Accelerated Computing Cudf
NVIDIA/cudf/skills/accelerated-computing-cudfanalysisOfficial
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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.
Files10 files
BENCHMARK.md146 lines
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Skill details
Versionv1.0.0
AuthorNVIDIA
Categoryanalysis
Skill IDNVIDIA/cudf/skills/accelerated-computing-cudf
Files10 files
Related skills
Build Test CudfUse this skill to build and test code changes inside a cudf devcontainer.Build Test Cudf JavaBuild and test cudf Java bindings (cudf-java) inside a cudf devcontainer. Use when the user asks to build, compile, or test Java code in the cudf repository.Cudf Query Engine BuilderBuilds a native cuDF proof of concept for a query engine with no working GPU path. Use when an engineer supplies target-engine or adapter code plus a query, operator sequence, or CPU implementation selected for the POC and needs working libcudf C++ or cuDF Java code, a runnable single-GPU program, and a CPU/reference correctness comparison. Do not use for standalone dataframe analysis, cuDF mainline changes, production or deployment integration, production fallback or I/O policy beyond the singDebug Cudf PandasDebug and fix pandas test suite failures under the cudf.pandas compatibility layer. Use when given pytest node IDs of failing pandas tests that need to be fixed for cudf.pandas compatibility.Perf Compare CudfBenchmark a cuDF branch, WIP changes, or a PR against the `main` branchReproduce CiReproduce cudf CI failures locally. Provide a GitHub Actions job URL to auto-discover parameters, or supply the container image and CI script directly.