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SageMaker HyperPod Adds Managed Ray Support on Amazon EKS

Amazon SageMaker HyperPod now provides managed Ray support on Amazon EKS, enabling users to create and monitor Ray clusters, connect notebooks, and run distributed training and inference.

By Illumora Editorial

Source · Aug 24, 2026, 7:32 PM · On Illumora · Aug 24, 2026, 7:37 PM

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Read the source →AWS Machine Learning Blog — Introducing new Ray capabilities on SageMaker HyperPod | Amazon Web Services
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The AWS Machine Learning Blog announced that Amazon SageMaker HyperPod now includes managed Ray support on Amazon EKS. This integration allows users to leverage Ray for distributed machine learning workloads within the SageMaker environment.

Key Points

  • Amazon SageMaker HyperPod now offers managed Ray support.
  • The Ray support is integrated with Amazon EKS.
  • Users can create and monitor Ray clusters.
  • JupyterLab and Code Editor notebooks can connect to live Ray clusters.
  • The offering includes out-of-the-box observability for Ray workloads.
  • It supports resilient distributed training and accelerated inference from SageMaker Studio.
  • The capabilities are built with open-source KubeRay and standard Ray APIs.

Context

According to the AWS Machine Learning Blog, this new capability allows users to manage Ray clusters directly within SageMaker HyperPod. The integration with Amazon EKS provides a scalable and resilient infrastructure for distributed machine learning tasks. This approach uses open-source KubeRay and standard Ray APIs.

Why It Matters

This update provides SageMaker users with a managed solution for distributed computing, potentially simplifying the deployment and management of complex machine learning workflows. Builders can now integrate Ray's distributed processing capabilities more directly into their SageMaker Studio projects.

What To Do

  • Review the SageMaker HyperPod documentation for details on Ray integration.
  • Explore connecting JupyterLab or Code Editor notebooks to Ray clusters in SageMaker Studio.
  • Test the distributed training and inference capabilities using the new Ray support.
  • Note the use of open-source KubeRay and standard Ray APIs for compatibility.