Chapter 3. Installing and deploying OpenShift AI

Procedure

1. Open a new terminal window.

2. Follow these steps to log in to your OpenShift cluster as a cluster administrator:

   1. In the upper-right corner of the OpenShift web console, click your user name and select Copy login command.
   2. After you have logged in, click Display token.

   3. Copy the Log in with this token command and paste it in the OpenShift command-line interface (CLI).

      $ oc login --token=<token> --server=<openshift_cluster_url>

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3. Create a namespace for installation of the Operator by performing the following actions:

   1. Create a namespace YAML file named rhods-operator-namespace.yaml.

      apiVersion: v1
      kind: Namespace
      metadata:
        name: redhat-ods-operator 

      1

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      1

      Defines the required redhat-ods-operator namespace for installation of the Operator.

   2. Create the namespace in your OpenShift cluster.

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

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      You see output similar to the following:

      namespace/redhat-ods-operator created

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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

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      1

      Defines the required redhat-ods-operator namespace.

   2. Create the OperatorGroup object in your OpenShift cluster.

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

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      You see output similar to the following:

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

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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: <channel> 

      2

      
        source: redhat-operators
        sourceNamespace: openshift-marketplace
        startingCSV: rhods-operator.x.y.z 

      3

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      1

      Defines the required redhat-ods-operator namespace.

      2

      Sets the update channel. You must specify a value of fast, stable, stable-x.y eus-x.y, or alpha. For more information, see Understanding update channels.

      3

      Optional: Sets the operator version. If you do not specify a value, the subscription defaults to the latest operator version. For more information, see the Red Hat OpenShift AI Self-Managed Life Cycle Knowledgebase article.

   2. Create the Subscription object in your OpenShift cluster to install the Operator.

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

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      You see output similar to the following:

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

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Procedure

1. Log in to the OpenShift 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 the available Operators or by typing Red Hat OpenShift AI into the Filter by keyword box.
4. Click the Red Hat OpenShift AI tile. The Red Hat OpenShift AI information pane opens.
5. Select a Channel. For information about subscription update channels, see Understanding update channels.
6. Select a Version.
7. Click Install. The Install Operator page opens.
8. Review or change the selected channel and version as needed.
9. For Installation mode, note 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.
10. For Installed Namespace, select Operator recommended Namespace: redhat-ods-operator.

11. For Update approval, select one of the following update strategies:

    • Automatic: New updates in the update channel are installed as soon as they become available.

    • Manual: A cluster administrator must approve any new updates before installation begins.

      Important

      By default, the Red Hat OpenShift AI Operator follows a sequential update process. This means that if there are several versions between the current version and the target version, Operator Lifecycle Manager (OLM) upgrades the Operator to each of the intermediate versions before it upgrades it to the final, target version.

      If you configure automatic upgrades, OLM automatically upgrades the Operator to the latest available version. If you configure manual upgrades, a cluster administrator must manually approve each sequential update between the current version and the final, target version.

      For information about supported versions, see the Red Hat OpenShift AI Life Cycle Knowledgebase article.

12. Click Install.

    The Installing Operators pane appears. When the installation finishes, a checkmark appears next to the Operator name.

Procedure

1. Open a new terminal window.

2. Follow these steps to log in to your OpenShift cluster as a cluster administrator:

   1. In the upper-right corner of the OpenShift web console, click your user name and select Copy login command.
   2. After you have logged in, click Display token.

   3. Copy the Log in with this token command and paste it in the OpenShift command-line interface (CLI).

      $ oc login --token=<token> --server=<openshift_cluster_url>

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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

   
       kueue:
         managementState: Removed
       modelmeshserving:
         managementState: Removed
       ray:
         managementState: Removed
       trainingoperator:
         managementState: Removed
       trustyai:
         managementState: Removed
       workbenches:
         managementState: Removed

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   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 Installing the single-model serving platform.

   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 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 components, see Installing the distributed workloads components.

5. Create the DataScienceCluster object in your OpenShift cluster to install the specified OpenShift AI components.

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

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   You see output similar to the following:

   datasciencecluster.datasciencecluster.opendatahub.io/default created

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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. Click the Data Science Cluster tab.
4. Click Create DataScienceCluster.

5. For Configure via, select YAML view.

   An embedded YAML editor opens showing a 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 

   1

   2

   
       kueue:
         managementState: Removed
       modelmeshserving:
         managementState: Removed
       ray:
         managementState: Removed
       trainingoperator:
         managementState: Removed
       trustyai:
         managementState: Removed
       workbenches:
         managementState: Removed

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   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 Installing the single-model serving platform.

   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.

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:

   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 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 components, see Installing the distributed workloads components.
7. Click Create.

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. 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 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

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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:

   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.

7. Click Save.

   For any components that you updated, OpenShift AI initiates a rollout that affects all pods to use the updated image.

Procedure

1. Log in to the OpenShift AI dashboard.
2. In the top navigation bar, click the help icon ( ) and then select About.