Collaboration with shared spaces

An Amazon SageMaker Studio Classic shared space consists of a shared JupyterServer application and a shared directory. A JupyterLab shared space consists of a shared JupyterLab application and a shared directory within Amazon SageMaker Studio. All user profiles in a domain have access to all shared spaces in the domain. Amazon SageMaker AI automatically scopes resources in a shared space within the context of the Amazon SageMaker Studio Classic application that you launch in that shared space. Resources in a shared space include notebooks, files, experiments, and models. Use shared spaces to collaborate with other users in real-time using features like automatic tagging, real time co-editing of notebooks, and customization.

Shared spaces are available in:

A Studio Classic shared space only supports Studio Classic and KernelGateway applications. A shared space only supports the use of a JupyterLab 3 image Amazon Resource Name (ARN). For more information, see JupyterLab Versioning.

Amazon SageMaker AI automatically tags all SageMaker AI resources that you create within the scope of a shared space. You can use these tags to monitor costs and plan budgets using tools, such as AWS Budgets.

A shared space uses the same VPC settings as the domain that it's created in.

Automatic tagging

All resources created in a shared space are automatically tagged with a domain ARN tag and shared space ARN tag. The domain ARN tag is based on the domain ID, while the shared space ARN tag is based on the shared space name. 

You can use these tags to monitor AWS CloudTrail usage. For more information, see Log Amazon SageMaker API Calls with AWS CloudTrail.

You can also use these tags to monitor costs with AWS Billing and Cost Management. For more information, see Using AWS cost allocation tags.

Real time co-editing of notebooks

A key benefit of a shared space is that it facilitates collaboration between members of the shared space in real time. Users collaborating in a workspace get access to a shared Studio Classic application where they can access, read, and edit their notebooks in real time. Real time collaboration is only supported for JupyterServer applications within a shared space.

Users with access to a shared space can simultaneously open, view, edit, and execute Jupyter notebooks in the shared Studio Classic or JupyterLab application in that space.

The notebook indicates each co-editing user with a different cursor that shows the user profile name. While multiple users can view the same notebook, co-editing is best suited for small groups of two to five users.

To track changes being made by multiple users, we strongly recommended using Studio Classic's built-in Git-based version control.

JupyterServer 2

To use shared spaces in Studio Classic, Jupyter Server version 2 is required. Certain JupyterLab extensions and packages can forcefully downgrade Jupyter Server to version 1. This prevents the use of shared space. Run the following from the command prompt to change the version number and continue using shared spaces.

conda activate studio
pip install jupyter-server==2.0.0rc3

Customize a shared space

To attach a lifecycle configuration or custom image to a shared space, you must use the AWS CLI. For more information about creating and attaching lifecycle configurations, see Create and associate a lifecycle configuration. For more information about creating and attaching custom images, see Bring your own SageMaker AI image.