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training-eval-cli

training-eval-cli

Use this repo skill to build and troubleshoot Swin-Transformer supervised ImageNet training, fine-tuning, evaluation, throughput, DDP, AMP, and config override commands.

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Documentation de la Skill

GitHub

name: training-eval-cli description: "Use this repo skill to build and troubleshoot Swin-Transformer supervised ImageNet training, fine-tuning, evaluation, throughput, DDP, AMP, and config override commands." disable-model-invocation: true metadata: disco-role: operating license: MIT

training-eval-cli

Use this sub-skill when a task asks for supervised main.py workflows: training from scratch, evaluation, fine-tuning from pretrained weights, throughput measurement, command-line flags, distributed launchers, memory levers, or config overrides.

Covers

  • main.py train/eval/fine-tune/throughput workflow shape.
  • --cfg, --data-path, --zip, --cache-mode, --pretrained, --resume, --accumulation-steps, --use-checkpoint, AMP and fused flags.
  • torchrun/distributed-launch command templates and LOCAL_RANK behavior.
  • Supervised config families under Swin V1, Swin V2, and Swin-MLP.
  • Safe command validation without running training.

Routes elsewhere

  • Data root or checkpoint-schema diagnosis: data-and-checkpoints.
  • Model constructor/API questions: core-models.
  • SimMIM pretraining/fine-tuning: simmim-workflows.
  • Swin-MoE/Tutel/fused CUDA extension: moe-and-acceleration.

Workflow

  1. Pick a config family using ../../references/model-zoo-and-configs.md.
  2. Validate data/checkpoint assumptions with data-and-checkpoints.
  3. Compose a command using ../../scripts/swin_cli_command_builder.py or the recipes in references/supervised-workflows.md.
  4. Validate the command shape with scripts/validate_swin_command.py --repo-root <checkout> -- <command>. The validator never launches training.
  5. If the command still fails, read references/troubleshooting.md for DDP, memory, config, AMP, and optional dependency issues.

Safety warning

Actual main.py training/evaluation is GPU/data/checkpoint heavy. Do not run full native training as a quick check. Use CPU model construction and command validation first.

Linked files

  • references/supervised-workflows.md - recipes for train, eval, fine-tune, and throughput.
  • references/cli-reference.md - flag meanings and config side effects.
  • references/troubleshooting.md - launcher, memory, and flag mistakes.
  • scripts/validate_swin_command.py - safe command-shape validator.
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