Seitenhistorie

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Jupyter allows you to work in with your own virtual environment using the Python modules installed on the HPC. Start by loading the Python interpreter/module you want to work with, . You can use e.g. conda for this task. Start by creating a new conda environment:

module purge   ##just in case you are using jupyter terminal
module load PythonMiniconda/3.11.3

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conda create -p /gpfs/project/$USER

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python -m venv --prompt py311 --system-site-packages /py310 python=3.10
conda activate /gpfs/project/$USER/py311

PIP_CONFIG_FILE=/software/python/pip.conf pip install --user --upgrade pip setuptools wheel

Activate this environment and install all packages you want to work with - at least ipykernel must be installed

py310

Hint: this only works if you have defined a .condarc with channels pointing to our repo server (see also Conda)

Install the programs that you need with conda install , at least  ipykernel must be installed:

conda 

source /gpfs/project/$USER/py311/bin/activate

PIP_CONFIG_FILE=/software/python/pip.conf pip install ipykernel

Create a new file "kernel.sh" in the main directory of your environment and make it executable

cd /gpfs/project/$USER/py311py310
vi kernel.sh

Contents of the file kernel.sh:

 
----------------------------------------------------
#!/bin/bash
source 
export PYTHONPATH=/gpfs/project/$USER/py310/lib/python3.10/site-packages
export PATH=/gpfs/project/$USER/py311py310/bin/activate:$PATH
exec python -m ipykernel $@
----------------------------------------------------

Make the file executable with

chmod a+x kernel.sh

Create a new directory for your kernel in your /home/.local/share folder

mkdir -p /home/$USER/.local/share/jupyter/kernels/py311py310
cd /home/$USER/.local/share/jupyter/kernels/py311py310

Create a new file "kernel.json" with contents (!!replace $USER with your explicit username here!!)

{
"argv": [
  "/gpfs/project/$USER/py311py310/kernel.sh",
  "-f",
  "{connection_file}"
 ],
 "display_name": "VenvPython 3.10 (py311conda)",
 "language": "python",
 "metadata": {
  "debugger": true
 }
}

In your next jupyterhub session a new kernel with the name "venv (py311Python 3.10 (conda)" will then be available.

Hint: This seems to only work with Python versions < 3.11 !