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advanced-extensions

advanced-extensions

Use Modin experimental Batch Pipeline, XGBoost, spreadsheet, NumPy, Polars, sklearn, and torch extensions.

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Documentation de la Skill

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name: advanced-extensions description: "Use Modin experimental Batch Pipeline, XGBoost, spreadsheet, NumPy, Polars, sklearn, and torch extensions." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Modin advanced and experimental extensions

Use this sub-skill when a task names modin.experimental, Batch Pipeline, Modin XGBoost, spreadsheet UI, Modin NumPy, Modin Polars, experimental sklearn, or the experimental PyTorch DataLoader.

Start here

  1. Read references/batch-pipeline.md for PandasQueryPipeline, Ray-only execution, output IDs, postprocessors, fan-out, and partition-callback rules.
  2. Read references/xgboost.md for Ray-only distributed XGBoost, DMatrix, train, feature metadata, and dependency compatibility.
  3. Read references/experimental-frontends.md for spreadsheet, Modin NumPy, Modin Polars, experimental sklearn, and PyTorch DataLoader boundaries.
  4. Read references/troubleshooting.md for version-sensitive optional dependency failures and Ray-only extension errors.
  5. Run scripts/batch_pipeline_smoke.py to verify a tiny Ray Batch Pipeline workflow.
  6. Run scripts/xgboost_smoke.py only after verifying the installed XGBoost package exposes Modin's required legacy Rabit APIs.

Routing boundaries

This sub-skill owns experimental APIs and optional frontends. Route stable DataFrame/Series work to ../core-pandas-api/SKILL.md, engine/resource setup to ../engines-configuration/SKILL.md, and experimental file/conversion work to ../io-interoperability/SKILL.md.

Operating pattern

  1. Treat every API here as version-sensitive. Verify imports and a tiny local fixture first.
  2. Configure the Ray engine before importing Modin for Batch Pipeline or XGBoost.
  3. Keep optional dependencies explicit: xgboost, scikit-learn, modin-spreadsheet, polars, and torch are not all covered by the base package.
  4. Do not scale training, UI, or network-dependent workflows until the bundled smoke or a local equivalent passes.
  5. Carry import or dependency incompatibilities as optional extension caveats rather than claiming stable package coverage.
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