jupyterhub_teaching_example.md

JupyterHub Teaching Example

Setting up a Jupyter Lab Course involves additional steps, beyond JupyterHub, such as creating course specific environments and allowing participants to link and activate these environments during the course. This page includes a work through of these additional steps, with best practice examples for each part.

Context

• The common situation described here is that one or several Jupyter Lab Notebooks (ipynb files) are available and prepared. Students are supposed to open these notebooks through the ZIH JupyterHub and work through them during a course.

• These notebooks are typically prepared for specific dependencies (Python packages) that need to be activated by participants in the course, when opening the notebooks.

• These environments can either be chosen based on the pre-configured ZIH virtualenv/conda environments, or built in advance. We will focus on the custom environment approach here.

Prerequisites

• A public git repository with the notebook files (ipynb) and all other starting files required by participants. One option to host the repository is the GitLab of TU Chemnitz.
• A HPC project for teaching, with students as registered participants
• For the tutor, a shell access to the HPC resources and project folder.

Preparation on the Lecturer's Side

The following part describes several steps for the preparation of a course with the JupyterHub at ZIH.

1. Creating a custom Python environment

Prepare a Python virtual environment (virtualenv) or conda virtual environment as described in Python virtual environments. Note, for preparing a custom environment for a Jupyter Lab course, all participants will need to have read-access to this environment. This is best done by storing the environment in either a workspace with a limited lifetime or in a projects folder (e.g. /projects/p_lv_jupyter_course/) without a limited lifetime.

2. Clone the repository and store environment setup

First prepare the requirements.txt or the environment.yml to persist the environment as described in Python virtual environments.

Then clone the repository of your course to your home directory or into a directory in the projects folder and add the file to the repository.

=== "virtualenv" ```console marie@compute$ git clone git@gitlab.hrz.tu-chemnitz.de:zih/projects/p_lv_jupyter_course/clone_marie/jupyterlab_course.git [...] marie@compute$ cp requirements.txt /projects/p_lv_jupyter_course/clone_marie/jupyterlab_course marie@compute$ cd /projects/p_lv_jupyter_course/clone_marie/jupyterlab_course marie@compute$ git add requirements.txt marie@compute$ git commit marie@compute$ git push

```

=== "conda" ```console marie@compute$ git clone git@gitlab.hrz.tu-chemnitz.de:zih/projects/p_lv_jupyter_course/clone_marie/jupyterlab_course.git [...] marie@compute$ cp requirements.txt /projects/p_lv_jupyter_course/clone_marie/jupyterlab_course marie@compute$ cd /projects/p_lv_jupyter_course/clone_marie/jupyterlab_course marie@compute$ git add environment.yml marie@compute$ git commit marie@compute$ git push

```

Now, you can re-create the environment and the whole course from the git repository in the future.

To test the activation of the environment use:

=== "virtualenv"

```console
marie@compute$ source /scratch/ws/1/python_virtual_environment_teaching/env/bin/activate #Activate virtual environment. Example output: (envtest) bash-4.2$

```

=== "conda"

```console
marie@compute$ conda activate /scratch/ws/1/conda_virtual_environment_teaching

```

3. Prepare an activation file

Create a file to install the ipykernel to the user-folder, linking the central workshop_env to the ZIH JupyterLab. An activate_workshop_env.sh should have the following content:

/projects/jupyterlab_course/workshop_env/bin/python -m ipykernel install --user --name workshop_env --display-name="workshop_env"

!!! note The file for installing the kernel should also be added to the git repository.

4. Prepare the spawn link

Have a look at the instructions to prepare a custom spawn link in combination with the git-pull feature.

Usage on the Student's Side

Preparing activation of the custom environment in notebooks

When students open the notebooks (e.g. through a Spawn Link that pulls the Git files and notebooks from our repository), the Python environment must be activated first by installing a Jupyter kernel. This can be done inside the first notebook using a shell command (.sh).

Therefore the students will need to run the activation_workshop_env.sh file, which can be done in the first cell of the first notebook (e.g. inside 01_intro.ipynb).

In a code cell in 01_intro.ipynb, add:

!cd .. && sh activate_workshop_env.sh

When students run this file, the following output signals a successful setup.

{: align="center"}

Afterwards, the workshop_env Jupyter kernel can be selected in the top-right corner of Jupyter Lab.

!!! note A few seconds may be needed until the environment becomes available in the list.

Test spawn link and environment activation

During testing, it may be necessary to reset the workspace to the initial state. There are two steps involved:

First, remove the cloned git repository in user home folder.

!!! warning Check carefully the syntax below, to avoid removing the wrong files.

cd ~
rm -rf ./jupyterlab_course.git

Second, the IPython Kernel must be un-linked from the user workshop_env.

jupyter kernelspec uninstall workshop_env

Summary

The following video shows an example of the process of opening the spawn link and activating the environment, from the students perspective. Note that this video shows the case for a conda virtual environment.

Your browser does not support the video tag.

!!! note - The spawn link may not work the first time a user logs in.

- Students must be advised to _not_ click "Start My Server" or edit the form,
if the server does not start automatically.

- If the server does not start automatically, click (or copy & paste) the spawn link again.