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HyperPod tabs in Studio - Amazon SageMaker AI
TasksMetricsSettingsIDE and NotebooksDetails

HyperPod tabs in Studio

In Amazon SageMaker Studio you can navigate to one of your clusters in HyperPod clusters (under Compute) and view your list of clusters. The displayed clusters contain information like tasks, hardware metrics, settings, and metadata details. This visibility can help your team identify the right candidate for your pre-training or finetuning workloads. The following sections provide information on each type of information.

Tasks

Amazon SageMaker HyperPod provides a view of your cluster tasks. Tasks are operations or jobs that are sent to the cluster. These can be machine learning operations, like training, running experiments, or inference. The following section provides information on your HyperPod cluster tasks.

In Amazon SageMaker Studio, you can navigate to one of your clusters in HyperPod clusters (under Compute) and view the Tasks information on your cluster. If you are having any issues with viewing tasks, see Troubleshooting.

The task table includes:

For Slurm clusters

For Slurm clusters, the tasks currently in the Slurm job scheduler queue are shown in the table. The information shown for each task includes the task name, status, job ID, partition, run time, nodes, created by, and actions.

For a list and details about past jobs, use the sacct command in JupyterLab or a Code Editor terminal. The sacct command is used to view historical information about jobs that have finished or are complete in the system. It provides accounting information, including job resources usage like memory and exit status.

By default, all Studio users can view, manage, and interact with all available Slurm tasks. To restrict the viewable tasks to Studio users, see Restrict task view in Studio for Slurm clusters.

For Amazon EKS clusters

For Amazon EKS clusters, kubeflow (PyTorch, MPI, TensorFlow) tasks are shown in the table. PyTorch tasks are shown by default. You can sort for PyTorch, MPI, and TensorFlow under Task type. The information that is shown for each task includes the task name, status, namespace, priority class, and creation time.

By default, all users can view jobs across all namespaces. To restrict the viewable Kubernetes namespaces available to Studio users, see Restrict task view in Studio for EKS clusters. If a user cannot view the tasks and is asked to provide a namespace, they need to get that information from the administrator.

Metrics

Amazon SageMaker HyperPod provides a view of your Slurm or Amazon EKS cluster utilization metrics. The following provides information on your HyperPod cluster metrics.

You will need to install the Amazon EKS add-on to view the following metrics. For more information, see Install the Amazon CloudWatch Observability EKS add-on.

In Amazon SageMaker Studio, you can navigate to one of your clusters in HyperPod clusters (under Compute) and view the Metrics details on your cluster. Metrics provides a comprehensive view of cluster utilization metrics, including hardware, team, and task metrics. This includes compute availability and usage, team allocation and utilization, and task run and wait time information.

Settings

Amazon SageMaker HyperPod provides a view of your cluster settings. The following provides information on your HyperPod cluster settings.

In Amazon SageMaker Studio you can navigate to one of your clusters in HyperPod clusters (under Compute) and view the Settings information on your cluster. The information includes the following:

  • Instances details, including instance ID, status, instance type, and instance group

  • Instance groups details, including instance group name, type, counts, and compute information

  • Orchestration details, including the orchestrator, version, and certification authority

  • Cluster resiliency details

  • Security details, including subnets and security groups

IDE and Notebooks

Amazon SageMaker HyperPod provides a view of the development spaces running on your cluster. Spaces are self-contained environments for running JupyterLab or Code Editor IDEs directly on your HyperPod EKS cluster. You can create, configure, start, stop, and open spaces directly from Studio.

In Amazon SageMaker Studio, navigate to one of your clusters in HyperPod clusters (under Compute) and choose the IDE and Notebooks tab.

Key capabilities available through Studio include:

  • Create spaces with configurable compute, storage, and image settings through a guided form.

  • View all spaces in a searchable table showing name, application type, status, access type, storage, GPU, and vCPU allocations.

  • Start and stop spaces with a single click to manage compute costs.

  • Open spaces directly in the browser (JupyterLab or Code Editor) or connect through a Remote IDE. For more information, see Remote access to SageMaker Spaces.

  • Delete spaces that are no longer needed.

  • Select namespaces to organize spaces by team with resource quotas and governance settings.

  • Apply templates for consistent space configurations across teams.

For information on creating a domain, see Guide to getting set up with Amazon SageMaker AI.

Prerequisites

  • Install the HyperPod add-on on your cluster. For more information, see Install SageMaker AI Spaces Add-on.

  • Set up your cluster to be used in Studio. For more information, see Setting up an Amazon EKS cluster in Studio.

  • To enable private spaces among users with the same execution role, you must ensure the ExecutionRoleSessionNameMode flag is set to USER_IDENTITY. With the HyperPod Spaces feature, the username used inside the space is automatically derived from the Studio authentication context, allowing users to have a consistent identity across Studio and HyperPod Spaces without requiring additional sign-in.

    For Studio domains configured in IAM authentication mode, the Spaces username is derived from the IAM role session name. This corresponds to the IAM session used to launch Studio, either through the AWS Management Console or through a presigned Studio URL. For Studio domains configured in IAM Identity Center authentication mode, the Spaces username is the sanitized authenticated IAM Identity Center username.

    This is set by default for new domains and can be overridden for older domains. This setting can also be overridden for each user profile. For more information, see Execution role session name mode.

How it works

Once the add-on is installed and access is configured, navigate to your HyperPod cluster in Studio and select the IDE and Notebooks tab to see the spaces management interface.

Creating a space

To create a new space, choose Create space. The creation form allows you to configure the following:

  • Namespace: Select your team namespace with resource quotas and governance settings. This determines your available compute allocation.

  • Space settings:

    • Template: Select a pre-configured template (for example, JupyterLab or Code Editor) to apply default settings.

    • Compute: Choose between GPU and CPU configurations with fine-grained control over GPUs, vCPUs, and memory.

    • GPU partition: If fractional GPU is enabled, you can choose a partition to use for your workspace.

    • Image: Select from available container images or custom images configured by your administrator.

    • EBS space storage: Configure persistent storage for your notebooks and data.

  • Task governance: When enabled for the namespace, spaces integrate with HyperPod task governance for resource management and priority scheduling. For more information, see Task governance for Interactive Spaces on HyperPod.

Managing spaces

The spaces table provides a consolidated view of all your environments, including status and resource allocations.

From the Actions column, you can:

  • Stop a running space to release compute resources while preserving your data on EBS storage.

  • Open the space in your browser to launch the JupyterLab or Code Editor web interface.

  • Connect using a Remote IDE. For more information, see Remote access to SageMaker Spaces.

Connecting to your space

Spaces support two connection methods:

Web UI access

Choose Open from the spaces table to launch the IDE directly in your browser. This opens a fully functional JupyterLab or Code Editor interface hosted on your HyperPod cluster. No local software installation is required beyond a web browser. This is ideal for quick iterations, notebook-based exploration, and collaborative work. To enable Web UI access on your cluster, see Web browser access.

Remote IDE connection

Choose Open in Remote IDE from the spaces table to connect your local IDE to the space running on HyperPod. This provides a secure connection without requiring you to manage SSH keys or expose port 22. You get the full power of your local development environment while running code on HyperPod cluster compute. For more information, see Remote access to SageMaker Spaces.

Details

Amazon SageMaker HyperPod provides a view of your cluster metadata details. The following paragraph provides information on how to get your HyperPod cluster details.

In Amazon SageMaker Studio, you can navigate to one of your clusters in HyperPod clusters (under Compute) and view the Details on your cluster. This includes the tags, logs, and metadata.