Skip to content

JupyterHub for Teaching

On this page, we want to introduce to you some useful features if you want to use JupyterHub for teaching.

Note

JupyterHub uses compute resources from ZIH systems.

Please be aware of the following notes:

  • ZIH systems operate at a lower availability level than your usual Enterprise Cloud VM. There can always be downtimes, e.g. of the filesystems or the batch system.
  • Scheduled downtimes are announced by email. Please plan your courses accordingly.
  • Access to HPC resources is handled through projects. See your course as a project. Projects need to be registered beforehand (more info on the page Access).
  • Don't forget to add your users (e.g. students or tutors) to your project.
  • It might be a good idea to request a reservation of part of the compute resources for your project/course to avoid unnecessary waiting times in the batch system queue.

This feature bases on nbgitpuller. Further information can be found in the external documentation about nbgitpuller.

This extension for Jupyter notebooks can clone every public git repository into the users work directory. It's offering a quick way to distribute notebooks and other material to your students.

Git pull progress screen

To create a shareable link, we recommend to use URL encoding instead of plain text for the link in order to avoid defective links. The nbgitpuller link generator supports you in generating valid links for sharing.

Example

A shareable link for this feature looks like this:

https://taurus.hrsk.tu-dresden.de/jupyter/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fjdwittenauer%2Fipython-notebooks&urlpath=tree%2Fipython-notebooks%2Fnotebooks%2Flanguage%2FIntro.ipynb

Warning

For illustration purposes, we use plain text links in the following parts. In practice, we highly recommend to use URL encoded links instead.

URL with git-pull parameters

This example would clone the repository https://github.com/jdwittenauer/ipython-notebooks and afterwards open the Intro.ipynb notebook in the given path.

The following parameters are available:

Parameter Info
repo path to git repository
branch branch in the repository to pull from default: master
urlpath URL to redirect the user to a certain file, more info about parameter urlpath
depth clone only a certain amount of latest commits not recommended

Spawn Options Pass-through with URL Parameters

The spawn form now offers a quick start mode by passing URL parameters.

Example

The following link would create a jupyter notebook session on the interactive partition with the test environment being loaded:

https://taurus.hrsk.tu-dresden.de/jupyter/hub/spawn#/~(partition~'interactive~environment~'test)

URL with quickstart parameters

Every parameter of the advanced form can be set with this parameter. If the parameter is not mentioned, the default value will be loaded.

Parameter Default Value
partition default
nodes 1
ntasks 1
cpuspertask 1
gres empty (no generic resources)
mempercpu 1000
runtime 8:00:00
reservation empty (use no reservation)
project empty (use default project)
modules empty (do not load additional modules)
environment production
launch JupyterLab
workspace_scope empty (home directory)

You can use the advanced form to generate a URL for the settings you want. The address bar contains the encoded parameters starting with #/.

Combination of Quickstart and Git-Pull Feature

You can combine both features in a single link:

https://taurus.hrsk.tu-dresden.de/jupyter/hub/user-redirect/git-pull?repo=https://github.com/jdwittenauer/ipython-notebooks&urlpath=/tree/ipython-notebooks/notebooks/language/Intro.ipynb#/~(partition~'interactive~environment~'test)

URL with quickstart parameters

With the following link you will be redirected to a certain file in your home directory.

https://taurus.hrsk.tu-dresden.de/jupyter/user-redirect/notebooks/demo.ipynb

The file needs to exist, otherwise a 404 error will be thrown.

URL with git-pull and quickstart parameters

This link would redirect to https://taurus.hrsk.tu-dresden.de/jupyter/user/{login}/notebooks/demo.ipynb.

Create a Shared Python Environment

To provide a consistent Python environment, you can create a shared workspace and prepare a Python virtual environment in it. Then use a custom Jupyter Kernel to use this environment in JupyterHub. Please note the following:

  • Set the correct permissions to the workspace and all relevant subdirectories and files via chmod.

  • Install all relevant Python packages in the shared Python virtual environment (either pip or conda). Note that standard environments (as production or test) are not available in that case.

  • Modules can also be loaded in the Jupyter spawner via preload modules (considering the Python version of your virtual environment).

Set up your shared Python virtual environment for JupyterHub.

Hint

For working with conda virtual environments, it may be necessary to configure your shell via conda init as described in Python virtual environments

marie@compute$ module load Python #Load default Python
[...]
marie@compute$ ws_allocate -F scratch python_virtual_environment_teaching 1
Info: creating workspace.
/scratch/ws/1/python_virtual_environment_teaching
[...]
marie@compute$ virtualenv --system-site-packages /scratch/ws/1/python_virtual_environment_teaching/env #Create virtual environment
[...]
marie@compute$ source /scratch/ws/1/python_virtual_environment_teaching/env/bin/activate    #Activate virtual environment. Example output: (envtest) bash-4.2$
marie@compute$ pip install ipykernel
Collecting ipykernel
[...]
Successfully installed ... ipykernel-5.1.0 ipython-7.5.0 ...
marie@compute$ pip install --upgrade pip
marie@compute$ python -m ipykernel install --user --name my-teaching-kernel --display-name="my teaching kernel"
Installed kernelspec my-teaching-kernel in .../.local/share/jupyter/kernels/my-teaching-kernel
marie@compute$ pip install [...] #Now install additional packages for your notebooks
marie@compute$ deactivate
marie@compute$ chmod g+rx /scratch/ws/1/python_virtual_environment_teaching -R #Make the environment accesible for others
marie@compute$ module load Anaconda3 #Load Anaconda
[...]
marie@compute$ ws_allocate -F scratch conda_virtual_environment_teaching 1
Info: creating workspace.
/scratch/ws/1/conda_virtual_environment_teaching
[...]
marie@compute$ conda create --prefix /scratch/ws/1/conda_virtual_environment_teaching/conda-env python=3.8 #create virtual environment with Python version 3.8
[...]
marie@compute$ conda activate /scratch/ws/1/conda_virtual_environment_teaching/conda-env #activate conda-env virtual environment
marie@compute$ conda install ipykernel
[...]
marie@compute$ python -m ipykernel install --user --name my-teaching-kernel --display-name="my teaching kernel"
Installed kernelspec my-teaching-kernel in .../.local/share/jupyter/kernels/my-teaching-kernel
marie@compute$ conda install [...] # now install additional packages for your notebooks
marie@compute$ conda deactivate
marie@compute$ chmod g+rx /scratch/ws/1/conda_virtual_environment_teaching -R #Make the environment accesible for others

Now, users have to install the kernel in order to use the shared Python virtual environment in JupyterHub:

marie@compute$ module load Python #Load default Python
[...]
marie@compute$ source /scratch/ws/1/python_virtual_environment_teaching/env/bin/activate #Activate virtual environment. Example output: (envtest) bash-4.2$
marie@compute$ python -m ipykernel install --user --name my-teaching-kernel --display-name="my teaching kernel"
Installed kernelspec my-teaching-kernel in .../.local/share/jupyter/kernels/my-teaching-kernel
marie@compute$ deactivate
marie@compute$ module load Anaconda3 #Load Anaconda
[...]
marie@compute$ conda activate /scratch/ws/1/conda_virtual_environment_teaching
marie@compute$ python -m ipykernel install --user --name my-teaching-kernel --display-name="my teaching kernel"
Installed kernelspec my-teaching-kernel in .../.local/share/jupyter/kernels/my-teaching-kernel
marie@compute$ conda deactivate

After spawning the Notebook, you can select the kernel with the created Python virtual environment.

Hint

You can also execute the commands for installing the kernel from the Jupyter as described in JupyterHub Teaching Example. Then users do not have to use the command line interface after the preparation.