Agent skill · data analytics · nvidia
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.
Why this skill is useful
Provides specific guidance on using NVIDIA cuDF for GPU DataFrames, including performance optimization techniques and ETL patterns that the AI wouldn't reliably generate on its own.
What it needs
Requires cuda installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
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 (pyversion group for Python versions, cudaversion 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 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 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. Critical Rules 1. Choose the right cuDF path. Use cudf.pandas for broad compatibility or minimal-change acceleration. Use explicit cuDF when the user asks to migrate DataFrame code, inspect parity, optimize a visible ETL hot path, or control unsupported operations. 2. Benchmark the working set. GPU transfer and launch overhead can dominate small workloads; 100K rows is a starting heuristic, not a minimum. Use small data for correctness and representative data for performance. 3. Keep conversions at boundaries. Use .topandas() or .tonumpy() for CPU-only libraries, display, or final output boundaries. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/accelerated-computing-cudf