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name: cli-workflows description: "Generate safe, correct train.py, validate.py, inference.py, and torchrun/distributed commands for timm reference scripts." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

timm CLI Workflows

Use this sub-skill when the user needs commands for timm's repository-root training, validation, or inference scripts, or when adapting examples into reproducible shell commands. Keep this focused on command construction and operational caveats; route API-level model/data/training internals to the model-library, data, or training sub-skills, and route benchmark/result-table analysis to benchmarking-and-results.

Routing Checklist

• Use references/train-validate-inference.md for command shapes, dataset layout expectations, config files, output flags, CPU/debug variants, pretrained/checkpoint behavior, and NaFlex flags.
• Use references/distributed-training.md for torchrun, distributed_train.sh equivalence, DDP environment assumptions, local rank handling, and multi-GPU command patterns.
• Use references/troubleshooting.md when a command fails because scripts are missing, the dataset split path is wrong, CUDA is selected on a CPU host, downloads fail, result/class-map files fail, DDP hangs, NaFlex flags mismatch the model, or validation OOMs.
• Use scripts/timm_cli_command_builder.py to print dry commands without importing timm, running training, or checking local files.

High-Value Defaults

• Prefer explicit --data-dir, --model, --batch-size, --workers, and --device rather than relying on script defaults.
• For CPU/debug commands, set --device cpu, small --batch-size, low --workers, and short training controls such as --epochs 1, --val-interval 1, or --no-aug via extra args.
• For CUDA commands, add --amp only when the target host has a supported accelerator and the requested dtype is valid for the workflow.
• For training datasets, point --data-dir at the base folder containing train and validation splits; for validation/inference, point it at the validation/image split folder unless a non-ImageFolder dataset string changes split handling.
• If the user installed timm from pip only, explain that the root scripts may not be installed; they need a checkout copy of train.py, validate.py, inference.py, or equivalent copied scripts.

Bundled Command Builder

python scripts/timm_cli_command_builder.py train \
  --data-dir /data/imagenet --model resnet50 --batch-size 32 --device cpu \
  -- --epochs 1 --no-aug

The builder prints a shell-quoted command. It is intentionally dry: it never imports timm, never validates model names, never probes devices, and never opens datasets. Put advanced script flags after --, or pass repeated --extra-arg=value tokens when a wrapper cannot use passthrough arguments.