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name: learn-and-integrations description: "Routes Mars Learn estimator, Dask-on-Mars, and optional ML framework integration requests to the bundled API and dependency guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Learn and Integrations

Use this sub-skill for Mars Learn estimators and optional integration packages. The core route is scikit-learn-style API usage on Mars tensors/DataFrames; the optional route is Dask, PyTorch, TensorFlow, XGBoost, LightGBM, Statsmodels, Joblib, or Proxima integration.

Trigger phrases

• "Use Mars Learn like scikit-learn."
• "Run KMeans, PCA, or NearestNeighbors on Mars data."
• "Use Dask on top of Mars."
• "Run a PyTorch or TensorFlow script through Mars."
• "Use XGBoost, LightGBM, Statsmodels, Joblib, or Proxima with Mars."

What belongs here

• Core Mars Learn estimators and utilities.
• Dataset generation and preprocessing helpers.
• Optional integration import and dependency guidance.
• Tiny CPU estimator smoke checks.

What stays elsewhere

• Tensor/DataFrame fundamentals -> tensor-dataframe-core.
• Remote callable DAGs not tied to Learn -> remote-and-scripts.
• Ray, GPU, Kubernetes, YARN, or service CLI startup -> deployment-and-backends.

Read these bundled files

• references/api-reference.md for bundled estimator and integration entry points.
• references/workflows.md for the core CPU estimator path and optional integration routing.
• references/troubleshooting.md for missing optional dependencies, shape errors, and script-runner failures.
• scripts/check_mars_learn.py for a tiny Mars Learn CPU smoke.

Minimal route

1. Start from local tensor data with mars.tensor as mt.
2. Import the estimator, for example from mars.learn.decomposition import PCA.
3. Fit or transform tiny data; many fit / predict style methods trigger execution internally.
4. If the user asks for an optional integration, identify the exact dependency first and do not install broad extras by default.

Common decisions

• Use Mars Learn core estimators for base package guidance.
• Treat TensorFlow, PyTorch, XGBoost, LightGBM, and Statsmodels as optional integrations, not required baseline dependencies.
• For Dask-on-Mars, ensure real dask is installed; a placeholder object means the optional integration is unavailable.
• For distributed deep-learning scripts, route runtime/back-end prerequisites to deployment-and-backends when GPUs, Ray, or clusters are involved.

Quality bar

A future agent should be able to distinguish a core Mars Learn estimator task from an optional integration task, run a tiny CPU estimator smoke, and explain what external package or backend is missing when an integration cannot import.