Run interactive IDEs on Amazon EKS with SageMaker AI to power up your AI workflows | Amazon Web Services
To power up AI workflows on Amazon Elastic Kubernetes Service (Amazon EKS), data scientists need interactive IDEs like JupyterLab and Code Editor. Yet running those IDEs usually means leaving the cluster that hosts their pipelines, moving to a standalone JupyterHub deployment or a local laptop. That switch leaves them without the GPU nodes, shared storage, and AWS Identity and Access Management (IAM) roles their pipelines depend on. The Amazon SageMaker AI Spaces add-on for Amazon EKS closes that gap. It runs managed JupyterLab and Code Editor environments on the cluster that you already operate. Standing up a standalone JupyterHub environment with GPU access, storage, and authentication typically takes a platform team 3–5 days. With the add-on, a data scientist launches a fully configured Space in about 5 minutes.
In this post, you install the SageMaker AI Spaces add-on on an Amazon EKS cluster. You set up the supporting add-ons...
Copyright of this story solely belongs to amazon.com. To see the full text click HERE
