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.

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

• 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