Chapter 2. Installing and deploying OpenShift AI


Red Hat OpenShift AI is a platform for data scientists and developers of artificial intelligence (AI) applications. It provides a fully supported environment that lets you rapidly develop, train, test, and deploy machine learning models on-premises and/or in the public cloud.

OpenShift AI is provided as a managed cloud service add-on for Red Hat OpenShift or as self-managed software that you can install on-premise or in the public cloud on OpenShift.

For information about installing OpenShift AI as self-managed software on your OpenShift cluster in a disconnected environment, see Installing and uninstalling OpenShift AI Self-Managed in a disconnected environment. For information about installing OpenShift AI as a managed cloud service add-on, see Installing and uninstalling OpenShift AI.

Installing OpenShift AI involves the following high-level tasks:

  1. Confirm that your OpenShift Container Platform cluster meets all requirements. See Requirements for OpenShift AI Self-Managed.
  2. Add administrative users for OpenShift Container Platform. See Adding administrative users for OpenShift Container Platform.
  3. Install the Red Hat OpenShift AI Operator. See Installing the Red Hat OpenShift AI Operator.
  4. Install OpenShift AI components. See Installing and managing Red Hat OpenShift AI components.
  5. Configure user and administrator groups to provide user access to OpenShift AI. See Adding users.
  6. Access the OpenShift AI dashboard. See Accessing the OpenShift AI dashboard.
  7. Optionally, enable graphics processing units (GPUs) in OpenShift AI to ensure that your data scientists can use compute-heavy workloads in their models. See Enabling GPU support in OpenShift AI.

2.1. Requirements for OpenShift AI Self-Managed

Your environment must meet certain requirements to receive support for Red Hat OpenShift AI.

Installation requirements

You must meet the following requirements before you are able to install OpenShift AI on your Red Hat OpenShift Container Platform cluster.

  • Product subscriptions

    • A subscription for Red Hat OpenShift AI Self-Managed

      Contact your Red Hat account manager to purchase new subscriptions. If you do not yet have an account manager, complete the form at https://www.redhat.com/en/contact to request one.

  • An OpenShift Container Platform cluster 4.12 or greater

    • Use an existing cluster or create a new cluster by following the OpenShift Container Platform documentation: OpenShift Container Platform installation overview.

      Your cluster must have at least 2 worker nodes with at least 8 CPUs and 32 GiB RAM available for OpenShift AI to use when you install the Operator. To ensure that OpenShift AI is usable, additional cluster resources are required beyond the minimum requirements.

    • A default storage class that can be dynamically provisioned must be configured.

      Confirm that a default storage class is configured by running the oc get storageclass command. If no storage classes are noted with (default) beside the name, follow the OpenShift Container Platform documentation to configure a default storage class: Changing the default storage class. For more information about dynamic provisioning, see Dynamic provisioning.

    • Open Data Hub must not be installed on the cluster.

      For more information about managing the machines that make up an OpenShift cluster, see Overview of machine management.

  • An identity provider configured for OpenShift Container Platform

    Access to the cluster as a user with the cluster-admin role; the kubeadmin user is not allowed.

    Red Hat OpenShift AI supports the same authentication systems as Red Hat OpenShift Container Platform. See Understanding identity provider configuration for more information on configuring identity providers.

  • Internet access

    Along with Internet access, the following domains must be accessible during the installation of OpenShift AI Self-Managed:

    For CUDA-based images, the following domains must be accessible:

  • OpenShift Pipelines operator installation

    • The Red Hat OpenShift Pipelines operator enables support for installation of pipelines in a self-managed environment.

      Before you use data science pipelines in OpenShift AI, you must install the Red Hat OpenShift Pipelines Operator. For more information, see Installing OpenShift Pipelines. If your deployment is in a disconnected self-managed environment, see Red Hat OpenShift Pipelines Operator in a restricted environment.

    • Before you can execute a pipeline in a disconnected environment, you must mirror any images used by your pipelines to a private registry.
    • You can store your pipeline artifacts in an Amazon Web Services (AWS) Simple Storage Service (S3) bucket to ensure that you do not consume local storage. To do this, you must first configure write access to your S3 bucket on your AWS account.

      If you do not have access to Amazon S3 storage, you must configure your own storage solution for use with pipelines.

  • Install KServe dependencies

    To support the KServe component, which is used by the single-model serving platform to serve large models, you must also install Operators for Red Hat OpenShift Serverless and Red Hat OpenShift Service Mesh and perform additional configuration. For more information, see Serving large models.

2.2. Adding administrative users for OpenShift Container Platform

Before you can install and configure OpenShift AI for your data scientist users, you must define administrative users. Only users with the cluster-admin role can install and configure OpenShift AI.

For more information about creating a cluster admin user, see Creating a cluster admin.

2.3. Installing the Red Hat OpenShift AI Operator

This section shows how to install the Red Hat OpenShift AI Operator on your OpenShift Container Platform cluster using the command-line interface (CLI) and the OpenShift web console.

Note

If you want to upgrade from a previous version of OpenShift AI rather than performing a new installation, see Upgrading OpenShift AI.

Note

If your OpenShift cluster uses a proxy to access the Internet, you can configure the proxy settings for the Red Hat OpenShift AI Operator. See Overriding proxy settings of an Operator for more information.

2.3.1. Installing the Red Hat OpenShift AI Operator by using the CLI

The following procedure shows how to use the OpenShift command-line interface (CLI) to install the Red Hat OpenShift AI Operator on your OpenShift Container Platform cluster. You must install the Operator before you can install OpenShift AI components on the cluster.

Prerequisites

  • You have a running OpenShift Container Platform cluster, version 4.12 or greater, configured with a default storage class that can be dynamically provisioned.
  • You have cluster administrator privileges for your OpenShift Container Platform cluster.
  • You have downloaded and installed the OpenShift command-line interface (CLI). See Installing the OpenShift CLI.
  • To support KServe components, you installed the dependent Operators, including the Red Hat OpenShift Serverless and Red Hat OpenShift Service Mesh Operators. For more information, see Serving large models.

Procedure

  1. Open a new terminal window.
  2. In the OpenShift command-line interface (CLI), log in to your OpenShift Container Platform cluster as a cluster administrator, as shown in the following example:

    $ oc login <openshift_cluster_url> -u <admin_username> -p <password>
  3. Create a namespace for installation of the Operator by performing the following actions:

    1. Create a namespace YAML file, for example, rhods-operator-namespace.yaml.

      apiVersion: v1
      kind: Namespace
      metadata:
        name: redhat-ods-operator 1
      1
      redhat-ods-operator is the recommended namespace for the Operator.
    2. Create the namespace in your OpenShift Container Platform cluster.

      $ oc create -f rhods-operator-namespace.yaml

      You see output similar to the following:

      namespace/redhat-ods-operator created
  4. Create an operator group for installation of the Operator by performing the following actions:

    1. Create an OperatorGroup object custom resource (CR) file, for example, rhods-operator-group.yaml.

      apiVersion: operators.coreos.com/v1
      kind: OperatorGroup
      metadata:
        name: rhods-operator
        namespace: redhat-ods-operator 1
      1
      You must specify the same namespace that you created earlier in this procedure.
    2. Create the OperatorGroup object in your OpenShift Container Platform cluster.

      $ oc create -f rhods-operator-group.yaml

      You see output similar to the following:

      operatorgroup.operators.coreos.com/rhods-operator created
  5. Create a subscription for installation of the Operator by performing the following actions:

    1. Create a Subscription object CR file, for example, rhods-operator-subscription.yaml.

      apiVersion: operators.coreos.com/v1alpha1
      kind: Subscription
      metadata:
        name: rhods-operator
        namespace: redhat-ods-operator 1
      spec:
        name: rhods-operator
        channel: stable 2
        source: redhat-operators
        sourceNamespace: openshift-marketplace
      1
      You must specify the same namespace that you created earlier in this procedure.
      2
      For channel, select a value of fast, stable, stable-2.8, eus-2.8, or alpha. The following table describes these channels. For more information, including the lifecycle associated with each of the available subscription channels, see Red Hat OpenShift AI Self-Managed Life Cycle.
      ChannelSupportRelease frequencyRecommended environment

      fast

      One month of full support

      Every month

      Production environments with access to the latest product features.

      Select this streaming channel with automatic upgrades to avoid manually upgrading every month.

      stable

      Three months of full support

      Every three months

      Production environments with stability prioritized over new feature availability.

      Select this streaming channel with automatic upgrades to access the latest stable release and avoid manually upgrading.

      stable-x.y

      Seven months of full support

      Every three months

      Production environments with stability prioritized over new feature availability.

      Select numbered stable channels (such as stable-2.8) to plan and execute the upgrade to the next stable release while keeping your deployment under full support.

      eus-x.y

      Seven months of full support followed by Extended Update Support for eleven months

      Every nine months

      Enterprise-grade environments that cannot upgrade within a seven month window.

      Select this streaming channel if you prioritize stability over new feature availability.

      alpha

      One month of full support

      Every month

      Development environments with early-access features that might not be functionally complete.

      Select this channel to use early-access features that enable you to test functionality and provide feedback during the development process. Early-access features are not supported with Red Hat production service level agreements (SLAs).

      For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope. For more information about the support scope of Red Hat Developer Preview features, see Developer Preview Features Support Scope.

      Note

      The embedded and beta channels are legacy channels that will be removed in a future release. Do not select the embedded or beta channels for a new installation of the Operator.

    2. As described in the preceding step, ensure that the subscription channel you specify is appropriate for your organization’s requirements.
    3. Create the Subscription object in your OpenShift Container Platform cluster to install the Operator.

      $ oc create -f rhods-operator-subscription.yaml

      You see output similar to the following:

      subscription.operators.coreos.com/rhods-operator created

Verification

  • In the OpenShift Container Platform web console, click Operators Installed Operators and confirm that the Red Hat OpenShift AI Operator shows one of the following statuses:

    • Installing - installation is in progress; wait for this to change to Succeeded. This might take several minutes.
    • Succeeded - installation is successful.
  • In the web console, click Home Projects and confirm that the following project namespaces are visible and listed as Active:

    • redhat-ods-applications
    • redhat-ods-monitoring
    • redhat-ods-operator

2.3.2. Installing the Red Hat OpenShift AI Operator by using the web console

The following procedure shows how to use the OpenShift Container Platform web console to install the Red Hat OpenShift AI Operator on your cluster. You must install the Operator before you can install OpenShift AI components on the cluster.

Prerequisites

  • You have a running OpenShift Container Platform cluster, version 4.12 or greater, configured with a default storage class that can be dynamically provisioned.
  • You have cluster administrator privileges for your OpenShift Container Platform cluster.
  • To support KServe components, you installed the dependent Operators, including the Red Hat OpenShift Serverless and Red Hat OpenShift Service Mesh Operators. For more information, see Serving large models.

Procedure

  1. Log in to the OpenShift Container Platform web console as a cluster administrator.
  2. In the web console, click Operators OperatorHub.
  3. On the OperatorHub page, locate the Red Hat OpenShift AI Operator by scrolling through available Operators or by typing Red Hat OpenShift AI into the Filter by keyword box.
  4. Select the Operator to display additional information.
  5. Read the information about the Operator and click Install.
  6. For Update channel, select a value of fast, stable, stable-2.8, eus-2.8, or alpha. The following table describes these channels. For more information, including the lifecycle associated with each of the available subscription channels, see Red Hat OpenShift AI Self-Managed Life Cycle.

    ChannelSupportRelease frequencyRecommended environment

    fast

    One month of full support

    Every month

    Production environments with access to the latest product features.

    Select this streaming channel with automatic upgrades to avoid manually upgrading every month.

    stable

    Three months of full support

    Every three months

    Production environments with stability prioritized over new feature availability.

    Select this streaming channel with automatic upgrades to access the latest stable release and avoid manually upgrading.

    stable-x.y

    Seven months of full support

    Every three months

    Production environments with stability prioritized over new feature availability.

    Select numbered stable channels (such as stable-2.8) to plan and execute the upgrade to the next stable release while keeping your deployment under full support.

    eus-x.y

    Seven months of full support followed by Extended Update Support for eleven months

    Every nine months

    Enterprise-grade environments that cannot upgrade within a seven month window.

    Select this streaming channel if you prioritize stability over new feature availability.

    alpha

    One month of full support

    Every month

    Development environments with early-access features that might not be functionally complete.

    Select this channel to use early-access features that enable you to test functionality and provide feedback during the development process. Early-access features are not supported with Red Hat production service level agreements (SLAs).

    For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope. For more information about the support scope of Red Hat Developer Preview features, see Developer Preview Features Support Scope.

    Note

    The embedded and beta channels are legacy channels that will be removed in a future release. Do not select the embedded or beta channels for a new installation of the Operator.

  7. For Installation mode, observe that the only available value is All namespaces on the cluster (default). This installation mode makes the Operator available to all namespaces in the cluster.
  8. For Installed Namespace, select redhat-ods-operator (Operator recommended).
  9. Under Update approval, select either Automatic or Manual.
  10. Click Install.

    An installation pane opens. When the installation finishes, a checkmark appears beside the Operator name in the installation pane.

Verification

  • In the OpenShift Container Platform web console, click Operators Installed Operators and confirm that the Red Hat OpenShift AI Operator shows one of the following statuses:

    • Installing - installation is in progress; wait for this to change to Succeeded. This might take several minutes.
    • Succeeded - installation is successful.
  • In the web console, click Home Projects and confirm that the following project namespaces are visible and listed as Active:

    • redhat-ods-applications
    • redhat-ods-monitoring
    • redhat-ods-operator

2.4. Installing and managing Red Hat OpenShift AI components

The following procedures show how to use the command-line interface (CLI) and OpenShift Container Platform web console to install and manage components of Red Hat OpenShift AI on your OpenShift Container Platform cluster.

2.4.1. Installing Red Hat OpenShift AI components by using the CLI

The following procedure shows how to use the OpenShift command-line interface (CLI) to install specific components of Red Hat OpenShift AI on your OpenShift Container Platform cluster.

Important

The following procedure describes how to create and configure a DataScienceCluster object to install Red Hat OpenShift AI components as part of a new installation. However, if you upgraded from version 1 of OpenShift AI (previously OpenShift Data Science), the upgrade process automatically created a default DataScienceCluster object. If you upgraded from version 2.4 to 2.5, the upgrade process uses the settings from the 2.4 version’s DataScienceCluster object. To inspect the default DataScienceCluster object and change the installation status of Red Hat OpenShift AI components, see Updating the installation status of Red Hat OpenShift AI components by using the web console.

Prerequisites

Procedure

  1. Open a new terminal window.
  2. In the OpenShift command-line interface (CLI), log in to your on your OpenShift Container Platform cluster as a cluster administrator, as shown in the following example:

    $ oc login <openshift_cluster_url> -u <admin_username> -p <password>
  3. Create a DataScienceCluster object custom resource (CR) file, for example, rhods-operator-dsc.yaml.

    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  1 2
        modelmeshserving:
          managementState: Removed
        ray:
          managementState: Removed
        workbenches:
          managementState: Removed
    1
    To fully install the KServe component, which is used by the single-model serving platform to serve large models, you must install Operators for Red Hat OpenShift Service Mesh and Red Hat OpenShift Serverless and perform additional configuration. See Serving large models.
    2
    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.
  4. 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 fully install the KServe component, which is used by the single model serving platform to serve large models, see Serving large models.
    • The CodeFlare and KubeRay components are Technology Preview features 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.
    • To learn how to configure the distributed workloads feature that uses the CodeFlare and KubeRay components, see Configuring distributed workloads.
  5. Create the DataScienceCluster object in your OpenShift Container Platform cluster to install the specified OpenShift AI components.

    $ oc create -f rhods-operator-dsc.yaml

    You see output similar to the following:

    datasciencecluster.datasciencecluster.opendatahub.io/default created

Verification

  • Confirm that there is a running pod for each component:

    1. In the OpenShift Container Platform web console, click Workloads Pods.
    2. In the Project list at the top of the page, select redhat-ods-applications.
    3. 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:

    1. In the OpenShift Container Platform web console, click Operators Installed Operators.
    2. Click the Red Hat OpenShift AI Operator.
    3. Click the Data Science Cluster tab and select the DataScienceCluster object called default-dsc.
    4. Select the YAML tab.
    5. 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.

2.4.2. Installing Red Hat OpenShift AI components by using the web console

The following procedure shows how to use the OpenShift Container Platform web console to install specific components of Red Hat OpenShift AI on your cluster.

Important

The following procedure describes how to create and configure a DataScienceCluster object to install Red Hat OpenShift AI components as part of a new installation. However, if you upgraded from version 1 of OpenShift AI (previously OpenShift Data Science), the upgrade process automatically created a default DataScienceCluster object. If you upgraded from a previous minor version, the upgrade process used the settings from the previous version’s DataScienceCluster object. To inspect the DataScienceCluster object and change the installation status of Red Hat OpenShift AI components, see Updating the installation status of Red Hat OpenShift AI components by using the web console.

Prerequisites

  • The Red Hat OpenShift AI Operator is installed on your OpenShift Container Platform cluster. See Installing the Red Hat OpenShift AI Operator.
  • You have cluster administrator privileges for your OpenShift Container Platform cluster.

Procedure

  1. Log in to the OpenShift Container Platform web console as a cluster administrator.
  2. In the web console, click Operators Installed Operators and then click the Red Hat OpenShift AI Operator.
  3. Create a DataScienceCluster object to install OpenShift AI components by performing the following actions:

    1. Click the Data Science Cluster tab.
    2. Click Create DataScienceCluster.
    3. For Configure via, select YAML view.

      An embedded YAML editor opens showing a default custom resource (CR) for the DataScienceCluster object.

    4. 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 Serving large models.
      • The CodeFlare and KubeRay components are Technology Preview features 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.
      • 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 configure the distributed workloads feature that uses the CodeFlare and KubeRay components, see Configuring distributed workloads.
  4. Click Create.

Verification

  • Confirm that there is a running pod for each component:

    1. In the OpenShift Container Platform web console, click Workloads Pods.
    2. In the Project list at the top of the page, select redhat-ods-applications.
    3. 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:

    1. In the OpenShift Container Platform web console, click Operators Installed Operators.
    2. Click the Red Hat OpenShift AI Operator.
    3. Click the Data Science Cluster tab and select the DataScienceCluster object called default-dsc.
    4. Select the YAML tab.
    5. 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.

2.4.3. Updating the installation status of Red Hat OpenShift AI components by using the web console

The following procedure shows how to use the OpenShift Container Platform web console to update the installation status of components of Red Hat OpenShift AI on your OpenShift Container Platform cluster.

Important

If you upgraded from version 1 to version 2 of OpenShift AI, the upgrade process automatically created a default DataScienceCluster object and enabled several components of OpenShift AI. If you upgraded from a previous minor version, the upgrade process used the settings from the previous version’s DataScienceCluster object.

The following procedure describes how to edit the DataScienceCluster object to do the following:

  • Change the installation status of the existing Red Hat OpenShift AI components
  • Add additional components to the DataScienceCluster object that were not available in the previous version of OpenShift AI.

Prerequisites

  • The Red Hat OpenShift AI Operator is installed on your OpenShift Container Platform cluster.
  • You have cluster administrator privileges for your OpenShift Container Platform cluster.

Procedure

  1. Log in to the OpenShift Container Platform web console as a cluster administrator.
  2. In the web console, click Operators Installed Operators and then click the Red Hat OpenShift AI Operator.
  3. Click the Data Science Cluster tab.
  4. On the DataScienceClusters page, click the default object.
  5. Click the YAML tab.

    An embedded YAML editor opens showing the custom resource (CR) file for the DataScienceCluster object.

  6. 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:

    Note

    If a component shows with the component-name: {} format in the spec.components section of the CR, the component is not installed.

    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 Serving large models.
    • 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.
    • If they are not already present in the CR file, you can install the CodeFlare and KubeRay features by adding components called codeflare and ray to the spec.components section of the CR and setting the managementState field for the components to Managed.
    • The CodeFlare and KubeRay components are Technology Preview features 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.
    • To learn how to configure the distributed workloads feature that uses the CodeFlare and KubeRay components, see Configuring distributed workloads.
  7. 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:

    1. In the OpenShift Container Platform web console, click Workloads Pods.
    2. In the Project list at the top of the page, select redhat-ods-applications.
    3. 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:

    1. In the OpenShift Container Platform web console, click Operators Installed Operators.
    2. Click the Red Hat OpenShift AI Operator.
    3. Click the Data Science Cluster tab and select the DataScienceCluster object called default-dsc.
    4. Select the YAML tab.
    5. 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.

2.4.4. Disabling KServe dependencies

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.

Prerequisites

  • You have used the OpenShift command-line interface (CLI) or web console to disable the KServe component.

Procedure

  1. Log in to the OpenShift web console as a cluster administrator.
  2. In the web console, click Operators Installed Operators and then click the Red Hat OpenShift AI Operator.
  3. Disable the OpenShift Service Mesh component as follows:

    1. Click the DSC Initialization tab.
    2. Click the default-dsci object.
    3. Click the YAML tab.
    4. In the spec section, add the serviceMesh component (if it is not already present) and configure the managementState field as shown:

      spec:
       serviceMesh:
         managementState: Removed
    5. Click Save.

Verification

  1. In the web console, click Operators Installed Operators and then click the Red Hat OpenShift AI Operator.

    The Operator details page opens.

  2. In the Conditions section, confirm that there is no ReconcileComplete condition with a status value of Unknown. \ No newline at end of file
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