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47 lines
2.2 KiB
Plaintext
47 lines
2.2 KiB
Plaintext
# solvers.ReliabilityModel { #mlsysim.solvers.ReliabilityModel }
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```python
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solvers.ReliabilityModel()
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```
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Calculates Mean Time Between Failures (MTBF) and optimal checkpointing intervals.
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This model handles the reliability modeling of massive clusters, helping
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determine the 'Goodput' of long-running training jobs. It identifies
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the probability of a job failure before completion and calculates the
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Young-Daly optimal interval to minimize wasted compute time.
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Literature Source:
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1. Young (1974), "A First-Order Approximation to the Optimum Checkpoint
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Interval."
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2. Daly (2006), "A Higher Order Estimate of the Optimum Checkpoint
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Interval for Restart-Dump Strategy."
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.solvers.ReliabilityModel.solve) | Calculates reliability and checkpointing metrics for a fleet. |
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### solve { #mlsysim.solvers.ReliabilityModel.solve }
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```python
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solvers.ReliabilityModel.solve(
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fleet,
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job_duration_hours,
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checkpoint_time_s=60.0,
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avg_recovery_time_s=300.0,
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)
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```
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Calculates reliability and checkpointing metrics for a fleet.
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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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| fleet | Fleet | The hardware cluster configuration. | _required_ |
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| job_duration_hours | float | Total job duration in hours. | _required_ |
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| checkpoint_time_s | float | Time to write one checkpoint in seconds (default 60s). | `60.0` |
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| avg_recovery_time_s | float | Average time to recover from a failure in seconds (default 300s). Includes checkpoint reload, process restart, and re-warmup. | `300.0` |
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