A collection of training and benchmarking examples built on LanceDB multimodal data lakehouse to demonstrate how LanceDB performs as the data layer across different model types, and training regimes along with benchmarks and best practices for training with LanceDB.

Why use LanceDB for training?

One LanceDB table for the entire training loop

• Curate & engineer features — compute new signals (detections, CLIP embeddings, scene tags, dedup flags) as columns via distributed, checkpointed UDF backfills, then slice with SQL, full-text, and vector search directly on the table. Create a dataloder directly using filtered reads from the table or create a training split as versioned materialized view, not a CSV manifest.
• Manage at scale — bytes, metadata, annotations, and embeddings all live in one schema-enforced table, and zero-copy schema evolution lets you add a column without rewriting the data: pre-tokenize or pre-embed a multi-TB corpus once and append it as a new column for free.
• Load & train — With random-access, zero-copy dataloading reads straight from LanceDB tables, from local or object storage, keeping the GPU fed and shards cleanly across Ray workers.

---

Projects using LanceDB

• stable-worldmodel — a platform for reproducible world-model research (paper) built on a LanceDB data layer. 3–4× faster data loading on Push-T vs HDF5 / MP4 at a fraction of the disk.
• le-wm — LeWorldModel, a stable joint-embedding predictive world model from pixels (paper), trained on the stable-worldmodel platform and its LanceDB data layer.
• lerobot-lancedb — a drop-in LanceDB backend for 🤗 LeRobot datasets, referenced in the official LeRobot docs. 2–4× faster data loading across PushT / ALOHA / Koch at identical training quality.

Building on LanceDB and want to be listed here? Open a PR.

Examples

Model type	Example
Object Detection (AV perception)	object-detection/
ViT (MFU benchmark across backends)	examples/ViT/
VLA (Vision-Language-Action)	examples/lerobot_ray_lance/
World Model / Video Generation	🚧
VLM	🚧
LLM	🚧

Repository layout

object-detection/                 # AV perception — BDD100K + Geneva + Faster R-CNN
examples/
  ViT/                            # MFU benchmark: LanceDB vs S3 vs Parquet
  lerobot_ray_lance/              # VLA: Ray + LeRobot Diffusion Policy
  leWorldModel/                   # CogVideo / world model fine-tuning

Each example is self-contained and targets one concrete question.

Setup

uv sync

See each example's own README for run instructions.