SageMaker HyperPod Adds Managed Ray Support on EKS
Key takeaways
- SageMaker HyperPod now supports managed Ray on Amazon EKS.
- This enables easier creation and monitoring of Ray clusters for distributed ML.
- Users can connect notebooks and perform resilient training and inference.
- The integration uses open-source KubeRay and standard Ray APIs.
Who benefits
Summary
Amazon SageMaker HyperPod now provides managed Ray support on Amazon EKS, enabling users to create and monitor Ray clusters directly. This integration facilitates distributed training and accelerated inference within SageMaker Studio, leveraging open-source KubeRay and standard Ray APIs.
Why it matters
This update simplifies the deployment and management of distributed AI workloads, allowing professionals to leverage Ray's capabilities for complex model training and inference more efficiently within the AWS ecosystem.
How to implement this in your domain
- 1Explore SageMaker HyperPod's new Ray integration for distributed ML workloads.
- 2Set up and monitor Ray clusters directly within SageMaker Studio for training and inference.
- 3Connect JupyterLab or Code Editor notebooks to live Ray clusters for interactive development.
- 4Utilize the out-of-the-box observability features to track cluster performance.
Original post by Nilesh PS
"Amazon SageMaker HyperPod now offers managed Ray support on Amazon EKS. Create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, get out-of-the-box observability, and run resilient distributed training and accelerated inference from SageMake…"
View on XOriginally posted by Nilesh PS on X · view source
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