Chapter 2. Installing and deploying OpenShift AI

Procedure

1. Visit your Private Offer with the URL link provided by your Red Hat Sales representative.
2. Click Accept Terms to subscribe to the AMI Private Offer named OpenShift AI from AWS Marketplace.
3. After accepting the offer terms, click Continue to Configuration.

Procedure

1. In the AWS Console, navigate to the AWS Marketplace. For example:

   1. Click the help icon and then select Getting Started Resource Center.
   2. Select AWS Marketplace > Browse AWS Marketplace.
2. In the top Search field, type: Red Hat OpenShift AI.

3. Select one of the two options depending on the geographical location of the billing address for your AWS account (note that this location might differ from the geographical location of the cluster):

   • Europe, the Middle East, and Africa (EMEA region)
   • North America and regions outside EMEA
4. Click Continue to Subscribe.
5. Click Continue to Configuration and then select the appropriate fulfillment options. Note that some selectors might have only one option.
6. Click Continue to Launch.

7. Link your AWS account with your Red Hat account to complete your registration:

   1. In the AWS Marketplace console, navigate to the Manage Subscriptions page.
   2. On the Red Hat OpenShift AI tile, click Set up product.

   3. On the top banner, click Set up account.

      This link takes you to the Red Hat Hybrid Cloud Console.

   4. If you are not already logged in, log in.

      Note

      You must use your Red Hat login to log in to your Red Hat account. Each Red Hat account has a user email address and a Red Hat login associated with it. The email address and the Red Hat login can be the same. However, if the email address and the Red Hat login are different, you must use the Red Hat login to log in to your Red Hat account; you cannot use your email address to log in.

      For more information, see Finding your Red Hat login.

   5. Review and then accept the terms and agreements.
   6. Click Connect accounts.

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 the default-dsc object.

5. Select the YAML tab.

   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:
         argoWorkflowsControllers:
           managementState: Removed 

   1

   
         managementState: Removed
       feastoperator:
         managementState: Removed
       kserve:
         managementState: Removed 

   2

   3

   
       kueue:
         defaultClusterQueueName: default
         defaultLocalQueueName: default
         managementState: Removed
       llamastackoperator:
         managementState: Removed
       modelmeshserving:
         managementState: Removed
       modelregistry:
         managementState: Removed
       ray:
         managementState: Removed
       trainingoperator:
         managementState: Removed
       trustyai:
         managementState: Removed
       workbenches:
         managementState: Removed

   Copy to Clipboard Toggle word wrap

   1

   To use your own Argo Workflows instance with the datasciencepipelines component, set argoWorkflowsControllers.managementState to Removed. This allows you to integrate with a managed Argo Workflows installation already on your OpenShift cluster and avoid conflicts with the embedded controller. See Configuring pipelines with your own Argo Workflows instance. <2>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 Save.

Verification

1. Confirm the status of all installed components:

   1. In the OpenShift web console, click Operators Installed Operators.
   2. Click the Red Hat OpenShift AI Operator.
   3. Click the Data Science Cluster tab.

   4. For the DataScienceCluster object called default-dsc, verify that the status is Phase: Ready.

      Note

      When you edit the spec.components section to change the installation status of a component, the default-dsc status also changes. During the initial installation, it might take a few minutes for the status phase to change from Progressing to Ready. You can access the OpenShift AI dashboard before the default-dsc status phase is Ready, but all components might not be ready.

   5. Click the default-dsc link to display the data science cluster details.
   6. Select the YAML tab.

   7. In the status.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. Confirm that there is at least one running pod for each component:

   1. In the OpenShift 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 one or more running pods for each of the OpenShift AI components that you installed.
3. In the OpenShift AI dashboard, users can view the list of the installed OpenShift AI components, their corresponding source (upstream) components, and the versions of the installed components, as described in Viewing installed OpenShift AI components.

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-dsc 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
       llamastackoperator:
         managementState: Removed
       modelmeshserving:
         managementState: Removed
       ray:
         managementState: Removed
       trainingoperator:
         managementState: Removed
       trustyai:
         managementState: Removed
       workbenches:
         managementState: Removed
         workbenchNamespace: rhods-notebooks

   Copy to Clipboard Toggle word wrap

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.

8. If you are upgrading from OpenShift AI 2.19 or earlier, upgrade the Authorino Operator to the stable update channel, version 1.2.1 or later.

   1. Update Authorino to the latest available release in the tech-preview-v1 channel (1.1.2), if you have not done so already.

   2. Switch to the stable channel:

      1. Navigate to the Subscription settings of the Authorino Operator.
      2. Under Update channel, click on the highlighted tech-preview-v1.
      3. Change the channel to stable.
   3. Select the update option for Authorino 1.2.1.

Verification

1. Confirm that there is at least one running pod for each component:

   1. In the OpenShift web console, click Workloads Pods.
   2. In the Project list at the top of the page, select redhat-ods-applications or your custom applications namespace.
   3. In the applications namespace, confirm that there are one or more running pods for each of the OpenShift AI components that you installed.

2. Confirm the status of all installed components:

   1. In the OpenShift 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 status.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.

3. In the OpenShift AI dashboard, users can view the list of the installed OpenShift AI components, their corresponding source (upstream) components, and the versions of the installed components, as described in Viewing installed OpenShift AI components.

Procedure

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