Chapter 2. The OpenShift AI user interface

The Red Hat OpenShift AI interface is based on the OpenShift web console user interface.

The Red Hat OpenShift AI user interface is divided into several areas:

  • The global navigation bar, which provides access to useful controls, such as Help and Notifications.

    Figure 2.1. The global navigation bar

    The global navigation bar
  • The side navigation menu, which contains different categories of pages available in OpenShift AI.

    Figure 2.2. The side navigation menu

    The side navigation menu
  • The main display area, which displays the current page and shares space with any drawers currently displaying information, such as notifications or quick start guides. The main display area also displays the Notebook server control panel where you can launch Jupyter by starting and configuring a notebook server. Administrators can also use the Notebook server control panel to manage other users' notebook servers.

    Figure 2.3. The main display area

    The main display area

2.2. Side navigation

There are several different pages in the side navigation:

Applications → Enabled

The Enabled page displays applications that are enabled and ready to use on OpenShift AI. This page is the default landing page for OpenShift AI.

Click the Launch application button on an application tile to open the application interface in a new tab. If an application has an associated quick start tour, click the drop-down menu on the application tile and select Open quick start to access it. This page also displays applications and components that have been disabled by your administrator. Disabled applications are denoted with Disabled on the application tile. Click Disabled on the application tile to access links allowing you to remove the tile itself, and to revalidate its license, if the license had previously expired.

Applications → Explore
The Explore page displays applications that are available for use with OpenShift AI. Click a tile for more information about the application or to access the Enable button. The Enable button is visible only if an application does not require an OpenShift Operator installation. 
Data Science Projects
The Data Science Projects page allows you to organize your data science work into a single project. From this page, you can create and manage data science projects. You can also enhance the capabilities of your data science project by adding workbenches, adding storage to your project’s cluster, adding data connections, and adding model servers.
Data Science Pipelines → Pipelines
The Pipelines page allows you to import, manage, track, and view data science pipelines. Using Red Hat OpenShift AI pipelines, you can standardize and automate machine learning workflows to enable you to develop and deploy your data science models.
Data Science Pipelines → Runs
The Runs page allows you to define, manage, and track executions of a data science pipeline. A pipeline run is a single execution of a data science pipeline. You can also view a record of previously executed and scheduled runs for your data science project.
Model Serving
The Model Serving page allows you to manage and view the status of your deployed models. You can use this page to deploy data science models to serve intelligent applications, or to view existing deployed models. You can also determine the inference endpoint of a deployed model.
Resources
The Resources page displays learning resources such as documentation, how-to material, and quick start tours. You can filter visible resources using the options displayed on the left, or enter terms into the search bar.
Settings → Notebook images
The Notebook images page allows you to configure custom notebook images that cater to your project’s specific requirements. After you have added custom notebook images to your deployment of OpenShift AI, they are available for selection when creating a notebook server.
Settings → Cluster settings

The Cluster settings page allows you to perform the following administrative tasks on your cluster:

  • Enable or disable Red Hat’s ability to collect data about OpenShift AI usage on your cluster.
  • Configure how resources are claimed within your cluster by changing the default size of the cluster’s persistent volume claim (PVC).
  • Reduce resource usage in your OpenShift AI deployment by stopping notebook servers that have been idle.
  • Schedule notebook pods on tainted nodes by adding tolerations.
Settings → Accelerator profiles

The Accelerator profiles page allows you to perform the following administrative tasks on your accelerator profiles:

  • Enable or disable an existing accelerator profile.
  • Create, update, or delete accelerator profiles.
  • Schedule pods on tainted nodes by adding tolerations.
Settings → Serving runtimes
The Serving runtimes page allows you to manage the model-serving runtimes in your OpenShift AI deployment. You can use this page to add, edit, and enable or disable model-serving runtimes. You specify a model-serving runtime when you configure a model server on the Data Science Projects page.
Settings → User management
The User management page allows you to define OpenShift AI user group and admin group membership.