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54 lines
2.1 KiB
Plaintext
54 lines
2.1 KiB
Plaintext
# solvers.TransformationModel { #mlsysim.solvers.TransformationModel }
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```python
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solvers.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.solvers.TransformationModel.solve) | Solves for CPU preprocessing bottleneck. |
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### solve { #mlsysim.solvers.TransformationModel.solve }
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```python
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solvers.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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