        # isn't on PATH; the compose up below (run from Windows Python) handles
-        # the deploy. Tell the script to only write .env and skip docker ops so
-        # it doesn't emit a spurious "build failed" error.
-        if _is_windows():
-            bootstrap_env_vars['WEIGHTSLAB_SKIP_DOCKER_OPS'] = '1'
-        script_args = []
-        if not secured:
-            script_args.append('--unsecure')
-        if getattr(args, "dev", False):
-            script_args.append('--dev')
-        logger.info(f"WEIGHTSLAB_CERTS_DIR={bootstrap_env_vars['WEIGHTSLAB_CERTS_DIR']}")
-        logger.info(f"WEIGHTSLAB_CERTS_DIR_HOST={bootstrap_env_vars['WEIGHTSLAB_CERTS_DIR_HOST']}")
-        logger.info(f"WEIGHTSLAB_ROOT={bootstrap_env_vars['WEIGHTSLAB_ROOT']}")
-        exit_code = _run_shell_script(bootstrap_script, script_args, bootstrap_env_vars)
-        if exit_code != 0:
-            logger.warning(f"Bootstrap script exited with code {exit_code}, continuing anyway...")
-    else:
-        logger.warning(f"Bootstrap script not found: {bootstrap_script}")
-
-    # docker compose gives an exported env var precedence over .env. Force the
-    # host-native certs path here (on every path, secured or not) so our
-    # compose call below bind-mounts a real folder — a stray /mnt/c or empty
-    # value would mount an empty/invalid dir and can crash Envoy.
-    os.environ["WEIGHTSLAB_CERTS_DIR"] = _to_docker_host_path(manager.certs_dir)
-
-    _compose_cmd(
-        _get_compose_file(),
-        _get_envoy_config(),
-        ["up", "-d", "--pull", "always"],
-    )
-    port = os.environ.get("VITE_PORT", "5173")
-    # HTTPS iff cert files actually exist in WEIGHTSLAB_CERTS_DIR — the same
-    # file-presence rule the deploy pipeline applies.
-    secured = manager.has_valid_certs()
-    protocol = "https" if secured else "http"
-    logger.info(f"Weights Studio UI is running at: {protocol}://localhost:{port}")
-    if secured:
-        certs_dir_str = str(manager.certs_dir)
-        # Persist first (it logs its own lines), so the export/setx reminder
-        # below stays the FINAL output and can't be missed.
-        # Persist when a custom dir was given (it won't match the default, so the
-        # backend must be told where to look) or when the var isn't already set.
-        if certs_dir_arg or not _CERTS_DIR_IN_ORIGINAL_ENV:
-            _persist_certs_dir(certs_dir_str)
-        # The backend and any new shell must point at the same certs dir, or
-        # they'll mismatch the UI's TLS/auth. Keep this the last thing printed.
-        logger.warning("")
-        logger.warning(" ACTION REQUIRED — TLS is ON. Set WEIGHTSLAB_CERTS_DIR so the "
-                       "training backend and new terminals use the same certificates:")
-        logger.warning(f" (bash) export WEIGHTSLAB_CERTS_DIR=\"{certs_dir_str}\"")
-        logger.warning(f" (Windows) setx WEIGHTSLAB_CERTS_DIR \"{certs_dir_str}\"")
-    else:
-        logger.info("UI is running UNSECURED (HTTP, no gRPC auth). "
-                    "Re-run with `weightslab ui launch --certs` for TLS.")
-
-
-def ui_secure_environment(args):
-    """`weightslab se`: create a certs directory with certs + gRPC token.
-
-    The directory is the single source of truth — WEIGHTSLAB_CERTS_DIR is exported
-    for this process and the user is asked to export it globally. Everything else
-    (TLS on/off, auth on/off) is derived from the files in that directory by the
-    backend and the deploy pipeline, so this command does not set any other env.
-    """
-    logger.info("Setting up secure environment...")
-    # Bundled .sh scripts ship without the execute bit (pip strips it); make them
-    # runnable so the cert-generation script below doesn't fail on "Permission denied".
-    _ensure_scripts_executable()
-
-    force_certs = getattr(args, "force_certs", False)
-    no_auth = getattr(args, "no_auth", False)
-    certs_dir = getattr(args, "certs_dir", None)
-    if certs_dir:
-        # Absolute path so Windows Python, WSL bash and docker agree on location.
-        certs_dir = str(Path(certs_dir).resolve())
-
-    # Resolve the target directory first (no filesystem work in __init__), so we
-    # can point the generation scripts at it via WEIGHTSLAB_CERTS_DIR.
-    manager = CertAuthManager(certs_dir=certs_dir, enable_auth=not no_auth)
-
-    exit_code = _generate_certs_with_fallback(force_certs=force_certs, certs_dir=manager.certs_dir)
-    if exit_code != 0:
-        logger.error("Certificate generation failed")
-        sys.exit(1)
-
-    manager.certs_dir.mkdir(parents=True, exist_ok=True)
-    manager.get_or_create_auth_token()
-
-    # Export ONLY the single source of truth for this process.
-    os.environ["WEIGHTSLAB_CERTS_DIR"] = str(manager.certs_dir)
-
-    logger.info(" Certificates generated successfully")
-    logger.info(" gRPC auth token created")
-    logger.info(f" Certs and token stored in: {manager.certs_dir}")
-    logger.info(f" WEIGHTSLAB_CERTS_DIR exported for this process: {manager.certs_dir}")
-    logger.info("Then launch the secured UI with: weightslab ui launch --certs")
-    # Keep this the FINAL output so the user can't miss the action they must take.
-    logger.warning("")
-    logger.warning(" ACTION REQUIRED — set WEIGHTSLAB_CERTS_DIR globally so new shells "
-                   "and the training backend find these certs (single source of truth):")
-    logger.warning(f" (bash) echo 'export WEIGHTSLAB_CERTS_DIR=\"{manager.certs_dir}\"' >> ~/.bashrc && source ~/.bashrc")
-    logger.warning(f" (Windows) setx WEIGHTSLAB_CERTS_DIR \"{manager.certs_dir}\"")
-
-
-# Bundled PyTorch examples, keyed by the CLI flag (e.g. --cls -> ws-classification).
-# Each value is (directory name under examples/PyTorch, human-readable label).
-# kind -> (dir_name, label, category) where category is the examples/ subfolder.
-_EXAMPLES = {
-    "cls": ("ws-classification", "classification", "PyTorch"),
-    "seg": ("ws-segmentation", "segmentation", "PyTorch"),
-    "det": ("ws-detection", "detection", "PyTorch"),
-    "clus": ("ws-clustering", "clustering", "PyTorch"),
-    "gen": ("ws-generation", "generation", "PyTorch"),
-    "3d_det": ("ws-3d-lidar-detection", "3D LiDAR detection", "Usecases"),
-    "2d_det": ("ws-2d-lidar-detection", "2D LiDAR detection", "Usecases"),
-}
-_DEFAULT_EXAMPLE = "cls"
-
-
-def _get_example_dir(name: str = "ws-classification", category: str = "PyTorch") -> Path:
-    """Path to a bundled example directory (under examples/<category>/<name>)."""
-    return Path(__file__).parent / 'examples' / category / name
-
-
-def _install_example_requirements(example_dir: Path) -> None:
-    """Install an example's requirements non-interactively, if a file is present.
-
-    Looks for requirements.txt (then requirements.in) in the example directory and
-    runs `pip install -r` with the current interpreter and `--no-input` so it never
-    prompts. Non-fatal: a failure is logged and the example is still attempted, so a
-    transient install hiccup doesn't block a run where deps are already satisfied.
-    """
-    for fname in ("requirements.txt", "requirements.in"):
-        req = example_dir / fname
-        if not req.exists():
-            continue
-        logger.info(f"Installing example requirements (non-interactive): {req}")
-        try:
-            subprocess.run(
-                [sys.executable, "-m", "pip", "install", "-r", str(req),
-                 "--no-input", "--disable-pip-version-check"],
-                check=True,
-            )
-        except subprocess.CalledProcessError as exc:
-            logger.warning(
-                f"Failed to install requirements ({req}): {exc}. "
-                "Continuing — the example may still run if deps are already installed."
-            )
-        return # only the first matching requirements file is used
-
-
-def example_start(args):
-    """`weightslab start example [--cls|--seg|--clus|--gen]`: run a bundled example.
-
-    Defaults to the classification (cls) example. First installs the example's
-    requirements (if a requirements file is present) without prompting, then runs
-    its main.py with the current Python interpreter from its own directory so it
-    resolves its sibling config.yaml. Runs in the foreground (serves until Ctrl+C).
-    """
-    kind = getattr(args, "example_kind", None) or _DEFAULT_EXAMPLE
-    dir_name, label, category = _EXAMPLES.get(kind, _EXAMPLES[_DEFAULT_EXAMPLE])
-
-    example_dir = _get_example_dir(dir_name, category)
-    main_py = example_dir / "main.py"
-    if not main_py.exists():
-        logger.error(f"{label.capitalize()} example not found: {main_py}")
-        sys.exit(1)
-
-    # Install the example's own requirements first, without any interaction.
-    _install_example_requirements(example_dir)
-
-    logger.info(f"Starting the WeightsLab {label} ({kind}) example...")
-    logger.info(f" {main_py}")
-    logger.info("In another terminal, launch the UI with: weightslab ui launch")
-    logger.info(f"Then open http://localhost:5173 — stop the example with Ctrl+C.")
-    if not _CERTS_DIR_IN_ORIGINAL_ENV:
-        manager = CertAuthManager.from_env_or_default()
-        if manager.has_valid_certs():
-            logger.warning(
-                "WEIGHTSLAB_CERTS_DIR is not set in your shell environment. "
-                "TLS will work this session (certs found at default location) "
-                "but may not persist across terminals."
-            )
-            _persist_certs_dir(str(manager.certs_dir))
-    try:
-        env = os.environ.copy()
-        env['WEIGHTSLAB_SUPPRESS_BANNER'] = '1'
-        result = subprocess.run([sys.executable, str(main_py)], cwd=str(example_dir), env=env)
-    except KeyboardInterrupt:
-        logger.info("Example stopped.")
-        return
-    if result.returncode != 0:
-        sys.exit(result.returncode)
-
-
-def cli_connect(args):
-    """`weightslab cli [--port N] [--host H]`: open an interactive terminal
-    connected to a currently-running experiment's CLI server.
-
-    With no --port, auto-discovers the running experiment (the backend advertises
-    its actual port on startup). Pass --port to target a specific server.
-    """
-    try:
-        import weightslab.backend.cli as cli_backend
-    except Exception as exc:
-        logger.error(f"Could not load the WeightsLab CLI client: {exc}")
-        sys.exit(1)
-
-    port = getattr(args, "port", None)
-    host = getattr(args, "host", None)
-    exit_code = cli_backend.cli_connect(cli_port=port, cli_host=host)
-    sys.exit(exit_code)
-
-
-def _add_example_kind_flags(p: argparse.ArgumentParser) -> None:
-    """Attach the mutually-exclusive example-kind flags (default: classification)."""
-    group = p.add_mutually_exclusive_group()
-    group.add_argument("--cls", action="store_const", dest="example_kind", const="cls",
-                       help="Run the classification example (default)")
-    group.add_argument("--seg", action="store_const", dest="example_kind", const="seg",
-                       help="Run the segmentation example")
-    group.add_argument("--det", action="store_const", dest="example_kind", const="det",
-                       help="Run the detection example")
-    group.add_argument("--clus", action="store_const", dest="example_kind", const="clus",
-                       help="Run the clustering example")
-    group.add_argument("--gen", action="store_const", dest="example_kind", const="gen",
-                       help="Run the generation example")
-    group.add_argument("--3d_det", action="store_const", dest="example_kind", const="3d_det",
-                       help="Run the 3D LiDAR point-cloud detection example")
-    group.add_argument("--2d_det", action="store_const", dest="example_kind", const="2d_det",
-                       help="Run the 2D LiDAR point-cloud detection example")
-    p.set_defaults(example_kind=_DEFAULT_EXAMPLE)
-
-
-def _build_parser() -> argparse.ArgumentParser:
-    """Build the top-level argument parser (banner + detailed command reference).
-
-    The CLI is intentionally minimal — exactly these commands:
-        weightslab --help | -h | help
-        weightslab se [--force-certs]
-        weightslab ui launch [--certs]
-        weightslab start example [--cls|--seg|--det|--clus|--gen|--3d_det|--2d_det]
-        weightslab cli [--port PORT] [--host HOST]
-    """
-    parser = argparse.ArgumentParser(
-        prog="weightslab",
-        description=_DESCRIPTION,
-        epilog=_EPILOG,
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-    )
-    # metavar lists only the documented commands; the `example` alias is accepted
-    # but intentionally omitted here (and help=SUPPRESS'd below) so it stays hidden.
-    sub = parser.add_subparsers(dest="command", metavar="{se,ui,start,cli,help}")
-
-    # weightslab se [--force-certs] [certs_dir]
-    se_parser = sub.add_parser("se", help="Set up the secure environment (TLS certs + gRPC auth token)")
-    se_parser.add_argument('--force-certs', action='store_true', help='Regenerate certificates even if they already exist')
-    se_parser.add_argument('certs_dir', nargs='?', default=None,
-                           help='Custom directory for certs/token (default: $WEIGHTSLAB_CERTS_DIR or ~/.weightslab-certs)')
-
-    # weightslab ui launch [--certs] [certs_dir]
-    ui_parser = sub.add_parser("ui", help="Manage the Weights Studio UI")
-    ui_sub = ui_parser.add_subparsers(dest="action")
-    launch_ui_parser = ui_sub.add_parser(
-        "launch", help="Clean stale Docker state, then launch the UI (unsecured by default; --certs for TLS)")
-    launch_ui_parser.add_argument('--certs', action='store_true', help='Generate (if missing) and use TLS certs + gRPC auth token (secured HTTPS). Default: unsecured HTTP.')
-    launch_ui_parser.add_argument('-i', '--image', default=None,
-                                  help='Frontend image repo to run/pull (default: graybx/weightslab). '
-                                       'e.g. guillaumep2705/weightslab')
-    launch_ui_parser.add_argument('-v', '--version', default=None,
-                                  help='Frontend image tag/version to pull (default: latest). e.g. v1.2.3')
-    launch_ui_parser.add_argument('certs_dir', nargs='?', default=None,
-                                  help='Custom directory for certs/token (default: $WEIGHTSLAB_CERTS_DIR or ~/.weightslab-certs)')
-
-    # weightslab cli [--port N] [--host H]
-    cli_parser = sub.add_parser(
-        "cli", help="Open an interactive terminal connected to the running experiment")
-    cli_parser.add_argument('--port', type=int, default=None,
-                            help='CLI server port (default: auto-discover the running experiment)')
-    cli_parser.add_argument('--host', default=None,
-                            help='CLI server host (default: localhost)')
-
-    # weightslab start example [--cls|--seg|--clus|--gen]
-    start_parser = sub.add_parser("start", help="Start a bundled WeightsLab resource")
-    start_sub = start_parser.add_subparsers(dest="start_target")
-    example_parser = start_sub.add_parser(
-        "example", help="Start a bundled PyTorch example (default: classification)")
-    _add_example_kind_flags(example_parser)
-
-    # Tolerate the swapped order: `weightslab example start [flags]` (and bare
-    # `weightslab example`) behave exactly like `weightslab start example`. Hidden
-    # from --help on purpose (argparse.SUPPRESS) — it's a forgiving fallback, not a
-    # documented command.
-    example_alias = sub.add_parser("example", help=argparse.SUPPRESS)
-    example_alias_sub = example_alias.add_subparsers(dest="example_action")
-    example_alias_start = example_alias_sub.add_parser(
-        "start", help="Start a bundled PyTorch example (default: classification)")
-    _add_example_kind_flags(example_alias_start)
-
-    sub.add_parser("help", help="Show this help message")
-
-    return parser, ui_parser, start_parser, cli_parser
-
-
-def main():
-    parser, ui_parser, start_parser, cli_parser = _build_parser()
-    args = parser.parse_args()
-
-    if args.command == "help" or args.command is None:
-        parser.print_help()
-    elif args.command == "cli":
-        cli_connect(args)
-    elif args.command == "se":
-        ui_secure_environment(args)
-    elif args.command == "ui":
-        if getattr(args, "action", None) == "launch":
-            ui_launch(args)
-        else:
-            ui_parser.print_help()
-    elif args.command == "start":
-        if getattr(args, "start_target", None) == "example":
-            example_start(args)
-        else:
-            start_parser.print_help()
-    elif args.command == "example":
-        # Alias for `start example` — tolerate the swapped subcommand order
-        # (`weightslab example start [flags]`) and the bare `weightslab example`.
-        example_start(args)
-    else:
-        parser.print_help()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/weightslab/utils/__init__.py b/weightslab/utils/__init__.py
index af11e464..6131e7c0 100644
--- a/weightslab/utils/__init__.py
+++ b/weightslab/utils/__init__.py
@@ -1,9 +1,36 @@
-from .tools import filter_kwargs_for_callable, safe_call_with_kwargs, capture_rng_state, restore_rng_state, recursive_update
-
-__all__ = [
-	'filter_kwargs_for_callable',
-	'safe_call_with_kwargs',
-	'capture_rng_state',
-	'restore_rng_state',
-	'recursive_update',
-]
+"""weightslab.utils package.
+
+The convenience re-exports from `.tools` are resolved lazily (PEP 562): `.tools`
+imports torch/numpy, so eager re-export here would pull the heavy stack into
+every `weightslab.utils.<x>` import (e.g. the torch-free `weightslab.utils.logs`
+used by the UI/CLI). Names still import normally on first access, e.g.
+``from weightslab.utils import filter_kwargs_for_callable``.
+"""
+import importlib
+
+_LAZY_EXPORTS = {
+    name: ".tools"
+    for name in (
+        "filter_kwargs_for_callable",
+        "safe_call_with_kwargs",
+        "capture_rng_state",
+        "restore_rng_state",
+        "recursive_update",
+    )
+}
+
+__all__ = list(_LAZY_EXPORTS)
+
+
+def __getattr__(name):  # PEP 562
+    module_name = _LAZY_EXPORTS.get(name)
+    if module_name is None:
+        raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+    module = importlib.import_module(module_name, __name__)
+    value = getattr(module, name)
+    globals()[name] = value
+    return value
+
+
+def __dir__():
+    return sorted(set(globals()) | set(_LAZY_EXPORTS))
diff --git a/weightslab/utils/telemetry.py b/weightslab/utils/telemetry.py
index 57c5fb27..8b2f2e03 100644
--- a/weightslab/utils/telemetry.py
+++ b/weightslab/utils/telemetry.py
@@ -1,4 +1,4 @@
-"""Anonymous usage telemetry — fired on first daily import and on `weightslab ui launch`.
+"""Anonymous usage telemetry — fired on first daily import and on `weightslab start`.
 
 Opt-out: set WL_NO_TELEMETRY=1 in your environment.
 No personally identifiable information is collected. The raw IP is used
@@ -87,7 +87,7 @@ def _payload(event: str, version: str) -> bytes:
         tz = "unknown"
     return json.dumps({
         "uuid": _get_or_create_uuid(),
-        "event": event,           # "import" | "ui_launch"
+        "event": event,           # "import" | "ui_start"
         "version": version,
         "python": sys.version.split()[0],
         "os": platform.system(),  # Windows / Linux / Darwin
@@ -138,15 +138,15 @@ def ping_import(version: str) -> None:
 
 
 def ping_ui_launch(version: str) -> None:
-    """Async ping on `weightslab ui launch` — at most once per 24 h or version change. No-op in CI."""
+    """Async ping on `weightslab start` — at most once per 24 h or version change. No-op in CI."""
     if _disabled() or _is_ci():
         return
     if not _ping_due(_LAST_UI_PING_FILE, version):
-        logger.debug("Telemetry ui_launch ping skipped (24 h cooldown active)")
+        logger.debug("Telemetry ui_start ping skipped (24 h cooldown active)")
         return
     _record_ping(_LAST_UI_PING_FILE, version)
     logger.info(
         "WeightsLab uses anonymous usage data (package version used, and OS name). "
         "Set WL_NO_TELEMETRY=1 to disable."
     )
-    _fire("ui_launch", version)
+    _fire("ui_start", version)
diff --git a/weightslab/utils/tools.py b/weightslab/utils/tools.py
index b57ff83a..f8f25e54 100644
--- a/weightslab/utils/tools.py
+++ b/weightslab/utils/tools.py
@@ -1,4 +1,5 @@
 import io
+import sys
 import xxhash
 import types
 import logging
@@ -22,6 +23,23 @@
 # -------------------------- Utils Functions ---------------------------------
 # ----------------------------------------------------------------------------
 
+def _running_in_notebook() -> bool:
+    """True when running inside a Jupyter notebook kernel or Google Colab.
+
+    Distinguishes a notebook kernel (``ZMQInteractiveShell``) / Colab from a
+    plain script or a terminal IPython session, so we only nudge users who can't
+    reach a locally-launched Weights Studio without a tunnel.
+    """
+    if "google.colab" in sys.modules:
+        return True
+    try:
+        from IPython import get_ipython
+        shell = get_ipython()
+        return shell is not None and shell.__class__.__name__ == "ZMQInteractiveShell"
+    except Exception:
+        return False
+
+
 def safe_reset_index(df: "pd.DataFrame") -> "pd.DataFrame":
     """Reset DataFrame index levels into columns, skipping any level whose name
     is already a column.