mirror of
https://github.com/harvard-edge/cs249r_book.git
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Vijay Janapa Reddi
cb0ae4082f
Run quartodoc build against the refactored package (engine/ extracted from core/, infra->infrastructure): regenerate the API reference so every stub points at the new paths. Adds engine.*/infrastructure.* stubs, removes the 41 stale core.solver.* / core.engine.* / core.scenarios.* / core.config.* / core.evaluation.* / infra*.qmd orphans, and refreshes the package pages (core now primitives-only) + index. Verified: all 47 documented symbols resolve in the new package; every index link resolves; zero stale mlsysim.core.<engine-mod> / mlsysim.infra references anywhere in docs/. (objects.json inventory is gitignored -- regenerated at build.)
35 lines
879 B
Plaintext
35 lines
879 B
Plaintext
# engine.solver.BatchingOptimizer { #mlsysim.engine.solver.BatchingOptimizer }
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```python
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engine.solver.BatchingOptimizer()
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```
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Finds the maximum batch size that satisfies a P99 latency SLA.
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Searches the continuous batching design space using an M/M/c queueing
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model to find the optimal balance between throughput and tail latency.
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.engine.solver.BatchingOptimizer.solve) | Determines the maximum batch size that satisfies a P99 tail latency SLA. |
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### solve { #mlsysim.engine.solver.BatchingOptimizer.solve }
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```python
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engine.solver.BatchingOptimizer.solve(
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model,
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hardware,
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seq_len,
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sla_latency_ms,
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arrival_rate_qps,
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num_replicas=1,
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precision='fp16',
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efficiency=0.5,
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max_search_batch=256,
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)
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```
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Determines the maximum batch size that satisfies a P99 tail latency SLA.
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