The mechanism operates by nesting quota shares inside AWS Batch job queues, giving administrators granular control over reservations and dynamic borrowing strategies. Workloads from shares that are currently under their allocated limits are actively prioritized, and borrowed capacity is reclaimed via automated cross-share preemption the moment original owners submit new tasks. Additionally, administrators can configure priority-based in-share preemption to halt lower-priority scheduled or running jobs so that critical operations launch immediately. Getting started requires an adequately provisioned AWS account, the SageMaker Python SDK v3, a Scheduling Policy, a Service Environment, and a c…
Key points
Setting up the feature requires the SageMaker Python SDK v3 alongside a Scheduling Policy…: Setting up the feature requires the SageMaker Python SDK v3 alongside a Scheduling Policy, Service Environment, and job queue.
Compute can be held in exclusive reservation, lent out to other teams, or set to dynamica…: Compute can be held in exclusive reservation, lent out to other teams, or set to dynamically lend and borrow idle capacity.
The automated system helps organizations maximize GPU hardware utilization while preventi…: The automated system helps organizations maximize GPU hardware utilization while preventing scheduling bottlenecks between separate project teams.
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Artificial Intelligence
Lend and borrow resources across teams with AWS Batch’s quota management for SageMaker Training jobs
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 (ML) training workloads without building their own infrastructure. Instead, they use the queuing and scheduling features of AWS Batch. As organizations scale their artificial intelligence and machine learning (AI/ML) operations and onboard more teams, coordinating access to shared graphics processing unit (GPU) capacity can introduce additional communication, coordination, and configuration overhead for administrators. This overhead includes setting up separate queues, adjusting capacity limits, and carefully orchestrating the right workloads to get the right resources at the right time. A core value proposition of AWS Batch is helping customers increase utilization of their compute, and as training environments grow, so does the opportunity to do more with existing capacity.
Working backward from customers managing multi-team training environments, we heard a consistent request: give us a way to define allocations per team or project, share idle capacity automatically, and…
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