• Jupyter is a large umbrella project that covers many different software offerings and tools, including the popular Jupyter Notebook and JupyterLab web-based notebook authoring and editing applications.

• Kernels are programming language specific processes that run independently and interact with the Jupyter Applications and their user interfaces.

  • They can be considered as the computational engine for notebooks.

  • The kernel process is a standalone process that runs in the background and executes the code that you write in your notebooks. The kernel process is responsible for running the code and returning the results to the frontend.

• ARCC provides the Jupyter with OnDemand workshop.

The general process involves updating the Conda environment to include kernel related packages, and then configuring the kernel spec to allow it to be picked up by the Jupyter service.

1. Activate you R Conda Environment.

2. Start R.

3. Install the IRkernel package.

4. Exit R.

5. Deactivate your Conda environment.

6. Copy the created kernelspec into your home ~/.local/share/jupyter/kernels/ folder.

7. Update the kernel.json.

Activate you R Conda Environment and start R.

[]$ module load miniconda3/24.3.0
[]$ conda activate /project/<project-name>/software/conda-envs/r_4.3.3_env/
(/project/<project-name>/software/conda-envs/r_4.3.3_env) []$ R
> install.packages('IRkernel')
...
also installing the dependencies ‘fastmap’, ‘fansi’, ‘utf8’, ‘htmltools’, ‘pillar’, ‘base64enc’, ‘repr’, ‘evaluate’, ‘IRdisplay’, ‘pbdZMQ’, ‘crayon’, ‘jsonlite’, ‘uuid’, ‘digest’
...
* DONE (IRkernel)

Installing the IRkernel package will create a kernel spec that we can use.

This can be found under the Conda environment location, under: lib/R/library/IRkernel/kernelspec/

[]$ ls /project/<project-name>/software/conda-envs/r_4.3.3_env/lib/R/library/IRkernel/kernelspec/
kernel.js  kernel.json  logo-64x64.png  logo-svg.svg

[salexan5@mblog2 ~]$ cat /project/<project-name>/software/conda-envs/r_4.3.3_env/lib/R/library/IRkernel/kernelspec/kernel.json
{"argv": ["R", "--slave", "-e", "IRkernel::main()", "--args", "{connection_file}"],
 "display_name":"R",
 "language":"R"
}

If you haven’t used the Jupyter service, then you might not have, and thus will need to create the following folders:

.local/share/jupyter/kernels/

Copy the created kernelspec into your home:

[~]$ cd .local/share/jupyter/kernels/
[]$ mkdir r_4.3.3
[]$ cd r_4.3.3
[]$ cp /project/<project-name>/software/conda-envs/r_4.3.3_env/lib/R/library/IRkernel/kernelspec/* .

Update the kernel.json file to:

• point to the Conda environment’s version of R.

• give this kernel a unique display name.

[]$ cat kernel.json
{"argv": ["/project/<project-name>/software/conda-envs/r_4.3.3_env/bin/R", "--slave", "-e", "IRkernel::main()", "--args", "{connection_file}"],
 "display_name":"R_4.3.3 (local)",
 "language":"R"
}

From OnDemand start a Jupyter session.

Notice how the newly configured kernel is available.

.libPaths()

# Cell Output:
'/cluster/medbow/project/<project-name>/software/conda-envs/r_4.3.3_env/lib/R/library'

write.table(installed.packages()[,c(1,2,3:4)])

# Cell Output:
"Package" "LibPath" "Version" "Priority"
"base" "base" "/cluster/medbow/project/<project-name>/software/conda-envs/r_4.3.3_env/lib/R/library" "4.3.3" "base"
...
"vctrs" "vctrs" "/cluster/medbow/project/<project-name>/software/conda-envs/r_4.3.3_env/lib/R/library" "0.6.5" NA

Note the library path is that of the Conda environment: /project/arcc/software/conda-envs/r_4.3.3_env/lib/R/library