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Guilherme Calandrini authoredGuilherme Calandrini authored
JupyterHub
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).
Disclaimer
!!! warning
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.
Access
!!! note 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 https://jupyterhub.hpc.tu-dresden.de.
Old taurus https://taurus.hrsk.tu-dresden.de/jupyter.
Login page
At login page please use your ZIH credential (without @tu-dresden.de).
For example: Username: ddde223z
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Start a Session
Start a new session by clicking on the Start my server button.
Standard Profiles
Our simple form offers you the most important settings to start quickly.
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We have created three profiles for each Cluster, namely:
| name | optimized for | specially recommended for |
|---|---|---|
| Alpha - 1 core, 1,5GB 1 hour | x86_64 (AMD) | python programming |
| Alpha - 2 Core, 3GB, 4 Hours | x86_64 (AMD) | R programming |
| Alpha - 4 Core, 8GB, 8 Hours | x86_64 (AMD) | |
| Barnard 1 core, 1,5GB 1 hour | x86_64 (Intel) | python programming |
| Barnard - 2 Core, 3GB, 4 Hours | x86_64 (Intel) | Julia and R programming |
| Barnard - 4 Core, 8GB, 8 Hours | x86_64 (Intel) | |
| Romeo - 1 core, 1,5GB 1 hour | x86_64 (AMD) | python programming |
| Romeo - 2 Core, 3GB, 4 Hours | x86_64 (AMD) | R programming |
| Romeo - 4 Core, 8GB, 8 Hours | x86_64 (AMD) | |
| VIS - 2 Core, 4GB, 2 Hours | Visualization | ANSYS |
| VIS - 4 Core, 8GB, 6 Hours | Visualization | ANSYS |
JupyterLab
After your session it is spawned you will be redirected to JupyterLab. The main interface looks like as following:
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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
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 currently offer one for Python, C++, MATLAB and R.
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.
!!! note
The Jupytext plugin is not installed on the ZIH system 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.
=== "JupyterLab"
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.
{: align="center"}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
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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.