SageMaker HyperPod task governance now supports fine-grained compute quota allocation of GPU, Trainium accelerator, vCPU, and vCPU memory within an instance. Administrators can allocate fine-grained compute quota across teams, optimizing compute resource distribution and staying within budget.
Data scientists often execute LLM tasks, like training or inference, that do not require entire HyperPod instances, leading to underutilization of accelerated compute resources. HyperPod task governance enables administrators to manage compute quota allocation across teams. With this capability, administrators can now strategically allocate compute resources, ensuring fair access, preventing resource monopolization, and maximizing cluster utilization. This capability enables fine-grained compute quota allocation in addition to instance-level allocation, aligning with organizational workload demands.
SageMaker HyperPod task governance is available in all AWS Regions where HyperPod is available: US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Stockholm), and South America (São Paulo).
To learn more, visit SageMaker HyperPod webpage, and HyperPod task governance documentation.
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Source: Amazon Web Services
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