Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio
Data scientists and ML engineers can now create, configure, start, stop, and open Amazon SageMaker Spaces on SageMaker HyperPod EKS clusters directly from SageMaker Studio. Launch JupyterLab and Code Editor environments in a few clicks, without using command-line tools.
Amazon SageMaker HyperPod now allows creation and management of Spaces within the SageMaker Studio UI. Data scientists and ML engineers can launch JupyterLab and Code Editor environments on HyperPod clusters without needing command-line tools, significantly reducing the time from cluster access to productive development to a few clicks.
HyperPod provides infrastructure for foundation model training and inference at scale, utilizing AWS Elastic Kubernetes Service (AWS EKS) for distributed training across hundreds of accelerators. Spaces functionality was introduced earlier this year, enabling ML developers to create interactive development environments directly on HyperPod EKS clusters.
Previously, creating and managing Spaces relied on HyperPod CLI or kubectl commands. This new SageMaker Studio capability eliminates the need for command-line tools and focuses on model development.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
