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name: models-training description: "Guides PyHealth model-family selection, dataset and vocabulary contracts, Trainer training/inference, device selection, checkpoints, and safe small-scale workflow adaptation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PyHealth models and training

Use this route after a task dataset emits validated samples. Read model overview for family selection and API reference for current signatures.

Workflow

1. Confirm task mode, input keys, processor output schemas, vocabulary sizes, label names, and a patient-safe split in clinical-tasks and data-pipelines.
2. Choose a model family that consumes those fields: classical, sequence, recurrent/attention, transformer, graph, image/signal, multimodal, or generative. Instantiate it with the task dataset where required.
3. Run scripts/check_model_contract.py or a one-batch CPU forward pass. Do not download weights or start a large run in a smoke test.
4. Construct Trainer(model, device=..., output_path=...); use explicit device and bounded epochs, steps_per_epoch, and output paths.
5. Train with validation and a mode-appropriate monitor; call evaluate or inference and route output arrays to evaluation.
6. Keep last.ckpt/best.ckpt provenance with model/task/config/device. Route text, image, signal, and external-weight constraints to medical-code-text.

CPU is the baseline; CUDA is optional and must be independently probed. Read workflows and troubleshooting before changing a model or checkpoint.