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name: once-for-all description: "Routes Once-for-All/OFA pretrained model loading, subnet evaluation, and predictor-driven architecture-search workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Once-for-All

Use this skill for the public Once-for-All (OFA) package when the request is about pretrained supernets, specialized subnet evaluation, PyTorch Hub entry points, or predictor-driven architecture search.

What this skill covers

• Load OFA supernets with ofa.model_zoo.ofa_net.
• Load specialized OFA models with ofa.model_zoo.ofa_specialized.
• Sample, freeze, and extract active subnets from the OFA supernets.
• Evaluate subnet or specialized-model behavior on ImageNet-style folders.
• Run the tutorial-style accuracy, FLOPs, latency, and evolutionary-search helpers.

What this skill does not cover

• Distributed OFA training with Horovod/MPI.
• ImageNet-scale training runs or long fine-tuning jobs.
• Maintainer release automation.

Install and smoke-check

Read references/dependencies.md before choosing extras. A practical baseline is:

pip install ofa torch torchvision filelock
pip install numpy gdown tqdm pyyaml matplotlib thop

If you are working from a source checkout, prefer a normal install over editable mode when modern packaging tools reject the repo's timestamped version string.

Minimal import smoke:

python - <<'PY'
from ofa.model_zoo import ofa_net
m = ofa_net('ofa_resnet50', pretrained=False)
print(type(m).__name__)
PY

For a broader install check, run scripts/check_install.py.

Specialized-model ids may still resolve small public config files even when you are not fetching pretrained weights.

Route map

sub-skills/inference/

Read this for model loading, subnet sampling, PyTorch Hub names, and ImageNet-style evaluation.

Typical user intents:

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

sub-skills/search/

Read this for accuracy predictors, FLOPs or latency tables, and evolutionary architecture search.

Typical user intents:

• "search for the best subnet"
• "use the accuracy predictor"
• "run the latency or FLOPs constrained tutorial"
• "build an OFA tradeoff curve"

Cross-cutting references

• references/repo-provenance.md — snapshot of the source checkout used to generate this skill.
• references/repo-routing-metadata.json — router metadata consumed by repo-skills-router.
• references/dependencies.md — package groups and backend notes.
• references/troubleshooting.md — install, import, download, and dataset pitfalls.

Cross-cutting script

• scripts/check_install.py — safe install smoke for the installed package and the core OFA helpers.

How to choose this skill

Choose once-for-all when the user names OFA, ofa_net, ofa_specialized, AccuracyPredictor, EvolutionFinder, eval_ofa_net.py, eval_specialized_net.py, or the Once-for-All tutorial notebook.

Choose a more generic computer-vision skill only when the task is not OFA-specific.