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name: export-and-interoperability description: "Build ONNX export and validation commands, package/load timm models via Hugging Face Hub or local directories, use Torch Hub compatibility, and safely clean or average checkpoints." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Export and Interoperability

Use this sub-skill when a task asks for timm model export, ONNX Runtime validation, pretrained model interchange through Hugging Face Hub or local directories, Torch Hub compatibility, or checkpoint cleanup/averaging before sharing weights.

Routing Checklist

• Use references/export-checkpoint-workflows.md for ONNX export/validation command options, dynamic input caveats, exportable=True, reparameterization, checkpoint cleaning, checkpoint averaging, hash naming, and safetensors choices.
• Use references/hub-and-conversion.md for hf-hub: and local-dir: model names, push_to_hf_hub, local config.json plus weight-file expectations, Torch Hub behavior, and conversion-script caveats.
• Use references/troubleshooting.md for missing optional dependencies, unsupported ONNX ops, dynamic-shape failures, checkpoint prefix/EMA mismatches, safe loading failures, local-dir packaging mistakes, Hugging Face auth/cache/network issues, and external conversion requirements.
• Use scripts/timm_onnx_command_builder.py to print dry onnx_export.py and onnx_validate.py commands without importing timm or requiring ONNX dependencies.
• Use scripts/timm_checkpoint_tools.py for self-contained checkpoint inspection, dry command construction for cleaning/averaging, and bundled averaging; cleaning remains a generated command for a timm script checkout rather than a write action inside this helper.

High-Value Defaults

• Start ONNX work with a small, common model and static shape: --model resnet18 --batch-size 1 --input-size 3 224 224; add --dynamic-size only when deployment needs variable height/width.
• Export through timm's export path, not ad hoc tracing: create models with exportable=True, use --checkpoint for local weights, and add --reparam for models that expose reparameterization before deployment.
• Treat --check-forward as optional verification because it requires ONNX/ONNX Runtime support and may expose numerical or operator-coverage differences rather than command-construction mistakes.
• Prefer safetensors for shared cleaned or averaged weights when the dependency is installed; keep .pth only for legacy consumers that need PyTorch serialization.
• Keep checkpoint tools non-destructive: never overwrite an existing output, inspect candidate keys first, and decide whether EMA weights should be used before cleaning or averaging.
• For hosted or portable timm models, package both config.json and a recognized weight filename; use exact hf-hub:org/model@revision or local-dir:/path/to/model_dir source prefixes when loading.

Boundary Routing

• Use ../model-library/ for ordinary timm.create_model, model discovery, pretrained config inspection, feature extraction, and generic checkpoint mismatch analysis before export.
• Use ../cli-workflows/ for train.py, validate.py, and inference.py commands that are not ONNX-specific.
• Use ../benchmarking-and-results/ when the task is performance measurement rather than export or validation correctness.
• Treat framework conversion scripts as reference-only starting points; do not promise turnkey conversion unless the external checkpoint format, framework package, and architecture mapping are available.

Bundled Helpers

python scripts/timm_onnx_command_builder.py export \
  --output model.onnx --model resnet18 --input-size 3 224 224 --batch-size 1 --check-forward

python scripts/timm_checkpoint_tools.py inspect \
  --checkpoint model_best.pth.tar

The helpers are intentionally conservative. The ONNX builder prints commands only. The checkpoint helper can inspect checkpoints, construct clean/average commands, and run bundled checkpoint averaging with overwrite refusal; checkpoint cleaning is emitted as a command for a trusted timm script checkout.