Sunsetting cuxfilter after RAPIDS Release v26.06

_notices/rsn0060.md

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+---
+layout: notice
+parent: RAPIDS Support Notices
+grand_parent: RAPIDS Notices
+nav_exclude: true
+notice_type: rsn
+# Update meta-data for notice
+notice_id: 60 # should match notice number
+notice_pin: true # set to true to pin to notice page
+
+title: "Sunsetting cuxfilter after RAPIDS Release v26.06"
+notice_author: RAPIDS TPM
+notice_status: In Progress
+notice_status_color: yellow
+# 'notice_status' and 'notice_status_color' combinations:
+#   "Proposal" - "blue"
+#   "Completed" - "green"
+#   "Review" - "purple"
+#   "In Progress" - "yellow"
+#   "Closed" - "red"
+notice_topic: Platform Support Change
+notice_rapids_version: "v26.06+"
+notice_created: 2026-05-22
+# 'notice_updated' should match 'notice_created' until an update is made
+notice_updated: 2026-05-22
+---
+
+## Overview
+
+RAPIDS v26.06 will be the final release to include updates for `cuxfilter`.
+After the v26.06 release, RAPIDS will stop publishing new `cuxfilter` packages,
+development of the `cuxfilter` repository will cease, and the repository will be
+archived with migration guidance.
+
+`cuxfilter` helped users build GPU-accelerated, notebook-first, cross-filtered
+dashboards over large datasets by connecting cuDF-backed data to visualization
+libraries such as Panel, Bokeh, HoloViews, Datashader, and deck.gl.
+
+The forward path for this workflow will be a skill-based replacement rather than
+a successor Python package. The skill will preserve the useful `cuxfilter`
+patterns and help users generate GPU-accelerated visual analytics directly with
+supported RAPIDS and Python visualization libraries.
+
+## Impact
+
+`cuxfilter` v26.06 will remain the final maintained version. No new `cuxfilter`
+conda or pip packages will be published for RAPIDS releases after v26.06, and
+future RAPIDS releases will not provide compatibility updates, bug fixes, or API
+support for `cuxfilter`.
+
+Beginning with the first RAPIDS release after v26.06, `cuxfilter` should be
+removed from RAPIDS release surfaces where applicable, including
+release manifests, install examples, documentation entry points, metapackage
+references, and container references.
+
+Existing users may continue to use the v26.06 package by pinning compatible
+RAPIDS, CUDA, Python, and visualization-library versions. That path is intended
+only for existing workloads that cannot migrate immediately. It should not be
+used as the starting point for new dashboard or notebook development.
+
+## Migration guidance
+
+There will not be a direct successor package that re-implements the `cuxfilter`
+API. New work should use the skill replacement and direct-library patterns:
+
+- Use `cuDF` for GPU dataframe loading, transformation, aggregation, and joins.
+- Use HoloViews, hvPlot, Datashader, and Panel for notebook-first visual
+  exploration and linked selections.
+- Use Plotly Dash, Streamlit, Bokeh, or PyDeck when the desired output is a
+  standalone application or a framework-specific dashboard.
+- Use pandas or Polars as a CPU fallback when a local GPU is unavailable.
+
+The skill replacement is intended to capture the relevant workflow knowledge:
+GPU dataframe use, visual aggregation, linked selections, layout, and controls
+for fast exploration of large datasets.
+
+## Mapping common cuxfilter concepts
+
+| Previous `cuxfilter` concept | Recommended replacement pattern |
+| --- | --- |
+| `cuxfilter.DataFrame` | Load and transform data directly with `cudf.DataFrame`; convert only at explicit visualization boundaries when a library requires CPU data. |
+| `dashboard([...])` and preset layouts | Compose the view with Panel, Dash, Streamlit, or another maintained dashboard framework. |
+| Charts, widgets, and linked filters | Use HoloViews/hvPlot/Datashader with `link_selections`, or framework-native callback/state patterns. |
+| Graph and geospatial examples | Use cuGraph or cuSpatial for GPU-side analytics, then visualize with Datashader, HoloViews, PyDeck, Bokeh, or Plotly. |
+
+## Continued GPU visual analytics support
+
+RAPIDS continues to support GPU-accelerated data preparation and analytics
+through projects such as cuDF, cuGraph, and related libraries. The
+replacement skill will point users to those libraries and provide examples and
+templates for building accelerated visual analytics workflows without importing
+`cuxfilter`.