AWS Batch introduces quota management for SageMaker Training jobs
Current overview (not historical)
AWS Batch has introduced quota management for SageMaker Training jobs, allowing administrators to allocate compute capacity across teams or projects via quota sharing policies. Quotas configured with the LEND_AND_BORROW policy can borrow out idle capacity and borrow in during busy periods, while supporting on-premise priority preemption where higher-priority jobs within the same quota can preempt lower-priority ones.
Written with AI from the reporting · updated 10/29/2026
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- 1Unclassified fact stationOct 30, 00:00recorded Oct 30, 01:32single source
AWS Batch quota management for SageMaker Training jobs
Read original source ↗ AWS Machine Learning BlogReplay this moment in the explorerEvidence & revisions · 1 source records
- Subject：
- aws
- Action：
- launched quota management capability
- Object：
- SageMaker Training jobs scheduling with GPU resource sharing
- Occurred (fact side)：
- Oct 30, 00:00
- Published (valid side)：
- Oct 30, 00:58
- First seen (known side)：
- Oct 30, 01:32
Fact stationSingle source · one-sided claimCopy may have been revised later; time replay is bounded. Layer labels group reading only, not causation between layers.
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- AWS Machine Learning BlogSelectedFirst-party sourceRelated fact: single sourceAWS Batch introduces quota management for SageMaker Training jobs to enable cross-team resource sharing
Report first recorded Oct 30, 01:32
AWS Batch now supports quota management for SageMaker Training jobs, allowing administrators to divide GPU capacity into quota shares organized by team or project. Each quota share can be configured with resource-sharing strategies such as LEND_AND_BORROW, which automatically lends idle capacity to other shares and borrows from them when work is available. The system also supports in-share preemption, enabling high-priority workloads to preempt lower-priority jobs within the same share. This eliminates the need for manual queue management and helps improve GPU utilization across multi-team environments.
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Event activity trend
- Latest comparable
- 3
- Comparable peak
- 10Oct 30, 02:00
- 24h change
- -49%
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