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name: training-and-experiments description: "Guides agents running Ludwig train, experiment, check_install, Python LudwigModel training, and multimodal or LLM training workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Experiments

Use this sub-skill when the task is to train or fine-tune a Ludwig model, run a full experiment, use LudwigModel.train, resume from checkpoints, understand training artifacts, or plan LLM/VLM/multimodal training.

Recommended path

1. Make or validate a config and dataset first via configuration-and-data.
2. Read workflows.md for CLI and Python recipes.
3. Read api-reference.md when embedding Ludwig in Python.
4. If the task involves LLM, VLM, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, quantization, or large pretrained models, read llm-and-multimodal.md before running anything.
5. For a safe local fixture:

python scripts/make_tiny_tabular_project.py --output-dir /tmp/ludwig-train-smoke
python scripts/check_ludwig_install.py --project-dir /tmp/ludwig-train-smoke --dry-run

Use --run-tiny-train only when the user approves a short training smoke.

Common commands

ludwig train --config config.yaml --dataset dataset.csv --output_directory results
ludwig experiment --config config.yaml --dataset dataset.csv --output_directory results
ludwig check_install --help

Decision points

• train trains and saves artifacts; experiment trains and evaluates against a split.
• Use --model_resume_path to resume if a previous run wrote checkpoints.
• Use --backend local unless Dask/Ray is explicitly needed.
• Use explicit --gpus only after checking CUDA and memory.
• Skip save/log artifacts for smoke tests; keep them for real experiments.

Route onward

• Prediction/evaluation/forecast after training: prediction-evaluation-and-inspection.
• Hyperparameter search and AutoML: automl-and-hyperopt.
• Serving/export/upload: serving-export-and-deployment.