---

name: training description: "Inspect and operate the MedSegDiff segmentation training workflow, including CLI configuration, model and diffusion factories, checkpoints, precision, batching, logging, and device setup." disable-model-invocation: true metadata: disco-role: operating license: MIT

MedSegDiff training

Use this sub-skill for the scripts/segmentation_train.py path and its factory/runtime wiring. It is an operating guide, not a promise that training is cheap or CPU-compatible.

Route

1. Start with references/workflows.md for dataset-branch selection, exact commands, documented configurations, checkpoint behavior, and the inspection/training boundary.
2. Use references/api-reference.md for verified defaults and signatures before changing a flag or constructing the factory directly.
3. Use references/troubleshooting.md when import, branch, shape, device, resume, precision, or version behavior is surprising.
4. Run scripts/inspect_train_cli.py for --help, defaults, and branch inspection. It is deliberately stdlib-only: it does not import the training launcher, Visdom, PyTorch, a dataset, or a model.

Hard boundary

• CLI/default inspection and a tiny direct factory construction are safe preflight operations when their dependencies are available.
• The real launcher imports Visdom, initializes a distributed process group, opens the dataset, builds the model, and enters an unbounded training loop unless --lr_anneal_steps is set. Treat it as CUDA-dependent and dataset/time intensive.
• A CPU run is not a truthful substitute for full training. Do not claim training success from parser or factory checks.
• dpm_solver changes the diffusion sampling path; it does not provide training data, shorten the need for training, or replace the training loop.

Operating invariants

• Preserve the exact lowercase option spellings and pass boolean values explicitly (True/False or another accepted token); these are not store_true switches.
• The launcher chooses ISIC, BRATS, or a custom 2-D/3-D fallback before setting in_ch. Dataset branch and model input channels must agree.
• Keep architecture, diffusion, version, and channel flags identical when resuming a checkpoint; inspect the resume caveats before trusting restored optimizer/EMA state.