Importing a project (Watson Studio)

You can create a project that is preloaded with assets by importing the project.

Use one of these methods:

Watch the following video to see how to create a project from a ZIP file and from a GitHub repository.

This video provides a visual method to learn the concepts and tasks in this documentation.

Importing a project from a local file or sample

You can import a project that you previously exported, or a sample project from the Gallery. You can download sample projects from Industry accelerators that contain a set of data science assets to accomplish a specific goal.

If you created a project from file, you can create it from a project ZIP file on your local system. The compressed file that you select must be an exported ZIP file of a Cloud Pak for Data project and must be from the same Cloud Pak for Data version you are currently using or from earlier versions. You cannot import a compressed file that was exported from a project in Cloud Pak for Data as a Service.

Note that if the imported project contains notebooks, you can only work with those notebooks in the notebook editor, even if the notebooks were created in JupyterLab. You cannot use the JupyterLab IDE in the project.

To create a project from a local file:

  1. From the navigation menu, choose Projects > All projects.
  2. Select Create a project from a file.
  3. Upload the project ZIP file or select a sample project.
  4. If the exported file that you select to import was encrypted, you must enter the password that was used for encryption to enable decrypting sensitive connection properties. If you enter an incorrect password, the project file imports successfully, but sensitive connection properties are falsely decrypted.
  5. You have the option to select these project configurations:
    • Mark the project as sensitive. The project has a sensitive tag and project collaborators can't move data assets out of the project. You cannot change this setting after the project is created.
    • Log project activity. Detailed project activity is tracked in a full activities log that you can download. You can enable or disable this option at any time on the project Settings page.
  6. Click Create.

Importing a project from a Git repository

You can import a project from a Git repository by using default Git integration or deprecated Git integration.

Before you begin

  1. Before you create a project with Git integration, you must understand the options that you have and the implications of choosing either option:
  2. You must configure access to a Git repository. This enables you to work in your clone of a branch from the remote repository associated with the project.

If the imported project contains notebooks, you can only work with those notebooks in the notebook editor, even if the notebooks were created in JupyterLab. You cannot use the JupyterLab IDE in the project.

Creating a project with a Git repository

To create a project with a Git repository:

  1. From the navigation menu, choose Projects > All projects.
  2. Select Create a project integrated with a Git repository.
  3. Enter a name.
  4. Select the integration type:
    • Default Git integration for true Git-based version control when working on files.
    • Deprecated Git integration for Git integration with locking. Although you can still create and use projects with this integration type, this project creation option is deprecated. You have these project configuration options:
      1. Either create a new empty project or import an existing project from a Git repository.
      2. Mark the project as sensitive. You cannot change this setting after the project is created.
      3. Log project activity. You can enable or disable this option at any time on the project Settings page.
      4. Designate the JupyterLab IDE as the only editor tool for notebooks and scripts by selection. You can enable this option at any time on the project Settings page.
  5. Select your token and Git repository.
  6. Click Create.
  7. After the project import is complete, view the project import summary to check that all the assets were successfully imported.
  8. Before you begin working with the imported assets in your new project, check for missing credentials, for example in notebooks and data connections, to enable successful relinking between project assets.
  9. Add collaborators by clicking the Access Control tab in your project, then click Add collaborators or Add user groups. All users will need to create access tokens for collaboration.

If the notebooks were created in JupyterLab, the required notebook kernel might not be found when you try opening the notebooks. In this case, you need to associate the notebook with an appropriate environment template. You can do this:

  • From the notebook opened in edit mode by:

    1. Clicking the Notebook Info icon (Notebook Info icon) from the notebook toolbar, clicking Environment and then selecting an environment template for the notebook from the list.

  • Before you open the notebook, from the project Assets page by:

    1. Selecting the notebook and unlocking it if it is locked. You can only change the environment of a notebook if the notebook is unlocked.
    2. Clicking Actions > Change environment and selecting an appropriate environment template for the notebook.

If you notice that notebooks or Python scripts are missing in the project import, they might not have been included in the original project export. Verify that the person who synchronized assets between the repository and your project explicitly selected those assets, and added them to the project export.

The status of a project import is also tracked on the project's Overview page. Check for a readme file on the Assets page of the project for information about the analytics use case of the added assets and the applied data analysis methods. Imported notebook jobs will always run on the latest available version of the notebook.

Learn more

Parent topic: Creating a project