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name: search-and-parallelism description: "Control auto-sklearn search spaces, parallel resources, ensembles, disk outputs, and result-inspection APIs." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Search and Parallelism

Use this sub-skill when the task is to configure or diagnose auto-sklearn search/resources, Dask parallelism, SMAC strategy callbacks, ensemble construction, disk outputs, or result inspection after a fit.

Route here for

• Choosing time_left_for_this_task, per_run_time_limit, memory_limit, n_jobs, dask_client, thread-environment limits, tmp_folder, and disk-retention knobs.
• Restricting the search space with include or exclude dictionaries and built-in component IDs.
• Selecting SMAC behavior with get_smac_object_callback, smac_scenario_args, random search/ROAR, successive halving, or get_trials_callback.
• Planning single-machine n_jobs runs, attaching a user-created Dask client, or explaining the internal LocalDask/UserDask behavior.
• Designing sequential search followed by post-hoc fit_ensemble().
• Controlling ensembles with ensemble_class, ensemble_kwargs, ensemble_nbest, max_models_on_disc, disable_evaluator_output, and load_models.
• Inspecting sprint_statistics(), leaderboard(), show_models(), cv_results_, performance_over_time_, runhistory_, and ensemble model weights.

Route elsewhere

• Basic estimator selection, fit, predict, predict_proba, score, persistence, and minimal smoke workflows: estimators.
• Data formats, train/test split semantics, resampling choices, metrics, custom scorers, and refit() requirements after CV/custom splits: data-metrics-validation.
• Writing or registering custom classifiers, regressors, preprocessors, or component search spaces: custom-components.
• Large metadata regeneration, metadata_directory, AutoSklearn2 portfolios, and maintenance scripts: metadata-maintenance.

Fast operating checklist

1. Decide the workflow mode:
   • sequential: one worker; optionally set ensemble_class=None during search and call fit_ensemble() later.
   • parallel: set n_jobs > 1 or pass a Dask Client; guard script entry points with if __name__ == "__main__":.
   • random: supply a get_smac_object_callback that returns a SMAC ROAR/random-search facade.
   • successive-halving: supply a get_smac_object_callback that returns SMAC4AC(..., intensifier=SuccessiveHalving, ...) and sets ta_kwargs["budget_type"].
2. Budget resources conservatively. In multi-process search, total model-training memory can approach n_jobs * memory_limit plus the parent process and Dask overhead. If the dataset is large, do not simply raise n_jobs.
3. Set thread caps before Python starts when using multiple workers or when predicting/scoring large ensembles:

   export OPENBLAS_NUM_THREADS=1
   export MKL_NUM_THREADS=1
   export OMP_NUM_THREADS=1
   

4. If using Dask workers across processes or machines, ensure all workers can read/write the same tmp_folder and output area.
5. Choose include/exclude with valid component IDs. Use include for tight, interpretable, or fast searches; use exclude to remove only known bad/expensive components. Do not set both.
6. Keep evaluator output enabled unless the workflow is analysis-only and does not need prediction or ensembles. disable_evaluator_output=True disables saved models and predictions, so predict() is unavailable.
7. Inspect results in layers: sprint_statistics() for run counts, leaderboard() for tabular ranking, show_models() for ensemble members, cv_results_ for GridSearchCV-like details, and performance_over_time_ for time curves.

Bundled references and helper

• Search and parallelism reference — constructor parameters, include/exclude IDs, Dask patterns, SMAC callbacks, and search-mode snippets.
• Results and ensembles reference — ensemble controls, disk outputs, fit_ensemble(), leaderboard(), show_models(), cv_results_, and performance_over_time_.
• Troubleshooting reference — fork/forkserver/main-guard failures, memory and time limits, disk growth, Dask cleanup, and disabled evaluator outputs.
• scripts/build_search_config.py — safe snippet generator; emits JSON/Python configuration examples without importing or running auto-sklearn.

Validation before a future run

• Print the generated constructor kwargs and verify there is no simultaneous include and exclude.
• Confirm time_left_for_this_task >= 2 * per_run_time_limit if an ensemble is expected; auto-sklearn may cap per-run time to leave room for at least two models.
• For parallel workflows, confirm code and data loading are inside a main guard or functions, shared storage is visible to workers, and user-owned Dask clients are closed by user code.
• For result inspection, verify fit() completed and evaluator output was not disabled in a way that removed required predictions/models.