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.

The process creates a kernel for an individual user from the previously created shared Conda environment.

Each individual from the shared space will need to follow this process to create their own kernel.

If anyone updated the packages within this shared Conda environment, then the update will effect everyone. Be warned.

[salexan5@mblog1 ~]$ module load miniconda3/24.3.0
[salexan5@mblog1 ~]$ conda activate /cluster/medbow/project/arcc/software/tensorflow/2.16
(/cluster/medbow/project/arcc/software/tensorflow/2.16) [salexan5@mblog1 ~]$ export PYTHONUSERBASE=$CONDA_PREFIX
(/cluster/medbow/project/arcc/software/tensorflow/2.16) [salexan5@mblog1 ~]$ conda install ipykernel
...
(/cluster/medbow/project/arcc/software/tensorflow/2.16) [salexan5@mblog1 ~]$ conda deactivate

Installing the ipykernel package will create a kernel spec related folder that we can use.

This can be found under the Conda environment location, under: share/jupyter/kernels/ named python3

[salexan5@mblog1 ~]$ ls /project/arcc/software/tensorflow/2.16/share/jupyter/kernels/python3/

[salexan5@mblog1 ~]$ cd /project/arcc/software/tensorflow/2.16
[salexan5@mblog1 2.16]$ cd share/jupyter/kernels/
[salexan5@mblog1 kernels]$ ls
python3

[salexan5@mblog1 kernels]$ ls python3
kernel.json  logo-32x32.png  logo-64x64.png  logo-svg.svg

Rename the folder to something more appropriate:

[salexan5@mblog1 kernels]$ mv python3 TF2.16

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 TF2.16 folder into your home:

[salexan5@mblog1 kernels]$ cp -r TF2.16/ ~/.local/share/jupyter/kernels/

Update the kernel.json file to:

• give this kernel a unique display name.

[salexan5@mblog1 kernels]$ ~/.local/share/jupyter/kernels/TF2.16/
[salexan5@mblog1 tf2.16]$ cat kernel.json
{
 "argv": [
  "/cluster/medbow/project/arcc/software/tensorflow/2.16/bin/python",
  "-m",
  "ipykernel_launcher",
  "-f",
  "{connection_file}"
 ],
 "display_name": "TF2.16 (local)",
 "language": "python",
 "metadata": {
  "debugger": true
 }
}

From OnDemand start a Jupyter session.

Notice how the newly configured kernel is now available.

import tensorflow as tf; 
print("TensorFlow Version: " + str( tf.__version__))

TensorFlow Version: 2.16.1