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Useful skills and ways to contribute#

There are many ways that you can contribute to the JupyterHub / Binder projects. This page suggests some pathways for community members to learn new skills and share their skills with the team.

Where to start?#

The short answer to this question is: follow your interests :-) There are many different ways to contribute to the JupyterHub community - technical, mentorship, communications, community work, and documentation are just a few. You can either apply your skills to help out, or use it as an opportunity to learn something new! We welcome contributions in any area, but if you can’t decide where to start why not answer some support questions in the community forum, or share your experiences of using Jupyter there.

A few general ways to help out#

  • Hang out in the Jupyter Community Forum and assist others with questions.

  • Look through the issues in a repository, and help respond to issues that haven’t been addressed already

  • Guide an issue’s conversation to help form a clear, actionable next step to move the issue forward

  • For general questions about using JupyterHub, assist the person and then improve the documentation so that the answer is easier to find.

  • For issues labeled “bug” try to clarify what is going on, and reproduce the bug on your machine

  • For issues with reproducible bugs, in a PR write a test that will fail because of the bug.

  • For issues with reproducible bugs, in a PR fix the bug and make sure that the test passes.

  • For issues that describe new features, engage in conversation in the issue to define an actionable path forward.

  • For issues that describe new features and with a clear path forward, open a PR that implements some or all of the feature.

Things to know about JupyterHub#

This section provides a combination of “helpful skills to learn as a contributor” and “places to apply your skills”.

JupyterHub-specific knowledge#

  • JupyterHub component architecture. JupyterHub is the underlying tool of many sub-projects in the JupyterHub/Binder ecosystem, and it’s good to understand its structure. This will make it easier to identify what to work on, what folks are talking about, and what repo might be the place to look at for a given task.

  • Code structure of the JupyterHub repository. Familiarizing yourself with the code structure of JupyterHub will help you navigate the repository, and help others to do so as well. There is currently no single place that documents this (if you’d like to create a PR to add it, we’d love to help! Here’s an issue to discuss).

  • Traitlets configuration. JupyterHub uses a tool called traitlets for configuration. You should familiarize yourself with Traitlets as well as the specific configuration values for JupyterHub. The traitlets documentation could be improved, if you’re interested in helping out, see this issue.

  • YAML Configuration. JupyterHub uses YAML, a way of structuring text as configuration files, in order to control the behavior of many tools in the JupyterHub ecosystem (as well as to configure CI/CD infrastructure). Here is a quick primer on YAML.

  • Testing infrastructure. JupyterHub projects generally use pytest for testing. However, some projects involve more complex tests than you’d normally find in a Python project. For example, mocking HTTP servers, or infrastructure that runs in the cloud.

Server infrastructure#

  • HTTP and REST APIs. These are the ways in which services, extensions, and some authenticators interact with a JupyterHub.

  • tornado / asyncio. JupyterHub uses these tools in order to send messages across JupyterHub infrastructure (e.g., between JupyterHub processes and user servers).

UI/UX#

  • HTML, CSS, and Jinja. HTML and CSS are used to generate user-facing pages throughout JupyterHub. Jinja is a templating engine that allows you to create HTML pages with variables inserted into them for customization.

  • Javascript. Some dynamic content on JupyterHub pages (or via extensions) uses Javascript to control the page’s behavior.

  • UX/UI knowledge. Aside from specific implementations of JupyterHub interfaces (which generally use HTML), it is also helpful to know about the structure and design of user interfaces, and considering the user experience as well.

Deployment Infrastructure#

  • Python. Most JupyterHub projects are distributed as Python packages on PyPi. Packages are often installed in virtual Python environments or Conda environments

  • Docker. JupyterHub uses Docker throughout its projects to provide environments that both JupyterHub and other communities can deploy. Docker is a core part of the Kubernetes deployments of JupyterHub, and can also be used to control user environments in a JupyterHub.

  • Kubernetes basics. A popular way to run JupyterHub is by using Kubernetes, a framework for orchestrating cloud infrastructure that is flexible and vendor-agnostic. Some projects of JupyterHub (in particular, JupyterHub for Kubernetes, Kubespawner, and BinderHub) all use Kubernetes for their infrastructure.

Community infrastructure#

  • Technical Writing. Every tool in the JupyterHub ecosystem is documented, and needs clear, concise, well-structured information to help others. Any contributions that improve documentation are appreciated.

  • Issue guidance and triage. Almost all enhancements to JupyterHub tools begin with an issue. They are the most common point of interaction with the broader community. To improve the content of our issues and facilitate improvements in JupyterHub, it is important to be a good listener, a respectful responder, and someone who encourages others to share their perspective.

  • The Jupyter Community Forum. The Discourse forum is busy with questions and discussions about Jupyter. Support for deploying, configuring, and generally using JupyterHub is primarily handled through this forum (as opposed to using GitHub issues which are scoped for actionable bug reports and development). Any help you can offer to other users is very welcome, and the forum is also a great place to exchange knowledge.

A note on complexity of tasks#

Depth/expertise required for certain kinds of tasks can be hard to nail down and not at all obvious. If you’d like to make a contribution and you’re not sure where to start, a good rule of thumb is to open an issue or reach out to another team member to help you scope the work you’d like to do, and to set expectations.

Resources for learning#

In this section, we gather some introductory materials for learning the aforementioned tools.

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