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name: cli-and-integrations description: "Use Lazy Predict CLI and integrations for CSV classification or regression runs, MLflow tracking, Dask and PySpark conversion, Spark MLlib classes, GPU or Intel optional checks, and integration troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

CLI and Integrations

Use this sub-skill when the user wants the lazypredict command-line interface, CSV target-column benchmarking, package environment checks, MLflow tracking, Dask/PySpark conversion, Spark MLlib classes, or optional acceleration and integration troubleshooting.

CLI quick check

The installed console command is lazypredict:

lazypredict --help
lazypredict --version

The CLI supports supervised classification and regression from a CSV file. It does not expose LazyForecaster, tuning, SHAP, or advanced integrations.

Run the bundled helper for a safe CLI command-object smoke test:

python scripts/smoke_cli.py --task classification

Use --skip-fit when only help/version behavior should be checked.

What to read

• references/cli-reference.md for CLI flags, CSV assumptions, output expectations, and limitations.
• references/integrations.md for MLflow, Dask/PySpark, Spark MLlib, GPU, Intel Extension, and optional dependency checks.
• references/troubleshooting.md for missing target columns, CLI not found, CSV data issues, MLflow/Spark/Dask/GPU dependency failures, and CUDA fallback semantics.

Route elsewhere

• Use supervised-benchmarking when the user needs Python API control over model lists, categorical encoders, custom metrics, predictions, persistence, or fitted pipelines.
• Use time-series-forecasting for LazyForecaster; the CLI is not a time-series interface.
• Use advanced-workflows for tuning, explainability, search spaces, SHAP, and optional plotting details.