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Best practices for Amazon SageMaker HyperPod administration and governance

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Best practices for Amazon SageMaker HyperPod administration and governance

Amazon SageMaker HyperPod gives machine learning (ML) teams access to large pools of accelerated compute for training and fine-tuning models. When several teams share one cluster, the technical setup is usually straightforward. The challenging part is governance. You must decide which teams can use the cluster, how much capacity each team gets, what happens when one team’s workload competes with another’s, and who is accountable when usage drifts from policy. Amazon SageMaker Unified Studio adds another consideration: You can connect a SageMaker HyperPod cluster to a project so team members can launch workloads from their project workspace. That convenience is valuable, but after multiple teams share visibility into the same cluster, the controls that govern who can do what become even more important. In this post, we show how to administer SageMaker HyperPod through SageMaker Unified Studio while preserving the underlying governance controls. We cover the four layers of control: organization, project, cluster, and workload. We also explain how to design identity, capacity, and observability policies across them. By the end, you will have a repeatable model for offering approved SageMaker HyperPod compute to ML teams in their project context while keeping cluster operations with the infrastructure team.

Amazon SageMaker HyperPod is a capability of Amazon SageMaker AI . Amazon SageMaker Unified Studio is the data and AI development environment where teams build with their data and tools. With SageMaker Unified Studio, you can connect a project to an existing SageMaker HyperPod cluster. Members can then launch machine learning workloads, review cluster and task information, and open a JupyterLab workflow.


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