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 Supported Configurations for 3.x Knowledgebase article.
5.1. Deprecated
- Deprecation of FMS Guardrails Orchestrator
- In OpenShift AI 3.5, the FMS Guardrails Orchestrator is deprecated and will be removed in a future release. NeMo Guardrails is now the single, recommended framework for all LLM safety guardrailing in OpenShift AI. While the FMS Guardrails Orchestrator remains functional in OpenShift AI 3.5, use NeMo Guardrails for all new deployments. If you have existing FMS Guardrails deployments, plan to migrate them to NeMo Guardrails before the FMS components are removed.
- Deprecated LM-Eval
-
The LM-Eval standalone evaluation service, including the
LMEvalJobcustom resource and the LM-Eval model evaluation UI, is deprecated and will be removed in a future release. Migrate evaluation workflows to EvalHub, which provides a unified evaluation platform with support for multiple evaluation frameworks, including LM Evaluation Harness.
- RStudio Server and CUDA - RStudio Server workbench images removed
Starting with OpenShift AI 3.5, the RStudio Server and CUDA - RStudio Server workbench images have been removed from Red Hat OpenShift AI due to licensing compliance requirements. The existing RStudio images contained packages with incorrect AGPL license declarations and proprietary binaries that do not meet Red Hat open source standards.
Running RStudio workbenches continue to operate after the upgrade, but you cannot create new RStudio workbenches or restart stopped ones from the dashboard.
RStudio remains available in OpenShift AI 2.25, with an end of life date of October 5, 2026, and OpenShift AI 3.3, with an end of life date of April 26, 2027.
To continue using RStudio on OpenShift AI 3.5 or later, you can build your own unsupported RStudio images by using the self-build recipes. For more information, see Building unsupported RStudio Server workbench images.
To migrate to a supported IDE for R workflows, see Alternative IDEs for R workflows and Set up R development in code-server or JupyterLab.
- Deprecation of OGX Evaluation API
- In OpenShift AI 3.5 EA1, the Evaluation REST API and its associated providers are deprecated and removed from the OGX Operator.
- Deprecation of the Safety and Shields APIs from OGX
- The Safety and Shields APIs and their respective providers have been deprecated in OGX, previously known as Llama Stack, in OpenShift AI 3.5 EA1.
- Deprecated default group creation for model registry
- Starting with OpenShift AI 3.4, the default group creation performed by the OpenShift AI Operator when a model registry is created is deprecated. This default group will be removed in a future release of OpenShift AI. The OpenShift administrator will then be responsible for creating this group after the model registry is created, if needed in their workflow.
- Deprecated SQLite as a production metadata store for OGX
-
Starting with OpenShift AI 3.2, SQLite is deprecated for use as a metadata store in production OGX deployments. PostgreSQL is required for production-grade environments to ensure adequate performance, concurrency, and scalability. SQLite remains available for local development and testing only and must be explicitly configured. This includes configurations that define SQLite backends such as
kv-sqliteorsql-sqlitein the OGX storage configuration. SQLite is not intended for production workloads.
- Deprecated annotation format for Connection Secrets
Starting with OpenShift AI 3.0, the
opendatahub.io/connection-type-refannotation format for creating Connection Secrets is deprecated.For all new Connection Secrets, use the
opendatahub.io/connection-type-protocolannotation instead. While both formats are currently supported,connection-type-protocoltakes precedence and should be used for future compatibility.- Deprecated Kubeflow Training operator v1
- The Kubeflow Training Operator (v1) is deprecated starting OpenShift AI 2.25 and is planned to be removed in a future release. This deprecation is part of our transition to Kubeflow Trainer v2, which delivers enhanced capabilities and improved functionality.
- Deprecated TrustyAI service CRD v1alpha1
-
Starting with OpenShift AI 2.25, the
v1alpha1version is deprecated and planned for removal in an upcoming release. You must update the TrustyAI Operator to versionv1to receive future Operator updates. - Deprecated KServe Serverless deployment mode
- Starting with OpenShift AI 2.25, The KServe Serverless deployment mode is deprecated. You can continue to deploy models by migrating to the KServe RawDeployment mode. If you are upgrading to Red Hat OpenShift AI 3.0, all workloads that use the retired Serverless or ModelMesh modes must be migrated before upgrading.
- Deprecated model registry API v1alpha1
-
Starting with OpenShift AI 2.24, the model registry API version
v1alpha1is deprecated and will be removed in a future release of OpenShift AI. The latest model registry API version isv1beta1. - 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.
- Accelerator Profiles and legacy Container Size selector deprecated
Starting with OpenShift AI 3.0, Accelerator Profiles and the Container Size selector for workbenches are deprecated.
These features are replaced by the more flexible and unified Hardware Profiles capability.
- 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.
- OpenShift AI dashboard user management moved from
OdhDashboardConfigtoAuthresource Previously, cluster administrators used the
groupsConfigoption in theOdhDashboardConfigresource 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 theAuthresource. If you have workflows (such as GitOps workflows) that interact withOdhDashboardConfig, you must update them to reference theAuthresource instead.Table 5.1. Updated configurations Resource 2.16 and earlier 2.17 and later versions apiVersionopendatahub.io/v1alphaservices.platform.opendatahub.io/v1alpha1kindOdhDashboardConfigAuthnameodh-dashboard-configauthAdmin groups
spec.groupsConfig.adminGroupsspec.adminGroupsUser groups
spec.groupsConfig.allowedGroupsspec.allowedGroups- 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_cpushead_cpu_requests,head_cpu_limitshead_memoryhead_memory_requests,head_memory_limitsmin_cpusworker_cpu_requestsmax_cpusworker_cpu_limitsmin_memoryworker_memory_requestsmax_memoryworker_memory_limitshead_gpushead_extended_resource_requestsnum_gpusworker_extended_resource_requestsYou can also use the new
extended_resource_mappingandoverwrite_default_resource_mappingparameters, as appropriate. For more information about these new parameters, see the CodeFlare SDK documentation (external).
5.2. Removed functionality
- RStudio Server and CUDA - RStudio Server workbench images removed
Starting with OpenShift AI 3.5, the RStudio Server and CUDA - RStudio Server workbench images have been removed from Red Hat OpenShift AI due to licensing compliance requirements. The existing RStudio images contained packages with incorrect AGPL license declarations and proprietary binaries that do not meet Red Hat open source standards.
Running RStudio workbenches continue to operate after the upgrade, but you cannot create new RStudio workbenches or restart stopped ones from the dashboard.
RStudio remains available in OpenShift AI 2.25, with an end of life date of October 5, 2026, and OpenShift AI 3.3, with an end of life date of April 26, 2027.
To continue using RStudio on OpenShift AI 3.5 or later, you can build your own unsupported RStudio images by using the self-build recipes. For more information, see Building unsupported RStudio Server workbench images.
To migrate to a supported IDE for R workflows, see Alternative IDEs for R workflows and Set up R development in code-server or JupyterLab.
- Training images removed for Kubeflow Training Operator v1
The following training runtime images for the Kubeflow Training Operator v1 (deprecated in OpenShift AI 3.4) have been removed:
-
registry.redhat.io/rhoai/odh-training-cuda121-torch24-py311-rhel9 -
registry.redhat.io/rhoai/odh-training-cuda124-torch25-py311-rhel9 -
registry.redhat.io/rhoai/odh-training-rocm62-torch24-py311-rhel9 registry.redhat.io/rhoai/odh-training-rocm62-torch25-py311-rhel9If you currently use these images, update your job specifications to use the following replacement images:
-
registry.redhat.io/rhoai/odh-training-cuda128-torch29-py312-rhel9 -
registry.redhat.io/rhoai/odh-training-rocm64-torch29-py312-rhel9
-
- FIPSEnabled configuration field and separate FIPS binaries removed
The
FIPSEnabledfield in the DSPO configuration and the separate FIPS binarieslauncher-v2-fipsandargoexec-fipsare removed. FIPS 140 mode is always enabled in Data Science Pipelines component binaries through the Go native FIPS 140-3 module.All cryptographic operations in the API server, persistence agent, and launcher use FIPS 140-validated algorithms. External services that Data Science Pipelines connects to must support TLS 1.2 or later with standard cipher suites: AES-GCM, AES-CBC with SHA-256 or SHA-384, and ECDHE with P-256 or P-384.
If you use external databases, object storage, or custom CA certificates, verify they meet the following requirements:
- External databases: Must support TLS 1.2 or later with FIPS-approved cipher suites. All modern MariaDB and MySQL versions and cloud-managed databases such as RDS, Cloud SQL, and Azure meet this requirement by default.
- External S3-compatible object storage: Must support TLS 1.2 or later. AWS S3, MinIO, Ceph, and GCS comply by default.
Custom CA certificates: Certificates signed with MD5 or using RSA keys shorter than 2048 bits are rejected. Verify your certificate chain uses RSA 2048-bit keys or larger, or ECDSA P-256 or P-384, with SHA-256 or stronger signatures.
FIPS enforcement applies only to Data Science Pipelines infrastructure components. User-authored pipeline steps and containers, operator-deployed MariaDB and MLMD instances, and Kubernetes API communication are not affected. If you previously configured
FIPSEnabled, remove this setting from your DSPO configuration. If you previously setFIPSEnabledtofalseto disable FIPS, note that FIPS is always enabled in this release; this is not expected to cause issues for compliant external services.
- tf2onnx package removed from TensorFlow images
The
tf2onnxpackage has been removed from TensorFlow workbench and runtime images. This package, which converts TensorFlow models to ONNX format, was incompatible with Keras 3 (used in TensorFlow 2.16+) and had irreconcilable dependency conflicts withprotobufversions required byonnx,tensorflow, andfeast. The upstream project has been abandoned since January 2024.If you require TensorFlow to ONNX conversion, see RHAIENG-1632 for alternative approaches.
- Caikit-NLP component removed
The
caikit-nlpcomponent has been formally deprecated and removed from OpenShift AI 3.0.This runtime is no longer included or supported in OpenShift AI. Users should migrate any dependent workloads to supported model serving runtimes.
- TGIS component removed
The TGIS component, which was deprecated in OpenShift AI 2.19, has been removed in OpenShift AI 3.0.
TGIS continued to be supported through the OpenShift AI 2.16 Extended Update Support (EUS) lifecycle, which ended in June 2025.
Starting with this release, TGIS is no longer available or supported. Users should migrate their model serving workloads to supported runtimes such as Caikit or Caikit-TGIS.
- AppWrapper Controller removed
The AppWrapper controller has been removed from OpenShift AI as part of the broader CodeFlare Operator removal process.
This change eliminates redundant functionality and reduces maintenance overhead and architectural complexity.
- CodeFlare Operator removed
Starting with OpenShift AI 3.0, the CodeFlare Operator has been removed.
The functionality previously provided by the CodeFlare Operator is now included in the KubeRay Operator, which provides equivalent capabilities such as mTLS, network isolation, and authentication.
- LAB-tuning feature removed
Starting with OpenShift AI 3.0, the LAB-tuning feature has been removed.
Users who previously relied on LAB-tuning for large language model customization should migrate to alternative fine-tuning or model customization methods.
- Embedded Kueue component removed
The embedded Kueue component, which was deprecated in OpenShift AI 2.24, has been removed in OpenShift AI 3.0.
OpenShift AI now uses the Red Hat Build of the Kueue Operator to provide enhanced workload scheduling across distributed training, workbench, and model serving workloads.
The embedded Kueue component is not supported in any Extended Update Support (EUS) release.
- Removal of DataSciencePipelinesApplication v1alpha1 API version
The
v1alpha1API version of theDataSciencePipelinesApplicationcustom resource (datasciencepipelinesapplications.opendatahub.io/v1alpha1) has been removed.OpenShift AI now uses the stable
v1API version (datasciencepipelinesapplications.opendatahub.io/v1).You must update any existing manifests or automation to reference the
v1API version to ensure compatibility with OpenShift AI 3.0 and later.
- Microsoft SQL Server command-line tool removal
- Starting with OpenShift AI 2.24, the Microsoft SQL Server command-line tools (sqlcmd, bcp) have been removed from workbenches. You can no longer manage Microsoft SQL Server using the preinstalled command-line client.
- Model registry ML Metadata (MLMD) server removal
Starting with OpenShift AI 2.23, the ML Metadata (MLMD) server has been removed from the model registry component. The model registry now interacts directly with the underlying database by using the existing model registry API and database schema. This change simplifies the overall architecture and ensures the long-term maintainability and efficiency of the model registry by transitioning from the
ml-metadatacomponent to direct database access within the model registry itself.If you see the following error for your model registry deployment, this means that your database schema migration has failed:
error: error connecting to datastore: Dirty database version {version}. Fix and force version.You can fix this issue by manually changing the database from a dirty state to 0 before traffic can be routed to the pod. Perform the following steps:
Find the name of your model registry database pod as follows:
kubectl get pods -n <your-namespace> | grep model-registry-dbReplace
<your-namespace>with the namespace where your model registry is deployed.Use
kubectl execto run the query on the model registry database pod as follows:kubectl exec -n <your-namespace> <your-db-pod-name> -c mysql -- mysql -u root -p"$MYSQL_ROOT_PASSWORD" -e "USE <your-db-name>; UPDATE schema_migrations SET dirty = 0;"Replace
<your-namespace>with your model registry namespace and<your-db-pod-name>with the pod name that you found in the previous step. Replace<your-db-name>with your model registry database name.This will reset the dirty state in the database, allowing the model registry to start correctly.
- Embedded subscription channel not used in some versions
-
For OpenShift AI 2.8 to 2.20 and 2.22 to 3.5, the
embeddedsubscription channel is not used. You cannot select theembeddedchannel for a new installation of the Operator for those versions. For more information about subscription channels, see Installing the Red Hat OpenShift AI Operator.
- 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*/'
- 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.
NoteFor 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.
- Beta subscription channel no longer used
-
Starting with OpenShift AI 2.5, the
betasubscription channel is no longer used. You can no longer select thebetachannel for a new installation of the Operator. For more information about subscription channels, see Installing the Red Hat OpenShift AI Operator.
- 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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