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.

Amazon SageMaker HyperPod provides purpose-built infrastructure for foundation model (FM) training and inference at scale. 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. In addition to training, HyperPod extends this EKS orchestrated infrastructure to serve low-latency, scalable inference for multi-billion-parameter foundation models.

Earlier this year, we launched Amazon SageMaker Spaces for HyperPod, an add-on ML developers can use to create interactive development environments directly on HyperPod EKS clusters. Organizations could then maximize their GPU investments by running interactive workloads alongside training jobs and model deployment on the same infrastructure, with support for fractional GPU allocations.

Previously, creating and managing Spaces relied primarily on the HyperPod CLI or kubectl commands. While this approach provides powerful, granular control for infrastructure administrators, data scientists who prefer a visual interface can now use this new SageMaker Studio capability to bypass command-line tools and focus entirely on model development.

With this new capability, data scientists can now create, configure, start, stop, and open Spaces directly from SageMaker Studio. The new IDE and Notebooks tab on the HyperPod cluster detail page provides a complete user interface for Space management, eliminating the need for CLI tools for day-to-day Space operations.

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