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Chapter 3. Requirements for upgrading OpenShift AI

When upgrading OpenShift AI, you must complete the following tasks.

Check the components in the DataScienceCluster object

When you upgrade Red Hat OpenShift AI, the upgrade process automatically uses the values from the previous DataScienceCluster object.

After the upgrade, you should inspect the DataScienceCluster object and optionally update the status of any components as described in Updating the installation status of Red Hat OpenShift AI components by using the web console.

Note

New components are not automatically added to the DataScienceCluster object during upgrade. If you want to use a new component, you must manually edit the DataScienceCluster object to add the component entry.

Migrate data science pipelines

Previously, data science pipelines in OpenShift AI were based on KubeFlow Pipelines v1. Starting with OpenShift AI 2.9, data science pipelines are based on KubeFlow Pipelines v2, which uses a different workflow engine. Data science pipelines 2.0 is enabled and deployed by default in OpenShift AI.

Starting with OpenShift AI 2.16, data science pipelines 1.0 resources are no longer supported or managed by OpenShift AI. It is no longer possible to deploy, view, or edit the details of pipelines that are based on data science pipelines 1.0 from either the dashboard or the KFP API server.

OpenShift AI does not automatically migrate existing data science pipelines 1.0 instances to 2.0. If you are upgrading to OpenShift AI 2.16 or later, you must manually migrate your existing data science pipelines 1.0 instances. For more information, see Migrating to data science pipelines 2.0.

Important

Data science pipelines 2.0 contains an installation of Argo Workflows. Red Hat does not support direct customer usage of this installation of Argo Workflows.

If you upgrade to OpenShift AI with data science pipelines 2.0 and an Argo Workflows installation that is not installed by data science pipelines exists on your cluster, OpenShift AI components will not be upgraded. To complete the component upgrade, disable data science pipelines or remove the separate installation of Argo Workflows. The component upgrade will complete automatically.

Address KServe requirements

For the KServe component, which is used by the single-model serving platform to serve large models, you must meet the following requirements:

  • To fully install and use KServe, 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.
  • If you want to add an authorization provider for the single-model serving platform, you must install the Red Hat - Authorino Operator. For information, see Adding an authorization provider for 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 in the DataScienceCluster object), you must also disable the dependent Service Mesh component to avoid errors. See Disabling KServe dependencies.

Update workflows interacting with OdhDashboardConfig resource

Previously, cluster administrators used the groupsConfig option in the OdhDashboardConfig resource to manage the OpenShift groups (both administrators and non-administrators) that can access the OpenShift AI dashboard. Starting with OpenShift AI 2.17, this functionality has moved to the Auth resource. If you have workflows (such as GitOps workflows) that interact with OdhDashboardConfig, you must update them to reference the Auth resource instead.

Table 3.1. User management resource update
 OpenShift AI 2.16 and earlierOpenShift AI 2.17 and later

apiVersion

opendatahub.io/v1alpha

services.platform.opendatahub.io/v1alpha1

kind

OdhDashboardConfig

Auth

name

odh-dashboard-config

auth

Admin groups

spec.groupsConfig.adminGroups

spec.adminGroups

User groups

spec.groupsConfig.allowedGroups

spec.allowedGroups

Update Kueue

In OpenShift AI, cluster administrators use Kueue to configure quota management for distributed workloads.

When upgrading from OpenShift AI 2.17 or earlier, the version of the MultiKueue Custom Resource Definitions (CRDs) changes from v1alpha1 to v1beta1.

However, if the kueue component is set to Managed, the Red Hat OpenShift AI Operator does not automatically remove the v1alpha1 MultiKueue CRDs during the upgrade. The deployment of the Kueue component then becomes blocked, as indicated in the default-dsc DataScienceCluster custom resource, where the value of the kueueReady condition remains set to False.

You can resolve this problem as follows:

Note

The MultiKueue feature is not currently supported in Red Hat OpenShift AI. If you created any resources based on the MultiKueue CRDs, those resources will be deleted when you delete the CRDs. If you do not want to lose your data, create a backup before deleting the CRDs.

  1. Log in to the OpenShift Console.
  2. In the Administrator perspective, click Administration CustomResourceDefinitions.
  3. In the search field, enter multik.

  4. Update the MultiKueueCluster CRD as follows:

    1. Click the CRD name, and click the YAML tab.

    2. Ensure that the metadata:labels section includes the following entry:

      Copy to Clipboard Toggle word wrap 
      app.opendatahub.io/kueue: 'true'
    3. Click Save.
  5. Repeat the above steps to update the MultiKueueConfig CRD.

  6. Remove the MultiKueueCluster and MultiKueueConfig CRDs, by completing the following steps for each CRD:

    1. Click the Actions menu.
    2. Click Delete CustomResourceDefinition.
    3. Click Delete to confirm the deletion.

The Red Hat OpenShift AI Operator starts the Kueue Controller, and Kueue then automatically creates the v1beta1 MultiKueue CRDs. In the default-dsc DataScienceCluster custom resource, the kueueReady condition changes to True. For information about how to check that the kueue-controller-manager-<pod-id> pod is Running, see Installing the distributed workloads components (for disconnected environments, see Installing the distributed workloads components).

Check the status of certificate management

You can use self-signed certificates in OpenShift AI.

After you upgrade, check the management status for Certificate Authority (CA) bundles as described in Working with certificates.

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