Known Issues and Limitations in ML Runtimes version 2024.10.01

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".

E.g. 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 less 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: "Could not fetch the image metadata" or "certificate signed by unknown authority". 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

Spark Runtime Add-on required for Spark 2 integration with Scala Runtimes

Scala Runtimes on Cloudera AI require Spark Runtime Addon to enable Spark2 integration. Spark3 is not supported with the Scala Runtime.

Impyla is not compatible with Python 3.12

The Python 3.12 editions of ML Runtimes, that is ML Runtimes 2024.10 version and higher versions, are not supported by the Impyla package. Therefore, Impyla cannot be used with these ML Runtimes to connect to Apache Impala.