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name: training description: "Guides config-driven PyTorch-VAE training, dry-runs, data layout checks, and experiment troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training

Use this sub-skill when the user wants to run, dry-run, resume, or debug a PyTorch-VAE experiment from a YAML config. It covers the generic runner pattern, CelebA-style data setup, logger/checkpoint output, and trainer knobs. The bundled command examples assume the generated skill directory is the current working directory.

Read first

• references/workflows.md for the end-to-end experiment flow.
• references/configuration.md for the config schema and model-specific fields.
• references/troubleshooting.md for data, trainer, and legacy-config failures.
• scripts/train_from_config.py for the bundled safe wrapper.

Include here

• Choosing the right configs/*.yaml file for a model.
• Understanding model_params, data_params, exp_params, trainer_params, and logging_params.
• Verifying CelebA extraction paths and sample output locations.
• Dry-running a config before a full fit.
• Full training runs when the user explicitly wants them.
• Trainer settings that affect GPUs, checkpointing, logging, or seeds.
• Special training knobs such as FactorVAE's dual optimizer or legacy VampVAE config handling.

Exclude or route elsewhere

• Constructor signatures, sample/generate behavior, and architecture selection -> sub-skills/model-reference/SKILL.md.
• Low-level architecture comparisons -> references/model-overview.md or model-reference.
• Repository provenance and staleness checks -> references/repo-provenance.md.

Typical triggers

• "train VAE"
• "run this config"
• "why does the trainer fail"
• "where are logs/checkpoints saved"
• "how do I point it at CelebA"
• "why is gpus failing"
• "how do I dry-run the experiment before a long fit"

Workflow

1. Pick the config that matches the target model family.
2. Confirm the data layout and data_path.
3. Run the bundled training wrapper without --fit for a safe validation pass.
4. Add --fit only after the config, data, and backend are ready.
5. Inspect logs under logs/<experiment-name>/version_* and checkpoints under the logger's checkpoints/ directory.

Hand off to model-reference when needed

If the user switches from "run the experiment" to "what arguments does the model take" or "what does sample() expect", route to the model-reference sub-skill.