User-defined kernels

The standard kernels in our JupyterHub setup include Python 3.6 and R 3.6.3, both with only few packages installed. These are sufficient for demonstration purposes, but may not contain specific packages that you may require. As a solution, you may set up your own kernel based on one of your local environments.

We will illustrate this process for both a MiniConda-installed Python and R environment. Please consult the VscDocumentation page on Python package management or the VscDocumentation page on R package management for MiniConda installation instructions.

Environment creation: Python

We will create a MiniConda environment with the following Python packages:

• python 3.9
• numpy
• scipy
• pandas
• tensorflow-gpu
• ipykernel

NOTE: Instead of creating a new environment you can also introduce an existing environment to your JupyterHub. In this case you can skip the creation of the environment. NOTE: Always include the ipykernel package in your environment, as this is necessary to install the kernel.

1. Create a new Conda environment (here named p39env):

   conda create -n p39env python=3.9 numpy scipy pandas tensorflow-gpu ipykernel
   

2. Activate the environment:

   conda activate p39env
   

3. Install the associated IPython kernel in ${VSC_HOME}/.local so that JupyterHub can find it:

   python -m ipykernel install  --prefix=${VSC_HOME}/.local/ --name 'p39env'
   

4. Connect to JupyterHub and verify that the new ‘p39env’ kernel appears. It should look like this:

5. Test the environment and make sure that it works as expected.

Environment creation: R

For R, let’s create an environment with following packages, and let’s name it r41env:

• R 4.1
• data.table
• ggplot2
• factoextra
• irkernel
• jupyter_client

NOTE: Instead of creating a new environment you can also introduce an existing environment to your JupyterHub. In this case you can skip the creation of the environment. NOTE: Always include the jupyter_client and the irkernel package, to be able to install the kernel.

1. Create the new R environment:

     conda create -n r41env r-base=4.1 r-data.table r-ggplot2 r-factoextra r-irkernel jupyter_client -c conda-forge
   

2. Activate the environment:

     conda activate r41env
   

3. Install the kernel in $VSC_HOME/.local to be able to use it in JupyterHub:

     Rscript -e 'IRkernel::installspec(prefix="${VSC_HOME}/.local/", name="r41env", displayname="r41env")'
   

4. Connect to JupyterHub and verify if you can find your newly created R environment in the kernel list.