Setting up your data science project

Before you begin, make sure that you are logged in to Red Hat OpenShift AI and that you can see the dashboard:

Dashboard Enabled

Note that you can start a Jupyter notebook from here, but it would be a one-off notebook run in isolation. To do data science as part of a workflow, you must create a data science project. Projects allow you and your team to organize and collaborate on resources within separated namespaces. From a project you can create multiple workbenches, each with their own Jupyter notebook environment, and each with their own data connections and cluster storage. In addition, the workbenches can share models and data with pipelines and model servers.

Procedure

  1. On the navigation menu, select Data Science Projects. This page shows a list of any existing projects that you have access to. From this page, you can select an existing project (if any) or create a new one.

    Data Science Projects List

  2. Click Create data science project.

  3. Enter a display name and description.

    1. Based on the display name, a resource name is automatically generated

    2. Beware that you are able to update the display name later on, but not the resource name.

      The project name should not be userX like in the screenshot. X should match your user id/number. This way all attendants will have a unique project name.
      New data science project form

  4. You can now see its initial state. There are five types of project components:

    New data science project

    • Workbenches are instances of your development and experimentation environment. They typically contain IDEs, such as JupyterLab, RStudio, and Visual Studio Code.

    • A Cluster storage is a volume that persists the files and data you’re working on within a workbench. A workbench has access to one or more cluster storage instances.

    • Data connections contain configuration parameters that are required to connect to a data source, such as an S3 object bucket.

    • Pipelines contain the Data Science pipelines that are executed within the project.

    • Models and model servers allow you to quickly serve a trained model for real-time inference. You can have multiple model servers per data science project.