Slurm Jupyter Kernel

Manage (create, list, modify and delete) and starting jupyter slurm kernels using sbatch.

slurmkernel is able to connect to a kernel started on a compute node using SSH port forwarding. You can specify a SSH proxy jump, if you have to jump over two hosts (e.g. a loadbalancer)

Slurm job 3251854 is in state "RUNNING"
Slurm job is in state running on compute node cn213
Starting SSH tunnel to forward kernel ports to localhost
Your started kernel is now ready to use on compute node cn213

Features & Use-Cases

• Start Remote Jupyter kernel using sbatch (Slurm)

* Access to your local filesystem with remote code execution

• Manage existing Slurm Jupyter kernel
• Use the template module to use pre-defined script templates for remote installation and local kernel creation
• Custom environment variables supported (e.g. JULIANUMTHREADS)

Table of Contents

• Slurm Jupyter Kernel

- Features \& Use-Cases - Table of Contents - Installation - Install using pip - Requirements for usage - Create a new kernel - Template module (Script templates) - Example - IPython Example - Remote Host - Localhost - Set kernel-specific environment - Using the kernel with Applications - Quarto Example - Troubleshooting - Kernel exceptions - Debugging - Get help

Installation

slurmjupyterkernel must be installed locally where the Jupyter notebooks will run.

Install using pip

python3 -m pip install slurmjupyterkernel

Requirements for usage

• SSH-Key based authentication

You need a running SSH agent with the loaded key file to access the loginnode without a password.

Create a new kernel

We assume to install the Jupyter kernel tools into your $HOME directory on your cluster.

Template module (Script templates)

With $ slurmkernel template {list, use, add, edit} you can use pre-defined script templates to initialize your remote environment (IJulia, IPython, ...), add new script templates or edit existing templates.

If you want to create your own script templates, see here: Create Script Templates

Example

Note: Add the parameter --dry-run to check the commands that will be executed!

$ slurmkernel template use --proxyjump lb.hpc.pc2.de --loginnode login001 --user hpcuser1 --template ipython

You will be interactively asked for the required information if you do not pass any arguments when calling slurmkernel template use

IPython Example

Remote Host

• load required software (if necessary)
• Create a Python virtual environment
• Install the IPython package (ipython, ipykernel)
• Create a wrapper script and mark it as executable

<pre><code class="lang-bash">remotehost ~$ module load lang Python remotehost ~$ python3 -m venv remotekernel/ remotehost ~$ source remotekernel/bin/activate (remotekernel) remotehost ~$ python3 -m pip install ipython ipykernel; deactivate remotehost ~$ echo -e &#39;#!/bin/bash\nmodule load lang Python\n\nsource remotekernel/bin/activate\n&quot;$@&quot;&#39; &gt; remotekernel/ipywrapper.sh &amp;&amp; chmod +x remotekernel/ipywrapper.sh</code></pre>

Localhost

• Kernel Remote Slurm kernel with command slurmkernel

<pre><code class="lang-bash">notebook ~$ slurmkernel create --displayname &quot;Python 3.8.2&quot; \ --slurm-parameter=&quot;account=slurmaccount,time=00:30:00,partition=normal&quot; \ --kernel-cmd=&quot;\$HOME/remotekernel/ipywrapper.sh ipython kernel -f {connectionfile}&quot; \ --proxyjump=&quot;lb.n1.pc2.uni-paderborn.de&quot; \ --loginnode=&quot;login-0001&quot; \ --language=&quot;python&quot;</code></pre>

Set kernel-specific environment

If you want to set kernel specific environment variables (e.g. JULIANUMTHREADS for the number of threads) just extend the jupyter kernelspec file with env.

Parameter for slurmkernel:

--envir

More information here: https://jupyter-client.readthedocs.io/en/stable/kernels.html

Using the kernel with Applications

• Install kernel as shown above

* Make sure that you pass the --language flag as well (e.g. python or julia`)

Quarto Example

Troubleshooting

Kernel exceptions

When you start a Jupyter slurm kernel, it throws an excpetion, depending on the error case. You can read the exception from the graphical user interface like JupyterLab. If you start a kernel in the classic notebook view, you can click on "Error" on the left of the kernel status. There you can also find the exception.

Debugging

If your Slurm jupyter kernel does not start, it can have many causes. Before we turn on the debug mode, check following things:

• SSH-Agent is active/running and my key is loaded

* If you can log in in to the loginnode of the HPC system passwordless (Shell) that should work

• Correct Proxyjump (Loadbalancer), Loginnode

Get help

$ slurmkernel --help
usage: Tool to manage (create, list, modify and delete) and starting jupyter slurm kernels using srun [-h] [--version] {create,list,edit,delete,template} ...
positional arguments:   {create,list,edit,delete,template}     create              create a new slurm kernel     list                list available slurm kernel     edit                edit an existing slurm kernel     delete              delete an existing slurm kernel     template            manage script templates (list, use, add, edit)
optional arguments:   -h, --help            show this help message and exit   --version             show program's version number and exit