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AWS SageMaker HyperPod integrates new Ray capabilities for foundation model training

Amazon SageMaker HyperPod now integrates new capabilities for the open-source Ray framework, simplifying foundation model training and serving. This integration allows data scientists to manage Ray clusters, access dashboards, and submit jobs directly from SageMaker Studio without needing to write Kubernetes manifests or use kubectl commands. The new features include automatic fault tolerance, tiered checkpointing for faster recovery, and seamless integration with SageMaker JumpStart for model deployment. AI

IMPACT Simplifies distributed training and serving of foundation models on AWS infrastructure.

RANK_REASON This is a product update from a cloud provider integrating an open-source framework, not a new frontier model release or significant industry-wide event.

Read on AWS Machine Learning Blog →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AWS SageMaker HyperPod integrates new Ray capabilities for foundation model training

COVERAGE [2]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Nilesh PS ·

    Introducing new Ray capabilities on SageMaker HyperPod

    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…

  2. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    AWS announced new Ray capabilities on SageMaker HyperPod integrating Ray with HyperPod infrastructure for foundation model training and serving. Source: AWS Mac

    AWS announced new Ray capabilities on SageMaker HyperPod integrating Ray with HyperPod infrastructure for foundation model training and serving. Source: AWS Machine Learning Blog https:// aws.amazon.com/blogs/machine-l earning/introducing-new-ray-capabilities-on-sagemaker-hyperpo…