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name: distributed-dask description: "Route cuML multi-GPU and distributed Dask workflows through LocalCUDACluster, partitioned data, supported estimators, and model collection." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

distributed-dask

Use this sub-skill for cuML workloads that need cuml.dask, dask_cuda.LocalCUDACluster, or multi-GPU / multi-node execution.

Use this sub-skill when

• the user wants a Dask-CUDA cluster for cuML training or inference
• data must be partitioned across workers before fitting or transforming
• the workflow needs a distributed cuml.dask estimator, dataset helper, or model collection step
• a quick smoke is needed to confirm optional Dask dependencies and CUDA visibility

Route elsewhere when

• the request is a single-GPU cuml estimator or utility workflow -> use python-estimators
• the request is a source build, C++ example, or MPI/C++ debugging task -> use native-build-and-cpp
• the request is generic Dask administration that does not involve cuML -> out of scope

Covered families

• clustering: KMeans, DBSCAN
• decomposition: PCA, TruncatedSVD
• ensemble: RandomForestClassifier, RandomForestRegressor
• linear models: LinearRegression, LogisticRegression, Ridge, Lasso, ElasticNet
• manifold: distributed UMAP transform around a fitted single-GPU model
• naive bayes: MultinomialNB
• neighbors: NearestNeighbors, KNeighborsClassifier, KNeighborsRegressor
• preprocessing: LabelBinarizer, OneHotEncoder, OrdinalEncoder
• text feature extraction: TfidfTransformer
• synthetic datasets: make_blobs, make_classification, make_regression

Operating sequence

1. Check that the Dask optional dependencies and CUDA runtime are present.
2. Start LocalCUDACluster and Client.
3. Partition data into Dask Array or Dask-cuDF collections.
4. Fit, predict, or transform with the chosen estimator family.
5. Collect combined models or outputs when the estimator supports that pattern.
6. Close the client and cluster explicitly.

See references/workflows.md for the exact data-layout patterns and references/troubleshooting.md for dependency and cluster failures. Use scripts/dask_cuml_smoke.py for a tiny parser / dependency / fit smoke.