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Managing Workspaces

A Workspace represents a user-driven environment (a “job”) for interactive data science or development—often running tools like Jupyter Notebook or VS Code. Each Workspace is powered by a Docker image, assigned specific resources (GPU, CPU, Memory), and optionally linked to persistent volumes for storage. Users can also enable checkpointing to periodically capture the state of their session for recovery purposes. This approach ensures that development and experimentation can be conducted in a consistent, easily reproducible environment.

Using Workspaces, your team can seamlessly develop and experiment with AI models, data processing scripts, or any other workload, all within a controlled, resource-managed environment.

Accessing the Workspace Dashboard

  1. Open the “Workspaces” Page

    • In the left navigation bar, select Workspaces.
    • You will land on the Workspaces dashboard, which shows all current Workspaces.
    • If no Workspaces exist yet, the page indicates “No workspace to display.”

    Workspace Dashboard

  2. Filter and Search

    • Use the Search Workspaces field to locate a specific Workspace by name.
    • Use the Project dropdown to display only Workspaces belonging to a particular project.
    • The dashboard provides summary information including the total number of Workspaces, and how many are Ready vs. NotReady.

Creating, Modifying, and Deleting Workspaces

To manage Workspaces on bahalf of users, you can refer to the following sub-pages: