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name: search description: "Routes Once-for-All accuracy-predictor, FLOPs/latency-table, and evolutionary architecture-search workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Search

Use this sub-skill when the user wants to search for a good OFA subnet using the tutorial-style accuracy predictor, latency or FLOPs tables, or evolutionary search loop.

Triggers

Choose this route for requests like:

• "search for the best subnet"
• "run the OFA architecture search"
• "use the accuracy predictor"
• "build a FLOPs tradeoff curve"
• "use the latency table"
• "adapt the notebook search workflow"

Included workflows

• Loading the search helpers from ofa.tutorial.
• Running a small offline search smoke with a dummy efficiency predictor.
• Running predictor-driven search with FLOPs or latency constraints.
• Interpreting the best-info tuple returned by EvolutionFinder.
• Reading the notebook-derived search flow for the published tutorial.

Excluded workflows

• Model loading and ImageNet evaluation. Route that to sub-skills/inference/.
• Distributed training. It is intentionally out of scope for this generated skill.

Read next

• references/api-reference.md for constructors and return-value contracts.
• references/workflows.md for the published search flow and the bundled offline smoke.
• references/troubleshooting.md for download, dependency, and constraint issues.

Bundled helper

• scripts/tiny_search.py — offline smoke for AccuracyPredictor plus the evolutionary loop.

Typical flow

1. Decide whether the user needs a smoke, a FLOPs search, or a latency-constrained search.
2. Load the accuracy predictor and the selected efficiency predictor.
3. Run the bundled smoke script for a quick offline verification, or use the workflow reference for the real notebook-style search.
4. Inspect the returned best sample, efficiency value, and predicted accuracy.

Practical notes

• AccuracyPredictor(pretrained=False) is enough for a smoke check.
• Real FLOPs or latency search may need optional downloads or a cached lookup table.
• The best_info tuple from EvolutionFinder contains the predicted accuracy, the sample dict, and the efficiency value.