This is a short guide to describe how to use jupyter nootebook on Izar through ssh port forwarding.
The key to successfully installing the tools is using python virtual environments.
Load the compiler you want to use and python using modules
$ module load gcc $ module load python |
$ module load intel $ module load python |
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:
$ 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. |
Activate virtual environment
$ source opt/venv-gcc/bin/activate (venv-gcc) [user@izar ~]$ |
Install Jupyter and ipyparallel
(venv-gcc) [user@izar ~]$ pip install jupyter ipyparallel Collecting jupyter .. .. |
Set a passwordless access to Izar by using ssh key and have the following in your ~/.ssh/config file on your personal computer. It is assumed that you are inside the EPFL network.
Host izar Hostname izar.epfl.ch User [username] |
Run jupyter notebook and ipcluster on Izar.
The script below is a template that allows you to start ipcluster 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.
#!/bin/bash -l
#SBATCH --job-name=ipcluster
#SBATCH --nodes=1
#SBATCH --exclusive
#SBATCH --time=01:00:00
#SBATCH --output jupyter-log-%J.out
module load gcc/8.4.0-cuda cuda/10.2.89 cudnn/7.6.5.32-10.2-linux-x64 mvapich2/2.3.4-cuda py-tensorflow
source opt/venv-gcc/bin/activate
profile=job_${SLURM_JOB_ID}
echo "creating profile: ${profile}"
ipython profile create ${profile}
echo "Launching controller"
ipcontroller --ip="*" --profile=${profile} &
sleep 10
echo "Launching engines"
srun ipengine --profile=${profile} --location=$(hostname) 2> /dev/null 1>&2 &
ipnport=$(shuf -i8000-9999 -n1)
XDG_RUNTIME_DIR=""
echo "${hostname}:${ipnport}" > jupyter-notebook-port-and-host
jupyter-notebook --no-browser --port=${ipnport} --ip=$(hostname -i) |
Launch your job as usual:
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:
Or copy and paste one of these URLs:
http://10.91.27.63:8504/?token=4b17ae5cfa505b5470dc84bb5240ab43ae714aa9480a163c
|
It has the form:
http://<IP ADDRESS>:<PORT NUMBER>/?token=<TOKEN> |
On your local machine do the following with the information provided by the above step
ssh -L <PORT NUMBER>:<IP ADDRESS>:<PORT NUMBER> izar -f -N |
For our example, this gives:
ssh -L 8504:10.91.27.63:8504 izar -f -N |
Now you should be able to access to Izar compute node through the web browser by pasting the following address
http://localhost:<PORT NUMBER>/?token=<TOKEN> |
For our example, this gives:
http://localhost:8504/?token=4b17ae5cfa505b5470dc84bb5240ab43ae714aa9480a163c |
Create a jupyter nootebook and add the following
import ipyparallel as ipp c = ipp.Client(profile='job_[SLURM_JOB_ID]') view = c[:] |
Replace [SLURM_JOB_ID] with the job number you obtain by running the command: squeue -u $USER
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