IDEs and Notebooks
Amazon SageMaker is introducing a new capability for SageMaker HyperPod EKS clusters, which allows AI developers to run their interactive machine learning workloads directly on the HyperPod EKS cluster. This feature introduces a new add-on called Amazon SageMaker Spaces, that enables AI developers to create and manage self-contained environments for running notebooks.
Administrators can use SageMaker HyperPod Console to install the add-on on their cluster, and define default space configurations such as images, compute resources, local storage for notebook settings (additional storage to be attached to their dev spaces), file systems, and initialization scripts. A one-click installation option will be available with default settings to simplify the admin experience. Admins can use the SageMaker HyperPod Console, kubectl, or HyperPod CLI to install the operator, create default settings, and manage all spaces in a centralized location.
AI developers can use HyperPod CLI to create, update, and delete dev spaces. They have the flexibility to use default configurations provided by admins or customize settings. AI developers can access their spaces on HyperPod using their local VS Code IDEs, and/or their web browser that hosts their JupyterLab or CodeEditor IDE on custom DNS domain configured by their admins. They can also use kubernetes’ port forwarding feature to access spaces in their web browsers.
Admin
Data scientist
SageMaker Spaces Managed Instance Pricing
The SageMaker Spaces Add-on/Operator does not incur any additional charge to the customer. However, to support the SSH-over-SSM tunneling required for the Remote IDE Connection feature, SageMaker Spaces uses an AWS-managed instance. This instance is registered as an Advanced On-Premises Instance under SSM, and therefore is billed per compute hour.
Please refer to the “On-Premises Instance Management” rate on the AWS Systems Manager
pricing page: AWS Systems Manager Pricing: https://aws.amazon.com/systems-manager/pricing/
