---

name: engine description: "Routes Ignite Engine, supervised helper, deterministic, and resume workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Ignite engine workflows

Use this sub-skill when the user is building or debugging the training loop itself: Engine, Events, supervised trainer/evaluator helpers, resume logic, custom events, or deterministic runs.

Include here

• ignite.engine.Engine, Events, EventEnum, State, and event-handler registration.
• create_supervised_trainer, create_supervised_evaluator, and the lower-level supervised step helpers.
• DeterministicEngine, reproducible dataflow helpers, and state-dict resume behavior.
• Gradient accumulation, AMP/Apex/TPU/MPS variants, and prepare_batch / model_fn / model_transform / output_transform customization.
• Training-loop patterns from the MNIST save/resume example, the super-resolution example, the reinforcement-learning examples, and other loop-centric recipes.

Exclude or route elsewhere

• Checkpoint storage, early stopping, schedulers, progress bars, and logger integrations belong in sub-skills/handlers/.
• Metric attachment, metric arithmetic, and metric-family semantics belong in sub-skills/metrics/.
• Parallel, idist, auto_*, and backend selection belong in sub-skills/distributed/.
• Deprecated ignite.contrib compatibility notes live in references/legacy-contrib.md.

Start here

• Read references/api-reference.md when you need exact signatures, default behaviors, or parameter semantics.
• Read references/workflows.md when you need an end-to-end trainer/evaluator recipe or a resume pattern.
• Read references/troubleshooting.md when the loop fails with resume, shape, epoch-length, AMP, or backend errors.
• Run scripts/resume_smoke.py for a tiny synthetic loop that exercises supervised training and state-dict resume.

Common triggers

• "How do I build an Ignite trainer or evaluator?"
• "How do I resume training from a saved state?"
• "Why does Engine.run complain about max_epochs or epoch_length?"
• "How do I use deterministic dataflow or custom events?"
• "How do I switch between CPU, CUDA AMP, MPS, or TPU helpers?"

Useful boundary notes

The engine route owns the loop skeleton and output semantics, but it does not own storage, logging, or distributed launch details. When a workflow spans engine plus handlers or distributed helpers, keep the training loop here and link the other concerns out to the owning sub-skill.