How to use Capacity Blocks for ML with AWS Batch

The article discusses how to use Capacity Blocks for ML (CBML) with AWS Batch to reserve and manage GPU-based EC2 instances for machine learning workloads.

• CBML allows reserving specific GPU instances for future machine learning tasks
• AWS Batch helps manage and scale these capacity block reservations
• Key steps include:
  • Purchasing a Capacity Block
  • Creating an EC2 launch template
  • Setting up an AWS Batch compute environment
  • Creating a job queue
• Important considerations:
• Match Availability Zone and instance type
• Use BEST_FIT allocation strategy
• Treat CBML compute environments as single-use

The approach allows researchers to efficiently manage and maximize utilization of reserved GPU instances for machine learning workloads.