Source time Edition date
IndustryAWS Machine Learning Blog
Lend and borrow resources across teams with AWS Batch’s quota management for SageMaker Training jobs
通过 AWS Batch 针对 SageMaker Training 作业的配额管理跨团队借出和借用资源
Teams trading GPU capacity this way must weigh automatic lending and preemption against the risk that lower-priority SageMaker Training jobs get interrupted, so quota owners set the priorities.
Judgment
Quota owners must decide whether to let AWS Batch lend idle GPU capacity across teams automatically, accepting that preemption can interrupt lower-priority SageMaker Training jobs in exchange for higher utilization.
Basis
AWS's Michelle Goodstein, David Lindskog, and Tom Burggraf describe AWS Batch support for SageMaker Training jobs as a fully managed way to queue, schedule, and retry ML training without building infrastructure. The mechanism is AWS Batch queuing and scheduling: administrators set separate queues and capacity limits, then orchestrate workloads so shared GPU capacity goes to the right jobs. The post frames the value as higher utilization of existing compute as training environments grow, but notes the coordination overhead of onboarding more teams and assigning priorities.
Unclear The source does not specify how preemption is ordered, what notice interrupted jobs receive, or whether quota lending is opt-in per team.
Next Check whether AWS Batch exposes per-queue preemption settings or loan limits that quota owners can audit before enabling cross-team lending.