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name: automl-and-hyperopt description: "Guides agents using Ludwig AutoML, init_config, hyperopt search spaces, Ray or Optuna executors, and tuning dependency troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AutoML and Hyperopt

Use this sub-skill when the task asks for Ludwig AutoML, init_config, auto_train, create_auto_config, ludwig hyperopt, search spaces, executors, Ray/Optuna dependencies, or tuning failures.

Start here

1. Validate the base dataset/config through configuration-and-data.
2. Read workflows.md for AutoML and HPO patterns.
3. Read api-reference.md for API/CLI surfaces.
4. Check prerequisites before running a search:

python scripts/check_hyperopt_prereqs.py
ludwig hyperopt --help

Key decision points

• init_config helps infer a starting config from a dataset and target.
• auto_train can create and train an AutoML-selected config within a time budget.
• hyperopt requires a config with a hyperopt section and an executor/search algorithm.
• Ray and Optuna paths are optional; missing imports should produce dependency guidance, not silent fallback.

Route elsewhere

• Ordinary one-run training: training-and-experiments.
• Serving/export of tuned models: serving-export-and-deployment.