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speedster-optimization

speedster-optimization

Guides Speedster inference optimization workflows, compiler/backend selection, save/load, and troubleshooting for optimized models.

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Documentation de la Skill

GitHub

name: speedster-optimization description: "Guides Speedster inference optimization workflows, compiler/backend selection, save/load, and troubleshooting for optimized models." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

Speedster optimization

Use this sub-skill when the user wants to accelerate an existing model, choose optimize_model parameters, save or load an optimized learner, or understand why a compiler/backend path was selected or skipped.

Triggers

  • Optimize a PyTorch, TensorFlow, ONNX, Hugging Face, or Diffusers model.
  • Choose metric_drop_ths, optimization_time, dynamic_info, ignore_compilers, ignore_compressors, or device.
  • Save or reload an optimized model.
  • Interpret telemetry, latency summaries, or backend-specific warnings.

Read next

  • references/api-reference.md for verified function signatures, accepted inputs, and return behavior.
  • references/workflows.md for end-to-end recipes and parameter choices.
  • references/framework-recipes.md for framework-specific notes and compiler/backend selection.
  • references/troubleshooting.md for install/import/backend and data-format failures.
  • ../nebullvm-backends/SKILL.md when the question is really about DataManager, device parsing, compiler lists, or optional dependency probes.
  • scripts/speedster_quick_probe.py when you want a safe import/signature/backend smoke probe.

What to include

  • optimize_model, save_model, and load_model.
  • Data input shapes and dynamic-shape guidance.
  • Device selection and backend selection.
  • Compatible compiler names and optional dependencies.
  • Telemetry opt-out and save/load behavior.

What to exclude

  • Full compiler installation procedures that belong to the NebullVM backend support sub-skill.
  • Long notebook or source-repo example paths.
  • Heavy benchmark runs and large optimization sweeps.

Quick decision rule

If the user asks “how do I make this model faster?”, start here. If the user asks “which backend/compiler package should I install?”, read the NebullVM backend sub-skill next.

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