POLICY · CODING · #2796
AWS Batch adds quota management to share GPUs for Amazon SageMaker Training jobs (SageMaker Python SDK v3)
AWS Batch introduced a quota management scheduling capability for Amazon SageMaker Training jobs that lets administrators define quota shares per team or project and configure resource-sharing strategies (RESERVE, LEND, LEND_AND_BORROW). The feature supports cross-share preemption to reclaim lent capacity and optional priority-based in-share preemption, and integrates with SageMaker via a Scheduling Policy, Service Environment, Job Queue, and examples in the SageMaker Python SDK v3.
KEY POINTS
- AWS Batch introduced a quota management scheduling capability for Amazon SageMaker Training jobs that lets administrators define quota shares per team or project and configure resource-sharing strategies (RESERVE, LEND, LEND_AND_BORROW).
- The feature supports cross-share preemption to reclaim lent capacity and optional priority-based in-share preemption, and integrates with SageMaker via a Scheduling Policy, Service Environment, Job Queue, and examples in the SageMaker Python SDK v3.
- This lets organizations allocate and automatically lend or reclaim constrained GPU capacity across teams, improving utilization and simplifying multi-team ML training coordination.
WHY IT MATTERS
This lets organizations allocate and automatically lend or reclaim constrained GPU capacity across teams, improving utilization and simplifying multi-team ML training coordination.