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在实现前澄清需求、探索设计选择,并形成清晰的开发方向。
training-eval-cli【开发与工程】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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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.
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.data-and-checkpoints.core-models.simmim-workflows.moe-and-acceleration.../../references/model-zoo-and-configs.md.data-and-checkpoints.../../scripts/swin_cli_command_builder.py or the recipes in references/supervised-workflows.md.scripts/validate_swin_command.py --repo-root <checkout> -- <command>. The validator never launches training.references/troubleshooting.md for DDP, memory, config, AMP, and optional dependency issues.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.
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.