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Create a Jupyter kernel for the workshop

Notebooks of the workshop use a shared Jupyter kernel, based on a Python virtual environment.

Setup

Clone this repository:

git clone https://github.com/nesi/ml102_workshop.git
cd ml102_workshop

Create the Conda environment and install dependencies:

module purge && module load Miniconda3
source $(conda info --base)/etc/profile.d/conda.sh
export PYTHONNOUSERSITE=1

CONDA_VENV=/nesi/project/nesi99991/ml102_20230713/jupyter_kernel_env
conda create -p "$CONDA_VENV" -y python=3.10.5
conda activate "$CONDA_VENV"
pip install -r requirements.lock.txt
conda deactivate

Then add it as a shared kernel for nesi99991:

module purge && module load JupyterLab
nesi-add-kernel -a nesi99991 -p "$CONDA_VENV" --shared tensorflow_ml102 TensorFlow/2.8.2-gimkl-2022a-Python-3.10.5

Alternative Kernel for Simplicity uses only the Tensorflow Module:

module purge && module load JupyterLab
nesi-add-kernel -a nesi99991 --shared tensorflow_ml102 TensorFlow/2.8.2-gimkl-2022a-Python-3.10.5

References

Relevant NeSI support pages: