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Chapter 6. Support removals
This section describes major changes in support for user-facing features in Red Hat OpenShift AI. For information about OpenShift AI supported software platforms, components, and dependencies, see Supported configurations.
6.1. Removed functionality
6.1.1. Pipeline logs for Python scripts running in Elyra pipelines are no longer stored in S3
Logs are no longer stored in S3-compatible storage for Python scripts which are running in Elyra pipelines. From OpenShift AI version 2.11, you can view these logs in the pipeline log viewer in the OpenShift AI dashboard.
For this change to take effect, you must use the Elyra runtime images provided in the 2024.1 or 2024.2 workbench images.
If you have an older workbench image version, update the Version selection field to 2024.1 or 2024.2, as described in Updating a project workbench.
Updating your workbench image version will clear any existing runtime image selections for your pipeline. After you have updated your workbench version, open your workbench IDE and update the properties of your pipeline to select a runtime image.
6.1.2. Data science pipelines v1 upgraded to v2
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. For more information, see Enabling 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 install or upgrade to OpenShift AI 2.9 or later with data science pipelines 2.0, ensure that there is no existing installation of Argo Workflows on your cluster.
It is no longer possible to deploy, view, or edit the details of pipelines that are based on data science pipelines 1.0 from the dashboard in OpenShift AI 2.15. If you already use data science pipelines, Red Hat recommends that you stay on OpenShift AI 2.8 until full feature parity in data science pipelines 2.0 has been delivered in a stable OpenShift AI release and you are ready to migrate to the new pipeline solution. For a detailed view of the release lifecycle, including the full support phase window, see Red Hat OpenShift AI Self-Managed Life Cycle.
If you want to use existing pipelines and workbenches with data science pipelines 2.0 after upgrading to OpenShift AI 2.15, you must update your workbenches to use the 2024.1 notebook image version or later and then manually migrate your pipelines from data science pipelines 1.0 to 2.0. For more information, see Upgrading to data science pipelines 2.0.
6.1.3. Embedded subscription channel no longer used
Starting with OpenShift AI 2.8, the embedded subscription channel is no longer used. You can no longer select the embedded channel for a new installation of the Operator. For more information about subscription channels, see Installing the Red Hat OpenShift AI Operator.
6.1.4. Version 1.2 notebook container images for workbenches are no longer supported
When you create a workbench, you specify a notebook container image to use with the workbench. Starting with OpenShift AI 2.5, when you create a new workbench, version 1.2 notebook container images are not available to select. Workbenches that are already running with a version 1.2 notebook image continue to work normally. However, Red Hat recommends that you update your workbench to use the latest notebook container image.
6.1.5. Beta subscription channel no longer used
Starting with OpenShift AI 2.5, the beta subscription channel is no longer used. You can no longer select the beta channel for a new installation of the Operator. For more information about subscription channels, see Installing the Red Hat OpenShift AI Operator.
6.1.6. HabanaAI workbench image removal
Support for the HabanaAI 1.10 workbench image has been removed. New installations of OpenShift AI from version 2.14 do not include the HabanaAI workbench image. However, if you upgrade OpenShift AI from a previous version, the HabanaAI workbench image remains available, and existing HabanaAI workbench images continue to function.
6.2. Deprecated functionality
6.2.1. Deprecated cluster configuration parameters
When using the CodeFlare SDK to run distributed workloads in Red Hat OpenShift AI, the following parameters in the Ray cluster configuration are now deprecated and should be replaced with the new parameters as indicated.
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You can also use the new extended_resource_mapping and overwrite_default_resource_mapping parameters, as appropriate. For more information about these new parameters, see the CodeFlare SDK documentation (external).
