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name: models-training description: "Fetch Nitrain architectures and train or evaluate Keras/TensorFlow or Torch/MONAI models." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Models and training

Use this sub-skill when a user needs to discover an architecture, fetch a pretrained network, build a Trainer, or run the CPU TorchTrainer path.

What belongs here

• fetch_architecture() and list_architectures() for architecture discovery.
• fetch_pretrained() when the workflow needs pretrained network access.
• Trainer for Keras/TensorFlow models.
• TorchTrainer for the CPU Torch/MONAI path.
• Framework detection and trainer-default behavior.

What does not belong here

• Dataset and reader construction: use sub-skills/datasets-readers/.
• Transform, sampler, and loader work: use sub-skills/preprocessing-and-loading/.
• Dataset-level prediction or explanation: use sub-skills/prediction-and-explanation/.

Typical user requests

• "Create a U-Net or VGG architecture"
• "List available architectures"
• "Train a regression or segmentation model"
• "Use TorchTrainer with MONAI"
• "Load pretrained weights"

Working pattern

1. Decide which framework the model actually uses: Keras/TensorFlow, torch, or monai-backed torch.
2. Pick task='regression', task='classification', or task='segmentation' when you want Trainer defaults.
3. Use Loader or Loader.to_keras() from the preprocessing sub-skill when the data needs batching first.
4. Keep training smoke tests tiny and CPU-only unless the user explicitly asks for a different backend.

Read these references

• references/api-reference.md for the verified signatures and default behavior.
• references/workflows.md for model-construction and training patterns.
• references/troubleshooting.md for task mismatches, import quirks, and backend/version conflicts.

Smoke checks

After installing dependencies, run the bundled helper scripts/check_install.py:

python scripts/check_install.py --mode models
python scripts/check_install.py --mode torch

Use the first command for Keras/TensorFlow workflows and the second for the CPU Torch/MONAI path.

Key decisions

• fetch_architecture(name, dim=None) returns a callable from antspynet.architectures.
• list_architectures() returns [name, dim] pairs.
• Trainer compiles Keras models automatically when the framework is Keras.
• TorchTrainer delegates to the torch training helpers and needs an optimizer, loss, metrics, and device.
• task=None is only valid when you also provide the optimizer and loss.

Common outcomes

• Keras models get compiled and can then call fit, evaluate, predict, summary, and save.
• Torch models are trained through the separate helper path rather than through model.fit.
• Trainer infers the framework from the model type string.

Watch for these signals

• A wrongtask or missing-optimizer error usually means the trainer defaults were not selected correctly.
• nitrain.fetch_pretrained is a module object in this snapshot, so the actual function lives in nitrain.models.fetch_pretrained.
• TorchTrainer lives under nitrain.trainers, not the package root.
• The Torch/MONAI path is CPU-only in the verified snapshot.

Before handing off

If the user really wants inference output post-processing, use the prediction sub-skill instead of stretching this one further.