Chapter 3. Requirements for upgrading OpenShift AI When upgrading OpenShift AI, you must complete the following tasks. Checking the components in the DataScienceCluster object When you upgrade Red Hat OpenShift AI, the upgrade process automatically uses the values from the 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. Migrating 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. OpenShift AI does not support direct 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. Addressing 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 . Updating 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 earlier OpenShift 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

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