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Python Jupyter notebooks are usually started on a localhost, which results in starting local webserver and using web browser to interact with the notebook.

On prometheus we cannot easily expose the web socket to external world, as calculations are done internally on a computing node, not visible from Internet.

How to use on Prometheus?

Via SSH tunnel

The trick is to start Jupyter via a job submitted to a computing node and creating a SSH tunnel to access it on a local PC.

Submit jupyter job to a computing node

Create a following file:

#SBATCH --partition plgrid-testing
#SBATCH --nodes 1
#SBATCH --ntasks-per-node 6
#SBATCH --time 0:30:00
#SBATCH --job-name jupyter-notebook-tunnel
#SBATCH --output jupyter-log-%J.txt

## get tunneling info
ipnport=$(shuf -i8000-9999 -n1)
ipnip=$(hostname -i)

## print tunneling instructions to jupyter-log-{jobid}.txt
echo -e "
    Copy/Paste this in your local terminal to ssh tunnel with remote
    ssh -o ServerAliveInterval=300 -N -L $ipnport:$ipnip:$ipnport ${user}

    Then open a browser on your local machine to the following address
    localhost:$ipnport  (prefix w/ https:// if using password)

module load plgrid/tools/python/3.6.5

## start an ipcluster instance and launch jupyter server
jupyter-notebook --no-browser --port=$ipnport --ip=$ipnip

Save it as pyton-notebook.slurm.

Send job to queue using sbatch command on login node of Prometheus

sbatch pyton-notebook.slurm

Wait until your job enters running state.

To check status of submitted job using squeue command

squeue -j <JobID>

or all jobs of user

squeue -u $USER

which lists all  current user jobs submitted to queue ($USER - is enviromental).

Common states of jobs:

  • PD - PENDING - Job is awaiting resource allocation.
  • R - RUNNING - Job currently has an allocation and is running.
  • CF - CONFIGURING  - Job has been allocated resources, but are waiting for them to become ready for use (e.g. booting). On Prometheus CF state could last for up to 8 minutes in case when nodes that have been in power save mode.
  • CG - COMPLETING  - Job is in the process of completing. Some processes on some nodes may still be active.

Make a tunnel

In your directory cat jupyter log file:

cat jupyter-log-XXXXXXX.txt

where `XXXXXXX` is your sbatch job id which is displayed after you run it f.e. `cat jupyter-log-7123485.txt`

It will show you something like this:

Copy/Paste this in your local terminal to ssh tunnel with remote
ssh -N -L 8511:
Then open a browser on your local machine to the following address
localhost:8511 (prefix w/ https:// if using password)
## You exec in another shell given command:
> ssh -o ServerAliveInterval=300 -N -L 8511:
## And you are set, you can open in browser:
> `localhost:8511`

Exec in another shell at your local computer given command to make a tunnel:

ssh -o ServerAliveInterval=300 -N -L 8511:

Start the notebook

Open in browser: `localhost:8511`

If you need jupyter token 'cat jupyter-log-XXXXXXX.txt` once again, and you can copy your token from there.

All informations from jupyter will be stored in this log file.

Stop job

if you wish to to end your sbatch, use scancel <JOBID> command, where JOBID is your tunnel JOBID you can look it up with pro-jobs or qsueue -u $USER commands.

scancel <JOBID>

Status of jobs and finished jobs data

To check submitted and running jobs use pro-jobs or qsueue -u $USER commands.

To check information about finished and historic jobs use pro-jobs-history command. For example with option "-d 30"  that command shows all user's jobs from last 30 days. More info in pro-jobs-history -h

pro-jobs-history -d 30

Additional software

Python module idact (Interactive Data Analysis Convenience Tools) could be used to automate Jupyter deployment on Prometheus cluster.

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