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 the Red Hat OpenShift AI: Supported Configurations Knowledgebase article. 6.1. Deprecated functionality 6.1.1. Deprecated OpenVINO Model Server (OVMS) plugin The CUDA plugin for the OpenVINO Model Server (OVMS) is now deprecated and will no longer be available in future releases of OpenShift AI. 6.1.2. OpenShift AI dashboard user management moved from OdhDashboardConfig to Auth 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 6.1. Updated configurations Resource 2.16 and earlier 2.17 and later versions 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 6.1.3. 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. Deprecated parameter Replaced by head_cpus head_cpu_requests , head_cpu_limits head_memory head_memory_requests , head_memory_limits min_cpus worker_cpu_requests max_cpus worker_cpu_limits min_memory worker_memory_requests max_memory worker_memory_limits head_gpus head_extended_resource_requests num_gpus worker_extended_resource_requests 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). 6.2. Removed functionality 6.2.1. Anaconda removal Anaconda is an open source distribution of the Python and R programming languages. Starting with OpenShift AI version 2.18, Anaconda is no longer included in OpenShift AI, and Anaconda resources are no longer supported or managed by OpenShift AI. If you previously installed Anaconda from OpenShift AI, a cluster administrator must complete the following steps from the OpenShift command-line interface to remove the Anaconda-related artifacts: Remove the secret that contains your Anaconda password: oc delete secret -n redhat-ods-applications anaconda-ce-access Remove the ConfigMap for the Anaconda validation cronjob: oc delete configmap -n redhat-ods-applications anaconda-ce-validation-result Remove the Anaconda image stream: oc delete imagestream -n redhat-ods-applications s2i-minimal-notebook-anaconda Remove the Anaconda job that validated the downloading of images: oc delete job -n redhat-ods-applications anaconda-ce-periodic-validator-job-custom-run Remove any pods related to Anaconda cronjob runs: oc get pods n redhat-ods-applications --no-headers=true | awk '/anaconda-ce-periodic-validator-job-custom-run*/' 6.2.2. Data science pipelines v1 support removed 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 customer usage of this installation of Argo Workflows. To install or upgrade to OpenShift AI 2.16 or later with data science pipelines 2.0, ensure that there is no existing installation of Argo Workflows on your cluster. 6.2.3. 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. Note 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. 6.2.4. 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 . 6.2.5. 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. 6.2.6. 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 . 6.2.7. 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 version, the HabanaAI workbench image remains available, and existing HabanaAI workbench images continue to function.

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