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