Chapter 7. Resolved issues

RHOAIENG-15033 - Model registry instances do not restart or update after upgrade from OpenShift AI 2.14 to 2.15

When you upgrade from OpenShift AI 2.14.z to 2.15, existing instances of the model registry component are not updated, which causes the instance pods to use older images than the ones referenced by the operator pod. This issue does not occur when you upgrade to 2.16.

RHOAIENG-15008 - Error when creating a bias metric from the CLI without a request name

Previously, the user interface sometimes displayed an error message when you viewed bias metrics if the requestName parameter was not set. If you used the user interface to view bias metrics, but wanted to configure them through the CLI, you had to specify a requestName parameter within your payload. This issue is now resolved.

RHOAIENG-14986 - Incorrect package path causes copy_demo_nbs to fail

Previously, the copy_demo_nbs() function of the CodeFlare SDK failed because of an incorrect path to the SDK package. Running this function resulted in a FileNotFound error. This issue is now resolved.

RHOAIENG-14552 - Workbench or notebook OAuth proxy fails with FIPS on OCP 4.16

Previously, when using OpenShift 4.16 or newer in a FIPS-enabled cluster, connecting to a running workbench failed because the connection between the internal component oauth-proxy and the OpenShift ingress failed with a TLS handshake error. When opening a workbench, the browser showed an "Application is not available" screen without any additional diagnostics. This issue is now resolved.

RHOAIENG-14095 - The dashboard is temporarily unavailable after the installing OpenShift AI Operator

Previously, after you installed the OpenShift AI Operator, the OpenShift AI dashboard was unavailable for approximately three minutes. As a result, a Cannot read properties of undefined page sometimes appeared. This issue is now resolved.

RHOAIENG-13633 - Cannot set a serving platform for a project without first deploying a model from outside of the model registry

Previously, you could not set a serving platform for a project without first deploying a model from outside of the model registry. You could not deploy a model from a model registry to a project unless the project already had single-model or multi-model serving selected. The only way to select single-model or multi-model serving from the OpenShift AI UI was to first deploy a model or model server from outside the registry. This issue is now resolved.

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

Previously, when you edited an Elyra pipeline in the JupyterLab IDE pipeline editor, and you clicked the PIPELINE PROPERTIES tab, and scrolled to the Generic Node Defaults section and edited the Runtime Image field, your changes were not saved. This issue is now resolved.