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name: training-custom-models description: "Retrain ProteinMPNN, validate training data layout, resume or debug training, and use custom training checkpoints for ProteinMPNN inference." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training Custom Models

Use this sub-skill when a user wants to retrain ProteinMPNN, inspect the training dataset format, run a tiny debug training smoke, resume from a checkpoint, or point inference at custom model weights. ProteinMPNN training is normally GPU/HPC work; keep full training runs explicit and treat them as expensive.

Quick Routing

• For training setup, dataset checks, SLURM adaptation, checkpoint naming, and training logs, use the references in this sub-skill.
• For actually designing or scoring sequences with a custom checkpoint after the model folder and model name are known, route to ../inference-design/ and pass --path_to_model_weights plus --model_name.
• For fixed positions, tied positions, PSSM, amino-acid bias, or other inference JSONL constraints, route to ../constraint-inputs/.
• Do not copy or bundle model checkpoints; users keep checkpoints in their ProteinMPNN checkout or training output directory.

Core Workflow

1. Confirm dependencies: Python, PyTorch, NumPy; training data parsing areas also need python-dateutil, SciPy, and pdbx/mmCIF tooling when preparing data from CIF files.
2. Validate the training data root before launching training:

   python scripts/check_training_layout.py --data-root path/to/pdb_2021aug02
   

3. For a tiny smoke check, use the sample dataset, --debug True, one epoch, a small token budget, and a scratch output folder.
4. For real training, adapt the SLURM recipe in references/training-workflow.md; expect long GPU runtime and large training data.
5. Use the resulting checkpoint folder with inference:

   python protein_mpnn_run.py --path_to_model_weights path/to/run/model_weights --model_name epoch_last --pdb_path path/to/input.pdb --out_folder path/to/out
   

References

• references/training-workflow.md: training flags, debug smoke, SLURM adaptation, resume behavior, output schema.
• references/data-layout.md: expected dataset files and .pt records, loader behavior, validation script use.
• references/custom-checkpoints.md: checkpoint names and inference flags for custom weights.
• references/troubleshooting.md: common training, data, dependency, and custom-checkpoint failures.