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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.)
54 lines
2.2 KiB
Plaintext
54 lines
2.2 KiB
Plaintext
# engine.solver.TransformationModel { #mlsysim.engine.solver.TransformationModel }
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```python
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engine.solver.TransformationModel()
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```
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Quantifies the CPU preprocessing bottleneck (Wall 9: Transformation).
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This model simulates the 'Transformation Wall' — the gap between CPU-bound
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data preprocessing (JPEG decode, tokenization, augmentation) and
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accelerator step time. When preprocessing cannot keep up, the accelerator
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starves and utilization drops.
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Literature Source:
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1. Mohan et al. (2022), "Analyzing and Mitigating Data Bottlenecks in
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Deep Learning Training."
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2. Murray et al. (2021), "tf.data: A Machine Learning Data Processing
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Framework." (Pipeline stall analysis.)
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3. NVIDIA DALI Documentation (2024). (GPU-accelerated preprocessing.)
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.engine.solver.TransformationModel.solve) | Solves for CPU preprocessing bottleneck. |
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### solve { #mlsysim.engine.solver.TransformationModel.solve }
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```python
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engine.solver.TransformationModel.solve(
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batch_size,
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sample_size_bytes,
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cpu_throughput,
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accelerator_step_time,
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)
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```
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Solves for CPU preprocessing bottleneck.
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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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| batch_size | int | Number of samples per batch. | _required_ |
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| sample_size_bytes | Quantity | Size of one sample in bytes (e.g., Q_("500 KB")). | _required_ |
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| cpu_throughput | Quantity | CPU preprocessing throughput (e.g., Q_("2 GB/s")). | _required_ |
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| accelerator_step_time | Quantity | Time for one accelerator training step (e.g., Q_("50 ms")). | _required_ |
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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\[str, Any\] | Transform time, bottleneck status, and accelerator utilization. |
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