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://taurus.hrsk.tu-dresden.de/jupyter.
Start a Session¶
Start a new session by clicking on the Start my server
button.
A form opens up where you can customize your session. Our simple form offers you the most important settings to start quickly.
For advanced users, we have an extended form where you can change many settings. You can:
- modify batch system parameters to your needs (more about batch system Slurm)
- assign your session to a project or reservation
- load modules from the module system
- choose a different standard environment (in preparation for future software updates or testing additional features)
You can save your own configurations as additional presets. Those are saved in your browser and are lost if you delete your browsing data. Use the import/export feature (available through the button) to save your presets in text files.
Info
The partition alpha is available only in the extended form.
Applications¶
You can choose between JupyterLab or classic Jupyter notebooks as outlined in the following.
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
Initially, your home directory is listed. You can open existing notebooks or files by navigating to the corresponding path and clicking on them.
Above the table on the right side, there is the button New
which lets you create new
notebooks, files, directories or terminals.
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.
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.
Click on the control panel button on the top right of your screen.
Now, you are back on the JupyterHub page and you can stop your server by clicking on
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 at Session Start¶
This message appears instantly, if your batch system parameters are not valid. Please check those settings against the available hardware. Useful pages for valid batch system parameters:
Hint
This message might also appear for other Slurm related problems, e.g. quota issues. That might be the case when the error appears for you but not for others while using the same system parameters. In this case, please ask for support as described in How to Ask for Support.
Error Message in JupyterLab¶
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 jupyter-session-<jobid>.log
.
Advanced Tips¶
Standard Environments¶
The default Python kernel uses conda environments based on the Watson Machine Learning Community Edition (formerly PowerAI) package suite. You can open a list with all included python-packages that are available within the standard environment through the spawner form:
This list shows all packages of the currently selected conda environment. This depends on your settings for partition (CPU architecture) and standard environment.
We have a number of standard environments, namely:
name | optimized for | specially recommended for | Modenv | Partitions |
---|---|---|---|---|
foss-2020b | x86_64, ppc64le | modenv/hiera, modenv/ml | romeo , alpha , ml |
|
fosscuda-2019b | GPUs | modenv/hiera, modenv/ml | romeo , alpha , ml |
|
fosscuda-2020b | GPUs | modenv/hiera, modenv/ml | romeo , alpha , ml |
|
gcccore-10.3.0_python-3.9.5_matlab-2021b | x86_64 | MATLAB | modenv/hiera | romeo , alpha |
hiera_gcccore-10.2.0_python-3.8.6 | x86_64 (AMD) | modenv/hiera | romeo , alpha |
|
hiera_gcccore-10.3.0_python-3.9.5_matlab-2021b | x86_64 (AMD) | MATLAB | modenv/hiera | romeo , alpha |
hiera_gcccore-10.3.0_python-3.9.5_r-4.1.0_rstudio-1.4.1717 | x86_64 (AMD) | RStudio | modenv/hiera | romeo , alpha |
production | x86_64, ppc64le | modenv/scs5, modenv/ml | default , haswell , interactive , gpu2 , hpdlf , dcv , julia , ml |
|
scs5_gcccore-10.2.0_python-3.8.6 | x86_64 (Intel) | modenv/scs5 | default , haswell , interactive , gpu2 , hpdlf , dcv , julia |
|
scs5_gcccore-10.3.0_python-3.9.5_matlab-2021b | x86_64 (Intel) | MATLAB | modenv/scs5 | default , haswell , interactive , gpu2 , hpdlf , dcv , julia |
scs5_gcccore-8.3.0_python-3.7.4 | x86_64 (Intel) | modenv/scs5 | default , haswell , interactive , gpu2 , hpdlf , dcv , julia |
Actually 'romeo', 'alpha' and 'ml' refer to their respective interactive partitions 'romeo-interactive', 'alpha-interactive', 'ml-interactive' to reduce job queue waiting time.
The standard environments in Jupyter are also closely related to the available Module Environments.
With these standard environments, we have tried to integrate a set of compatible software:
In this environment, we provide DCV and WebVNC access.
You can load more software using our module system, by selecting these through the option 'Preload modules (module load)'.
We integrated MATLAB into it, such that you can use it in your browser.
You can load more software using our module system, by selecting these through the option 'Preload modules (module load)'.
We integrated R & RStudio into it, such that you can use it in your browser. Please click on RStudio's Icon for it, when you are in the JupyterHub overview.
Can be utilized with the partitions gpu2
, alpha
and ml
.
It's specially geared towards GPU support.
Loading Modules¶
You have now the option to preload modules from the module system.
Select multiple modules that will be preloaded before your notebook server
starts. The list of available modules depends on the module environment you want
to start the session in (scs5
, hiera
or ml
). The right module environment will be
chosen by your selected partition.
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.