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UW-IT JupyterHub for Teaching

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.

Read more about JupyterHub at the UW:

Highlights of the UW winter and spring 2020
Jupyter Hub for Teaching pilot
Full report: UW winter 2020 JupyterHub for Teaching pilot
Profile of one UW professor’s experience with JupyterHub for Teaching Full report: UW spring 2021 JupyterHub for Teaching pilot

Sign up for UW-IT JupyterHub for Teaching service

Instructors can request to have a JupyterHub environment set up for one or more of their classes by filling out a registration form during an open registration period. Please note: Late registration requests may require two business days or more to be processed.

Open Registration Periods

For fall quarter 2021 courses: September 7 – 24, 2021   REGISTER NOW 

For winter quarter 2022 courses: December 13 – 31, 2022

Future registration periods to be announced.

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 or R 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)
CPU: Default is 2 cores, with option of up to 4 cores

Instructors can choose from six JupyterHub deployment environment options:

  1. Datascience
  2. SciPy
  3. R
  4. RStudio 
  5. TensorFlow
  6. An instructor-supported custom image sourced from the Jupyter community that the instructor provides and maintains. Please note that those who choose this option are responsible for all support, testing, troubleshooting, bug fixing and updates; UW-IT will not provide support outside of general cloud platform support.

The SciPy, R, and TensorFlow images share the following features:

  • 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
  • Ipywidgets for interactive visualizations in Python notebooks
  • NBGitPuller for syncing a git repository to a user’s home directory
  • NBResUse to show memory usage and limits in the Notebook UI
  • The Github links for each image provide more detailed information

How do I set up a custom JupyterHub environment?
Instructors can request to use their own custom image before a quarter starts during an open registration period. When choosing this option, instructors must build an image sourced from the Jupyter community, test it, publish it on DockerHub or another repository, and provide a location URL to the JupyterHub for Teaching support team.

Instructors who originally registered to use a stock image can choose to convert it to a custom image at any time during the quarter by sending a request and image URL to

Note: Instructors who choose the Custom Image option are responsible for all support, testing, troubleshooting, bug fixing, and updates; UW-IT will not provide support outside of general cloud platform support.

Can I modify the stock R, SciPy, or TensorFlow images?
Send modification requests for the stock R, SciPy, or TensorFlow images to Changes, if approved and successfully tested, will only be applied to the stock images between quarters to ensure system continuity and stability for all users. Alternatively, instructors can use one of the stock images as a starting point to create a custom image that can be used at any time.

Will new stock images be added in addition to the R, SciPy and TensorFlow images?
We are happy to review requests for new stock images, please send them to New stock images, if approved and successfully tested, will be implemented between quarters.

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?

Each student in your course will receive access to a Jupyter notebook, created from your selected Jupyter image. Access to the notebooks is restricted to the user UW NetIDs that are present in the course in Canvas. 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.

Are the best practices for using JupyterHub at the UW?

Yes! Review best practices here.

How long is my instance of JupyterHub available?

JupyterHub for Teaching instances will be maintained for at least one quarter after their associated course has completed. Announcements will be sent to instructors approximately two weeks before the deletion date.

JupyterHub active course quarter Deleted on or after
Fall 2020 April 12, 2021
Winter 2021 June 11, 2021
Spring 2021 Sept 29, 2021
Summer 2021 Dec 17, 2021
Last reviewed August 5, 2021