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Chapter 5. 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.
5.1. Deprecated functionality
5.1.1. Multi-model serving platform (ModelMesh)
Starting with OpenShift AI version 2.19, the multi-model serving platform based on ModelMesh is deprecated. You can continue to deploy models on the multi-model serving platform, but it is recommended that you migrate to the single-model serving platform.
For more information or for help on using the single-model serving platform, contact your account manager.
5.1.2. Deprecated Text Generation Inference Server (TGIS)
Starting with OpenShift AI version 2.19, the Text Generation Inference Server (TGIS) is deprecated. TGIS will continue to be supported through the OpenShift AI 2.16 EUS lifecycle. Caikit-TGIS and Caikit are not affected and will continue to be supported. The out-of-the-box serving runtime template will no longer be deployed. vLLM is recommended as a replacement runtime for TGIS.
5.1.3. Deprecated accelerator profiles
Accelerator profiles are now deprecated. To target specific worker nodes for workbenches or model serving workloads, use hardware profiles.
5.1.4. 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.
5.1.5. 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.
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5.1.6. 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).
5.2. Removed functionality
5.2.1. Embedded subscription channel not used in some versions
For OpenShift AI 2.8 to 2.20 and 2.22, the embedded subscription channel is not used. You cannot select the embedded channel for a new installation of the Operator for those versions. For more information about subscription channels, see Installing the Red Hat OpenShift AI Operator.
5.2.2. Standalone script for InstructLab removed
The standalone script for running Distributed InstructLab training has been removed. To run the InstructLab training flow, use the LAB-tuning Technology Preview feature. For more information, see Enabling LAB-tuning and Customizing models with LAB-tuning.
The LAB-tuning feature is currently not supported for disconnected environments.
5.2.3. 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-accessRemove the
ConfigMapfor the Anaconda validation cronjob:oc delete configmap -n redhat-ods-applications anaconda-ce-validation-resultRemove the Anaconda image stream:
oc delete imagestream -n redhat-ods-applications s2i-minimal-notebook-anacondaRemove the Anaconda job that validated the downloading of images:
oc delete job -n redhat-ods-applications anaconda-ce-periodic-validator-job-custom-runRemove 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*/'
5.2.4. 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.
Data science pipelines 2.0 contains an installation of Argo Workflows. Red Hat 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.
5.2.5. 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 workbench images at version 2024.1 or later.
If you have an older workbench image version, update the Version selection field to a compatible workbench image version, for example, 2024.1, 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.
5.2.6. Version 1.2 container images for workbenches are no longer supported
When you create a workbench, you specify a container image to use with the workbench. Starting with OpenShift AI 2.5, when you create a new workbench, version 1.2 container images are not available to select. Workbenches that are already running with a version 1.2 image continue to work normally. However, Red Hat recommends that you update your workbench to use the latest container image.
5.2.7. 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.
5.2.8. 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.
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