Power up your ML workflows with interactive IDEs on SageMaker HyperPod

Amazon SageMaker HyperPod clusters with Amazon Elastic Kubernetes Service (EKS) orchestration now support creating and managing interactive development environments such as JupyterLab and open source Visual Studio Code, streamlining the ML development lifecycle by providing managed environments for familiar tools to data scientists. This feature introduces a new add-on called Amazon SageMaker Spaces for AI developers to create and manage self-contained environments for running notebooks. Organizations can now maximize their GPU investments by running both interactive workloads and their training jobs on the same infrastructure, with support for fractional GPU allocations to improve cost efficiency. This feature reduces the complexity of managing multiple development environments and focus on building and deploying their AI and ML models.

This post shows how HyperPod administrators can configure Spaces for their clusters, and how data scientists can create and connect to these Spaces. You’ll also learn how to connect directly from your local ...