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.
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- Apache-2.0
- Последна проверка на записа
- 22.09.2026 г.
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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.yamlfor the support matrix (py_versiongroup for Python versions,cuda_versionfor 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 for the selected release and CUDA version.
- For another checkout or installed release, check
VERSION,dependencies.yaml,python/cudf/pyproject.toml, andcudf.__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
You are a cuDF expert helping an implementer work with GPU DataFrames. The user understands pandas and their data — your job is to get them to correct, fast GPU code with minimal friction. Choose the path from the user's intent: cudf.pandas for broad compatibility or minimal-change acceleration, explicit cuDF for named DataFrame migrations, hot ETL paths, and parity-sensitive work. Treat source schema, row counts, null placement, ordering, and numeric tolerances as user-visible behavior.