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Release notes

Red Hat OpenShift AI Self-Managed 2.13

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 2.13.1 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 Supported configurations.

Chapter 2. New features and enhancements

This section describes new features and enhancements in Red Hat OpenShift AI 2.13.

Important

This version of OpenShift AI supports using data science pipelines version 2.0. If you are using OpenShift AI 2.8 and want to continue using data science pipelines version 1.0, Red Hat recommends that you stay on OpenShift AI 2.8. For more information, see Support Removals.

2.1. New Features

Support for Red Hat Enterprise Linux AI models
You can now serve and deploy Granite family large language models (LLM) included with Red Hat Enterprise Linux AI. For more information about deploying models, see Deploying models by using the single-model serving platform. For more information about the supported models, see Downloading models in the Red Hat Enterprise Linux AI documentation.

2.2. Enhancements

Artifacts API in data science pipelines
You can now visualize artifacts from S3 storage in the data science pipelines dashboard. Whether created by the pipeline or imported externally, artifacts stored in S3 can be securely retrieved with API calls and downloaded or displayed directly in the dashboard.

Chapter 3. Technology Preview features

Important

This section describes Technology Preview features in Red Hat OpenShift AI 2.13. 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.

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.

Data drift monitoring (TrustyAI)

With data drift monitoring, data scientists can detect significant changes in input data distributions for their deployed models, helping to ensure that model predictions remain accurate and reliable over time.

TrustyAI data drift monitoring metrics compare the latest real-world data to the original training data and provide a quantitative measure of the alignment between the training data and the inference data.

To use data drift monitoring, see Monitoring data drift in the Open Data Hub documentation.

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

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.

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.

code-server workbench image

Red Hat OpenShift AI now includes the code-server workbench image. See code-server in GitHub for more information.

With the code-server workbench image, you can customize your workbench environment by using a variety of extensions to add new languages, themes, debuggers, and connect to additional services. You can also enhance the efficiency of your data science work with syntax highlighting, auto-indentation, and bracket matching.

Note

Elyra-based pipelines are not available with the code-server workbench image.

The code-server workbench image is currently available in Red Hat OpenShift AI 2.13 as a Technology Preview feature. This feature was first introduced in OpenShift AI 2.6.

Chapter 4. Developer Preview features

Important

This section describes Developer Preview features in Red Hat OpenShift AI 2.13. 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 AMD GPUs
The AMD ROCm workbench image adds support for the AMD graphics processing units (GPU) Operator, significantly boosting the processing performance for compute-intensive activities. This provides you with access to drivers, development tools, and APIs that support AI workloads and a wide range of models. Additionally, the AMD ROCm workbench image includes machine learning libraries to support AI frameworks such as TensorFlow and PyTorch. The Developer Preview release also enables access to images that can be used to explore serving and training/tuning use cases with AMD GPUs. For more information, see Using AMD GPUs with workbenches in OpenShift AI.
KServe Modelcars

The KServe component of OpenShift AI includes Modelcars as a Developer Preview feature. The Modelcars feature streamlines model fetching by using Open Container Initiative (OCI) images that contain your model data. This behavior can decrease startup times for large models, reduce disk space usage, and enhance performance.

The Modelcars feature is not enabled by default. You must modify your KServe configuration to use the feature.

For more information, see Serving models with OCI images in the KServe documentation and the Enhancing KServe Model Fetching with Modelcars design document.

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.

Chapter 5. Limited Availability features

Important

This section describes Limited Availability features in Red Hat OpenShift AI 2.13. 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.

Model-serving on single node OpenShift

This feature extends support for the model-serving capabilities of OpenShift AI on single node OpenShift.

You can now deploy a machine learning model by using the KServe component of OpenShift AI in RawDeployment mode, which means that KServe does not have any other components as dependencies.

For more information, see Deploy a machine learning model by using KServe RawDeployment mode on RHOAI with single node OpenShift.

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. Removed functionality

6.1.1. 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 be using the latest runtime images for Elyra, which are provided in the 2024.1 workbench images.

If you have an older workbench image version, update the Version selection field to 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.

6.1.2. Data science pipelines v1 upgraded to v2

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. For more information, see Enabling 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 2.9 or later with data science pipelines 2.0, ensure that there is no existing installation of Argo Workflows on your cluster.

It is no longer possible to deploy, view, or edit the details of pipelines that are based on data science pipelines 1.0 from the dashboard in OpenShift AI 2.13. If you already use data science pipelines, Red Hat recommends that you stay on OpenShift AI 2.8 until full feature parity in data science pipelines 2.0 has been delivered in a stable OpenShift AI release and you are ready to migrate to the new pipeline solution. For a detailed view of the release lifecycle, including the full support phase window, see Red Hat OpenShift AI Self-Managed Life Cycle.

Important

If you want to use existing pipelines and workbenches with data science pipelines 2.0 after upgrading to OpenShift AI 2.13, you must update your workbenches to use the 2024.1 notebook image version and then manually migrate your pipelines from data science pipelines 1.0 to 2.0. For more information, see Upgrading to data science pipelines 2.0.

6.1.3. 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.

6.1.4. Removal of bias detection (TrustyAI)

Starting with OpenShift AI 2.7, the (TrustyAI) bias detection functionality has been removed. If you previously had this functionality enabled, upgrading to OpenShift AI 2.7 or later will remove the feature. The default TrustyAI notebook image remains supported.

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

When you create a workbench, you specify a notebook container image to use with the workbench. Starting with OpenShift AI 2.5, when you create a new workbench, version 1.2 notebook container images are not available to select. Workbenches that are already running with a version 1.2 notebook image continue to work normally. However, Red Hat recommends that you update your workbench to use the latest notebook container image.

6.1.6. Beta subscription channel no longer used

Starting with OpenShift AI 2.5, the beta subscription channel is no longer used. You can no longer select the beta channel for a new installation of the Operator. For more information about subscription channels, see Installing the Red Hat OpenShift AI Operator.

6.2. Deprecated functionality

6.2.1. 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 parameterReplaced by

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).

Chapter 7. Resolved issues

The following notable issues are resolved in Red Hat OpenShift AI 2.13.1. Security updates, bug fixes, and enhancements for Red Hat OpenShift AI 2.13 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 2.13

Incorrect logo on dashboard after upgrading

Previously, after upgrading from OpenShift AI 2.11 to OpenShift AI 2.12, the dashboard could incorrectly display the Open Data Hub logo instead of the Red Hat OpenShift AI logo. This issue is now resolved.

RHOAIENG-11297 - Authentication failure after pipeline run

Previously, during the execution of a pipeline run, a connection error could occur due to a certificate authentication failure. This certificate authentication failure could be caused by the use of a multi-line string separator for customCABundle in the default-dsci object, which was not supported by data science pipelines. This issue is now resolved.

RHOAIENG-11232 - Distributed workloads: Kueue alerts do not provide runbook link

After a Kueue alert fires, the cluster administrator can click Observe → Alerting → Alerts and click the name of the alert to open its Alert details page. On the Alert details page, the Runbook section now provides a link to the appropriate runbook to help to diagnose and resolve the issues that triggered the alert. Previously, the runbook link was missing.

RHOAIENG-10665 - Unable to query Speculating with a draft model for granite model

Previously, you could not use speculative decoding on the granite-7b model and granite-7b-accelerator draft model. When querying these models, the queries failed with an internal error. This issue is now resolved.

RHOAIENG-9481 - Pipeline runs menu glitches when clicking action menu

Previously, when you clicked the action menu (⋮) next to a pipeline run on the Experiments > Experiments and runs page, the menu that appeared was not fully visible, and you had to scroll to see all of the menu items. This issue is now resolved.

RHOAIENG-8553 - Workbench created with custom image shows !Deleted flag

Previously, if you disabled the internal image registry on your OpenShift cluster and then created a workbench with a custom image that was imported by using the image tag, for example: quay.io/my-wb-images/my-image:tag, a !Deleted flag was shown in the Notebook image column on the Workbenches tab of the Data Science Projects page. If you stopped the workbench, you could not restart it. This issue is now resolved.

RHOAIENG-6376 - Pipeline run creation fails after setting pip_index_urls in a pipeline component to a URL that contains a port number and path

Previously, when you created a pipeline and set the pip_index_urls value for a component to a URL that contains a port number and path, compiling the pipeline code and then creating a pipeline run could result in an error. This issue is now resolved.

RHOAIENG-4240 - Jobs fail to submit to Ray cluster in unsecured environment

Previously, when running distributed data science workloads from notebooks in an unsecured OpenShift cluster, a ConnectionError: Failed to connect to Ray error message might be shown. This issue is now resolved.

Chapter 8. Known issues

This section describes known issues in Red Hat OpenShift AI 2.13.1 and any known methods of working around these issues.

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-11895 - Unable to clone a GitHub repo in JupyterLab when configuring a custom CA bundle using |-

If you configure a custom Certificate Authority (CA) bundle in the DSCInitialization (DSCI) object using |-, cloning a repo from JupyterLab fails with the following error: 

Cloning into 'ods-ci-notebooks-main'... fatal: unable to access 'https://github.com/redhat-rhods-qe/ods-ci-notebooks-main/': error setting certificate file: /etc/pki/tls/custom-certs/ca-bundle.crt
Workaround

  1. In the DSCI, replace customCABundle: |- with customCABundle: |. For example: 

      trustedCABundle:
    customCABundle: |
  2. Wait a few minutes, then recreate the workbench and clone the repo.

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

Manually removing the opendatahub.io/managed annotation from any component deployment YAML file might cause resource entry values in the file to be erased.

Workaround

To remove the annotation from a deployment, use the following steps to delete the deployment.

The controller pod for the component will redeploy automatically with default values.

  1. Log in to the OpenShift console as a cluster administrator.
  2. In the Administrator perspective, click Workloads > Deployments.
  3. From the Project drop-down list, select redhat-ods-applications.
  4. Click the options menu Options menu beside the deployment for which you want to remove the annotation.
  5. Click Delete Deployment.

RHOAIENG-10790 - Pipeline Schedules fail after upgrading to OpenShift AI 2.12 or later, or manually enabling/disabling podToPodTLS DSPA option

When you upgrade to OpenShift AI 2.12 or later from an earlier version, data science pipeline scheduled runs that existed before the upgrade fail to execute. An error reading server preface: EOF error is displayed in the task pod.

The same error occurs when you manually change the value of the spec.podToPodTLS field in the DataSciencePipelinesApplication resource of the project.

Workaround
Delete the scheduled run that is failing to execute, and then recreate it. Alternatively, you can duplicate the existing scheduled run.

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

  1. Delete the existing AppWrapper CRD. 

    $ oc delete crd appwrappers.workload.codeflare.dev

  2. Install the latest version of the AppWrapper CRD. 

    $ oc apply -f https://raw.githubusercontent.com/project-codeflare/codeflare-operator/main/config/crd/crd-appwrapper.yml

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-7887 - Kueue fails to monitor RayCluster or PyTorchJob resources

When you create a DataScienceCluster CR with all components enabled, the Kueue component is installed before the Ray component and the Training Operator component. As a result, the Kueue component does not monitor RayCluster or PyTorchJob resources.

Workaround

Perform one of the following actions:

  • After installing the Ray component and the Training Operator component, restart the Kueue controller pod in the redhat-ods-applications namespace.
  • Alternatively, edit the DataScienceCluster CR to mark the kueue component as Removed, wait until Kueue is uninstalled, and then mark the kueue component as Managed again.

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.

  1. From the OpenShift AI dashboard, click Data Science Pipelines → Runs.
  2. From the Project drop-down menu, click your data science project.
  3. From the Runs tab, click the pipeline run that you want to check the status of.

  4. Expand the condition group and click a child task.

    A panel that contains information about the child task is displayed

  5. 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 Enabling 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:

  1. In your S3-compatible storage bucket, place your model files in a directory called 1/, for example, /<s3_storage_bucket>/models/1/<model_files>.

  2. 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-2759 - Model deployment fails when both secured and regular model servers are present in a project

When you create a second model server in a project where one server is using token authentication, and the other server does not use authentication, the deployment of the second model might fail to start.

Workaround
None.

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-545 - Cannot specify a generic default node runtime image in JupyterLab pipeline editor

When you edit an Elyra pipeline in the JupyterLab IDE pipeline editor, and you click the PIPELINE PROPERTIES tab, and scroll to the Generic Node Defaults section and edit the Runtime Image field, your changes are not saved.

Workaround
Define the required runtime image explicitly for each node. Click the NODE PROPERTIES tab, and specify the required image in the Runtime Image field.

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:

  1. Log in to your cluster by using the oc client.

  2. 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
  1. Stop the running notebook.
  2. 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.
  3. Restart the notebook.
  4. In the left sidebar of JupyterLab, click Runtimes.
  5. 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.

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-5646 (previously documented as NOTEBOOKS-218) - Data science pipelines saved from the Elyra pipeline editor reference an incompatible runtime

When you save a pipeline in the Elyra pipeline editor with the format .pipeline in OpenShift AI version 1.31 or earlier, the pipeline references a runtime that is incompatible with OpenShift AI version 1.32 or later.

As a result, the pipeline fails to run after you upgrade OpenShift AI to version 1.32 or later.

Workaround
After you upgrade to OpenShift AI to version 1.32 or later, select the relevant runtime images again.

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.

RHOAIENG-583 (previously documented as RHODS-8921 and RHODS-6373) - You cannot create a pipeline server or start a workbench when cumulative character limit is exceeded

When the cumulative character limit of a data science project name and a pipeline server name exceeds 62 characters, you are unable to successfully create a pipeline server. Similarly, when the cumulative character limit of a data science project name and a workbench name exceeds 62 characters, workbenches fail to start.

Workaround
Rename your data science project so that it does not exceed 30 characters.

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-1132 (previously documented as RHODS-6383) - An ImagePullBackOff error message is not displayed when required during the workbench creation process

Pods can experience issues pulling container images from the container registry. If an error occurs, the relevant pod enters into an ImagePullBackOff state. During the workbench creation process, if an ImagePullBackOff error occurs, an appropriate message is not displayed.

Workaround
Check the event log for further information on the ImagePullBackOff error. To do this, click on the workbench status when it is starting.

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.

RHOAIENG-1135 (previously documented as RHODS-3985) - Dashboard does not display Enabled page content after ISV operator uninstall

After an ISV operator is uninstalled, no content is displayed on the Enabled page on the dashboard. Instead, the following error is displayed: 

Error loading components
HTTP request failed
Workaround
Wait 30-40 seconds and then refresh the page in your browser.

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.

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