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name: training-workflows description: "Compose timm API-level training loops with optimizer and scheduler factories, loss selection, task wrappers, EMA, AMP scaling, metrics, checkpoint state, and safe CPU smoke checks. Use when writing or debugging custom training code rather than cataloging train.py CLI flags or building data loaders." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training Workflows

Use this sub-skill when an agent needs to assemble or debug timm training components in Python code: create_optimizer_v2, optimizer_kwargs, create_scheduler_v2, loss modules, task wrappers, EMA, AMP scaling, metrics, and checkpoint-saving concepts.

Route by Need

• Optimizer or parameter groups: Start with references/optimizer-scheduler-guide.md for create_optimizer_v2, weight-decay filtering, layer decay, hybrid fallback groups, and optimizer_kwargs config translation.
• LR schedule behavior: Use references/optimizer-scheduler-guide.md for create_scheduler_v2, epoch-vs-update stepping, warmup, cycles, plateau metrics, and returned adjusted epoch counts.
• Losses and task wrappers: Use references/training-api.md for LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, BinaryCrossEntropy, JsdCrossEntropy, ClassificationTask, and distillation task routing.
• EMA, AMP, metrics, checkpoints: Use references/training-api.md for ModelEma variants, NativeScaler, accuracy, AverageMeter, task checkpoint state, and CheckpointSaver concepts.
• Failure diagnosis: Use references/troubleshooting.md for unsupported optimizer names, layer-decay grouping, scheduler step confusion, mixup/BCE target shape, distillation shape mismatches, EMA resume, and AMP/device mismatch.
• Smoke construction: Run or adapt scripts/training_api_smoke.py to verify a model, optimizer, scheduler, loss, optional task wrapper, metrics, EMA, and one CPU backward pass.

Boundaries

• This sub-skill covers API-level composition. Leave train script flag catalogs and launch recipes to cli-workflows.
• This sub-skill assumes tensors already come from a valid pipeline. Leave dataset, loader, transforms, mixup/cutmix construction, and collation details to data-pipelines.
• Do not make generated examples depend on repository-local files; keep examples importable from installed timm and standard PyTorch.

Safe Defaults

For a minimal custom loop, prefer create_model(..., pretrained=False), create_optimizer_v2(model, opt='adamw', lr=...), create_scheduler_v2(optimizer, sched='cosine', num_epochs=..., warmup_epochs=...), a target-compatible loss, accuracy/AverageMeter for logging, and optional ModelEmaV3 after model/device placement.

Run the smoke script before recommending a larger recipe:

python scripts/training_api_smoke.py --model resnet18 --opt adamw --sched cosine