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accelerated-computing-cudf

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

skills/accelerated-computing-cudf/SKILL.md

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# cuDF & dask-cuDF Implementer's Guide

## Compatibility

- Development release tracked by this skill: 26.12 (`VERSION`: `26.12.00`). Use the selected installed release for deployment requirements.
- Read `dependencies.yaml` for the support matrix (`py_version` group for Python versions, `cuda_version` for CUDA versions). Match cuDF, pylibcudf, libcudf, and RMM release versions, and match pip wheel suffixes (`-cu12` / `-cu13`) to the CUDA major version.
- Requires a supported NVIDIA GPU and compatible driver. Check the [installation requirements](https://docs.nvidia.com/datascience/install/#system-req) for the selected release and CUDA version.
- For another checkout or installed release, check `VERSION`, `dependencies.yaml`, `python/cudf/pyproject.toml`, and `cudf.__version__` before choosing versions or relying on an API.

## Naming

Use NVIDIA library-first wording in user-facing answers. Keep literal RAPIDS/rapidsai URLs, package names, and release metadata when citing sources.

## Role