Known Issues and Limitations

The modify-ml-serving-app command fails on Cloudera AI Inference service Azure cluster in the us-west-2 workload region (DSE-39826)

When you run modify-ml-serving-app API on Cloudera AI Inference service Azure cluster, the status of the application is displayed as modify:failed.

Workaround: You must first delete the instance group you want to modify using the delete-instance-group-ml-serving-app API. Then, recreate the instance group, modify the configuration based on your requirements, and add the instance group using the add-instance-groups-ml-serving-app API.

Automatic synchronization of teams and users feature is disabled in the UI (DSE-36718)

Due to some issues with the automatic synchronization of teams and users, this feature is disabled in the UI in the September 26, 2024 release for version 2.0.46-b200. New installation or upgraded workspace does not have the auto synchronization option in the UI.

Workaround: You can manually synchronize teams and synchronize users.

Web pod crashes if a project forking takes more than 60 minutes (DSE-35251)

The web pod crashes if a project forking takes more than 60 minutes. This is because the timeout is set to 60 minutes using the grpc_git_clone_timeout_minutes property. The following error is displayed after the web pod crash:

2024-04-23 22:52:36.384   1737    ERROR      AppServer.VFS.grpc                    crossCopy grpc error    data = [{"error":"1"},{"code":4,"details":"2","metadata":"3"},"Deadline exceeded",{}]
["Error: 4 DEADLINE_EXCEEDED: Deadline exceeded\n    at callErrorFromStatus (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/call.js:31:19)\n    at Object.onReceiveStatus (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/client.js:192:76)\n    at Object.onReceiveStatus (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/client-interceptors.js:360:141)\n    at Object.onReceiveStatus (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/client-interceptors.js:323:181)\n    at /home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/resolving-call.js:94:78\n    at process.processTicksAndRejections (node:internal/process/task_queues:77:11)\nfor call at\n    at ServiceClientImpl.makeUnaryRequest (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/client.js:160:34)\n    at ServiceClientImpl.crossCopy (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/make-client.js:105:19)\n    at /home/cdswint/services/web/server-dist/grpc/vfs-client.js:235:19\n    at new Promise (<anonymous>)\n    at Object.crossCopy (/home/cdswint/services/web/server-dist/grpc/vfs-client.js:234:12)\n    at Object.crossCopy (/home/cdswint/services/web/server-dist/models/vfs.js:280:38)\n    at projectForkAsyncWrapper (/home/cdswint/services/web/server-dist/models/projects/projects-create.js:229:19)"]
node:internal/process/promises:288
            triggerUncaughtException(err, true /* fromPromise */);
            ^Error: 4 DEADLINE_EXCEEDED: Deadline exceeded
    at callErrorFromStatus (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/call.js:31:19)
    at Object.onReceiveStatus (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/client.js:192:76)
    at Object.onReceiveStatus (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/client-interceptors.js:360:141)
    at Object.onReceiveStatus (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/client-interceptors.js:323:181)
    at /home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/resolving-call.js:94:78
    at process.processTicksAndRejections (node:internal/process/task_queues:77:11)
for call at
    at ServiceClientImpl.makeUnaryRequest (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/client.js:160:34)
    at ServiceClientImpl.crossCopy (/home/cdswint/services/web/node_modules/@grpc/grpc-js/build/src/make-client.js:105:19)
    at /home/cdswint/services/web/server-dist/grpc/vfs-client.js:235:19
    at new Promise (<anonymous>)
    at Object.crossCopy (/home/cdswint/services/web/server-dist/grpc/vfs-client.js:234:12)
    at Object.crossCopy (/home/cdswint/services/web/server-dist/models/vfs.js:280:38)
    at projectForkAsyncWrapper (/home/cdswint/services/web/server-dist/models/projects/projects-create.js:229:19) {
  code: 4,
  details: 'Deadline exceeded',
  metadata: Metadata { internalRepr: Map(0) {}, options: {} }
}

Workaround: Increase the timeout limit using the grpc_git_clone_timeout_minutes property. For example, 120 minutes.

UPDATE site_config SET grpc_git_clone_timeout_minutes = <new value>;

Enabling Service Accounts (DSE-32943)

Teams in the Cloudera Machine Learning Workspace can only run workloads within team projects with the Run as option for service accounts if they have previously manually added service accounts as a collaborator to the team.

Working with files larger than 1 MB in Jupyter causes error (OPSAPS-61524)

While working on files or saving files of size larger than 1 MB, Jupyter Notebook may display an error message such as 413 Request Entity Too Large.

Workaround:

Clean up the notebook cell results often to keep the notebook below 1 MB. Use the kubectl CLI to add the following annotation to the ingress corresponding to the session.

annotations:
        nginx.ingress.kubernetes.io/proxy-body-size: "0"

Get the session ID (the alphanumeric suffix in the URL) from the web UI.
Get the corresponding namespace:
```
kubectl get pods -A | grep <session ID>
```

List the ingress in the namespace

kubectl get ingress -n <user-namespace> | grep <session ID>

In the metadata, add the annotation.

kubectl edit ingress <ingress corresponding to the session> -n <user-namespace>

Terminal does not stop after time-out (DSE-12064)

After a web session times out, the terminal should stop running, but it remaings functional.

Cloudera Machine Learning Workspace upgrades disabled with NTP

Upgrades are disabled for Cloudera Machine Learning Workspace configured with non-transparent proxy (NTP). This issue is anticipated to be fixed in a subsequent hotfix release.

Using dollar character in environment variables in Cloudera Machine Learning

Environment variables with the dollar ($) character are not parsed correctly by Cloudera Machine Learning. For example, if you set PASSWORD="pass$123" in the project environment variables, and then try to read it using the echo command, the following output will be displayed: pass23

Workaround: Use one of the following commands to print the $ sign:

echo 24 | xxd -r -p
or
echo JAo= | base64 -d

Insert the value of the environment variable by wrapping it in the command substitution using $() or ``. For example, if you want to set the environment variable to ABC$123, specify:

ABC$(echo 24 | xxd -r -p)123 
or 
ABC`echo 24 | xxd -r -p`123

Models: Some API endpoints not fully supported

In the create_run api, run_name is not supported.

Also, search_experiments only supports pagination.

When a team added as a collaborator, it does not appear in the UI. (DSE-31570)

Run Job as displays even if the job is enabled on a service account. (DSE-31573)

If the job is enabled on a service account, the Run Job as option should not display. Even if me is selected at this point, the job still runs in the service account.

AMP archive upload fails if Project does not contain metadata YAML file

To create a zip file to deploy AMPs in Cloudera Machine Learning, do the following:

Download the AMP zip file from GitHub
Unzip it to a temp directory
From the command line navigate to the root directory of the zip
Run this command to create the new zip file: zip -r amp.zip .

Make sure you see the .project-metadata/yaml in the root of the zip file.

Cloning from Git using SSH is not supported via HTTP proxy

Workaround: Cloudera Machine Learning Projects support HTTPS for cloning git projects. It is suggested to use this as the workaround.

Model deployments requiring outbound access via proxy do not honor HTTP_PROXY, HTTPS_PROXY environment variables

Workaround: Add the HTTP_PROXY, HTTPS_PROXY, http_proxy and https_proxy environment variables to the cdsw-build.sh file of the Project Repository.

Application does not restart after upgrade or migration

An application may fail to automatically restart after a workspace upgrade or migration. In this case, manually restart the application.

Do not use backtick characters in environment variable names

Avoid using backtick characters ( ` ) in environment variable names, as this will cause sessions to fail with exit code 2.

Cloudera Model Registry is not supported on R models

Cloudera Model Registry is not supported on R models.

The mlflow.log_model registered model files might not be available on NFS Server (DSE-27709)

When using mlflow.log_model, registered model files might not be available on the NFS server due to NFS server settings or network connections. This could cause the model to remain in the registering status.

Workaround:

Re-register the model. It will register as an additional version, but it should correct the problem.
Add the ARTIFACT_SYNC_PERIOD environment variable to hdfscli-server Kubernetes deployment and set it to an integer value. This will set the model registry retry operation to twice the number of seconds specified by the artifact sync period integer value. If the ARTIFACT_SYNC_PERIOD is set to 30 seconds then model registry will retry for 60 seconds. The default value is 10 and model registry retries for 20 seconds. For example: -name: ARTIFACT_SYNC_PERIOD value: “30”.

Applications appear in failed state after upgrade (DSE-23330)

After upgrading Cloudera Machine Learning from version 1.29.0 on AWS, some applications may be in a Failed state. The workaround is to restart the application.

Cannot use hashtag character in JDBC connection string

The special character # (hashtag) cannot be used in a password that is then used in a JDBC connection string. Avoid using this special character, or use '%23' instead.

Cloudera Machine Learning Workspace installation fails

Cloudera Machine Learning Workspace installation with Azure NetApp Files on NFS v4.1 fails. The workaround is to use NFS v3.

Spark executors fail due to insufficient disk space

Generally, the administrator should estimate the shuffle data set size before provisioning the workspace, and then specify the root volume size of the compute node that is appropriate given that estimate. For more specific guidelines, see the following resources.

Runtime Addon fails to load (DSE-16200)

A Spark runtime add-on may fail when upgrading a workspace.

Solution: To resolve this problem, try to reload the add-on. In Site Administration > Runtime/Engine, in the option menu next to the failed add-on, select Reload.

Cloudera Machine Learning Workspace provisioning times out

When provisioning a Cloudera Machine Learning Workspace, the process may time out with an error similar to Warning FailedMount or Failed to sync secret cache:timed out waiting for the condition. This can happen on AWS or Azure.

Solution: Delete the workspace and retry provisioning.

Cloudera Machine Learning endpoint connectivity from Cloudera Data Hub and Cloudera Data Engineering (DSE-14882)

When Cloudera services connect to Cloudera Machine Learning services, if the Cloudera Machine Learning Workspace is provisioned on a public subnet, traffic is routed out of the VPC first, and then routed back in. On Cloudera Private Cloud Cloudera Machine Learning, traffic is not routed externally.

NFS performance issues on AWS EFS (DSE-12404)

Cloudera Machine Learning uses NFS as the filesystem for storing application and user data. NFS performance may be much slower than expected in situations where a data scientist writes a very large number (typically in the thousands) of small files. Example tasks include: using git clone to clone a very large source repository (such as TensorFlow), or using pip to install a Python package that includes JavaScript code (such as plotly). Reduced performance is particularly common with Cloudera Machine Learning on AWS (which uses EFS), but it may be seen in other environments.

Disable file upload and download (DSE-12065)

You cannot disable file upload and download when using the Jupyter Notebook.

Remove Workspace operation fails (DSE-8834)

Remove Workspace operation fails if workspace creation is still in progress.

API does not enforce a maximum number of nodes for Cloudera Machine Learning Workspace

Problem: When the API is used to provision new Cloudera Machine Learning Workspace, it does not enforce an upper limit on the autoscale range.

Downscaling Cloudera Machine Learning Workspace nodes does not work as expected (MLX-637, MLX-638)

Problem: Downscaling nodes does not work as seamlessly as expected due to a lack of Bin Packing on the Spark default scheduler, and because dynamic allocation is not currently enabled. As a result, currently infrastructure pods, Spark driver/executor pods, and session pods are tagged as non-evictable using the cluster-autoscaler.kubernetes.io/safe-to-evict: "false" annotation.

Technical Service Bulletins

TSB 2024-761: Orphan EBS Volumes in Cloudera Machine Learning Workspace: Cloudera Machine Learning provisions Elastic Block Store (EBS) volumes during provisioning of a workspace. Due to missing labels on Cloudera Machine Learning Workspace, delete operations on previously restored Cloudera Machine Learning Workspace didn’t clean up a subset of the provisioned block volumes.
Knowledge article: For the latest update on this issue see the corresponding Knowledge article: TSB 2024-761: Orphan EBS Volumes in Cloudera Machine Learning Workspace
TSB 2023-628: Sensitive user data getting collected in Cloudera Machine Learning Workspace or CDSW workspace diagnostic bundles: When using Cloudera Data Science Workbench (CDSW), Cloudera recommends users to store sensitive information, such as passwords or access keys, in environment variables rather than in the code. See Engine Environment Variables in the official Cloudera documentation for details. Cloudera recently learned that all session environment variables in the affected releases of CDSW and Cloudera Machine Learning are logged in web pod logs, which may be included in support diagnostic bundles sent to Cloudera as part of support tickets.
Knowledge article: For the latest update on this issue see the corresponding Knowledge article: TSB-2023-628: Sensitive user data getting collected in Cloudera Machine Learning Workspace or in CDSW workspace diagnostic bundles
TSB 2022-588: Kubeconfig and new version of aws-iam-authenticator: Regenerate Kubeconfig and in conjunction use a newer version of aws-iam-authenticator on AWS. Kubeconfig in Cloudera Cloudera Public Cloud Data Services needs to be regenerated because the Kubeconfig generated before June 15, 2022 uses an old APIVersion (client.authentication.k8s.io/v1alpha1) which is no longer supported. This causes compatibility issues with aws-iam-authenticator starting from v0.5.7. To be able to use the new aws-iam-authenticator, the Kubeconfig needs to be regenerated.
Knowledge article: For the latest update on this issue see the corresponding Knowledge article: TSB-2022-588: Kubeconfig and new version of aws-iam-authenticator