cuML

Use this repo skill for NVIDIA cuML tasks: direct GPU-native Python estimators, zero-code-change cuml.accel acceleration, cuml.dask multi-GPU workflows, supporting data/metrics/preprocessing utilities, or cuML source-build and C++/CUDA maintenance.

cuML is a CUDA/RAPIDS package. Direct cuml runtime proof requires a CUDA-capable installation and an NVIDIA GPU; CPU-only imports are not a substitute for estimator execution.

First checks

1. Confirm the task is about cuML/RAPIDS GPU machine learning, cuml.accel, cuml.dask, libcuml, or source-build/C++ APIs.

2. Read references/repo-provenance.md when deciding whether this skill is current for a checkout or when refreshing it.

3. Read references/package-overview.md for package surfaces, install variants, required/optional dependencies, output/device/memory behavior, and supported-version notes.

4. Run the bundled probe when an installed package is expected:

   python scripts/cuml_environment_probe.py --checks import cuda
   python scripts/cuml_environment_probe.py --checks import cuda health optional
   

5. If any check fails, read references/troubleshooting.md before changing dependencies or broadening the scope.

Route by task

User task	Read
Write, convert, debug, or validate direct single-GPU cuml estimator code with fit, predict, transform, output types, or model persistence.	sub-skills/python-estimators/SKILL.md
Keep existing scikit-learn, UMAP, or HDBSCAN code unchanged and accelerate it with python -m cuml.accel, notebook magics, environment variables, or programmatic activation.	sub-skills/sklearn-accel/SKILL.md
Build a cuml.dask workflow with Dask-CUDA, partitioned data, multi-GPU estimators, LocalCUDACluster, or distributed model collection.	sub-skills/distributed-dask/SKILL.md
Generate synthetic data, split/encode/scale data, compute metrics, vectorize text, use explainers, or prepare time-series utility inputs.	sub-skills/data-pipeline-utilities/SKILL.md
Build cuML from source, diagnose libcuml/CMake/CUDA toolchain issues, select native tests, or work with C++ examples/APIs.	sub-skills/native-build-and-cpp/SKILL.md

Installation and backend guidance

• Prefer the RAPIDS installation selector for current release/nightly commands and choose the CUDA/Python/package-manager combination that matches the host.
• Pip package names are CUDA-suffixed in current RAPIDS wheels, for example cuml-cu12 or cuml-cu13; dependencies such as libcuml, cudf, rmm, pylibraft, and CuPy must match the same CUDA/RAPIDS generation.
• cuml.dask requires additional Dask RAPIDS dependencies (rapids-dask-dependency, dask-cudf, raft-dask) matching the cuML version; dask-cuda/dask-ml are needed for many test or local-cluster workflows.
• cuml.accel should be enabled before importing scikit-learn, UMAP, or HDBSCAN. Unsupported estimators, inputs, parameters, or versions fall back to CPU and should be diagnosed with logging/profiling rather than treated as cuML estimator failures.
• Source builds are separate from ordinary package use. They need a full CUDA/toolchain/RAPIDS development environment and should start with the non-mutating probe in sub-skills/native-build-and-cpp/scripts/source_build_probe.py.

Validation policy

• Always validate with tiny generated data before scaling to production-sized datasets.
• Prefer quality metrics (accuracy_score, r2_score, adjusted Rand score, trustworthiness) over exact coefficient or label equality when comparing GPU and CPU algorithms.
• For model serialization, load only trusted pickle/joblib artifacts. Do not unpickle data from untrusted sources.
• For direct cuml, do not claim a CPU fallback. For cuml.accel, CPU fallback is expected for unsupported operations and should be made visible.
• For Dask and source-build workflows, distinguish optional unverified capability from required CUDA runtime failures.

Generated helpers

• scripts/cuml_environment_probe.py: shared import/CUDA/health/optional dependency check for installed cuML.
• sub-skills/python-estimators/scripts/single_gpu_estimator_smoke.py: tiny KMeans, LinearRegression, RandomForest, and pickle checks.
• sub-skills/sklearn-accel/scripts/cuml_accel_smoke.py: accelerator CLI/programmatic/profiling checks.
• sub-skills/distributed-dask/scripts/dask_cuml_smoke.py: optional LocalCUDACluster + distributed KMeans smoke with missing-dependency diagnostics.
• sub-skills/data-pipeline-utilities/scripts/data_utility_smoke.py: datasets, preprocessing, and metric checks.
• sub-skills/native-build-and-cpp/scripts/source_build_probe.py: non-mutating source-build prerequisite probe.

Avoid using this skill when

• The task is general scikit-learn usage with no GPU/RAPIDS/cuML surface.
• The task is generic Dask administration rather than cuML estimators on Dask collections.
• The task is CUDA kernel development unrelated to cuML/libcuml APIs.
• The task requires downloading external datasets or running long benchmarks without explicit user approval.