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. 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. 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 want to use existing pipelines and workbenches with data science pipelines 2.0 after upgrading OpenShift AI, 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 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 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. 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. 6.2.5. NVIDIA GPU Operator replaces NVIDIA GPU add-on Previously, to enable graphics processing units (GPUs) to help with compute-heavy workloads, you installed the NVIDIA GPU add-on. OpenShift AI no longer supports this add-on. Now, to enable GPU support, you must install the NVIDIA GPU Operator. To learn how to install the GPU Operator, see NVIDIA GPU Operator on Red Hat OpenShift Container Platform (external). 6.2.6. Kubeflow Notebook Controller replaces JupyterHub In OpenShift AI 1.15 and earlier, JupyterHub was used to create and launch notebook server environments. In OpenShift AI 1.16 and later, JupyterHub is no longer included, and its functionality is replaced by Kubeflow Notebook Controller. This change provides the following benefits: Users can now immediately cancel a request, make changes, and retry the request, instead of waiting 5+ minutes for the initial request to time out. This means that users do not wait as long when requests fail, for example, when a notebook server does not start correctly. The architecture no longer prevents a single user from having more than one notebook server session, expanding future feature possibilities. The removal of the PostgreSQL database requirement allows for future expanded environment support in OpenShift AI. However, this update also creates the following behavior changes: For IT Operations administrators, the notebook server administration interface does not currently allow login access to data scientist users' notebook servers. This is planned to be added in future releases. For data scientists, the JupyterHub interface URL is no longer valid. Update your bookmarks to point to the OpenShift AI Dashboard. The JupyterLab interface is unchanged and data scientists can continue to use JupyterLab to work with their notebook files as usual. 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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