Lend and borrow resources across teams with AWS Batch’s quota management for SageMaker Training jobs
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AWS has introduced quota management for AWS Batch to support Amazon SageMaker Training jobs, allowing organizations to better coordinate shared GPU capacity across multiple teams. This new scheduling capability enables administrators to define quota shares for specific teams or projects, utilizing strategies such as lending, borrowing, or reserving idle compute resources. The system automatically handles cross-share preemption to reclaim lent capacity when a lending team needs its resources back, ensuring priority workloads are not delayed. Additionally, in-share preemption allows higher-priority jobs within the same quota share to interrupt lower-priority running jobs if necessary. Administrators can configure these settings using the SageMaker Python SDK, which now includes an aws_batch module for programmatic job submission. The article demonstrates how different configurations, such as LEND_AND_BORROW versus RESERVE, cater to varying workload priorities and interruption tolerances. By automating resource allocation and prioritization, this feature helps increase compute utilization while reducing the operational overhead for infrastructure administrators.
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