What changed
AWS has launched support for quota management in AWS Batch for SageMaker Training jobs. This feature allows administrators to define allocations per team or project, share idle GPU capacity automatically, and prioritize workloads. The new scheduling capability enables organizations to manage compute resources more effectively as they scale AI/ML operations.
What this means in practice
With quota management, administrators can divide compute capacity into quota shares organized by team, project, or workload. Each quota share uses configurable resource-sharing strategies such as RESERVE, LEND, or LEND_AND_BORROW. These strategies determine how idle compute is shared or reclaimed when needed. AWS Batch prioritizes quota shares under their capacity limits and performs cross-share preemption to reclaim resources for lending quota shares. In-share preemption allows higher priority jobs to start sooner by preempting lower priority jobs in the SCHEDULED, STARTING, or RUNNING states.
Limitations and availability
The feature is available for AWS Batch users with an AWS account and the necessary permissions to create and manage AWS Batch resources. The example uses the SageMaker Python SDK v3, and users must install a compatible version. The quota shares are configured with specific instance types, such as ml.g5.xlarge, and capacity limits. The feature is not yet generally available but is being integrated with the aws_batch module in the SageMaker Python SDK v3 through a sample notebook.
