To perform work within a Data Science & AI Workbench project, open it from the Projects page. The project’s Settings page opens by default. Use the left-hand navigation from here to work with and manage different aspects of your project: Session - Sessions provide an Integrated Development Environment (IDE) for project development. They enable you to write code, explore and visualize data, and develop and evaluate models in a collaborative environment, incorporating familiar Git-based branching and merging workflows for version management. Deployments - Deployments make developed applications or models accessible for end-user interaction, or for automated tasks, such as a web service, REST API, or scheduled job. For more information, see Deployments. Schedules - Setting a schedule for your project instructs it to perform specific tasks automatically at predetermined times. You can update data, generate a report, execute a pipeline, run backup operations, and more. For more information, see Scheduling deployments. Runs - In Workbench, a run refers to the execution of a part of your project, similar to running a script or a program. When you initiate a run, Workbench allocates resources to execute the task. This could be any automated task you have established in your project. Share - Sharing projects with collaborators in Workbench facilitates teamwork by ensuring that all team members have the ability to contribute and have access to the project’s contents. Each project in Workbench has its own internal Git repository that is accessible by the project creator and collaborators that are added to the project. For more information, see Sharing a project.
If your organization would prefer to use its own supported external version control repository, your administrator can configure Workbench to use that repository instead of the internal GitHub server. For more information, see Connecting to an external version control repository.Once complete, you will be prompted for your personal access token before you create your first project in Workbench. For more information, see Configuring access to version control.
Audit Trail - The audit trail is like a project history timeline. It shows detailed records of all activities and changes made within a project. This includes user actions, version changes, access logs, deployment records, resource usage, and system notifications. The audit trail provides transparency, accountability, and is a useful resource when diagnosing issues within a project. Settings - Project settings allow you to configure various aspects of your project. From this page, you can:
  • Set your project’s name.
  • Select a resource profile. This is a critical step for meeting computational demands of your project. For more information, see Understanding resource profiles.
  • Choose a default editor for your project. The default selection is JupyterLab.
  • Assign tags to your project. For more information, see Project tagging.
  • Delete the project. For more information, see Deleting a project.`
Logs - Project logs provide comprehensive insights into the operational health of the project’s components. Each project runs an editor, sync, and proxy container, and logs for each container are accessible to aid in troubleshooting issues you may encounter with your project. For more information, see Project logs.

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Working offline

To work on a project offline, download an archived version (tar.gz) of the project from the projects page. When you are ready, you can upload the files you’ve modified back to the project in Workbench and commit your changes to make them available for other project collaborators. For more information, see Saving and committing changes in a project