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 in a disconnected environment. A disconnected environment is a network restricted environment where Operator Lifecycle Manager (OLM) cannot access the default OperatorHub and image registries, which require Internet connectivity.
For information about upgrading OpenShift AI as self-managed software on your OpenShift cluster in a connected environment, see Upgrading OpenShift AI Self-Managed.
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.
After you upgrade to OpenShift AI 2.9 or later, pipelines created with data science pipelines 1.0 continue to run, but are inaccessible from the OpenShift AI dashboard. If you are a current data science pipelines user, do not upgrade to OpenShift AI with data science pipelines 2.0 until you are ready to migrate to the new pipelines solution.
- If you configure automatic upgrades, when a new version of the Red Hat OpenShift AI Operator is available, and you have updated your mirror registry content, 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 and you have updated your mirror registry content, 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.
Red Hat supports the current version and three previous minor versions of OpenShift AI Self-Managed. For more information, see the Red Hat OpenShift AI Self-Managed Life Cycle knowledgebase article.
- When 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.
NoteNotebook images are constructed externally; they are prebuilt images that undergo quarterly changes and they do not change with every OpenShift AI upgrade.
Additional resources
