---

name: handlers description: "Routes Ignite checkpointing, logging, scheduling, progress, and profiling workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Ignite handlers workflows

Use this sub-skill when the request is about attaching helpers around an Ignite training loop: checkpointing, early stopping, EMA, progress bars, logger integrations, parameter schedulers, LR finding, timers, or profiling.

Include here

• Checkpoint, DiskSaver, ModelCheckpoint, and checkpoint-retention / restore patterns.
• EarlyStopping, TerminateOnNan, TimeLimit, Timer, and EMAHandler.
• ProgressBar, BaseLogger, TensorboardLogger, WandBLogger, MLflowLogger, NeptuneLogger, ClearMLLogger, PolyaxonLogger, VisdomLogger, and the logger setup helpers.
• Parameter schedulers, state schedulers, BatchSizeScheduler, ReduceLROnPlateauScheduler, and LR-finder helpers.
• Time profilers and related training instrumentation.
• Common handler recipes from the MNIST logging examples, CIFAR10 logging examples, and other monitoring-oriented workflows.

Exclude or route elsewhere

• Training-loop structure, resume state mechanics, and deterministic engine details belong in sub-skills/engine/.
• Metric definitions and metric math belong in sub-skills/metrics/.
• Distributed backend selection and Parallel belong in sub-skills/distributed/.
• Legacy ignite.contrib notes live in references/legacy-contrib.md.

Start here

• Read references/api-reference.md when you need exact class names, handler groups, or setup helper signatures.
• Read references/workflows.md for checkpoint/resume, early-stopping, logger, and scheduler recipes.
• Read references/troubleshooting.md when optional packages, logger credentials, or handler wiring fail.
• Run scripts/smoke_handlers.py for a small synthetic check that exercises checkpointing, early stopping, and scheduler previews.

Common triggers

• "How do I save the best checkpoint?"
• "How do I stop training early when validation stops improving?"
• "How do I attach TensorBoard, W&B, MLflow, or ClearML logging?"
• "How do I configure a learning-rate scheduler or LR finder?"
• "How do I add a progress bar or a time profiler?"

Useful boundary notes

This sub-skill owns the training extras that sit around the loop, but not the loop itself. If the user mainly needs to build or resume the loop, stay in sub-skills/engine/ and link out to this route for the checkpoint or logging parts.