Introducing new Ray capabilities on SageMaker HyperPod
aws ·
The article announces new Ray capabilities on Amazon SageMaker HyperPod that integrate the Ray framework with AWS's purpose-built infrastructure for large-scale ML training and serving. These capabilities let data scientists manage Ray clusters directly from SageMaker Studio, eliminating the need to write Kubernetes manifests or use kubectl port-forwarding. Users can open Ray Dashboard and Amazon Managed Grafana dashboards, connect JupyterLab or Code Editor workspaces to Ray clusters, submit jobs remotely, and configure hung job detection. The integration provides automatic fault tolerance for Ray training jobs through HyperPod's node health monitoring and recovery, plus tiered checkpointing for faster job resumption. For inference, SageMaker JumpStart can load model weights directly into Ray Serve endpoints, with KV cache offloading to tiered storage for long-context requests. These features work with open-source KubeRay and standard Ray APIs, so existing scripts run unchanged, and the post explains prerequisites and setup steps.
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