Known Issues and Limitations in ML Runtimes version 2025.01.3

You might run into some known issues while using ML Runtimes 2025.01.3.

DSE-42076: Conda -updated with Conda Technical Preview Runtime- fails🔗

Conda, that has been updated with Conda Technical Preview Runtime, fails when invoked from the Jupyter terminal. Use the Cloudera AI web terminal when interacting with Conda through the command line interface.

DSE-35335: PBJ R Runtimes print some output to logs instead of printing it to the session UI🔗

In PBJ R Runtime, messages generated in R (using IRkernel) are sent to the logs instead of being displayed in the UI. This behavior is due to the IRkernel, which sends the stderr output to the logs, rather than forwarding it to the UI. This behavior is not related to the PBJ R Runtime, but observed related to the IRkernel operation.

DSE-25143: Assembling plots in PBJ R Runtimes does not work🔗

When trying to plot additional content on already existing plots, PBJ R Runtimes throw an error. Plots can only be created using the plot function.

DSE-32839: Extra configuration needed when using Spark in a PBJ Workbench-based R Runtime🔗

When using Spark in R workloads that are running PBJ Workbench Runtimes, the environmental variable R_LIBS_USER must be passed to the Spark executors with the value set to /home/cdsw/.local/lib/R/<R_VERSION>/library.

For example, when using sparklyr with a PBJ Workbench R 4.3 Runtime, the correct way to set up a sparklyr connection is:

library(sparklyr)
     config <- spark_config()
     config$spark.executorEnv.R_LIBS_USER="/home/cdsw/.local/lib/R/4.3/library"
     sc <- spark_connect(config = config)

Packages can fail to load in a session🔗

When installing R or Python packages in a session, the kernel might not be able to load the package in the same session, if a previous version of the package or its newly installed dependencies have been loaded in the same session. Such issues are observable more often in PBJ R Runtimes, which automatically load basic R packages like vctrs, lifecycle, rlang, cli at session startup.

Workaround: Start a new session, import and use the newly installed package there.

Version of jupyter-client Python package must be of lower version than version 8 for PBJ Runtimes🔗

Upgrading the Python package jupyter-client with a version greater than 7.4.9 can temporarily break a project. Workloads using PBJ Runtimes will not be able to start Projects if the jupyter-client version is greater than 7.4.9.

Workaround: Launch the same version of Python, but not on a PBJ Runtime (either Workbench or JupyterLab). Open a Terminal window and uninstall the jupyter-client package from the Project by executing pip3 uninstall jupyter-client. Verify your change by running pip3 list and checking that the version of the jupyter-client package is less than version 8.

Adding a new ML Runtimes when using a custom root certificate might generate error messages🔗

When trying to add new ML Runtimes, a number of error messages might appear in various places when using a custom root certificate. For example, you might see: the Could not fetch the image metadata or the certificate signed by unknown authority messages. This is caused by the runtime-puller pods not having access to the custom root certificate that is in use.

Workaround:

Create a directory at any location on the master node:
For example:
mkdir -p /certs/
Copy the full server certificate chain into this folder. It is usually easier to create a single file with all of your certificates (server, intermediate(s), root):
```
# copy all certificates into a single file: 
        cat server-cert.pem intermediate.pem root.pem > /certs/cert-chain.crt
```
(Optional) If you are using a custom docker registry that has its own certificate, you need to copy this certificate chain into this same file:
```
cat docker-registry-cert.pem >> /certs/cert-chain.crt
```

Copy the global CA certificates into this new file:

# cat /etc/ssl/certs/ca-bundle.crt >> /certs/cert-chain.crt

Edit your deployment of runtime manager and add the new mount.
Do not delete any existing objects.
#kubectl edit deployment runtime-manager

Under VolumeMounts, add the following lines.

Note that the text is white-space sensitive - use spaces and not tabs.

- mountPath: /etc/ssl/certs/ca-certificates.crt 
        name: mycert 
        subPath: cert-chain.crt #this should match the new file name created in step 4

Under Volumes add the following text in the same edit:

- hostPath: 
        path: /certs/  #this needs to match the folder created in step 1
        type: "" 
        name: mycert

Save your changes:
wq!
Once saved, you will receive the message "deployment.apps/runtime-manager edited" and the pod will be restarted with your new changes.
To persist these changes across cluster restarts, use the following Knowledge Base article to create a kubernetes patch file for the runtime-manager deployment: https://community.cloudera.com/t5/Customer/Patching-CDSW-Kubernetes-deployments/ta-p/90241

Cloudera Bug: DSE-20530

DSE-17981: Disable Scala runtimes in models, experiments and applications runtime selection🔗

Scala Runtimes should not appear as an option for Models, Experiments, and Applications in the user interface. Currently Scala Runtimes only support Session and Jobs.