Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio

What happened

Manage Amazon SageMaker (Python library) HyperPod Spaces directly from SageMaker Studio, AWS Machine Learning Blog announced. 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.

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. Background Amazon SageMaker HyperPod provides purpose-built infrastructure for foundation model (FM) training and inference at scale. With Amazon Elastic Kubernetes (software that runs and manages applications across many servers) Service (Amazon EKS) orchestration, teams can run distributed training jobs across hundreds of accelerators with built-in resiliency and automatic fault recovery.

Key facts

• Background Amazon SageMaker HyperPod — provides: purpose-built infrastructure for foundation model (FM) training and inference at scale