Release notes

Red Hat OpenShift AI Self-Managed 2.17

Features, enhancements, resolved issues, and known issues associated with this release

Abstract

These release notes provide an overview of new features, enhancements, resolved issues, and known issues in version 2.17 of Red Hat OpenShift AI.

Chapter 1. Overview of OpenShift AI

Red Hat OpenShift AI is a platform for data scientists and developers of artificial intelligence and machine learning (AI/ML) applications.

OpenShift AI provides an environment to develop, train, serve, test, and monitor AI/ML models and applications on-premise or in the cloud.

For data scientists, OpenShift AI includes Jupyter and a collection of default notebook images optimized with the tools and libraries required for model development, and the TensorFlow and PyTorch frameworks. Deploy and host your models, integrate models into external applications, and export models to host them in any hybrid cloud environment. You can enhance your data science projects on OpenShift AI by building portable machine learning (ML) workflows with data science pipelines, using Docker containers. You can also accelerate your data science experiments through the use of graphics processing units (GPUs) and Intel Gaudi AI accelerators.

For administrators, OpenShift AI enables data science workloads in an existing Red Hat OpenShift or ROSA environment. Manage users with your existing OpenShift identity provider, and manage the resources available to notebook servers to ensure data scientists have what they require to create, train, and host models. Use accelerators to reduce costs and allow your data scientists to enhance the performance of their end-to-end data science workflows using graphics processing units (GPUs) and Intel Gaudi AI accelerators.

OpenShift AI has two deployment options:

Self-managed software that you can install on-premise or in the cloud. You can install OpenShift AI Self-Managed in a self-managed environment such as OpenShift Container Platform, or in Red Hat-managed cloud environments such as Red Hat OpenShift Dedicated (with a Customer Cloud Subscription for AWS or GCP), Red Hat OpenShift Service on Amazon Web Services (ROSA Classic or ROSA HCP), or Microsoft Azure Red Hat OpenShift.
For information about OpenShift AI as self-managed software on your OpenShift cluster in a connected or a disconnected environment, see Product Documentation for Red Hat OpenShift AI Self-Managed.
A managed cloud service, installed as an add-on in Red Hat OpenShift Dedicated (with a Customer Cloud Subscription for AWS or GCP) or in Red Hat OpenShift Service on Amazon Web Services (ROSA Classic).
For information about OpenShift AI Cloud Service, see Product Documentation for Red Hat OpenShift AI.

For information about OpenShift AI supported software platforms, components, and dependencies, see the Supported configurations Knowledgebase article.

For a detailed view of the 2.17 release lifecycle, including the full support phase window, see the Red Hat OpenShift AI Self-Managed Life Cycle Knowledgebase article.

Chapter 2. New features and enhancements

This section describes new features and enhancements in Red Hat OpenShift AI 2.17.

2.1. New features

Support for NVIDIA NIM metrics

You can now view metrics for a NIM model deployed on the NVIDIA NIM model serving platform. The following metrics are available:

GPU cache usage over time
Current running, waiting, and max requests count
Token count
Time to first token
Time per output token
Requests outcomes

For more information, see About the NVIDIA NIM model serving platform.

Note

NIM metrics are automatically enabled for new deployments in 2.17. If you are upgrading to 2.17 from a previous version of Red Hat OpenShift AI and have an existing NIM deployment, you must manually enable NIM metrics for the deployment. For more information, see the NVIDIA NIM model serving platform documentation.

2.2. Enhancements

Custom images no longer have custom- prefix in image name: You can now import a custom workbench image in the OpenShift AI dashboard, the custom- prefix is no longer applied to the image name.

Updated OpenShift AI user interface: The user interface for OpenShift AI has been updated to PatternFly 6. The update includes enhanced theming capabilities and support for an optional dark mode.

Customizable namespaces: You can now define a custom namespace for the operator and DSCI.applicationNamespace instead of the default predefined namespaces used by OpenShift AI. To use a custom namespace, label the namespace with opendatahub.io/application-namespace: true.

Chapter 3. Technology Preview features

Important

This section describes Technology Preview features in Red Hat OpenShift AI 2.17. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

Mandatory Kueue local-queue labeling policy for Ray cluster creation

Cluster administrators can use the Validating Admission Policy feature to enforce the mandatory labeling of Ray cluster resources with Kueue local-queue identifiers. This labeling ensures that workloads are properly categorized and routed based on queue management policies, which prevents resource contention and enhances operational efficiency.

When the local-queue labeling policy is enforced, Ray clusters are created only if they are configured to use a local queue, and the Ray cluster resources are then managed by Kueue. The local-queue labeling policy is enforced for all projects by default, but can be disabled for some or all projects. For more information about the local-queue labeling policy, see Enforcing the use of local queues.

Note

This feature might introduce a breaking change for users who did not previously use Kueue local queues to manage their Ray cluster resources.

RStudio Server notebook image

With the RStudio Server notebook image, you can access the RStudio IDE, an integrated development environment for R. The R programming language is used for statistical computing and graphics to support data analysis and predictions.

To use the RStudio Server notebook image, you must first build it by creating a secret and triggering the BuildConfig, and then enable it in the OpenShift AI UI by editing the rstudio-rhel9 image stream. For more information, see Building the RStudio Server workbench images.

Important

Disclaimer: Red Hat supports managing workbenches in OpenShift AI. However, Red Hat does not provide support for the RStudio software. RStudio Server is available through rstudio.org and is subject to their licensing terms. You should review their licensing terms before you use this sample workbench.

CUDA - RStudio Server notebook image

With the CUDA - RStudio Server notebook image, you can access the RStudio IDE and NVIDIA CUDA Toolkit. The RStudio IDE is an integrated development environment for the R programming language for statistical computing and graphics. With the NVIDIA CUDA toolkit, you can enhance your work by using GPU-accelerated libraries and optimization tools.

To use the CUDA - RStudio Server notebook image, you must first build it by creating a secret and triggering the BuildConfig, and then enable it in the OpenShift AI UI by editing the rstudio-rhel9 image stream. For more information, see Building the RStudio Server workbench images.

Important

The CUDA - RStudio Server notebook image contains NVIDIA CUDA technology. CUDA licensing information is available in the CUDA Toolkit documentation. You should review their licensing terms before you use this sample workbench.

Model Registry

OpenShift AI now supports the Model Registry Operator. The Model Registry Operator is not installed by default in Technology Preview mode. The model registry is a central repository that contains metadata related to machine learning models from inception to deployment.

OCI containers for model storage

You can use OCI storage as an alternative to cloud storage services for model serving. First, you create an OCI container image to contain the model. The image is uploaded to an OCI-compatible registry, such as Quay. Later, when deploying a model, the model serving platform references the repository of the containerized model.

Using an OCI container can provide the following advantages:

Reduced startup times, because the cluster keeps a cache of downloaded images. Restarting the model pod does not download the model again.
Lower disk space usage, because the model is not downloaded on each pod replica, assuming pods are scheduled on the same node.
Enhanced performance when pre-fetching images or asynchronous loading.
Compatibility and integration, because it can be easily integrated with KServe. No additional dependencies are required and the infrastructure might already be available.

Support for multinode deployment of very large models: Serving models over multiple graphical processing unit (GPU) nodes when using a single-model serving runtime is now available as a Technology Preview feature. Deploy your models across multiple GPU nodes to improve efficiency when deploying large models such as large language models (LLMs). For more information, see Deploying models across multiple GPU nodes.

Chapter 4. Developer Preview features

Important

This section describes Developer Preview features in Red Hat OpenShift AI 2.17. Developer Preview features are not supported by Red Hat in any way and are not functionally complete or production-ready. Do not use Developer Preview features for production or business-critical workloads. Developer Preview features provide early access to functionality in advance of possible inclusion in a Red Hat product offering. Customers can use these features to test functionality and provide feedback during the development process. Developer Preview features might not have any documentation, are subject to change or removal at any time, and have received limited testing. Red Hat might provide ways to submit feedback on Developer Preview features without an associated SLA.

For more information about the support scope of Red Hat Developer Preview features, see Developer Preview Support Scope.

Support for AppWrapper in Kueue: AppWrapper support in Kueue is available as a Developer Preview feature. The experimental API enables the use of AppWrapper-based workloads with the distributed workloads feature.

Distributed InstructLab training

InstructLab is an open-source project for enhancing large language models (LLMs) in generative artificial intelligence (gen AI) applications. It fine-tunes models using synthetic data generation (SDG) techniques and a structured taxonomy to create diverse, high-quality training datasets.

There are two ways to run the InstructLab training flow: the InstructLab pipeline, which is the recommended approach, and the standalone script:

InstructLab pipeline

The InstructLab pipeline is now available as a Developer Preview feature, enabling you to run the full InstructLab workflow through a data science pipeline in OpenShift AI. For prerequisites and setup instructions to run this pipeline, see Running InstructLab Pipeline with Data Science Pipelines on RHOAI.

Important

You must have NVIDIA GPU Operator 24.6 installed to use the InstructLab pipeline in OpenShift AI 2.17.

Standalone script

Distributed InstructLab training is available as a Developer Preview feature, offering enhanced performance for training tasks in distributed environments compared to single-node setups. By orchestrating multi-node training jobs, this feature improves efficiency and scalability and allows users to leverage distributed resources for more effective AI model development. To implement the full InstructLab training flow with the standalone script, including data preparation, transfer, and distributed execution, see Distributed InstructLab Training on RHOAI.

Chapter 5. Limited Availability features

Important

This section describes Limited Availability features in Red Hat OpenShift AI 2.17. Limited Availability means that you can install and receive support for the feature only with specific approval from Red Hat. Without such approval, the feature is unsupported. This applies to all features described in this section.

Tuning in OpenShift AI: Tuning in OpenShift AI is available as a Limited Availability feature. The Kubeflow Training Operator and the Hugging Face Supervised Fine-tuning Trainer (SFT Trainer) enable users to fine-tune and train their models easily in a distributed environment. In this release, you can use this feature for models that are based on the PyTorch machine-learning framework.

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. Deprecated functionality

6.1.1. 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`

6.1.2. 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. 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.

6.2.2. 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.

6.2.3. 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.

6.2.4. 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. 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.

6.2.6. 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.

Chapter 7. Resolved issues

The following notable issues are resolved in Red Hat OpenShift AI 2.17. Security updates, bug fixes, and enhancements for Red Hat OpenShift AI 2.17 are released as asynchronous errata. All OpenShift AI errata advisories are published on the Red Hat Customer Portal.

7.1. Issues resolved in Red Hat OpenShift AI 2.17 (February 2025)

RHOAIENG-16900 - Space-separated format in serving-runtime arguments can cause deployment failure

Previously, when deploying models, using a space-separated format to specify additional serving runtime arguments could cause unrecognized arguments errors. This issue is now resolved.

RHOAIENG-16073 - Attribute error when retrieving the job client for a cluster object

Previously, when initializing a cluster with the get_cluster method, assigning client = cluster.job_client sometimes resulted in an AttributeError: 'Cluster' object has no attribute '_job_submission_client' error. This issue is now resolved.

RHOAIENG-15773 - Cannot add a new model registry user

Previously, when managing the permissions of a model registry, you could not add a new user, group, or project as described in Managing model registry permissions. An HTTP request failed error was displayed. This issue is now resolved.

RHOAIENG-14197 - Tooltip text for CPU and Memory graphs is clipped and therefore unreadable

Previously, when you hovered the cursor over the CPU and Memory graphs in the Top resource-consuming distributed workloads section on the Project metrics tab of the Distributed Workloads Metrics page, the tooltip text was clipped, and therefore unreadable. This issue is now resolved.

RHOAIENG-11024 - Resources entries get wiped out after removing opendatahub.io/managed annotation

Previously, manually removing the opendatahub.io/managed annotation from any component deployment YAML file might have caused resource entry values in the file to be erased. This issue is now resolved.

RHOAIENG-8102 - Incorrect requested resources reported when cluster has multiple cluster queues

Previously, when a cluster had multiple cluster queues, the resources requested by all projects was incorrectly reported as zero instead of the true value. This issue is now resolved.

Chapter 8. Known issues

This section describes known issues in Red Hat OpenShift AI 2.17 and any known methods of working around these issues.

RHOAIENG-19716 - The system-authenticated user group cannot be removed by using the dashboard

After installing or upgrading Red Hat OpenShift AI, the system-authenticated user group might display in Settings > User management under Data science user groups. If you remove this user group from Data science user groups and save the changes, the group is erroneously added again.

Workaround

To remove the system-authenticated user group, delete the groupsConfig key from the OdhDashboardConfig custom resource:

Log in to the OpenShift console as a cluster administrator.
In the Administrator perspective, click Administration → CustomResourceDefinitions.
Search for OdhDashboardConfig and then select the OdhDashboardConfig custom resource definition (CRD).
Click the YAML tab.
Delete the groupsConfig key and all data within it, such as adminGroups and allowedGroups.
Click Save.

RHOAIENG-19711 - Kueue-controller-manager uses old metrics port after upgrade from 2.16.0 to 2.17.0

After upgrade, the Kueue Operator continues to use the old port (8080) instead of the new port (8443) for metrics. As a result, the OpenShift console Observe > Targets page shows that the status of the Kueue Operator is Down.

Workaround: Restart the Kueue Operator Pod (kueue-controller-manager-*) or delete the kueue-controller-manager deployment. The Kueue Operator status changes to Up.

RHOAIENG-18933 - Increased workbench image size can delay workbench startup

The 2024.2 workbench images now include the kubeflow-training Python SDK, enabling you to interact with the Training Operator for fine-tuning. This addition increases the workbench image size, which can cause a delay when starting the workbench.

Workaround: None.

RHOAIENG-18884 - Enabling NIM account setup is incomplete

When you try to enable the NVIDIA NIM model serving platform, the odh-model-controller deployment starts before the NIM account setup is complete. As a result, the NIM account setup is incomplete and the platform is not enabled.

Workaround: Restart the odh-model-controller deployment.

RHOAIENG-18675 - Workbenches component fails after upgrading

When upgrading to OpenShift AI 2.17, the workbench component does not upgrade correctly. Specifically, BuildConfigs and resources that follow it (such as RStudio BuildConfigs and ROCm imagestreams) are not updated, causing the workbench component reconciliation in the DataScienceCluster to fail.

Workaround: Before upgrading, delete the existing BuildConfig objects in your cluster to avoid these issues.

RHOAIENG-18590 - The KnativeServing resource fails after disabling and enabling KServe

After disabling and enabling the kserve component in the DataScienceCluster, the KnativeServing resource might fail.

Workaround

Delete all ValidatingWebhookConfiguration and MutatingWebhookConfiguration webhooks related to Knative:

Get the webhooks:

oc get ValidatingWebhookConfiguration,MutatingWebhookConfiguration | grep -i knative

Ensure KServe is disabled.

Get the webhooks:

oc get ValidatingWebhookConfiguration,MutatingWebhookConfiguration | grep -i knative

Delete the webhooks.
Enable KServe.
Verify that the KServe pod can successfully spawn, and that pods in the knative-serving namespace are active and operational.

RHOAIENG-19261 - The TrustyAI installation might fail due to missing custom resource definitions (CRDs)

When installing or upgrading OpenShift AI, the TrustyAI installation might fail due to missing InferenceService and ServingRuntime CRDs. As a result, the Trusty AI controller goes into the CrashLoopBackOff state.

Workaround

Depending on your deployment, enable the Kserve or ModelMesh component in the DataScienceCluster.
Restart the trustyai-service-operator-controller-manager pod in the redhat-ods-applications namespace.

RHOAIENG-18238 - Inference endpoints for deployed models return 403 error after upgrading the Authorino Operator

After upgrading the Authorino Operator, the automatic Istio sidecar injection might not be reapplied. Without the sidecar, Authorino is not correctly integrated into the service mesh, causing inference endpoint requests to fail with an HTTP 403 error.

Workaround: Restart the OpenShift AI Operator pod. For a detailed workaround, see RHOAI Single-Model Serving Endpoints Return HTTP 403 Errors After Authorino Update.

RHOAIENG-16247 - Elyra pipeline run outputs are overwritten when runs are launched from OpenShift AI dashboard

When a pipeline is created and run from Elyra, outputs generated by the pipeline run are stored in the folder bucket-name/pipeline-name-timestamp of object storage.

When a pipeline is created from Elyra and the pipeline run is started from the OpenShift AI dashboard, the timestamp value is not updated. This can cause pipeline runs to overwrite files created by previous pipeline runs of the same pipeline.

This issue does not affect pipelines compiled and imported using the OpenShift AI dashboard because runid is always added to the folder used in object storage. For more information about storage locations used in data science pipelines, see Storing data with data science pipelines.

Workaround: When storing files in an Elyra pipeline, use different subfolder names on each pipeline run.

OCPBUGS-49422 - AMD GPUs and AMD ROCm workbench images are not supported in a disconnected environment

This release of OpenShift AI does not support AMD GPUs and AMD ROCm workbench images in a disconnected environment because installing the AMD GPU Operator requires internet access to fetch dependencies needed to compile GPU drivers.

Workaround: None.

RHOAIENG-16146 - Connection sometimes not preselected when deploying a model from model registry

When deploying a model from a model registry, the object storage connection (previously called data connection) might not be preselected. The behavior depends on how many S3-compatible object storage connections in the target project match the location of the registered model version being deployed.

If the project has no matching connection: When you are prompted to create a connection, the location fields are not prefilled and the alert explaining the prefilling behavior is not shown.
If the project has multiple matching connections: In the connections list, the matching connections are not marked as Recommended.

If there is exactly one matching connection, the problem does not occur because the connection is correctly preselected.

Workaround

The workaround depends on the number of matching connections as follows:

If the project has no matching connection: Manually re-enter the model location details.
If the project has multiple matching connections: Inspect your connections in the project’s Connections view and select the connection that matches your preferred model location.

RHOAIENG-11371 - Incorrect run status reported for runs using ExitHandler

When using pipeline exit handlers (dsl.ExitHandler), if a task inside the handle fails but the exit task succeeds, the overall pipeline run status is inaccurately reported as Succeeded instead of Failed.

Workaround: None.

RHOAIENG-15123 (also documented as RHOAIENG-10790 and RHOAIENG-14265) - Pipelines schedule might fail after upgrading

When you upgrade to OpenShift AI 2.17, data science pipeline scheduled runs that existed before the upgrade might fail to execute, resulting in an error message in the task pod.

Workaround: Follow the steps in the How to resolve scheduled pipeline run failures after upgrading to Red Hat OpenShift AI 2.16.0 Knowledgebase solution.

RHOAIENG-14271 - Compatibility errors occur when using different Python versions in Ray clusters with Jupyter notebooks

When you use Python version 3.11 in a Jupyter notebook, and then create a Ray cluster, the cluster defaults to a workbench image that contains Ray version 2.35 and Python version 3.9. This mismatch can cause compatibility errors, as the Ray Python client requires a Python version that matches your workbench configuration.

Workaround: Use a workbench image with your Ray cluster that contains a matching Python version with the ClusterConfiguration argument.

RHOAIENG-12516 - fast releases are available in unintended release channels

Due to a known issue with the stream image delivery process, fast releases are currently available on unintended streaming channels, for example, stable, and stable-x.y. For accurate release type, channel, and support lifecycle information, refer to the Life-cycle Dates table on the Red Hat OpenShift AI Self-Managed Life Cycle page.

Workaround: None.

RHOAIENG-12233 - The chat_template parameter is required when using the /v1/chat/completions endpoint

When configuring the vLLM ServingRuntime for KServe runtime and querying a model using the /v1/chat/completions endpoint, the model fails with the following 400 Bad Request error:

As of transformers v4.44, default chat template is no longer allowed, so you must provide a chat template if the tokenizer does not define one

Transformers v4.44.2 is shipped with vLLM v0.5.5. As of vLLM v0.5.5, you must provide a chat template while querying a model using the /v1/chat/completions endpoint.

Workaround

If your model does not include a predefined chat template, you can use the chat-template command-line parameter to specify a chat template in your custom vLLM runtime, as shown in the example. Replace <CHAT_TEMPLATE> with the path to your template.

containers:
  - args:
      - --chat-template=<CHAT_TEMPLATE>

You can use the chat templates that are available as .jinja files in https://github.com/opendatahub-io/vllm/tree/main/examples or with the vLLM image under /apps/data/template. For more information, see Chat templates.

RHOAIENG-8294 - CodeFlare error when upgrading OpenShift AI 2.8 to version 2.10 or later

If you try to upgrade OpenShift AI 2.8 to version 2.10 or later, the following error message is shown for the CodeFlare component, due to a mismatch with the AppWrapper custom resource definition (CRD) version.

ReconcileCompletedWithComponentErrors DataScienceCluster resource reconciled with component errors: 1 error occurred: * CustomResourceDefinition.apiextensions.k8s.io "appwrappers.workload.codeflare.dev" is invalid: status.storedVersions[0]: Invalid value: "v1beta1": must appear in spec.versions

Workaround

Delete the existing AppWrapper CRD:

$ oc delete crd appwrappers.workload.codeflare.dev

Wait for about 20 seconds, and then ensure that a new AppWrapper CRD is automatically applied, as shown in the following example:

$ oc get crd appwrappers.workload.codeflare.dev
NAME                                 CREATED AT
appwrappers.workload.codeflare.dev   2024-11-22T18:35:04Z

RHOAIENG-7947 - Model serving fails during query in KServe

If you initially install the ModelMesh component and enable the multi-model serving platform, but later install the KServe component and enable the single-model serving platform, inference requests to models deployed on the single-model serving platform might fail. In these cases, inference requests return a 404 - Not Found error and the logs for the odh-model-controller deployment object show a Reconciler error message.

Workaround: In OpenShift, restart the odh-model-controller deployment object.

RHOAIENG-7716 - Pipeline condition group status does not update

When you run a pipeline that has condition groups, for example, dsl.lf, the UI displays a Running status for the groups, even after the pipeline execution is complete.

Workaround

You can confirm if a pipeline is still running by checking that no child tasks remain active.

From the OpenShift AI dashboard, click Data Science Pipelines → Runs.
From the Project list, click your data science project.
From the Runs tab, click the pipeline run that you want to check the status of.
Expand the condition group and click a child task.
A panel that contains information about the child task is displayed
On the panel, click the Task details tab.
The Status field displays the correct status for the child task.

RHOAIENG-6486 - Pod labels, annotations, and tolerations cannot be configured when using the Elyra JupyterLab extension with the TensorFlow 2024.1 notebook image

When using the Elyra JupyterLab extension with the TensorFlow 2024.1 notebook image, you cannot configure pod labels, annotations, or tolerations from an executed pipeline. This is due to a dependency conflict with the kfp and tf2onnx packages.

Workaround

If you are working with the TensorFlow 2024.1 notebook image, after you have completed your work, change the assigned workbench notebook image to the Standard Data Science 2024.1 notebook image.

In the Pipeline properties tab in the Elyra JupyterLab extension, set the Tensorflow runtime image as the default runtime image for the pipeline node individually, along with the relevant pod label, annotation or toleration, for each pipeline node.

RHOAIENG-6435 - Distributed workloads resources are not included in Project metrics

When you click Distributed Workloads Metrics > Project metrics and view the Requested resources section, the Requested by all projects value currently excludes the resources for distributed workloads that have not yet been admitted to the queue.

Workaround: None.

RHOAIENG-6409 - Cannot save parameter errors appear in pipeline logs for successful runs

When you run a pipeline more than once with data science pipelines 2.0, Cannot save parameter errors appear in the pipeline logs for successful pipeline runs. You can safely ignore these errors.

Workaround: None.

RHOAIENG-12294 (previously documented as RHOAIENG-4812) - Distributed workload metrics exclude GPU metrics

In this release of OpenShift AI, the distributed workload metrics exclude GPU metrics.

Workaround: None.

RHOAIENG-4570 - Existing Argo Workflows installation conflicts with install or upgrade

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 OpenShift AI with data science pipelines 2.0, ensure that there is no existing installation of Argo Workflows on your cluster. For more information, see Migrating to data science pipelines 2.0.

Workaround: Remove the existing Argo Workflows installation or set datasciencepipelines to Removed, and then proceed with the installation or upgrade.

RHOAIENG-3913 - Red Hat OpenShift AI Operator incorrectly shows Degraded condition of False with an error

If you have enabled the KServe component in the DataScienceCluster (DSC) object used by the OpenShift AI Operator, but have not installed the dependent Red Hat OpenShift Service Mesh and Red Hat OpenShift Serverless Operators, the kserveReady condition in the DSC object correctly shows that KServe is not ready. However, the Degraded condition incorrectly shows a value of False.

Workaround: Install the Red Hat OpenShift Serverless and Red Hat OpenShift Service Mesh Operators, and then recreate the DSC.

RHOAIENG-3025 - OVMS expected directory layout conflicts with the KServe StoragePuller layout

When you use the OpenVINO Model Server (OVMS) runtime to deploy a model on the single-model serving platform (which uses KServe), there is a mismatch between the directory layout expected by OVMS and that of the model-pulling logic used by KServe. Specifically, OVMS requires the model files to be in the /<mnt>/models/1/ directory, while KServe places them in the /<mnt>/models/ directory.

Workaround

Perform the following actions:

In your S3-compatible storage bucket, place your model files in a directory called 1/, for example, /<s3_storage_bucket>/models/1/<model_files>.
To use the OVMS runtime to deploy a model on the single-model serving platform, choose one of the following options to specify the path to your model files:
- If you are using the OpenShift AI dashboard to deploy your model, in the Path field for your data connection, use the /<s3_storage_bucket>/models/ format to specify the path to your model files. Do not specify the 1/ directory as part of the path.
- If you are creating your own InferenceService custom resource to deploy your model, configure the value of the storageURI field as /<s3_storage_bucket>/models/. Do not specify the 1/ directory as part of the path.

KServe pulls model files from the subdirectory in the path that you specified. In this case, KServe correctly pulls model files from the /<s3_storage_bucket>/models/1/ directory in your S3-compatible storage.

RHOAIENG-3018 - OVMS on KServe does not expose the correct endpoint in the dashboard

When you use the OpenVINO Model Server (OVMS) runtime to deploy a model on the single-model serving platform, the URL shown in the Inference endpoint field for the deployed model is not complete.

Workaround: To send queries to the model, you must add the /v2/models/_<model-name>_/infer string to the end of the URL. Replace _<model-name>_ with the name of your deployed model.

RHOAIENG-2602 - “Average response time" server metric graph shows multiple lines due to ModelMesh pod restart

The Average response time server metric graph shows multiple lines if the ModelMesh pod is restarted.

Workaround: None.

RHOAIENG-2585 - UI does not display an error/warning when UWM is not enabled in the cluster

Red Hat OpenShift AI does not correctly warn users if User Workload Monitoring (UWM) is disabled in the cluster. UWM is necessary for the correct functionality of model metrics.

Workaround: Manually ensure that UWM is enabled in your cluster, as described in Enabling monitoring for user-defined projects.

RHOAIENG-2555 - Model framework selector does not reset when changing Serving Runtime in form

When you use the Deploy model dialog to deploy a model on the single-model serving platform, if you select a runtime and a supported framework, but then switch to a different runtime, the existing framework selection is not reset. This means that it is possible to deploy the model with a framework that is not supported for the selected runtime.

Workaround: While deploying a model, if you change your selected runtime, click the Select a framework list again and select a supported framework.

RHOAIENG-2468 - Services in the same project as KServe might become inaccessible in OpenShift

If you deploy a non-OpenShift AI service in a data science project that contains models deployed on the single-model serving platform (which uses KServe), the accessibility of the service might be affected by the network configuration of your OpenShift cluster. This is particularly likely if you are using the OVN-Kubernetes network plugin in combination with host network namespaces.

Workaround

Perform one of the following actions:

Deploy the service in another data science project that does not contain models deployed on the single-model serving platform. Or, deploy the service in another OpenShift project.

In the data science project where the service is, add a network policy to accept ingress traffic to your application pods, as shown in the following example:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-ingress-to-myapp
spec:
  podSelector:
    matchLabels:
      app: myapp
  ingress:
     - {}

RHOAIENG-2228 - The performance metrics graph changes constantly when the interval is set to 15 seconds

On the Endpoint performance tab of the model metrics screen, if you set the Refresh interval to 15 seconds and the Time range to 1 hour, the graph results change continuously.

Workaround: None.

RHOAIENG-2183 - Endpoint performance graphs might show incorrect labels

In the Endpoint performance tab of the model metrics screen, the graph tooltip might show incorrect labels.

Workaround: None.

RHOAIENG-1919 - Model Serving page fails to fetch or report the model route URL soon after its deployment

When deploying a model from the OpenShift AI dashboard, the system displays the following warning message while the Status column of your model indicates success with an OK/green checkmark.

Failed to get endpoint for this deployed model. routes.rout.openshift.io"<model_name>" not found

Workaround: Refresh your browser page.

RHOAIENG-404 - No Components Found page randomly appears instead of Enabled page in OpenShift AI dashboard

A No Components Found page might appear when you access the Red Hat OpenShift AI dashboard.

Workaround: Refresh the browser page.

RHOAIENG-234 - Unable to view .ipynb files in VSCode in Insecured cluster

When you use the code-server notebook image on Google Chrome in an insecure cluster, you cannot view .ipynb files.

Workaround: Use a different browser.

RHOAIENG-1128 - Unclear error message displays when attempting to increase the size of a Persistent Volume (PV) that is not connected to a workbench

When attempting to increase the size of a Persistent Volume (PV) that is not connected to a workbench, an unclear error message is displayed.

Workaround: Verify that your PV is connected to a workbench before attempting to increase the size.

RHOAIENG-497 - Removing DSCI Results In OpenShift Service Mesh CR Being Deleted Without User Notification

If you delete the DSCInitialization resource, the OpenShift Service Mesh CR is also deleted. A warning message is not shown.

Workaround: None.

RHOAIENG-282 - Workload should not be dispatched if required resources are not available

Sometimes a workload is dispatched even though a single machine instance does not have sufficient resources to provision the RayCluster successfully. The AppWrapper CRD remains in a Running state and related pods are stuck in a Pending state indefinitely.

Workaround: Add extra resources to the cluster.

RHOAIENG-131 - gRPC endpoint not responding properly after the InferenceService reports as Loaded

When numerous InferenceService instances are generated and directed requests, Service Mesh Control Plane (SMCP) becomes unresponsive. The status of the InferenceService instance is Loaded, but the call to the gRPC endpoint returns with errors.

Workaround: Edit the ServiceMeshControlPlane custom resource (CR) to increase the memory limit of the Istio egress and ingress pods.

RHOAIENG-130 - Synchronization issue when the model is just launched

When the status of the KServe container is Ready, a request is accepted even though the TGIS container is not ready.

Workaround: Wait a few seconds to ensure that all initialization has completed and the TGIS container is actually ready, and then review the request output.

RHOAIENG-3115 - Model cannot be queried for a few seconds after it is shown as ready

Models deployed using the multi-model serving platform might be unresponsive to queries despite appearing as Ready in the dashboard. You might see an “Application is not available" response when querying the model endpoint.

Workaround: Wait 30-40 seconds and then refresh the page in your browser.

RHOAIENG-1619 (previously documented as DATA-SCIENCE-PIPELINES-165) - Poor error message when S3 bucket is not writable

When you set up a data connection and the S3 bucket is not writable, and you try to upload a pipeline, the error message Failed to store pipelines is not helpful.

Workaround: Verify that your data connection credentials are correct and that you have write access to the bucket you specified.

RHOAIENG-1207 (previously documented as ODH-DASHBOARD-1758) - Error duplicating OOTB custom serving runtimes several times

If you duplicate a model-serving runtime several times, the duplication fails with the Serving runtime name "<name>" already exists error message.

Workaround: Change the metadata.name field to a unique value.

RHOAIENG-1201 (previously documented as ODH-DASHBOARD-1908) - Cannot create workbench with an empty environment variable

When creating a workbench, if you click Add variable but do not select an environment variable type from the list, you cannot create the workbench. The field is not marked as required, and no error message is shown.

Workaround: None.

RHOAIENG-432 (previously documented as RHODS-12928) - Using unsupported characters can generate Kubernetes resource names with multiple dashes

When you create a resource and you specify unsupported characters in the name, then each space is replaced with a dash and other unsupported characters are removed, which can result in an invalid resource name.

Workaround: None.

RHOAIENG-226 (previously documented as RHODS-12432) - Deletion of the notebook-culler ConfigMap causes Permission Denied on dashboard

If you delete the notebook-controller-culler-config ConfigMap in the redhat-ods-applications namespace, you can no longer save changes to the Cluster Settings page on the OpenShift AI dashboard. The save operation fails with an HTTP request has failed error.

Workaround

Complete the following steps as a user with cluster-admin permissions:

Enter the following command to update the OdhDashboardConfig custom resource in the redhat-ods-applications application namespace:

$ oc patch OdhDashboardConfig odh-dashboard-config -n redhat-ods-applications --type=merge -p '{"spec": {"dashboardConfig": {"notebookController.enabled": true}}}'

RHOAIENG-133 - Existing workbench cannot run Elyra pipeline after notebook restart

If you use the Elyra JupyterLab extension to create and run data science pipelines within JupyterLab, and you configure the pipeline server after you created a workbench and specified a notebook image within the workbench, you cannot execute the pipeline, even after restarting the notebook.

Workaround

Stop the running notebook.
Edit the workbench to make a small modification. For example, add a new dummy environment variable, or delete an existing unnecessary environment variable. Save your changes.
Restart the notebook.
In the left sidebar of JupyterLab, click Runtimes.
Confirm that the default runtime is selected.

RHOAIENG-11 - Separately installed instance of CodeFlare Operator not supported

In Red Hat OpenShift AI, the CodeFlare Operator is included in the base product and not in a separate Operator. Separately installed instances of the CodeFlare Operator from Red Hat or the community are not supported.

Workaround

Delete any installed CodeFlare Operators, and install and configure Red Hat OpenShift AI, as described in the Red Hat Knowledgebase solution How to migrate from a separately installed CodeFlare Operator in your data science cluster.

For more information about Red Hat OpenShift AI Operator dependencies, see Red Hat OpenShift AI Supported Configurations.

RHODS-12798 - Pods fail with "unable to init seccomp" error

Pods fail with CreateContainerError status or Pending status instead of Running status, because of a known kernel bug that introduced a seccomp memory leak. When you check the events on the namespace where the pod is failing, or run the oc describe pod command, the following error appears:

runc create failed: unable to start container process: unable to init seccomp: error loading seccomp filter into kernel: error loading seccomp filter: errno 524

Workaround: Increase the value of net.core.bpf_jit_limit as described in the Red Hat Knowledgebase solution Pods failing with error loading seccomp filter into kernel: errno 524 in OpenShift 4.

KUBEFLOW-177 - Bearer token from application not forwarded by OAuth-proxy

You cannot use an application as a custom workbench image if its internal authentication mechanism is based on a bearer token. The OAuth-proxy configuration removes the bearer token from the headers, and the application cannot work properly.

Workaround: None.

RHOAIENG-16568 (previously documented as NOTEBOOKS-210) - A notebook fails to export as a PDF file in Jupyter

When you export a notebook as a PDF file in Jupyter, the export process fails with an error.

Workaround: None.

RHOAIENG-1210 (previously documented as ODH-DASHBOARD-1699) - Workbench does not automatically restart for all configuration changes

When you edit the configuration settings of a workbench, a warning message appears stating that the workbench will restart if you make any changes to its configuration settings. This warning is misleading because in the following cases, the workbench does not automatically restart:

Edit name
Edit description
Edit, add, or remove keys and values of existing environment variables

Workaround: Manually restart the workbench.

RHOAIENG-1208 (previously documented as ODH-DASHBOARD-1741) - Cannot create a workbench whose name begins with a number

If you try to create a workbench whose name begins with a number, the workbench does not start.

Workaround: Delete the workbench and create a new one with a name that begins with a letter.

KUBEFLOW-157 - Logging out of JupyterLab does not work if you are already logged out of the OpenShift AI dashboard

If you log out of the OpenShift AI dashboard before you log out of JupyterLab, then logging out of JupyterLab is not successful. For example, if you know the URL for a Jupyter notebook, you are able to open this again in your browser.

Workaround: Log out of JupyterLab before you log out of the OpenShift AI dashboard.

RHODS-9789 - Pipeline servers fail to start if they contain a custom database that includes a dash in its database name or username field

When you create a pipeline server that uses a custom database, if the value that you set for the dbname field or username field includes a dash, the pipeline server fails to start.

Workaround: Edit the pipeline server to omit the dash from the affected fields.

RHOAIENG-580 (previously documented as RHODS-9412) - Elyra pipeline fails to run if workbench is created by a user with edit permissions

If a user who has been granted edit permissions for a project creates a project workbench, that user sees the following behavior:

During the workbench creation process, the user sees an Error creating workbench message related to the creation of Kubernetes role bindings.
Despite the preceding error message, OpenShift AI still creates the workbench. However, the error message means that the user will not be able to use the workbench to run Elyra data science pipelines.
If the user tries to use the workbench to run an Elyra pipeline, Jupyter shows an Error making request message that describes failed initialization.
Workaround
A user with administrator permissions (for example, the project owner) must create the workbench on behalf of the user with edit permissions. That user can then use the workbench to run Elyra pipelines.

RHODS-7718 - User without dashboard permissions is able to continue using their running notebooks and workbenches indefinitely

When a Red Hat OpenShift AI administrator revokes a user’s permissions, the user can continue to use their running notebooks and workbenches indefinitely.

Workaround: When the OpenShift AI administrator revokes a user’s permissions, the administrator should also stop any running notebooks and workbenches for that user.

RHOAIENG-1157 (previously documented as RHODS-6955) - An error can occur when trying to edit a workbench

When editing a workbench, an error similar to the following can occur:

Error creating workbench
Operation cannot be fulfilled on notebooks.kubeflow.org "workbench-name": the object has been modified; please apply your changes to the latest version and try again

Workaround: None.

RHOAIENG-1152 (previously documented as RHODS-6356) - The notebook creation process fails for users who have never logged in to the dashboard

The dashboard’s notebook Administration page displays users belonging to the user group and admin group in OpenShift. However, if an administrator attempts to start a notebook server on behalf of a user who has never logged in to the dashboard, the server creation process fails and displays the following error message:

Request invalid against a username that does not exist.

Workaround: Request that the relevant user logs into the dashboard.

RHODS-5763 - Incorrect package version displayed during notebook selection

The Start a notebook server page displays an incorrect version number for the Anaconda notebook image.

Workaround: None.

RHODS-5543 - When using the NVIDIA GPU Operator, more nodes than needed are created by the Node Autoscaler

When a pod cannot be scheduled due to insufficient available resources, the Node Autoscaler creates a new node. There is a delay until the newly created node receives the relevant GPU workload. Consequently, the pod cannot be scheduled and the Node Autoscaler’s continuously creates additional new nodes until one of the nodes is ready to receive the GPU workload. For more information about this issue, see the Red Hat Knowledgebase solution When using the NVIDIA GPU Operator, more nodes than needed are created by the Node Autoscaler.

Workaround: Apply the cluster-api/accelerator label in machineset.spec.template.spec.metadata. This causes the autoscaler to consider those nodes as unready until the GPU driver has been deployed.

RHOAIENG-1149 (previously documented RHODS-5216) - The application launcher menu incorrectly displays a link to OpenShift Cluster Manager

Red Hat OpenShift AI incorrectly displays a link to the OpenShift Cluster Manager from the application launcher menu. Clicking this link results in a "Page Not Found" error because the URL is not valid.

Workaround: None.

RHOAIENG-1137 (previously documented as RHODS-5251) - Notebook server administration page shows users who have lost permission access

If a user who previously started a notebook server in Jupyter loses their permissions to do so (for example, if an OpenShift AI administrator changes the user’s group settings or removes the user from a permitted group), administrators continue to see the user’s notebook servers on the server Administration page. As a consequence, an administrator is able to restart notebook servers that belong to the user whose permissions were revoked.

Workaround: None.

RHODS-4799 - Tensorboard requires manual steps to view

When a user has TensorFlow or PyTorch notebook images and wants to use TensorBoard to display data, manual steps are necessary to include environment variables in the notebook environment, and to import those variables for use in your code.

Workaround

When you start your notebook server, use the following code to set the value for the TENSORBOARD_PROXY_URL environment variable to use your OpenShift AI user ID.

import os
os.environ["TENSORBOARD_PROXY_URL"]= os.environ["NB_PREFIX"]+"/proxy/6006/"

RHODS-4718 - The Intel® oneAPI AI Analytics Toolkits quick start references nonexistent sample notebooks

The Intel® oneAPI AI Analytics Toolkits quick start, located on the Resources page on the dashboard, requires the user to load sample notebooks as part of the instruction steps, but refers to notebooks that do not exist in the associated repository.

Workaround: None.

RHODS-4627 - The CronJob responsible for validating Anaconda Professional Edition’s license is suspended and does not run daily

The CronJob responsible for validating Anaconda Professional Edition’s license is automatically suspended by the OpenShift AI operator. As a result, the CronJob does not run daily as scheduled. In addition, when Anaconda Professional Edition’s license expires, Anaconda Professional Edition is not indicated as disabled on the OpenShift AI dashboard.

Workaround: None.

RHOAIENG-1141 (previously documented as RHODS-4502) - The NVIDIA GPU Operator tile on the dashboard displays button unnecessarily

GPUs are automatically available in Jupyter after the NVIDIA GPU Operator is installed. The Enable button, located on the NVIDIA GPU Operator tile on the Explore page, is therefore redundant. In addition, clicking the Enable button moves the NVIDIA GPU Operator tile to the Enabled page, even if the Operator is not installed.

Workaround: None.

RHODS-3984 - Incorrect package versions displayed during notebook selection

In the OpenShift AI interface, the Start a notebook server page displays incorrect version numbers for the JupyterLab and Notebook packages included in the oneAPI AI Analytics Toolkit notebook image. The page might also show an incorrect value for the Python version used by this image.

Workaround: When you start your oneAPI AI Analytics Toolkit notebook server, you can check which Python packages are installed on your notebook server and which version of the package you have by running the !pip list command in a notebook cell.

RHODS-2956 - Error can occur when creating a notebook instance

When creating a notebook instance in Jupyter, a Directory not found error appears intermittently. This error message can be ignored by clicking Dismiss.

Workaround: None.

RHOAING-1147 (previously documented as RHODS-2881) - Actions on dashboard not clearly visible

The dashboard actions to revalidate a disabled application license and to remove a disabled application tile are not clearly visible to the user. These actions appear when the user clicks on the application tile’s Disabled label. As a result, the intended workflows might not be clear to the user.

Workaround: None.

RHOAIENG-1134 (previously documented as RHODS-2879) - License revalidation action appears unnecessarily

The dashboard action to revalidate a disabled application license appears unnecessarily for applications that do not have a license validation or activation system. In addition, when a user attempts to revalidate a license that cannot be revalidated, feedback is not displayed to state why the action cannot be completed.

Workaround: None.

RHOAIENG-2305 (previously documented as RHODS-2650) - Error can occur during Pachyderm deployment

When creating an instance of the Pachyderm operator, a webhook error appears intermittently, preventing the creation process from starting successfully. The webhook error is indicative that, either the Pachyderm operator failed a health check, causing it to restart, or that the operator process exceeded its container’s allocated memory limit, triggering an Out of Memory (OOM) kill.

Workaround: Repeat the Pachyderm instance creation process until the error no longer appears.

RHODS-2096 - IBM Watson Studio not available in OpenShift AI

IBM Watson Studio is not available when OpenShift AI is installed on OpenShift Dedicated 4.9 or higher, because it is not compatible with these versions of OpenShift Dedicated.

Workaround: Contact Marketplace support for assistance manually configuring Watson Studio on OpenShift Dedicated 4.9 and higher.

Chapter 9. Product features

Red Hat OpenShift AI provides a rich set of features for data scientists and IT operations administrators. To learn more, see Introduction to Red Hat OpenShift AI.

Legal Notice

The text of and illustrations in this document are licensed by Red Hat under a Creative Commons Attribution–Share Alike 3.0 Unported license ("CC-BY-SA"). An explanation of CC-BY-SA is available at http://creativecommons.org/licenses/by-sa/3.0/. In accordance with CC-BY-SA, if you distribute this document or an adaptation of it, you must provide the URL for the original version.

Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law.

Red Hat, Red Hat Enterprise Linux, the Shadowman logo, the Red Hat logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries.

Linux® is the registered trademark of Linus Torvalds in the United States and other countries.

Java® is a registered trademark of Oracle and/or its affiliates.

XFS® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries.

MySQL® is a registered trademark of MySQL AB in the United States, the European Union and other countries.

Node.js® is an official trademark of Joyent. Red Hat is not formally related to or endorsed by the official Joyent Node.js open source or commercial project.

The OpenStack® Word Mark and OpenStack logo are either registered trademarks/service marks or trademarks/service marks of the OpenStack Foundation, in the United States and other countries and are used with the OpenStack Foundation's permission. We are not affiliated with, endorsed or sponsored by the OpenStack Foundation, or the OpenStack community.

All other trademarks are the property of their respective owners.

Release notes

Features, enhancements, resolved issues, and known issues associated with this release

Chapter 1. Overview of OpenShift AI

Chapter 2. New features and enhancements

2.1. New features

2.2. Enhancements

Chapter 3. Technology Preview features

Chapter 4. Developer Preview features

Chapter 5. Limited Availability features

Chapter 6. Support removals

6.1. Deprecated functionality

6.1.1. OpenShift AI dashboard user management moved from `OdhDashboardConfig` to `Auth` resource

6.1.2. Deprecated cluster configuration parameters

6.2. Removed functionality

6.2.1. Data science pipelines v1 support removed

6.2.2. Pipeline logs for Python scripts running in Elyra pipelines are no longer stored in S3

6.2.3. Embedded subscription channel no longer used

6.2.4. Version 1.2 notebook container images for workbenches are no longer supported

6.2.5. Beta subscription channel no longer used

6.2.6. HabanaAI workbench image removal

Chapter 7. Resolved issues

7.1. Issues resolved in Red Hat OpenShift AI 2.17 (February 2025)

Chapter 8. Known issues

Chapter 9. Product features

Legal Notice

Learn

Try, buy, & sell

Communities

About Red Hat Documentation

Making open source more inclusive

About Red Hat

Red Hat legal and privacy links

Red Hat legal and privacy links

Release notes

Features, enhancements, resolved issues, and known issues associated with this release

Chapter 1. Overview of OpenShift AI

Chapter 2. New features and enhancements

2.1. New features

2.2. Enhancements

Chapter 3. Technology Preview features

Chapter 4. Developer Preview features

Chapter 5. Limited Availability features

Chapter 6. Support removals

6.1. Deprecated functionality

6.1.1. OpenShift AI dashboard user management moved from OdhDashboardConfig to Auth resource

6.1.2. Deprecated cluster configuration parameters

6.2. Removed functionality

6.2.1. Data science pipelines v1 support removed

6.2.2. Pipeline logs for Python scripts running in Elyra pipelines are no longer stored in S3

6.2.3. Embedded subscription channel no longer used

6.2.4. Version 1.2 notebook container images for workbenches are no longer supported

6.2.5. Beta subscription channel no longer used

6.2.6. HabanaAI workbench image removal

Chapter 7. Resolved issues

7.1. Issues resolved in Red Hat OpenShift AI 2.17 (February 2025)

Chapter 8. Known issues

Chapter 9. Product features

Legal Notice

Learn

Try, buy, & sell

Communities

About Red Hat Documentation

Making open source more inclusive

About Red Hat

Red Hat legal and privacy links

Red Hat legal and privacy links

6.1.1. OpenShift AI dashboard user management moved from `OdhDashboardConfig` to `Auth` resource