Known issues in 1.5.4 SP2

DSE-42079: Cloudera AI Workbenches are not compatible with the Unified timezone feature

When you enable the Unified timezone feature, the Cloudera Embedded Container Service cluster timezone is synchronized with the Cloudera Manager Base time zone, and the Cloudera AI sessions fails to launch with Exit Code 34. Timestamp discrepancies with workloads are displayed.

Workaround:

If you use Cloudera AI Workbenches, disable the Unified Timezone feature by following the instructions in Cloudera Embedded Container Service unified time zone.

DSE-41757: Python workloads running multiple-line comments or strings might fail

Python workloads running multiple-line comments or strings might fail to run when using the Workbench Editor.

Workaround:

Run the code using the PBJ Workbench Editor.

DSE-42509: Creating a project does not work on NTP/Airgap proxy when using private repository with SSH key and SSH URL

When creating a project on NTP/Airgap proxy, using a private repository with an SSH key and an SSH URL, an error message is displayed that the project cannot be created.

No workaround is available.

DSE-42510: Fetching Cloudera or Huggingface AMP catalog is failing on NTP/Airgap proxy

When fetching Cloudera or Huggingface Cloudera Accelerators for Machine Learning Project (AMP) catalog on NTP/Airgap proxy, an error message is displayed Error fetching AMP catalog source URL.

No workaround is available.

DSE-44698: Restart Stabilty - Cloudera AI Workbench pod preemption by system-critical workloads

During control plane upgrades or cluster restarts, Cloudera AI Workbench pods may transition into the Init:Error or Init:ContainerStatusUnknown state.

This issue may arise during cluster startup or under resource pressure when the scheduler preempts lower-priority Cloudera AI Workbench pods to allocate resources for higher-priority system pods, such as critical system components. Additionally, Kubernetes does not automatically clean up preempted pods, leaving them in failed Init states.

This is the expected Kubernetes scheduler behavior. There is no permanent fix available, as pod preemption is controlled by the Kubernetes scheduler and is necessary for system stability during resource constraints.

Workaround:

Delete the affected pod, and Kubernetes automatically attempts to reschedule it if sufficient resources are available:

kubectl delete pod [***POD NAME***] -n [***CLOUDERA AI WORKBENCH NAMESPACE***]

DSE-40756: Resource Profile field displays empty on Job Settings page

After creating or upgrading a job and saving a Resource Profile, the Resource Profile field on the Job Settings pages might appear empty.

There is no workaround.

DSE-47904: Resource Profile is not visible for jobs and applications

After creating or upgrading a job and saving a Resource Profile, the Resource Profile field on the Application Settings pages might appear empty.

There is no workaround.

DSE-43884: Default timeout insufficient for large databases to be migrated from CDSW to Cloudera AI

When migrating a Cloudera Data Science Workbench workspace containing large database tables, such as a dashboards table with several hundred thousand unstopped rows, the database update process inside the cml-db-migrate pod can take several hours to complete. Because the default job completion timeout is set to 10 minutes, the migration fails prematurely due to a timeout waiting for the cml-db-migrate job to finish.

Workaround:

note

Both WORKLOAD_IMAGE_REGISTRY and MLX_CP_VERSION for the setup can be obtained from the dp-mlx-control-plane-app deployment in the CDP namespace.

kubectl get deployment dp-mlx-control-plane-app -n cdp -o yaml | grep WORKLOAD_IMAGE_REGISTRY -A 1

        - name: WORKLOAD_IMAGE_REGISTRY
          value: docker-private.infra.cloudera.com/cloudera

kubectl get deployment dp-mlx-control-plane-app -n cdp -o yaml | grep MLX_CP_VERSION -A 1

        - name: MLX_CP_VERSION
          value: 1.47.0-b58

Open a Shell session on the Cloudera Embedded Container Service node.
Change to the Docker parcel directory: cd /opt/cloudera/parcels/ECS/docker.
Export WORKLOAD_IMAGE_REGISTRY=docker-private.infra.cloudera.com/cloudera.
Export MLX_CP_VERSION=1.47.0-b58.

Pull the migrator image:

./docker pull $WORKLOAD_IMAGE_REGISTRY/mlx-control-plane-app-cdsw-migrator:$MLX_CP_VERSION

Tag the existing image as a backup:

./docker tag $WORKLOAD_IMAGE_REGISTRY/mlx-control-plane-app-cdsw-migrator:$MLX_CP_VERSION $WORKLOAD_IMAGE_REGISTRY/mlx-control-plane-app-cdsw-migrator:backup

Run the container:

./docker run  --name mig_container $WORKLOAD_IMAGE_REGISTRY/mlx-control-plane-app-cdsw-migrator:$MLX_CP_VERSION /bin/bash

Copy the migration script from the container to the host:

./docker cp mig_container:/cdsw-migrator/cdsw_to_pvc_migration.sh /tmp/cdsw_to_pvc_migration.sh

Update MAX_ITERATIONS=180, to three hours or more, based on database size, in the /tmp/cdsw_to_pvc_migration.sh script.

Copy the updated script back into the container:

./docker cp /tmp/cdsw_to_pvc_migration.sh mig_container:/cdsw-migrator/cdsw_to_pvc_migration.sh

Commit the container changes to the image:

./docker commit mig_container $WORKLOAD_IMAGE_REGISTRY/mlx-control-plane-app-cdsw-migrator:$MLX_CP_VERSION

Push the updated image to the registry:

./docker push $WORKLOAD_IMAGE_REGISTRY/mlx-control-plane-app-cdsw-migrator:$MLX_CP_VERSION

Remove the container:
```
./docker rm mig_container
```
Restart the Cloudera Data Science Workbench to Cloudera AI migration.