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Vijay Janapa Reddi
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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.)
44 lines
1.8 KiB
Plaintext
44 lines
1.8 KiB
Plaintext
# engine.solver.ScalingModel { #mlsysim.engine.solver.ScalingModel }
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```python
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engine.solver.ScalingModel()
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```
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Analyzes the 'Scaling Physics' of model training (Chinchilla Laws).
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This model determines the optimal model size (P) and dataset size (D)
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given a compute budget (C), following the compute-optimal training
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regime where D ≈ 20P.
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Literature Source:
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1. Hoffmann et al. (2022), "Training Compute-Optimal Large Language Models."
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2. Kaplan et al. (2020), "Scaling Laws for Neural Language Models."
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3. McCandlish et al. (2018), "An Empirical Model of Large-Batch Training."
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.engine.solver.ScalingModel.solve) | Solves for compute-optimal model and dataset parameters. |
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### solve { #mlsysim.engine.solver.ScalingModel.solve }
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```python
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engine.solver.ScalingModel.solve(compute_budget, target_model_size=None)
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```
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Solves for compute-optimal model and dataset parameters.
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#### Parameters {.doc-section .doc-section-parameters}
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| Name | Type | Description | Default |
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|-------------------|----------|---------------------------------------------------------------------------|------------|
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| compute_budget | Quantity | Total training budget (e.g., in TFLOPs or H100-GPU-days). | _required_ |
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| target_model_size | Quantity | If provided, calculates the required tokens for this specific model size. | `None` |
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#### Returns {.doc-section .doc-section-returns}
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| Name | Type | Description |
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|--------|------------------|-------------------------------------------------------------------|
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| | Dict\[str, Any\] | Optimal parameters, token count, and training duration estimates. |
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