How Amazon Search increased ML training twofold using AWS Batch for Amazon SageMaker Training jobs

The article discusses how Amazon Search optimized its machine learning (ML) training infrastructure by leveraging AWS Batch for SageMaker Training jobs, significantly improving GPU instance utilization.

• Increased peak GPU utilization from 40% to over 80%
• Used AWS Batch's fair-share scheduling to prioritize workloads
• Implemented three key technologies: Service Environments, Share Identifiers, and Amazon CloudWatch
• Enabled researchers to submit multiple concurrent jobs without manual resource management
• Supports different priority levels: production, exploratory, and batch inference jobs

The solution allows for dynamic resource allocation, preemption of lower-priority jobs, and improved operational efficiency across GPU clusters without building custom monitoring systems.