Accelerated Computing Cudf
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration...
Install
npx skills add NVIDIA/skills --skill accelerated-computing-cudf -g -a claude-code -y- Repository
- NVIDIA/skills3.8k
- Category
- Data and ML
- Updated
- 5d ago
- License
- CC-BY-4.0 AND Apache-2.0
- Context cost
- Mediumabout 2,460 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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.
