BDA5.4 HPC Optimization for ML

This node covers performance tuning strategies that enhance machine learning training efficiency on HPC systems. It includes batch size tuning, mixed precision training, and mechanisms for recovery and checkpointing.

• Optimize batch sizes and parallelism settings to improve training scalability.
• Apply mixed precision techniques and implement robust checkpointing strategies for long-running jobs.

• BDA5.4.1 Batch Size and Data Parallelism
• BDA5.4.2 Mixed Precision Training
• BDA5.4.3 Checkpointing and Recovery

Caution: All text is AI generated