RAPIDS
Official@rapidsai
Open GPU Data Science
Agent Skills by RAPIDS
Showing 8 vetted skills indexed across 1 GitHub repositories.
rapidsaireview-cudf-polars-expressions
Implements and reviews Polars expression support in the cudf-polars GPU engine.
rapidsaiperf-compare-cudf
Benchmarks libcudf NVBench suites on a branch or PR against main and reports performance differences.
rapidsaiupdate-narwhals-testing
Updates Narwhals version pins and pytest skip lists for cudf CI testing.
rapidsaibuild-test-cudf-java
Build and test cuDF Java bindings with Maven against nightly or local libcudf builds.
rapidsaidebug-cudf-pandas
Diagnose and fix pandas test suite failures under the cudf.pandas GPU compatibility layer.
rapidsaiaccelerated-computing-cudf
Migrate pandas DataFrame workloads to GPU-accelerated cuDF and dask-cuDF pipelines.
rapidsaibuild-test-cudf
Build and test code changes inside a cudf devcontainer.
rapidsaireview-cudf
Automates cudf pull request evaluation by collecting metadata, diffs, and context.
Frequently Asked Questions About RAPIDS
FAQPage SchemaWhat specific tasks does this enable for developers?▼
These capabilities enable developers to build and test cuDF modifications within isolated containerized environments. It further facilitates the review process by automatically aggregating pull request metadata, code diffs, and relevant technical context to expedite code validation and integration.
Which engineering personas benefit from these capabilities?▼
Data engineers, GPU kernel developers, and open-source contributors working on high-performance data processing libraries benefit from these capabilities. It is specifically designed for those maintaining or extending cuDF who require consistent build environments and efficient peer review cycles.
What are the prerequisites for running these environments?▼
Users require a system with NVIDIA GPU support and a compatible container runtime environment. The setup relies on pre-configured devcontainer definitions to ensure that all necessary GPU drivers and build dependencies are correctly mapped for local compilation and testing.