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#Amazon SageMaker HyperPod
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  1. AWS Machine Learning Blog

    Best practices for Amazon SageMaker HyperPod administration and governance

    中文摘要

    通过 Amazon SageMaker Unified Studio 管理 Amazon SageMaker HyperPod 的方法，同时保持集群治理。本文展示了平台团队如何设计基础设施边界、治理访问权限、分配共享容量，并在组织、项目、集群和工作负载控制层面上一致地运行 HyperPod。

    英文原文

    Learn how to administer Amazon SageMaker HyperPod through Amazon SageMaker Unified Studio while preserving cluster governance. This post shows platform teams how to design infrastructure boundaries, govern access, allocate shared capacity, and operate HyperPod consistently across the organization, project, cluster, and workload control layers.

  2. AWS Machine Learning Blog

    Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio

    中文摘要

    数据科学家和机器学习工程师现在可以直接从SageMaker Studio在SageMaker HyperPod EKS集群上创建、配置、启动、停止和打开Amazon SageMaker Spaces。只需点击几下即可启动JupyterLab和代码编辑器环境，而无需使用命令行工具。

    英文原文

    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.

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