tag: Ray

Amazon SageMaker HyperPod now brings managed Ray support to Amazon EKS, letting you spin up and monitor Ray clusters directly from SageMaker Studio. You can connect JupyterLab and Code Editor notebooks to live clusters, get built-in observability, and run distributed training plus accelerated inference using open-source KubeRay and standard Ray APIs—no extra setup needed.

Amazon SageMaker HyperPod now makes running Ray clusters way smoother with interactive development environments, automatic job recovery, and Grafana dashboards—no more kubectl wrestling matches. You can iterate on cluster-scale compute directly from JupyterLab or your local IDE, while HyperPod handles GPU faults and hung jobs automatically, plus accelerates Ray Serve inference with tiered KV caching.