Configure AWS Batch quota sharing for SageMaker Training jobs across teams
Lend and borrow resources across teams with AWS Batch’s quota management for SageMaker Training jobs
- Who
- AWS
- What
- explains how to configure and use AWS Batch quota management for SageMaker Training jobs
What happened
AWS explains how to configure quota management for SageMaker Training jobs using AWS Batch and the SageMaker Python SDK v3. The walkthrough sets up three quota shares with LEND_AND_BORROW, LEND, and RESERVE strategies, then demonstrates reclaiming lent capacity through cross-share preemption. It also shows how updating a submitted job’s priority can trigger in-share preemption so higher-priority work runs sooner.
Why it matters
The three quota configurations offer a transferable way to match shared GPU allocation policies to workload urgency, spare capacity needs, and tolerance for preemption.
Read next
See the full storyAWS Shows How Teams Can Share SageMaker Training Quotas Through AWS BatchThe story so far
AWS published a walkthrough for configuring AWS Batch quota sharing for SageMaker Training jobs using the SageMaker Python SDK v3, showing how teams can lend, borrow and reclaim capacity and prioritize training work. The walkthrough configures three quota shares with LEND_AND_BORROW, LEND and RESERVE strategies, then demonstrates how cross-share preemption reclaims lent capacity. It also shows how changing a submitted job’s priority can trigger in-share preemption so higher-priority work runs sooner.
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