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Jupyter notebook


It is feasible to establish a Jupyter notebook operating on the HPC systems. This procedure is uncomplicated on GNU/Linux and macOS, owing to the presence of integrated terminal programs and an SSH client.

To begin with, you will need to submit an interactive job using the salloc command:

$ salloc --nodes=1 --ntasks=4 --qos=regular --time=2:00:00 --mem=20gb srun --pty /bin/bash

Upon executing the command, you will be instantly connected to the computing node. From there, you will need to load a Python module that is equipped to support Jupyter or leverage your own Python installation.

As an illustration, the following command will load the default Python version on the compute nodes:

$ module load Python

Now you would be ready to start a Jupyter notebook:

$ jupyter notebook --no-browser --ip

[I 23:32:12.176 NotebookApp]
[I 23:32:12.176 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 23:32:12.178 NotebookApp]

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:

Now it is time to create a tunnel to redirect the port 8888 in the compute node to a local port of your selection (in the example we will keep both ports the same). In a new terminal:

ssh -L 8888:atlas-XXX:8888 -N

Open up a browser and go to:


Tokens will be different each time you start up a new Jupyter notebook.

In the event the port 8888 is already taken you can change it using --port option when starting the notebook:

jupyter notebook --no-browser --port <port of your choice> --ip

Adding custom kernels to your notebooks

Given the following enviroments

$ conda env list
# conda environments:
base                  *  /scicomp/EasyBuild/CentOS/7.4.1708/Skylake/software/Anaconda3/2019.10
tensorflow-2.1.0-gpu_py37h7a4bb67_0     /scicomp/EasyBuild/CentOS/7.4.1708/Skylake/software/Anaconda3/2019.10/envs/tensorflow-2.1.0-gpu_py37h7a4bb67_0

We can add the custom kernel by

conda activate tensorflow-2.1.0-gpu_py37h7a4bb67_0
python -m ipykernel install --user --name=tensorflow-2.1.0-gpu_py37h7a4bb67_0


You need to activate the target enviroment before adding the custom kernel.

Now if we list all available kernels in the notebook, the custom kernel should appear:

Custom Kernels