Chapter 1. Overview of upgrading OpenShift AI Self-Managed
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 . Important Previously, data science pipelines in OpenShift AI were based on KubeFlow Pipelines v1. Data science pipelines are now 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. After upgrading to OpenShift AI 2.16 or later, 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. If you are a current data science pipelines user, do not upgrade to OpenShift AI 2.16 or later until you are ready to migrate to data science pipelines 2.0. 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 and update your workbenches. For more information, see Migrating to data science pipelines 2.0 . Data science pipelines 2.0 contains an installation of Argo Workflows. OpenShift AI does not support direct customer usage of this installation of Argo Workflows. To upgrade to OpenShift AI 2.9 or later with data science pipelines, ensure that no separate installation of Argo Workflows exists on your cluster. 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. 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. If your OpenShift instance has an accelerator, its accelerator profile is preserved after an upgrade. For more information about accelerators, see Working with accelerators . Notebook images are integrated into the image stream during the upgrade and subsequently appear in the OpenShift AI dashboard. Note Notebook images are constructed externally; they are prebuilt images that undergo quarterly changes and they do not change with every OpenShift AI upgrade. Additional resources Operator Lifecycle Manager workflow