# BDA5.4.3 Checkpointing and Recovery

This skill covers techniques for saving and restoring training state to ensure fault tolerance and efficient recovery in long-running ML jobs. It includes strategies for storage management, frequency control, and integration with batch schedulers.

## Requirements

* External: Familiarity with training loops and storage systems
* Internal: None

## Learning Outcomes

* Explain the importance of checkpointing for resiliency in HPC training workflows.
* Implement model, optimizer, and scheduler state saving in popular ML frameworks.
* Choose checkpointing frequency based on job length, stability, and system load.
* Manage checkpoint file size, compression, and storage placement.
* Integrate checkpointing with job resubmission and monitoring tools in HPC environments.

** Caution: All text is AI generated **