Managing resources

Procedure

1. Log in to the OpenShift console as a user with OpenShift AI administrator privileges.
2. In the Administrator perspective, click Home → API Explorer.
3. In the search bar, enter OdhDashboardConfig to filter by kind.
4. Click the OdhDashboardConfig custom resource (CR) to open the resource details page.
5. From the Project list, select the OpenShift AI application namespace; the default is redhat-ods-applications.
6. Click the Instances tab.
7. Click the odh-dashboard-config instance to open the details page.
8. Click the YAML tab.

9. Edit the values of the options that you want to change.

   For example, to show or hide a menu item in the dashboard navigation menu, update the spec.dashboardConfig section to edit the relevant dashboard configuration option.

   Note

   If a dashboard configuration option is not included in the OdhDashboardConfig CR, the default value is used.

   To change the default behavior for such options, edit the OdhDashboardConfig CR to add the missing entry to the spec.dashboardConfig section, and set the preferred value:

   • To show the feature, set the value to false
   • To hide the feature, set the value to true

   Example

   By default, the Distributed workloads menu item is shown in the dashboard navigation menu. To hide this menu item, set the disableDistributedWorkloads value to true, as follows:

   disableDistributedWorkloads: true

   Copy to Clipboard Toggle word wrap

   For more information about dashboard configuration options and their default values, see Dashboard configuration options.

10. Click Save to apply your changes and then click Reload to synchronize your changes to the cluster.

Procedure

1. From the OpenShift AI dashboard, click Settings → Cluster settings.
2. Under PVC size, enter a new size in gibibytes or mebibytes.
3. Click Save changes.

Procedure

1. From the OpenShift AI dashboard, click Settings → Cluster settings.
2. Click Restore Default to restore the default PVC size of 20GiB.
3. Click Save changes.

Procedure

1. From the OpenShift AI dashboard, click Settings → Connection types.

   The Connection types page appears, displaying the available connection types for the current project.

2. Optionally, you can select the Options menu and then click Preview to see how the connection form associated with the connection type appears to your users.

Procedure

1. From the OpenShift AI dashboard, click Settings → Connection types.

   The Connection types page appears, displaying the available connection types.

2. Click Create connection type.

3. In the Create connection type form, enter the following information:

   1. Enter a name for the connection type.

      A resource name is generated based on the name of the connection type. A resource name is the label for the underlying resource in OpenShift.

   2. Optionally, edit the default resource name. Note that you cannot change the resource name after you create the connection type.
   3. Optionally, provide a description of the connection type.

   4. Specify at least one category label. By default, the category labels are database, model registry, object storage, and URI. Optionally, you can create a new category by typing the new category label in the field. You can specify more than one category.

      The category label is for descriptive purposes only. It allows you and the users in your origanization to sort the available connection types when viewing them in the OpenShift AI dashboard interface.

   5. Check the Enable users in your organization to use this connection type when adding connections" option if you want the connection type to appear in the list of connections available to users, for example, when they configure a workbench, a model server, or a pipeline.

      Note that you can also enable/disable the connection type after you create it.

   6. For the Fields section, add the fields and section headings that you want your users to see in the form when they add a connection to a project resource (such as a workbench or a model server).

      Note that the connection name and description fields are included by default, so you do not need to add them.

      • Optionally, select a model serving compatible type to automatically add the fields required to use its corresponding model serving method.
      • Click Add field to add a field to prompt users to input information, and optionally assign default values to those fields.
      • Click Add section heading to organize the fields under headings.
4. Click Preview to open a preview of the connection form as it will appear to your users.
5. Click Save.

Verification

1. On the Settings → Connection types page, the new connection type appears in the list.

Procedure

1. From the OpenShift AI dashboard, click Settings → Connection types.

2. From the list of available connection types, find the connection type that you want to duplicate.

   Optionally, you can select the Options menu and then click Preview to see how the related connection form appears to your users.

3. Click the Options menu , and then click Duplicate.

   The Create connection type form appears populated with the information from the connection type that you duplicated.

4. Edit the form according to your use case.
5. Click Preview to open a preview of the connection form as it will appear to your users and verify that the form appears as you expect.
6. Click Save.

Procedure

1. From the OpenShift AI dashboard, click Settings → Connection types.
2. From the list of available connection types, find the connection type that you want to edit.

3. Click the Options menu , and then click Edit.

   The Edit connection type form appears.

4. Edit the form fields and sections.
5. Click Preview to open a preview of the connection form as it will appear to your users and verify that the form appears as you expect.
6. Click Save.

Procedure

1. From the OpenShift AI dashboard, click Settings → Connection types.
2. From the list of available connection types, find the connection type that you want to enable or disable.
3. On the row containing the connection type, click the toggle in the Enable column.

Procedure

1. From the OpenShift AI dashboard, click Settings → Connection types.

2. From the list of available connection types, find the connection type that you want to delete.

   Optionally, you can select the Options menu and then click Preview to see how the related connection form appears to your users.

3. Click the Options menu , and then click Delete.
4. In the Delete connection type? form, type the name of the connection type that you want to delete and then click Delete.

Procedure

1. From the OpenShift AI dashboard, click Settings → Storage classes.

   The Storage classes page appears, displaying the storage classes for your cluster as defined in OpenShift.

2. To enable or disable a storage class for users, on the row containing the storage class, click the toggle in the Enable column.

3. To edit a storage class, on the row containing the storage class, click the action menu (⋮) and then select Edit.

   The Edit storage class details dialog opens.

4. Optional: In the Display Name field, update the name for the storage class. This name is used only in OpenShift AI and does not impact the storage class within OpenShift.
5. Optional: In the Description field, update the description for the storage class. This description is used only in OpenShift AI and does not impact the storage class within OpenShift.

6. For storage classes that support multiple access modes, select an Access mode to define how the volume can be accessed. For more information, see About persistent storage.

   Only the access modes that have been enabled for the storage class by your cluster and OpenShift AI administrators are visible.

7. Click Save.

Procedure

1. From the OpenShift AI dashboard, click Settings → Storage classes.

   The Storage classes page appears, displaying the storage classes for your cluster as defined in OpenShift.

2. If the storage class that you want to set as the default is not enabled, on the row containing the storage class, click the toggle in the Enable column.
3. To set a storage class as the default for OpenShift AI, on the row containing the storage class, select Set as default.

1. In OpenShift AI, in the Applications section of the left menu, click Enabled.
2. Locate the Start basic workbench tile and click Open application.

3. On the page that opens, click the Administration tab.

   The Administration page opens.

Procedure

1. On the page that opens when you launch a basic workbench, click the Administration tab.

2. On the Administration tab, perform the following actions:

   1. In the Users section, locate the user whose workbench you want to start.
   2. Click Start workbench beside the relevant user.
   3. Complete the Start a basic workbench page.
   4. Optional: Select the Start workbench in current tab checkbox if necessary.

   5. Click Start workbench.

      After the server starts, you see one of the following behaviors:

      • If you previously selected the Start workbench in current tab checkbox, the JupyterLab interface opens in the current tab of your web browser.

      • If you did not previously select the Start workbench in current tab checkbox, the Workbench status dialog box prompts you to open the server in a new browser tab or in the current tab.

        The JupyterLab interface opens according to your selection.

Procedure

1. On the page that opens when you launch a basic workbench, click the Administration tab.

2. On the Administration page, perform the following actions:

   1. In the Users section, locate the user that the workbench belongs to.
   2. Click View server beside the relevant user.
   3. On the Workbench control panel page, click Access workbench.

Procedure

1. On the page that opens when you launch a basic workbench, click the Administration tab.

2. Stop one or more servers.

   • If you want to stop one or more specific servers, perform the following actions:

     1. In the Users section, locate the user that the workbench belongs to.

     2. To stop the workbench, perform one of the following actions:

        • Click the action menu (⋮) beside the relevant user and select Stop server.

        • Click View server beside the relevant user and then click Stop workbench.

          The Stop server dialog box appears.

     3. Click Stop server.

   • If you want to stop all workbenches, perform the following actions:

     1. Click the Stop all workbenches button.
     2. Click OK to confirm stopping all servers.

Procedure

1. From the OpenShift AI dashboard, click Settings → Cluster settings.
2. Under Idle workbench timeout, select Stop idle workbenches after defined period.
3. Enter a time limit, in hours and minutes, for when idle workbenches are stopped.
4. Click Save changes.

Procedure

1. From the OpenShift AI dashboard, click Settings → Cluster settings.
2. Under Workbench pod tolerations, select Add a toleration to workbench pods to allow them to be scheduled to tainted nodes.
3. In the Toleration key for workbench pods field, enter a toleration key. The key is any string, up to 253 characters. The key must begin with a letter or number, and can contain letters, numbers, hyphens, dots, and underscores. For example, workbenches-only.

4. Click Save changes. The toleration key is applied to new workbench pods when they are created.

   For existing workbench pods, the toleration key is applied when the workbench pods are restarted.

Verification

1. In the OpenShift console, select your data science project, then click Workloads → StatefulSet. You can see how many pods are running, either zero or one, depending on whether your workbench is currently started or stopped.
2. Search for your workbench pod name and then click the name to open the pod details page.
3. Confirm that the assigned Node and Tolerations are correct.

Procedure

1. From the OpenShift AI dashboard, click Settings → Cluster settings.
2. Locate the Usage data collection section.
3. Select the Allow collection of usage data checkbox.
4. Click Save changes.

Procedure

1. From the OpenShift AI dashboard, click Settings → Cluster settings.
2. Locate the Usage data collection section.
3. Clear the Allow collection of usage data checkbox.
4. Click Save changes.