Run interactive IDEs on Amazon EKS with SageMaker AI to power up your AI workflows

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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 and IAM roles, deploy the AWS Load Balancer Controller, request a TLS certificate, and create an AWS Key Management System (AWS KMS) encryption key. You then create your first Space and reach it through a presigned URL in the browser and from VS Code over SSH-over-SSM. Finally, you review how to move your team to OpenID Connect (OIDC) sign-in with Amazon Cognito. Solution overview The solution runs on a single EKS cluster in three layers: Network and access. Amazon Route 53 resolves a wildcard domain to an internet-facing Application Load Balancer (ALB) with TLS from AWS Certificate Manager (ACM). For VS Code, AWS Systems Manager tunnels …

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• Originalquelle / Hersteller-Advisory lesen: https://aws.amazon.com/blogs/machine-learning/run-interactive-ides-on-amazon-eks-with-sagemaker-ai-to-power-up-your-ai-workflows/
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