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Red Hat OpenShift AI Self-Managed

Upgrading OpenShift AI Self-Managed

Upgrade OpenShift AI on OpenShift

Last Updated: 2025-10-

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Abstract

Upgrade OpenShift AI on OpenShift.

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Table of Contents

PREFACE CHAPTER 1. OVERVIEW OF UPGRADING OPENSHIFT AI SELF-MANAGED CHAPTER 2. CONFIGURING THE UPGRADE STRATEGY FOR OPENSHIFT AI CHAPTER 3. REQUIREMENTS FOR UPGRADING OPENSHIFT AI CHAPTER 4. UPDATING THE INSTALLATION STATUS OF RED HAT OPENSHIFT AI COMPONENTS BY USING THE WEB CONSOLE 3 4 6 7 10 Table of Contents

PREFACE As a cluster administrator, you can configure either automatic or manual upgrade of the OpenShift AI Operator. PREFACE

CHAPTER 1. OVERVIEW OF UPGRADING OPENSHIFT AI SELF- MANAGED As a cluster administrator, you can configure either automatic or manual upgrades for the Red Hat OpenShift AI Operator.

NOTE

For information about upgrading OpenShift AI as self-managed software on your OpenShift cluster in a disconnected environment, see Upgrading OpenShift AI Self- Managed in a disconnected environment. If you configure automatic upgrades, when a new version of the Red Hat OpenShift AI Operator is available, Operator Lifecycle Manager (OLM) automatically upgrades the running instance of your Operator without human intervention. If you configure manual upgrades, when a new version of the Red Hat OpenShift AI Operator is available, OLM creates an update request. A cluster administrator must manually approve the update request to update the Operator to the new version. See Manually approving a pending Operator upgrade for more information about approving a pending Operator upgrade. By default, the Red Hat OpenShift AI Operator follows a sequential update process. This means that if there are several minor versions between the current version and the version that you plan to upgrade to, Operator Lifecycle Manager (OLM) upgrades the Operator to each of the minor 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, without human intervention. If you configure manual upgrades, a cluster administrator must manually approve each sequential update between the current version and the final, target version. To view information regarding the supported and tested upgrade paths for Red Hat OpenShift AI, see Red Hat OpenShift AI Upgrade Path Information. For information about OpenShift AI Self-Managed release types and supported versions, see the Red Hat OpenShift AI Self-Managed Life Cycle Knowledgebase article. Before you upgrade OpenShift AI, you should complete the Requirements for upgrading OpenShift AI. Before you can use an accelerator in OpenShift AI, your instance must have the associated accelerator profile or hardware profile. If your OpenShift instance has an accelerator, its accelerator profile or hardware profile is preserved after an upgrade. For more information about accelerators, see Working with accelerators.

IMPORTANT

By default, hardware profiles are hidden in the dashboard navigation menu and user interface, while accelerator profiles remain visible. In addition, user interface components associated with the deprecated accelerator profiles functionality are still displayed. To show the Settings → Hardware profiles option in the dashboard navigation menu, and the user interface components associated with hardware profiles, set the disableHardwareProfiles value to false in the OdhDashboardConfig custom resource (CR) in OpenShift. For more information about setting dashboard configuration options, see Customizing the dashboard. Red Hat OpenShift AI Self-Managed 2.25 Upgrading OpenShift AI Self-Managed

CHAPTER 2. CONFIGURING THE UPGRADE STRATEGY FOR OPENSHIFT AI As a cluster administrator, you can configure either an automatic or manual upgrade strategy for the Red Hat OpenShift AI Operator.

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 version that you intend to upgrade to, 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, without human intervention. 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 Self-Managed Life Cycle Knowledgebase article. Prerequisites You have cluster administrator privileges for your OpenShift cluster. The Red Hat OpenShift AI Operator is installed. Procedure

1. Log in to the OpenShift cluster web console as a cluster administrator.
2. In the Administrator perspective, in the left menu, select Operators → Installed Operators.
3. Click the Red Hat OpenShift AI Operator.
4. Click the Subscription tab.
5. Under Update approval, click the pencil icon and select one of the following update strategies: Automatic : New updates are installed as soon as they become available. Manual : A cluster administrator must approve any new update before installation begins.
6. Click Save. Additional resources For more information about the subscription channels that are available in version 2 of the Red Hat OpenShift AI Operator, see Installing the Red Hat OpenShift AI Operator. For more information about upgrading Operators that have been installed by using OLM, see Updating installed Operators in the OpenShift documentation. Red Hat OpenShift AI Self-Managed 2.25 Upgrading OpenShift AI Self-Managed

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.

NOTE

If you are upgrading OpenShift AI on a cluster running in FIPS mode, any custom container images for data science pipelines must be based on UBI 9 or RHEL 9. This ensures compatibility with FIPS-approved pipeline components and prevents errors related to mismatched OpenSSL or GNU C Library (glibc) versions.

Migrate from embedded Kueue to Red Hat build of Kueue

The embedded Kueue component for managing distributed workloads is deprecated. OpenShift AI now uses the Red Hat build of Kueue Operator to provide enhanced workload scheduling for distributed training, workbench, and model serving workloads. Before upgrading OpenShift AI, check if your environment is using the embedded Kueue component by verifying the spec.components.kueue.managementState field in the DataScienceCluster custom resource. If the field is set to Managed , you must complete the migration to the Red Hat build of Kueue Operator to avoid controller conflicts and ensure continued support for queue-based workloads. This migration requires OpenShift 4.18 or later. For more information, see Migrating to the Red Hat build of Kueue Operator.

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 more 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 CHAPTER 3. REQUIREMENTS FOR UPGRADING OPENSHIFT AI

Red Hat OpenShift AI Upgrade Path Information CHAPTER 3. REQUIREMENTS FOR UPGRADING OPENSHIFT AI

CHAPTER 4. UPDATING THE INSTALLATION STATUS OF RED HAT OPENSHIFT AI COMPONENTS BY USING THE WEB CONSOLE You can use the OpenShift web console to update the installation status of components of Red Hat OpenShift AI on your OpenShift cluster.

IMPORTANT

If you upgraded OpenShift AI, the upgrade process automatically used the values of the previous version’s DataScienceCluster object. New components are not automatically added to the DataScienceCluster object. After upgrading OpenShift AI: Inspect the default DataScienceCluster object to check and optionally update the managementState status of the existing components. Add any new components to the DataScienceCluster object. Prerequisites The Red Hat OpenShift AI Operator is installed on your OpenShift cluster. You have cluster administrator privileges for your OpenShift cluster. 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/v kind: DataScienceCluster metadata: name: default-dsc spec: components: codeflare: managementState: Removed dashboard: managementState: Removed datasciencepipelines: managementState: Removed kserve: Red Hat OpenShift AI Self-Managed 2.25 Upgrading OpenShift AI Self-Managed

ii. Under Update channel, click on the highlighted tech-preview-v1. iii. Change the channel to stable. c. Select the update option for Authorino 1.2.1. Verification

1. Confirm that there is at least one running pod for each component: a. In the OpenShift web console, click Workloads → Pods. b. In the Project list at the top of the page, select redhat-ods-applications or your custom applications namespace. c. 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: a. In the OpenShift web console, click Operators → Installed Operators. b. Click the Red Hat OpenShift AI Operator. c. Click the Data Science Cluster tab and select the DataScienceCluster object called default-dsc. d. Select the YAML tab. e. 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. Red Hat OpenShift AI Self-Managed 2.25 Upgrading OpenShift AI Self-Managed