AWS Batch adds quota management for sharing GPU capacity across SageMaker Training teams
Overview
AWS Batch now offers quota management for SageMaker Training jobs, letting administrators divide GPU capacity into quota shares by team, project or workload, according to an AWS Machine Learning Blog post.
Each share sets a resource-sharing strategy of RESERVE, LEND or LEND_AND_BORROW, and can turn on in-share preemption so higher-priority jobs run first.
The post says the strategies maximize GPU utilization while honoring each team's allocation. Its demonstration uses the SageMaker Python SDK v3 with three shares, each capped at one ml.g5.xlarge instance.
Written by AI from the articles below · updated Oct 29, 1:21 PM ET
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- AWS Machine Learning BlogOfficialAWS Batch adds quota management to share GPU capacity across SageMaker Training teams
AIAWS Batch now offers quota management for SageMaker Training jobs, letting administrators divide GPU capacity into quota shares by team or project. Each share sets a resource-sharing strategy (RESERVE, LEND, or LEND_AND_BORROW) and can turn on in-share preemption so higher-priority jobs run first. The post demonstrates the setup with the SageMaker Python SDK v3 using three shares, each capped at one ml.g5.xlarge instance.
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