AWS Batch Quota Management for SageMaker Training Jobs
According to AWS. AWS introduces quota management in AWS Batch to support Amazon SageMaker Training jobs across multi-team environments.
- • AWS Batch now supports quota management for Amazon SageMaker Training jobs.
- • Administrators can organize compute capacity into quota shares by team or project.
- • Resource-sharing strategies include reserve, lend, and borrow options for idle compute.
Overview and Architecture
With the launch of AWS Batch support for Amazon SageMaker Training jobs, customers gained a new fully managed way to queue, schedule, and retry machine learning training workloads without building their own infrastructure. As organizations scale their artificial intelligence and machine learning operations, coordinating access to shared GPU capacity can introduce additional configuration overhead.
Quota Shares and Preemption
Administrators can divide compute capacity into quota shares organized by team, project, or workload. Each quota share uses configurable resource-sharing strategies that maximize GPU utilization while honoring each team's allocation when it matters most, utilizing features such as cross-share and in-share preemption.
- 1. aws.amazon.comOpen source