Chapter 4. Updating the installation status of Red Hat OpenShift AI components by using the web console You can use the OpenShift web console to update the installation status of components of Red Hat OpenShift AI on your OpenShift cluster. Important If you upgraded OpenShift AI, the upgrade process automatically used the values of the version's DataScienceCluster object. New components are not automatically added to the DataScienceCluster object. After upgrading OpenShift AI: Inspect the default DataScienceCluster object to check and optionally update the managementState status of the existing components. Add any new components to the DataScienceCluster object. Prerequisites The Red Hat OpenShift AI Operator is installed on your OpenShift cluster. You have cluster administrator privileges for your OpenShift cluster. Procedure Log in to the OpenShift web console as a cluster administrator. In the web console, click Operators Installed Operators and then click the Red Hat OpenShift AI Operator. Click the Data Science Cluster tab. On the DataScienceClusters page, click the default object. Click the YAML tab. An embedded YAML editor opens showing the default custom resource (CR) for the DataScienceCluster object, similar to the following example: apiVersion: datasciencecluster.opendatahub.io/v1 kind: DataScienceCluster metadata: name: default-dsc spec: components: codeflare: managementState: Removed dashboard: managementState: Removed datasciencepipelines: managementState: Removed kserve: managementState: Removed kueue: managementState: Removed modelmeshserving: managementState: Removed ray: managementState: Removed trainingoperator: managementState: Removed trustyai: managementState: Removed workbenches: managementState: Removed In the spec.components section of the CR, for each OpenShift AI component shown, set the value of the managementState field to either Managed or Removed . These values are defined as follows: Managed The Operator actively manages the component, installs it, and tries to keep it active. The Operator will upgrade the component only if it is safe to do so. Removed The Operator actively manages the component but does not install it. If the component is already installed, the Operator will try to remove it. Important To learn how to install the KServe component, which is used by the single-model serving platform to serve large models, see Installing the single-model serving platform . If you have not enabled the KServe component (that is, you set the value of the managementState field to Removed ), you must also disable the dependent Service Mesh component to avoid errors. See Disabling KServe dependencies . To learn how to install the distributed workloads feature, see Installing the distributed workloads components . To learn how to run distributed workloads in a disconnected environment, see Running distributed data science workloads in a disconnected environment . Click Save . For any components that you updated, OpenShift AI initiates a rollout that affects all pods to use the updated image. Verification Confirm that there is a running pod for each component: In the OpenShift web console, click Workloads Pods . In the Project list at the top of the page, select redhat-ods-applications . In the applications namespace, confirm that there are running pods for each of the OpenShift AI components that you installed. Confirm the status of all installed components: In the OpenShift web console, click Operators Installed Operators . Click the Red Hat OpenShift AI Operator. Click the Data Science Cluster tab and select the DataScienceCluster object called default-dsc . Select the YAML tab. In the installedComponents section, confirm that the components you installed have a status value of true . Note If a component shows with the component-name: {} format in the spec.components section of the CR, the component is not installed.

Commands:
apiVersion: datasciencecluster.opendatahub.io/v1 kind: DataScienceCluster metadata: name: default-dsc spec: components: codeflare: managementState: Removed dashboard: managementState: Removed datasciencepipelines: managementState: Removed kserve: managementState: Removed kueue: managementState: Removed modelmeshserving: managementState: Removed ray: managementState: Removed trainingoperator: managementState: Removed trustyai: managementState: Removed workbenches: managementState: Removed