---

name: nnunet description: "Guides the official PyTorch nnU-Net workflow for UNet++ training, inference, preprocessing, ensembling, pretrained models, and trainer/model-selection utilities." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

nnU-Net

Use this sub-skill for the repository's official PyTorch stack. It covers the nnunet distribution, the nnUNet_* console scripts, and the official UNet++ trainer/model variants built on nnU-Net.

Route here when the user asks about

• nnUNet_train, nnUNet_predict, nnUNet_plan_and_preprocess, nnUNet_convert_decathlon_task, nnUNet_ensemble, nnUNet_determine_postprocessing, nnUNet_find_best_configuration, nnUNet_download_pretrained_model, nnUNet_print_available_pretrained_models, nnUNet_print_pretrained_model_info, nnUNet_export_model_to_zip, nnUNet_install_pretrained_model_from_zip, or nnUNet_change_trainer_class.
• Task-style dataset folders such as Task003_Liver.
• Path variables such as nnUNet_raw_data_base, nnUNet_preprocessed, or RESULTS_FOLDER.
• 3D training/inference, cascade workflows, model selection, or pretrained model sharing.

What this sub-skill does

• Explains the expected data layout and environment variables for nnU-Net.
• Routes preprocessing, training, validation, inference, postprocessing, ensembling, and pretrained-model management.
• Helps diagnose version mismatches, missing imports, and stale advanced trainers.
• Provides a tiny runtime smoke script for safe inspection of the package, CUDA status, and sliding-window helpers.

What to read first

• references/paths-and-environment.md for dataset folders, environment variables, and install assumptions.
• references/cli-reference.md for the public command set and important flags.
• references/workflows.md for end-to-end flows.
• references/pretrained-models.md for the built-in pretrained model catalog and download/install cautions.
• references/troubleshooting.md for common failure modes.
• references/api-reference.md for important classes and functions used by the workflows.

Recommended workflow order

1. Confirm the TaskXXX dataset layout and the three nnU-Net path variables.
2. Verify the installed nnunet package and CUDA support.
3. Run nnUNet_plan_and_preprocess only after the dataset root is sane.
4. Train with nnUNet_train or inspect with nnUNet_predict.
5. Use nnUNet_determine_postprocessing, nnUNet_ensemble, or nnUNet_find_best_configuration only after validation outputs exist.
6. Use pretrained-model helpers only when the user actually wants downloads, packaging, or inspection of available TaskXXX models.

Important guardrails

• A CPU import does not prove the training path is ready. The verified runtime smoke should include CUDA status when the host provides a GPU.
• The inspected repo snapshot needed batchgenerators==0.21 because newer releases no longer exposed MultiThreadedAugmenter from the import path used by nnU-Net.
• matplotlib is needed for some CLI imports, and requests is needed for the pretrained-model helpers.
• nnUNet_change_trainer_class is the public CLI name in this snapshot; the underlying module is nnunet.inference.change_trainer.
• nnUNet_train_DP and nnUNet_train_DDP are advanced multi-GPU entry points and should be treated cautiously if the installed source snapshot and runtime expectations drift.

Bundled runtime helper

• scripts/check-nnunet-runtime.py is a safe import / CLI / CUDA / sliding-window smoke helper. Use it when you need a quick confidence check without running a full dataset workflow.

Common questions this sub-skill answers

• How should a TaskXXX dataset be arranged?
• Which environment variables must be set before planning or training?
• How do I list, inspect, download, export, or install pretrained nnU-Net models?
• Why does an nnU-Net CLI fail to import matplotlib, requests, or MultiThreadedAugmenter?
• How do I choose between 3d_lowres, 3d_fullres, and 3d_cascade_fullres?
• How do I pick the right trainer or recover an old checkpoint's trainer name?

Where to go next

• Use references/workflows.md for the concrete command sequences.
• Use references/troubleshooting.md when the symptom is an import error, path error, data-layout problem, or advanced multi-GPU mismatch.
• Use the root ../../SKILL.md only if the user has not yet chosen between nnU-Net and the Keras stack.