Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio

Key points

• Data scientists and machine learning (ML) engineers can now launch JupyterLab and Code Editor environments on HyperPod clusters without leaving their browser or using command-line tools, reducing the time from cluster access to productive development to a few clicks.1
• Amazon SageMaker HyperPod provides purpose-built infrastructure for foundation model (FM) training and inference at scale.1
• With Amazon Elastic Kubernetes Service (Amazon EKS) orchestration, teams can run distributed training jobs across hundreds of accelerators with built-in resiliency and automatic fault recovery.1
• Administrators install the SageMaker Spaces add-on on their HyperPod EKS cluster using either the Quick install (one-click with optimized defaults) or Custom install option (required to set up web UI access) from their SageMaker HyperPod EKS cluster’s IDE and Notebooks tab.1

Sources (1)

• [1]Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio
  AWS Machine Learning Blog · Oct 6, 03:47 PM

  We recently introduced the ability to create and manage Amazon SageMaker Spaces on Amazon SageMaker HyperPod EKS clusters directly from the Amazon SageMaker Studio UI.

  Data scientists and machine learning (ML) engineers can now launch JupyterLab and Code Editor environments on HyperPod clusters without leaving their browser or using command-line tools, reducing the time from cluster access to productive development to a few clicks.

Extractive summary: sentences quoted from the sources.