---

name: sklearn-accel description: "Zero-code-change acceleration for scikit-learn, UMAP, and HDBSCAN." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

sklearn-accel

Use this sub-skill when existing scikit-learn, UMAP, or HDBSCAN code should run through cuml.accel without rewriting the workflow to direct cuML APIs.

Route here when

• you need CLI, notebook, environment-variable, or programmatic activation
• you need to confirm proxying, fallback, logging, or profiling
• you need to explain compatibility-driven CPU fallback or result differences

Keep out

• direct cuML estimator rewrites and GPU-native tuning -> ../python-estimators/SKILL.md
• Dask or multi-GPU workflow changes -> ../distributed-dask/SKILL.md
• generic scikit-learn advice that is not tied to cuml.accel

Core rules

• activate before importing sklearn, umap, or hdbscan
• use python -m cuml.accel for scripts, modules, or inline commands
• use %load_ext cuml.accel in notebooks before library imports
• use CUML_ACCEL_ENABLED=1 only when you do not control the app entrypoint
• use cuml.accel.install(disable_uvm=False, log_level=None) for programmatic activation
• use cuml.accel.enabled(), cuml.accel.is_proxy(), and cuml.accel.profile() to check behavior
• rely on logs and profiler output to confirm GPU versus CPU execution; is_proxy() alone only proves proxy activation

What belongs here

• CLI forwarding and profiling flags from python -m cuml.accel
• notebook and IPython activation, log-level, and profiler magics
• fallback compatibility by estimator family, input type, parameter, and dependency version
• expected numerical differences and when to compare metrics instead of fitted attributes

References

• references/compatibility-and-profiling.md
• references/troubleshooting.md
• scripts/cuml_accel_smoke.py