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name: inference description: "Routes Once-for-All model loading, subnet sampling, hub entry points, and ImageNet-style evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Inference

Use this sub-skill when the user wants to load an OFA supernet, sample or fix an active subnet, inspect the hub entry points, or evaluate a specialized OFA model on ImageNet-style data.

Triggers

Choose this route for requests like:

• "load an OFA supernet"
• "sample a subnet from the supernet"
• "evaluate a specialized OFA model"
• "use the hubconf models"
• "run or adapt eval_ofa_net.py"
• "run or adapt eval_specialized_net.py"

Included workflows

• Constructing ofa_net supernets.
• Constructing ofa_specialized models.
• Sampling active subnets and extracting a stand-alone subnet.
• Running a lightweight forward smoke on CPU or CUDA.
• Evaluating a model on an ImageNet-style ImageFolder layout.
• Reading and using the repo's PyTorch Hub shortcuts.

Excluded workflows

• Predictor-driven search. Route that to sub-skills/search/.
• Distributed training. It is intentionally out of scope for this generated skill.

Read next

• references/model-overview.md for the supported supernet and specialized-model families.
• references/api-reference.md for verified constructors and subnet methods.
• references/workflows.md for end-to-end model-loading and evaluation recipes.
• references/troubleshooting.md for download, dataset-layout, and backend issues.

Bundled helper

• scripts/evaluate_ofa.py — safe wrapper for model smoke, optional subnet sampling, and ImageNet-style evaluation.

Typical flow

1. Identify the model family and whether the user needs a supernet or a specialized model.
2. Load the model with ofa_net or ofa_specialized.
3. For supernets, sample or fix the active subnet if needed.
4. Run the bundled helper for a smoke pass or a small ImageNet-style evaluation.
5. Use the workflow reference when the user needs benchmark-quality subnet evaluation or a published specialized-model id.

Practical notes

• ofa_net(..., pretrained=False) is the fastest safe smoke.
• ofa_specialized(...) may still resolve public config files, and pretrained behavior can also fetch weights.
• Real specialized-model benchmarking is best on CUDA with an ImageNet validation split.
• If the user only needs a shape check, the bundled helper can run without a dataset.