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This is a short guide to describe how to use jupyter nootebook on Izar through ssh port forwarding.

Installing IPython and Jupyter

The key to successfully installing the tools is using python virtual environments.

1. Load the compiler you want to use and python using modules
   

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   language bash
   $ module load gcc $ module load python

   
   

   Code Block
   language bash
   $ module load intel $ module load python

   
   

2. Create a virtual environment

   In the following, we use GCC but the same procedure applies to Intel compiler. In your home folder, create a virtual environment:

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   language bash
   $ virtualenv -p python3 --system-site-packages opt/venv-gcc Running virtualenv with interpreter /ssoft/spack/arvine/v1/opt/spack/linux-rhel7-skylake_avx512/gcc-8.4.0/python-3.7.7-drpdlwdbo3lmtkcbckq227ypnzno4ek3/bin/python3 Already using interpreter /ssoft/spack/arvine/v1/opt/spack/linux-rhel7-skylake_avx512/gcc-8.4.0/python-3.7.7-drpdlwdbo3lmtkcbckq227ypnzno4ek3/bin/python3 Using base prefix '/ssoft/spack/arvine/v1/opt/spack/linux-rhel7-skylake_avx512/gcc-8.4.0/python-3.7.7-drpdlwdbo3lmtkcbckq227ypnzno4ek3' New python executable in /home/user/opt/venv-gcc/bin/python3 Also creating executable in /home/user/opt/venv-gcc/bin/python Installing setuptools, pip, wheel... done.

   
   

3. Activate virtual environment

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   language bash
   $ source opt/venv-gcc/bin/activate (venv-gcc) [user@izar ~]$

   
   

4. Install Jupyter
   

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   language bash
   (venv-gcc) [user@izar ~]$ pip install jupyter Collecting jupyter .. ..

   
   

5. Set passwordless access to Izar by using ssh key and have the following in your ~/.ssh/config file on your personal computer (How to generate an SSH key). It is assumed that you are inside the EPFL network (or connected through VPN).
   
   

   Code Block
   language bash
   Host izar Hostname izar.epfl.ch User [username] Host i*.izar ProxyCommand ssh -W $(echo "%h" | sed -e 's/.izar//'):%p izar

   
   

6. Run jupyter notebook on Izar.
   The script below is a template that allows you to start jupyter notebook on a compute node of Izar. You can copy it in a file called launch_jupyter.sh for example. It has to be placed in your home (or you have to modify it accordingly). Note that all the modules that jupyter may need have to be loaded here. As an example, here, we loaded the modules that allow us to use TensorFlow in the notebook.

   Code Block
   language bash title launch_jupyter.sh
   #!/bin/bash -l #SBATCH --job-name=ipython-trial #SBATCH --nodes=1 #SBATCH --gres=gpu:1 #SBATCH --exclusive #SBATCH --time=01:00:00 #SBATCH --output jupyter-log-%J.out module load gcc mvapich2 py-tensorflow source opt/venv-gcc/bin/activate ipnport=$(shuf -i8000-9999 -n1) jupyter-notebook --no-browser --port=${ipnport} --ip=$(hostname -i)

   Launch your job as usual:

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   language bash
   sbatch launch_jupyter.sh

   Once the job is running analyze the output jupyter-log-[SLURM_ID].out. Then, look for a line like the following:

   Code Block
   language bash
   Or copy and paste one of these URLs: http://10.91.27.63:8504/?token=4b17ae5cfa505b5470dc84bb5240ab43ae714aa9480a163c

   It has the form:

   Code Block
   language bash
   http://<IP ADDRESS>:<PORT NUMBER>/?token=<TOKEN>

   
   

7. On your local machine do the following with the information provided by the above step
   

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   language bash
   ssh -L <PORT NUMBER>:<IP ADDRESS>:<PORT NUMBER> -l <USERNAME> izar.epfl.ch -f -N ## Or if you created the ssh config from step 5 ssh -L <PORT NUMBER>:<IP ADDRESS>:<PORT NUMBER> izar -f -N

   For our example, this gives:
   

   Code Block
   language bash
   ssh -L 8504:10.91.27.63:8504 -l user izar.epfl.ch -f -N ## with config ssh -L 8504:10.91.27.63:8504 izar -f -N

   
   

8. Now you should be able to access to Izar compute node through the web browser by pasting the following address
   
   

   Code Block
   language bash
   http://localhost:<PORT NUMBER>/?token=<TOKEN>

   For our example, this gives:

   Code Block
   language bash
   http://localhost:8504/?token=4b17ae5cfa505b5470dc84bb5240ab43ae714aa9480a163c

   
   

Here is a screenshot of a jupyter session running a simple TensorFlow example. The computations are run on the cluster node, but you access the results directly from your browser.

Image Modified

If you wish to use tensorboard you must open a second connection as follow

...

ssh -L 6006:localhost:6006 -o ProxyCommand="ssh -W %h:%p izar.epfl.ch" -l user i39

## with config
ssh -L 6006:localhost:6006 i39.izar

Image Modified

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