Sweeper

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Sweeper searches deployment configurations with a black-box optimizer. It turns every suggestion into a versioned ReplaySpec, sends that specification to an injected RunnerFactory, and returns ranked candidates or a Pareto front.

The aisimulate package owns only backend-neutral simulation behavior. Optional feature packages can register a SweepConfigProvider that contributes search dimensions and materializes its part of a replay. Sweeper imports a provider only when its adapter name appears in the configuration.

Start Here

• Quickstart runs a small backend-neutral sweep.
• Tutorial explains a complete sweep configuration.
• Architecture shows the provider, replay, and worker boundaries.
• Configuration describes core and adapter-owned search spaces.
• Traffic defines trace, request-rate, concurrency, and KV-load workloads.
• Optimization Goals defines scalar and Pareto objectives.
• Results describes ReplaySpec and Candidate output.
• Sweep Configuration Providers documents the extension ABI.

Python Entry Point

Sweeper is the only public execution interface. Supply a replay runtime explicitly:

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from aisimulate.sweeper import SmartSearchConfig, Sweeper
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config = SmartSearchConfig.from_yaml("sweep.yaml")
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candidates = Sweeper(runner_factory=my_runner_factory).run(config)

The standalone python -m aisimulate.sweeper command validates configuration but deliberately does not choose a replay implementation.

Compatibility

• A provider is imported only when its adapter name appears under adapters.
• The runner advertises supported ReplaySpec versions, backend/topology pairs, and runtime hooks before a study starts.
• Every Sweeper.run call owns fresh optimizer studies, result caches, runners, and worker pools.
• KVBM search fields are rejected. Native G2 replaces that path; Sweeper does not forward the old host or disk offload settings.