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Code Block
[]$ module purge
[]$ module load miniconda3/24.3.0
[]$ conda activate /cluster/medbow/project/<project-name>/software/tensorflow/2.16
(/cluster/medbow/project/<project-name>/software/tensorflow/2.16) []$ export PYTHONUSERBASE=$CONDA_PREFIX
(/cluster/medbow/project/<project-name>/software/tensorflow/2.16) []$ conda install ipykernel
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(/cluster/medbow/project/<project-name>/software/tensorflow/2.16) []$ python -m ipykernel install --user --name=TF2.16-local

(/cluster/medbow/project/<project-name>/software/tensorflow/2.16) []$ conda deactivate
[]$

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The Related Kernel Details

Info

Installing Using 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

Code Block
[]$ cd

python -m ipykernel install --user --name=TF2.16-local above will result in the following output of the form:

Code Block
() (/project/<project-name>/software/tensorflow/2.16) []$ cd share/jupyter/kernels/
[]$ ls
python3

[]$ ls python3
kernel.json  logo-32x32.png  logo-64x64.png  logo-svg.svg
Info

Rename the folder to something more appropriate:

Code Block
[]$ mv python3 TF2.16

Configure Your Jupyter Environment

Note

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

~
python -m ipykernel install --user --name=TF2.16-local
Installed kernelspec TF2.16-local in /cluster/medbow/home/<username>/.local/share/jupyter/kernels/tf2.16-local
Info

Copy the created TF2.16 folder into your homeNotice: This file is created in the user's home folder, and can be accessed and viewed by the user:

Code Block
[]$ cp -r TF2.16/ ~ls /cluster/medbow/home/<username>/.local/share/jupyter/kernels/
Info

Update the kernel.json file to:

  • give this kernel a unique display name.

Code Block
[]$ ~/tf2.16-local/
kernel.json  logo-32x32.png  logo-64x64.png  logo-svg.svg

[]$ cat /cluster/medbow/home/<username>/.local/share/jupyter/kernels/TF2tf2.16/
[]$ cat -local/kernel.json
{
 "argv": [
  "/cluster/medbow/project/<project-name>/software/tensorflow/2.16/bin/python",
  "-Xfrozen_modules=off",
  "-m",
  "ipykernel_launcher",
  "-f",
  "{connection_file}"
 ],
 "display_name": "TF2.16 (-local)",
 "language": "python",
 "metadata": {
  "debugger": true
 }
}
Info

Suggestion: Using “-local” will help to distinguish that this is a local kernel, based on a Conda environment, that the user has created, and not something provided by ARCC.

Info

Side Note: Installing the ipykernel package creates a kernel spec related folder within the Conda environment, under: share/jupyter/kernels/ named python3.

Running python -m ipykernel will copy and update this folder.

Note

Kernel names can only contain ASCII letters and numbers and these separators: - . _ (hyphen, period, and underscore)

Names can not use whitespace such as --name="TF2.16 (local)"

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Start Jupyter

Info

From OnDemand start a Jupyter session.

Notice how the newly configured kernel is now available.

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