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cs249r_book/mlsysim/docs/api/engine.solver.CheckpointModel.qmd
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Vijay Janapa Reddi cb0ae4082f docs(mlsysim): regenerate quartodoc API stubs for the new module layout
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# engine.solver.CheckpointModel { #mlsysim.engine.solver.CheckpointModel }
```python
engine.solver.CheckpointModel()
```
Analyzes the storage constraints and I/O burst penalties of saving model states.
Training massive models requires saving hundreds of gigabytes (Weights +
Optimizer States) to persistent storage. This model calculates the time
spent blocked on I/O, subtracting from the cluster's Model FLOPs Utilization.
Literature Source:
1. Eisenman et al. (2022), "Check-N-Run: A Checkpointing System for
Training Large Language Models."
## Methods
| Name | Description |
| --- | --- |
| [solve](#mlsysim.engine.solver.CheckpointModel.solve) | Solves for checkpoint size, write time, and resulting MFU penalty. |
### solve { #mlsysim.engine.solver.CheckpointModel.solve }
```python
engine.solver.CheckpointModel.solve(
model,
hardware,
optimizer='adam',
checkpoint_interval_hours=4.0,
n_writers=1,
filesystem_limit_gbs=500.0,
)
```
Solves for checkpoint size, write time, and resulting MFU penalty.
#### Parameters {.doc-section .doc-section-parameters}
| Name | Type | Description | Default |
|----------------------|--------|-------------------------------------------------------------------------------------------------------------------------------|-----------|
| n_writers | int | Number of parallel checkpoint writers (default 1). Distributed checkpointing (e.g., FSDP) shards the write across workers. | `1` |
| filesystem_limit_gbs | float | Maximum aggregate filesystem write bandwidth in GB/s (default 500). Prevents over-optimistic scaling when n_writers is large. | `500.0` |