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name: training-configuration description: "Configure AlphaFold 3 training datasets, Trainer/DataLoader construction, YAML/Pydantic factories, optimization, evaluation, and bounded diagnostics." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training configuration

Use this sub-skill for Trainer, its wrapped DataLoader, dataset selection, Pydantic/YAML configuration, conductor phases, checkpointing, optimizer/EMA choices, Fabric setup, and bounded training preflight. Start with scripts/validate_config.py; it parses and validates a YAML document without constructing a model or trainer.

Read the smallest applicable reference:

• references/trainer-api.md for public classes, collation, lifecycle, optimizer/EMA, logging, evaluation, and checkpoints.
• references/configuration.md for model, trainer, dataset, weighted-sampler, conductor, extra-field, and dotpath contracts.
• references/workflows.md for safe preflight, direct-API, YAML, multi-phase, collation, resume, and bounded diagnostics.
• references/troubleshooting.md for validation failures, backend/precision problems, checkpoint hazards, and resource stop conditions.

Routing boundaries

• Route generic Alphafold3Input, atom feature semantics, and input conversion to input-representation.
• Route mmCIF/PDB/MSA/template preparation, cropping, and dataset-scale curation to data-pipeline.
• Route architecture, forward/loss versus sampling modes, and model shape or checkpoint internals to model-inference.
• Route Click, Gradio, and command construction to cli-serving.

Operating rules

1. Establish the model dimensions, training objective, dataset kind, train/valid /test split policy, accelerator, precision, checkpoint policy, and an explicit step/batch budget before construction.
2. Validate the selected YAML root and optional dotpath first. A successful parse is not a successful trainer plan: inspect warnings for missing paths, extra keys, conflicting dataset sources, unsafe overwrite, and unbounded resource settings.
3. Keep structure preparation separate from training. This skill consumes ready PDBInput/AtomInput datasets; it does not curate large databases or define molecule/atom feature semantics.
4. Construct Trainer only after a human-approved bounded plan. Construction launches Lightning Fabric and creates the checkpoint directory; the validator deliberately does neither. Never use a full production model or dataset for a smoke check.
5. Treat use_ema, fp16, distributed_eval, optimizer switches, and other extra trainer keys as runtime options, not as proof that a run is affordable or that a backend is available. Record the actual Fabric accelerator and precision selected at runtime.
6. For a conductor, validate every phase and the exact training_order before selecting a phase. Root checkpoint folder/prefix values are composed with each phase; do not reuse a phase output namespace accidentally.

The bundled validator is intentionally read-only: it does not instantiate Trainer, call a factory's create_instance, call Fabric.launch, create checkpoint/log directories, download data, train, or start a server.