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name: training-evaluation description: "Assemble MONAI training and evaluation loops with Ignite-based engines, handlers, schedulers, checkpointing, validation, logging, AMP/distributed caveats, and tiny workflow checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MONAI Training and Evaluation

Use this sub-skill when a task asks for MONAI training/evaluation loops, monai.engines trainer/evaluator wiring, Ignite event handlers, validation callbacks, checkpointing, stats/logging, MONAI optimizer utilities, or debugging engine/handler behavior.

Route by task

• For a handwritten PyTorch loop that needs MONAI structure, read references/workflows.md to choose plain PyTorch versus SupervisedTrainer/SupervisedEvaluator and wire loaders, network, loss, optimizer, metrics, handlers, and validation.
• For constructor arguments, handler attachment points, output keys, metric transforms, or scheduler utilities, read references/api-reference.md before coding against monai.engines, monai.handlers, or monai.optimizers.
• For runtime errors, silent logs, stale metrics, missing checkpoints, AMP/CUDA assumptions, distributed sampler issues, or optional tracking dependencies, read references/troubleshooting.md.
• To prove the installed MONAI plus Ignite stack can run a tiny CPU training/validation workflow, run scripts/tiny_supervised_workflow.py --help first, then scripts/tiny_supervised_workflow.py.

Boundaries

• This sub-skill owns training/evaluation orchestration: monai.engines, monai.handlers, monai.optimizers, trainer/evaluator events, validation, checkpoints, stats/logging, AMP and distributed considerations.
• For data dictionaries, transforms, datasets, caches, and dataloaders, use the data-transforms sub-skill first, then return here for engine wiring.
• For model architecture, losses, inferers, postprocessing transforms, and metric selection details, use the modeling-inference sub-skill first, then return here for loop integration.
• For config-driven bundle training/evaluation, use the bundle-config sub-skill instead of hand-assembling engines.
• For Auto3DSeg orchestration, use the apps-auto3dseg sub-skill instead of writing trainer/evaluator loops manually.