---

name: training-configuration description: "Configure and launch nnU-Net v2 training runs after planning/preprocessing is complete: folds, configurations, resume/validation flags, devices/DDP, pretrained weights, trainer selection, checkpoints, logs, manual splits, and command matrices." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

nnU-Net v2 Training Configuration

Use this sub-skill when the user needs to train nnU-Net v2 models on already planned and preprocessed data, choose folds/configurations, recover validation exports, resume runs, select devices, or understand training outputs.

Do not use this sub-skill for dataset conversion, planning, preprocessing, inference, best-configuration selection, ensembling, or implementing new trainer subclasses. For custom trainer code, route to customization-extension; this sub-skill only shows how to select trainer class names in training commands.

Fast Path

1. Confirm nnUNet_raw, nnUNet_preprocessed, and nnUNet_results are set and that the dataset has been planned/preprocessed.
2. Choose a trained configuration from the dataset plans, commonly 2d, 3d_fullres, 3d_lowres, or 3d_cascade_fullres.
3. Train folds with --npz if model selection or ensembling may be needed later:

   nnUNetv2_train DATASET001_Example 3d_fullres 0 --npz
   

4. Train all folds by repeating folds 0 through 4, or train one final model on all cases with fold all when cross-validation is not needed:

   nnUNetv2_train DATASET001_Example 3d_fullres all
   

5. For cascade training, complete 3d_lowres first, then train 3d_cascade_fullres.
6. Inspect outputs under nnUNet_results/DatasetXXX_Name/TRAINER__PLANS__CONFIGURATION/fold_X/.

Bundled References

• references/cli-reference.md: exact nnUNetv2_train arguments, Python API mapping, flags, device/DDP semantics, and output paths.
• references/training-workflows.md: common training matrices, cascade ordering, manual splits, pretraining/fine-tuning, logging, and command generation.
• references/troubleshooting.md: failure-mode triage for missing preprocessing, cascade dependencies, --npz recovery, resume/validation choices, DDP, checkpoints, CPU/MPS expectations, and manual split placement.
• scripts/plan_training_matrix.py: prints command matrices for folds/configurations without running training.

Command Matrix Helper

Use the bundled helper to produce reviewable shell commands before launching expensive jobs:

python sub-skills/training-configuration/scripts/plan_training_matrix.py DATASET001_Example --configs 2d 3d_fullres --folds 0 1 2 3 4 --npz

It only prints commands. It does not inspect data, allocate GPUs, submit cluster jobs, or run nnUNetv2_train.