SageMaker Studio 可直接管理 HyperPod Spaces
AWS 宣布，用户现在可以直接在 Amazon SageMaker Studio 界面中创建、配置、启动、停止并打开 Amazon SageMaker HyperPod EKS 集群上的 SageMaker Spaces，无需再使用 HyperPod CLI 或 kubectl。集群详情页新增的 “IDE and Notebooks” 标签页提供完整的 Space 管理界面，可打开 JupyterLab、Code Editor 或通过 SSH-over-SSM 连接本地 VS Code。AWS 称配合 Karpenter 预热节点，Space 启动时间可从 5–7 分钟缩短到约 30–40 秒。
对使用 HyperPod 的团队来说，交互式开发环境的管理门槛降低，数据科学家不必再依赖命令行即可在训练集群上启动开发环境。
We recently introduced the ability to create and manage Amazon SageMaker Spaces on Amazon SageMaker HyperPod EKS clusters directly from the Amazon SageMaker Studio UI. 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 Service (Amazon EKS) orchestration, teams can run distributed training jobs across hundreds of accelerators with built-in resiliency and automatic fault recovery. In addition to training, HyperPod extends this EKS orchestrated infrastructure to serve low-latency, scalable inference for multi-billion-parameter foundation models.
Earlier this year, we launched Amazon SageMaker Spaces for HyperPod, an add-on ML developers can use to create interactive development environments directly on HyperPod EKS clusters. Organizations could then maximize their GPU investments by running interactive workloads alongside training jobs and model deployment on the same infrastructure, with support for fractional GPU allocations.
#Amazon Web Services#Amazon SageMaker HyperPod#Amazon SageMaker Studio#Amazon EKS#Karpenter#JupyterLab#Visual Studio Code