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Research Technologies for Teaching and Learning

JupyterHub for Teaching gives users access to Jupyter Notebooks (computational environments and resources) without the hassle of installation and maintenance tasks. Instructors can make individual workspaces available to students using shared resources, which can be managed efficiently by system administrators.

Sign up for UW-IT JupyterHub for Teaching Service

Thank you for your interest. Signups are now closed for autumn quarter 2020.

If you are interested in using UW-IT JupyterHub for Teaching in a winter quarter 2021 course, registrations will open on Monday, November 23. Please check back then.

Frequently Asked Questions

I’m new to JupyterHub. Is there an easy way to learn more about how it can support teaching and learning?

I have experience using Python in my classes. What’s the difference between using Python and JupyterHub?

The hosted JupyterHub system provides a browser based Python IDE, with a consistent environment for all users. As there are no local dependencies, complications arising from students using a variety of platforms are significantly reduced.

What will my Jupyter environment include?

Per-user resource allocation:
User Storage: 5 GB
Max Memory: 2GB or 4GB (chosen by instructor)

The deployed environments are based on the JupyterHub SciPy image, with several extra usability features. The image contains the following environment:

  • Ubuntu Bionic
  • Miniconda Python 3.x in /opt/conda
  • Unprivileged user jovyan with ownership over the /home/jovyan and /opt/conda paths
  • Pandoc and TeX Live for notebook document conversion
  • Git, emacs, jed, nano, tzdata, and unzip
  • Pandas, numexpr, matplotlib, scipy, seaborn, scikit-learn, scikit-image, sympy, cython, patsy, statsmodel, cloudpickle, dill, numba, bokeh, sqlalchemy, hdf5, vincent, beautifulsoup, protobuf, xlrd, emcee, pymc3, astroML, astropy, astroplan, astroquery, gatspy and pytorch packages
  • Ipywidgets for interactive visualizations in Python notebooks
  • Facets for visualizing machine learning datasets
  • NBGitPuller for syncing a git repository to a user’s home directory
  • NBResUse to show memory usage and limits in the Notebook UI

How technical do students need to be to learn successfully with JupyterHub?

Experience with other coding platforms (e.g., MATLAB, ArcGIS) and languages can provide a useful foundation. If students do not have experience with Python, instructors should consider providing time and resources to getting students comfortable using it.

What support can I expect from UW-IT?

A Docker container will be set up for you to use or, alternatively, you can provide and configure your own Docker container; each student in your course will receive access to a Jupyter notebook. Access to the notebooks is restricted to a whitelist of UW NetIDs and is granted via a Canvas integration. UW-IT will run the infrastructure at no cost to you.

Note: UW-IT is not expert in the pedagogy of using JupyterHub. While we are providing JupyterHub, we strongly encourage users to review JupyterHub’s support documentation. UW-IT is also working with advanced users of Jupyter Notebooks to build a campus support network. As details about that support become available, they will be shared here.

For general questions about JupyterHub for Teaching, contact For faster service, please include “Jupyter” in the subject line.

What kind of support should I be prepared to set up on my own?

Experienced students, familiar with Jupyter notebooks and JupyterHub and who are capable of managing back end development and technical problems, may be an important source of support for faculty. This type of support might be included in an existing teaching assistant’s job duties. With limited permissions to the cloud computing administrator interface, teaching assistants could troubleshoot common issues with students in real time.

Is the JupyterHub for Teaching service really free?

There are currently no costs associated with using the service.


Last reviewed August 10, 2020