Chapter 6. Resolved issues

This release provides security updates. For a complete list of updates, see the associated errata advisory on the Red Hat Customer Portal.

RHOAIENG-26537 - Users cannot access the dashboard after installing OpenShift AI 2.21

After you installed OpenShift AI 2.21 and created a DataScienceCluster on a new cluster, you could not access the dashboard because the Auth custom resource was created without the default group configuration. This issue is now resolved.

RHOAIENG-26464 - InstructLab training phase1 pods restart when using default value due to insufficient memory in RHOAI 2.21

When you ran the InstructLab pipeline using the default value for the train_memory_per_worker input parameter (100 GiB), the phase1 training task failed because of insufficient pod memory. This issue is now resolved.

RHOAIENG-26263 - Node selector not cleared when changing the hardware profile for a workbench or model deployment

If you edited an existing workbench or model deployment to change the hardware profile from one that included a node selector to one that did not, the previous node placement settings could not be removed. With this release, the issue is resolved.

RHOAIENG-26099 - Environment variable HTTP_PROXY and HTTPS_PROXY added to notebooks

Previously, the notebook controller injected a cluster-wide OpenShift Proxy configuration to all newly created and restarted workbenches. With this release, proxy configurations are not injected unless a cluster administrator enables proxy configuration through the ConfigMap.

To enable proxy configuration, run the following command:

oc create configmap notebook-controller-setting-config --from-literal=INJECT_CLUSTER_PROXY_ENV=true -n redhat-ods-applications

$ oc create configmap notebook-controller-setting-config --from-literal=INJECT_CLUSTER_PROXY_ENV=true -n redhat-ods-applications

oc delete pod -l app=odh-notebook-controller -A

$ oc delete pod -l app=odh-notebook-controller -A

oc rollout restart -n redhat-ods-applications deployment/odh-notebook-controller-manager

$ oc rollout restart -n redhat-ods-applications deployment/odh-notebook-controller-manager

RHOAIENG-23475 - Inference requests on IBM Power in a disconnected environment fail with a timeout error

Previously, when you used the IBM Power architecture to send longer prompts of more than 100 input tokens to the inference service, there was no response from the inference service. With this release, the issue is resolved.

RHOAIENG-20595 - Pipelines tasks fail to run when defining an http_proxy environment variable

The pipeline tasks failed to run if you attempted to set the http_proxy or https_proxy environment variables in a pipeline task. With this release, the issue is resolved.

RHOAIENG-16568 - Unable to download notebook as a PDF from JupyterLab Workbenches

Previously, you could not download a notebook as a PDF file in Jupyter. With this release, the issue is resolved.

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

Previously, when you used Python version 3.11 in a Jupyter notebook and then created a Ray cluster, the cluster defaulted to a workbench image that contained both Ray version 2.35 and Python version 3.9, which caused compatibility errors. With this release, the issue is resolved.

RHOAIENG-7947 - Model serving fails during query in KServe

Previously, if you initially installed the ModelMesh component and enabled the multi-model serving platform, but later installed the KServe component and enable the single-model serving platform, inference requests to models deployed on the single-model serving platform could have failed. This issue no longer occurs.

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

If you were granted edit permissions for a project and created a project workbench, you saw the following behavior:

RHOAIENG-24682 - [vLLM-Cuda] Unable to deploy model on FIPS enabled cluster

Previously, if you deployed a model by using the vLLM NVIDIA GPU ServingRuntime for KServe or vLLM ServingRuntime Multi-Node for KServe runtimes on NVIDIA accelerators in a FIPS-enabled cluster, the deployment could fail. This issue is now resolved.

RHOAIENG-23596 - Inference requests on IBM Power with longer prompts to the inference service fail with a timeout error

Previously, when using the IBM Power architecture to send longer prompts of more than 100 input tokens to the inference service, there was no response from the inference service. This issue no longer occurs.