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name: python-estimators description: "Direct single-GPU cuML Python estimator workflows on NVIDIA GPUs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

python-estimators

Use this sub-skill for direct, GPU-native cuml Python estimators on one NVIDIA GPU. The expected workflow is scikit-learn-style construction plus fit, predict, transform, fit_predict, or fit_transform, with explicit handling of GPU inputs, output containers, validation, and trusted model persistence.

Route here when

• Rewriting or authoring explicit cuml estimator code rather than keeping scikit-learn imports unchanged.
• Choosing among single-GPU estimator families: clustering, linear models, random forests, decomposition, nearest neighbors, SVMs, manifold learning, or estimator-like time-series models.
• Controlling output_type, set_global_output_type, using_output_type, fitted attributes, prediction outputs, or safe pickle/joblib round-trips.
• Running a tiny single-GPU validation before a larger workflow.
• Debugging direct estimator failures caused by CUDA visibility, RMM memory, input containers, dtypes, output conversion, model state, or serialization.

Route away

• cuml.dask, Dask-CUDA, multi-GPU, or multi-node workflows -> sibling sub-skill distributed-dask.
• Zero-code-change acceleration for existing scikit-learn, UMAP, or HDBSCAN code -> sibling sub-skill sklearn-accel.
• Detailed dataset generation, preprocessing, metrics, model selection, text features, explainers, or time-series utility schemas -> sibling sub-skill data-pipeline-utilities.
• Source builds, C++ APIs, native CI test selection, CUDA compiler, or linker/toolchain issues -> sibling sub-skill native-build-and-cpp or root troubleshooting.

Covered estimator families

• Clustering: KMeans, DBSCAN, HDBSCAN plus HDBSCAN approximate prediction and membership helper routing notes.
• Linear models: LinearRegression, LogisticRegression, Ridge, Lasso, ElasticNet.
• Ensembles: RandomForestClassifier, RandomForestRegressor.
• Decomposition: PCA, TruncatedSVD.
• Neighbors: NearestNeighbors, KNeighborsClassifier, KNeighborsRegressor.
• SVMs: SVC, SVR, LinearSVC, LinearSVR.
• Manifold route notes: UMAP, TSNE.
• Estimator-like time-series route notes: ARIMA, AutoARIMA, ExponentialSmoothing with deprecation cautions.

Operating procedure

1. Read references/api-reference.md to select the estimator family and confirm constructor defaults, common methods, output-type behavior, and routing boundaries.

2. Read references/workflows.md for direct fit/predict/transform recipes, health checks, output-type management, serialization, and tiny validation patterns.

3. Read references/troubleshooting.md before changing environments when imports, CUDA, RMM, input shape/type, fitted-state, or pickle/joblib behavior fails.

4. Keep work single-GPU unless the user explicitly asks for Dask; select a GPU before Python starts with CUDA_VISIBLE_DEVICES when the host has multiple GPUs.

5. Validate tiny code paths before scaling data. From this sub-skill directory, use:

   python scripts/single_gpu_estimator_smoke.py --help
   python scripts/single_gpu_estimator_smoke.py --case all --check-pickle
   

Do not claim CPU fallback for direct cuml estimators. A CUDA-capable cuML installation and visible NVIDIA GPU are required for runtime proof.