Skip to content


With our JupyterHub service, we offer you a quick and easy way to work with Jupyter notebooks on ZIH systems. This page covers starting and stopping JupyterHub sessions, error handling and customizing the environment.

We also provide a comprehensive documentation on how to use JupyterHub for Teaching (git-pull feature, quickstart links, direct links to notebook files).



The JupyterHub service is provided as-is, use at your own discretion.

Please understand that JupyterHub is a complex software system of which we are not the developers and don't have any downstream support contracts for, so we merely offer an installation of it but cannot give extensive support in every case.



This service is only available for users with an active HPC project. See Application for Login and Resources, if you need to apply for an HPC project.

JupyterHub is available at

Old taurus

Login Page

At login page please use your ZIH credentials (without

Simple form

Start a Session

Start a new session by clicking on the Start button.

Standard Profiles

Our simple form offers you the most important settings to start quickly.

Advanced form

We have created three profiles for each cluster, namely:

Cluster Resources Optimized for Recommended for
Alpha 1 core, 1.5 GB 1 hour x86_64 (AMD) Python programming
Alpha 2 core, 3 GB, 4 hours x86_64 (AMD) R programming
Alpha 4 core, 8 GB, 8 hours x86_64 (AMD)
Barnard 1 core, 1.5 GB, 1 hour x86_64 (Intel) Python programming
Barnard 2 core, 3 GB, 4 hours x86_64 (Intel) Julia and R programming
Barnard 4 core, 8 GB, 8 hours x86_64 (Intel)
Romeo 1 core, 1.5 GB 1 hour x86_64 (AMD) Python programming
Romeo 2 core, 3 GB, 4 hours x86_64 (AMD) R programming
Romeo 4 core, 8 GB, 8 hours x86_64 (AMD)
VIS 1 core, 1.5 GB, 1 hours Visualization ANSYS
VIS 2 core, 4 GB, 2 hours Visualization ANSYS
VIS 4 core, 8 GB, 6 hours Visualization ANSYS

Advanced Options

Aside of the standard profiles there is the possibility to specify custom parameters to the job spawner. This can be activated cby licking at the Advanced button located below to the profile list.

Advanced form


After your session it is spawned you will be redirected to JupyterLab. The main interface looks like as following:

JupyterLab overview


Your interface may differ from the picture above as not all icons (launchers) are available at all profiles.

The main workspace is used for multiple notebooks, consoles or terminals. Those documents are organized with tabs and a very versatile split screen feature. On the left side of the screen you can open several views:

  • file manager
  • controller for running kernels and terminals
  • overview of commands and settings
  • details about selected notebook cell
  • list of open tabs

At the following table it's possible to see what is available at each cluster.

Cluster Julia R RStudio MATLAB MATLAB Web WebVNC DCV
Alpha - OK OK OK OK* OK* -
Barnard OK OK OK OK OK* OK* -
Romeo - OK OK OK OK* OK* -


All small profiles for all clusters do not include MATLAB Web neither WebVNC.

Jupyter Notebooks in General

In JupyterHub, you can create scripts in notebooks. Notebooks are programs which are split into multiple logical code blocks. Each block can be executed individually. In between those code blocks, you can insert text blocks for documentation. Each notebook is paired with a kernel running the code.

We offer some custom kernel for Julia*, MATLAB and R.


Some kernels may not be available depending of the amounting of resources requested for the job or due to the availability of modules to an specific cluster.

Version Control of Jupyter Notebooks with Git

Since Jupyter notebooks are files containing multiple blocks for input code, documentation, output and further information, it is difficult to use them with Git. Version tracking of the .ipynb notebook files can be improved with the Jupytext plugin. Jupytext will provide Markdown (.md) and Python (.py) conversions of notebooks on the fly, next to .ipynb. Tracking these files will then provide a cleaner Git history. A further advantage is that Python notebook versions can be imported, allowing to split larger notebooks into smaller ones, based on chained imports.


The Jupytext plugin is not installed on the ZIH systems at the moment. Currently, it can be installed by the users with parameter --user. Therefore, ipynb files need to be made available in a repository for shared usage within the ZIH system.

Stop a Session

It is good practice to stop your session once your work is done. This releases resources for other users and your quota is less charged. If you just log out or close the window, your server continues running and will not stop until the Slurm job runtime hits the limit (usually 8 hours).

At first, you have to open the JupyterHub control panel.

Open the file menu and then click on Logout. You can also click on Hub Control Panel, which opens the control panel in a new tab instead.

JupyterLab logout

Error Handling

We want to explain some errors that you might face sooner or later. If you need help, open a ticket and ask for support as described in How to Ask for Support.

Error Message in JupyterLab

JupyterLab error directory not found

If the connection to your notebook server unexpectedly breaks, you will get this error message. Sometimes your notebook server might hit a batch system or hardware limit and gets killed. Then, the log file of the corresponding batch job usually contains useful information. These log files are located in your home directory and have the name jupyterhub-<clustername>.log.

Advanced Tips

Loading Modules

Inside your terminal session you can load modules from the module system.

Custom Kernels

As you might have noticed, after launching JupyterLab, there are several boxes with icons therein visible in the Launcher. Each box therein represents a so called 'Kernel'. (note that these are not to be confused with operating system kernel, but similarly provide basic functionality for running your use cases, e.g. Python or R)

You can also create your own Kernels.