Chapter 2. Customize the dashboard
The OpenShift AI dashboard provides features that are designed to work for most scenarios. These features are configured in the OdhDashboardConfig custom resource (CR).
To see a description of the options in the OpenShift AI dashboard configuration, see Dashboard configuration options.
As an OpenShift AI administrator, you can customize the interface of the dashboard. For example, you can show or hide some of the dashboard navigation menu items. To change the default settings of the dashboard, edit the OdhDashboardConfig CR as described in Editing the dashboard configuration.
2.1. Edit the dashboard configuration
As an OpenShift AI administrator, you can customize the interface of the dashboard by editing the dashboard configuration.
Prerequisites
- You have OpenShift AI administrator privileges.
Procedure
- Log in to the OpenShift console as a user with OpenShift AI administrator privileges.
-
In the Administrator perspective, click Home
API Explorer. -
In the search bar, enter
OdhDashboardConfigto filter by kind. -
Click the
OdhDashboardConfigcustom resource (CR) to open the resource details page. -
From the Project list, select the OpenShift AI application namespace; the default is
redhat-ods-applications. - Click the Instances tab.
-
Click the
odh-dashboard-configinstance to open the details page. - Click the YAML tab.
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.dashboardConfigsection to edit the relevant dashboard configuration option.NoteIf a dashboard configuration option is not included in the
OdhDashboardConfigCR, the default value is used.To change the default behavior for such options, edit the
OdhDashboardConfigCR to add the missing entry to thespec.dashboardConfigsection, 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 Observe & monitor
Workload metrics menu item is shown in the dashboard navigation menu. To hide this menu item, set the disableDistributedWorkloadsvalue totrue, as follows:disableDistributedWorkloads: trueFor more information about dashboard configuration options and their default values, see Dashboard configuration options.
-
To show the feature, set the value to
- Click Save to apply your changes and then click Reload to synchronize your changes to the cluster.
Verification
- Log in to OpenShift AI and verify that your dashboard configurations apply.
2.2. Configure global prompt registry namespaces
Global prompt registry namespaces is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.
For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.
Share organization-curated prompts with users across projects by configuring the global MLflow prompt registry namespace. When you configure the global namespace, the MLflow Operator creates the required RBAC bindings automatically.
You can configure the global namespace from the OpenShift AI dashboard or by editing the OdhDashboardConfig custom resource (CR) directly.
Prerequisites
- You have OpenShift AI administrator privileges.
-
The
mlflowoperatorcomponent is set toManagedin theDataScienceClustercustom resource (CR). -
Gen AI Studio is enabled:
spec.dashboardConfig.genAiStudiois set totruein theOdhDashboardConfigCR. -
Prompt management is enabled:
spec.dashboardConfig.promptManagementis set totruein theOdhDashboardConfigCR. - An MLflow server instance is running in the target namespace with one or more prompts registered. The MLflow Operator creates RBAC bindings only when an active MLflow server is present in the namespace.
Procedure
To configure the global namespace from the dashboard:
-
In the OpenShift AI dashboard, click Settings
General settings. - In the Global project section, select the project that contains the prompts you want to share.
- Click Save changes.
-
In the OpenShift AI dashboard, click Settings
To configure the global namespace by editing the CR:
- Log in to OpenShift as a user with OpenShift AI administrator privileges.
Edit the
OdhDashboardConfigCR to add the target namespace to thespec.globalMLflowNamespacesfield.In the following example, replace
_<namespace>_with the name of the namespace that contains the MLflow prompt registry you want to share:apiVersion: opendatahub.io/v1alpha kind: OdhDashboardConfig metadata: name: odh-dashboard-config namespace: pass:attributes[{dbd-config-default-namespace}] spec: globalMLflowNamespaces: - <namespace> # ...NoteThe
globalMLflowNamespacesfield is limited to a single namespace entry. This field is located directly underspec, not underspec.dashboardConfig.Apply the
opendatahub.io/global-mlflow-workspacelabel to the target namespace.In the following command, replace
_<namespace>_with the same namespace name you added toglobalMLflowNamespaces:$ oc label namespace <namespace> opendatahub.io/global-mlflow-workspace=mlflowThis label triggers the MLflow Operator to create
mlflow-viewRBAC bindings in the target namespace so that users can browse prompts from that namespace. The Operator responds to this label only when an active MLflow server instance is present in the namespace.- Allow up to 60 seconds for the configuration change to take effect.
Verification
Verify that the
mlflow-viewRoleBinding was created in the target namespace:$ oc get rolebindings -n <namespace> | grep mlflow-view- Log in to the OpenShift AI dashboard as a non-administrator user who has a project with a configured playground. Open the Gen AI Studio playground, click Load Prompt, and verify that the Global prompts tab displays prompts from the configured namespace.
Additional resources
2.3. Dashboard configuration options
The OpenShift AI dashboard includes a set of core features enabled by default that are designed to work for most scenarios. OpenShift AI administrators can configure the OpenShift AI dashboard from the OdhDashboardConfig custom resource (CR) in OpenShift.
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
For more information about setting dashboard configuration options, see Editing the dashboard configuration.
Features denoted with (Technology Preview) in this table are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using Technology Preview features in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.
| Feature configuration option | Default | Description |
|---|---|---|
|
(Developer Preview) |
|
Shows agent management features in the Gen AI Studio Playground, including the ability to save, load, rename, delete, and create variant copies of playground agents. To enable this feature, set the value to |
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Enables the Agent Ops features in the Gen AI Studio. To enable this feature, set the value to |
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Shows the Agents Catalog page in the dashboard navigation menu. To enable this feature, set the value to |
|
(Technology Preview) |
|
Shows the Gen AI studio |
|
|
|
Enables Automated Machine Learning (AutoML) features in the dashboard. To enable these features, set the value to |
|
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Enables Automated Retrieval-Augmented Generation (AutoRAG) features in the dashboard. To enable these features, set the value to |
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|
Enables connection verification in the dashboard. When enabled, users can verify that connection credentials are valid and endpoints are reachable before saving a connection. The Connections tab also displays a Status column showing verification results. To enable this feature, set the value to |
|
(Technology Preview) |
|
Shows a preview of the YAML for the |
|
| No longer used | Deprecated as of OpenShift AI 3.0. Accelerator profiles have been replaced by hardware profiles. |
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Shows the Settings |
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|
Shows the Settings |
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|
Shows the Settings |
|
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|
Shows the Settings |
|
|
|
Shows the Workload metrics menu item under Observe and monitor in the dashboard navigation menu. To hide this menu item, set the value to |
|
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Shows the Feature store menu item in the dashboard navigation menu. To hide this menu item, set the value to |
|
| No longer used | Deprecated. This option has no effect. |
|
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|
Hides Kueue-related options in the dashboard. Set the value to |
|
|
|
When true, it hides Eval Hub evaluation features from the dashboard navigation Develop & train |
|
| No longer used | Deprecated as of OpenShift AI 3.0. Hardware profiles are generally available and cannot be disabled. |
|
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Shows the Home menu item in the dashboard navigation menu. To hide this menu item, set the value to |
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On the Applications |
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|
Shows the label on a tile that indicates whether the application is |
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|
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Enables the ability to select KServe as a model-serving platform. To disable this ability, set the value to |
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Enables the ability to use authentication with KServe. To disable this ability, set the value to |
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Enables the ability to view KServe metrics. To disable this ability, set the value to |
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On the Settings |
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|
Controls the state of the Enable distributed inference with llm-d toggle on the Settings |
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Shows the AI hub |
|
| No longer used |
Deprecated as of OpenShift AI 3.0. The |
|
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Shows the AI hub |
|
|
|
Shows the Add CA certificate to secure database connection section in the Create model registry dialog and the Edit model registry dialog. To hide this section, set the value to |
|
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Shows the AI hub |
|
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|
Enables the ability to select NVIDIA NIM as a model-serving platform. To disable this ability, set the value to |
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Shows the Endpoint Performance tab on the Deployments page. To hide this tab, set the value to |
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Shows the Develop and train menu item in the dashboard navigation menu. To hide this menu item, set the value to |
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Shows the Projects menu item in the dashboard navigation menu. To hide this menu item, set the value to |
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Controls the enhanced Permissions tab on the Projects page, which provides role-based access control (RBAC) for project members. To show this tab, set the value to |
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Enables custom role management features in Data Science Projects, including the Roles tab, the custom role creation form, built-in role templates, and edit and duplicate actions. To disable these features, set the value to |
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Distinguishes between global items and project-scoped items (if project-scoped items exist) in the OpenShift AI web console. This option applies to workbench images, hardware profiles, and model-serving runtimes for KServe. To disable this functionality, set the value to |
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Enables project sharing so that users can share access to their projects with other users. To prevent users from sharing projects, set the value to |
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Shows the Configuration parameters section in the Deploy model dialog and the Edit model dialog when using the model serving platform. To hide this section, set the value to |
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Shows the Settings |
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Shows the Support menu item when a user clicks the Help icon in the dashboard toolbar. To hide this menu item, set the value to |
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Hides the Usage data collection section from Settings |
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Shows the Model Bias tab on the Models page. To hide this tab, set the value to |
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Shows the Settings |
|
|
|
Enables OpenShift AI administrators to add applications to the OpenShift AI dashboard Applications |
|
(Technology Preview) |
|
Enables the external models page in the dashboard. To enable this feature, set the value to |
|
(Technology Preview) |
|
When you enable this feature, the Vector stores tab displays on the AI asset endpoints page. The pre-configured vector stores are defined in the |
|
(Technology Preview) |
|
Enables the Feature Store admin UI, including the create wizard and manage page. To enable this feature, set the value to |
|
|
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Shows or hides the Gen AI studio and AI asset endpoints navigation items in the dashboard. To show these items, set the value to |
|
(Technology Preview) |
|
Enables tracing support in the Gen AI Studio playground. To enable this feature, set the value to |
|
(Technology Preview) |
|
Shows the global project setting for prompts in the Settings |
|
|
|
Shows the GPUaaS Infrastructure page for cluster capacity and accelerator utilization monitoring. To hide this page, set the value to |
|
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Enables guardrails configuration for model deployments. To enable this feature, set the value to |
|
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|
Shows or hides a drop-down list of global and local project-scoped gateways in the Advanced Settings step of your model deployment. Users can access the model deployment through these gateways. The selected gateway is applied to the |
|
(Technology Preview) |
|
Enables llm-d topology and routing configuration fields in the deployment wizard and admin settings pages. To enable this feature, set the value to |
|
| No longer used | Deprecated. This option has no effect. |
|
(Technology Preview) |
|
Enable this feature to browse and deploy MCP servers from Red Hat partners and other providers. In the left navigation menu, click AI hub |
|
(Technology Preview) |
|
Shows the Registry tab within the MCP Servers page. To enable this feature, set the value to |
|
| No longer used |
Deprecated. The |
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Shows or hides the Model as a service tab on the AI assets endpoint page. To hide this tab, set the value to |
|
|
|
Shows the Observe and monitor |
|
|
| Shows or hides prompt management features in the Gen AI Studio Chat Playground. When enabled, you can load prompts from the MLflow prompt registry, and save new prompts or versions of existing prompts. You can search, preview versions, and load prompts into the playground with the prompt library modal. |
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Enables project role management features for custom RBAC roles. To disable this feature, set the value to |
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Shows or hides the Develop and train |
|
(Technology Preview) |
|
Enables tool calling configuration such as filters, labels and commands for validated models in the model catalog. To enable this feature, set the value to |
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Shows or hides the option to use the new default deployment method for basic vLLM generative AI models. Previously, you could only deploy an
These vLLM options are displayed from preconfigured and configurable |
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Specifies an array of domains that the cluster treats as internal, in addition to |
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Shows the Gen AI studio |
|
|
|
Specifies the namespace that contains the MLflow prompt registry to share across projects. When you add a namespace to this list and label it with |
|
| No longer used |
Read-only. To configure access to the OpenShift AI dashboard, use the |
|
| No longer used | Deprecated. Use hardware profiles instead to customize names and resources for model servers. |
|
|
|
Shows the Start basic workbench tile in the Applications section, and the Start basic workbench button on the Projects page. To hide these items, set the value to |
|
| No longer used | Deprecated. Use hardware profiles instead to customize names and resources for workbenches. |
|
|
| Specifies the order of custom Serving Runtime templates. When the user creates a new template, it is added to this list. |
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