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General Jupyter contributor guidelines

If you're reading this section, you're probably interested in contributing to Jupyter. Welcome and thanks for your interest in contributing!

Please take a look at the Contributor documentation, familiarize yourself with using the Jupyter Server, and introduce yourself on the mailing list and share what area of the project you are interested in working on.

For general documentation about contributing to Jupyter projects, see the Project Jupyter Contributor Documentation.

Setting Up a Development Environment

Installing the Jupyter Server

The development version of the server requires node and pip.

Once you have installed the dependencies mentioned above, use the following steps:

pip install --upgrade setuptools pip
git clone https://github.com/jupyter/jupyter_server
cd jupyter_server
pip install -e ".[test]"

If you are using a system-wide Python installation and you only want to install the server for you, you can add --user to the install commands.

Once you have done this, you can launch the main branch of Jupyter server from any directory in your system with:

jupyter server

Code Styling

jupyter_server has adopted automatic code formatting so you shouldn't need to worry too much about your code style. As long as your code is valid, the pre-commit hook should take care of how it should look. pre-commit and its associated hooks will automatically be installed when you run pip install -e ".[test]"

To install pre-commit manually, run the following:

pip install pre-commit
pre-commit install

You can invoke the pre-commit hook by hand at any time with:

pre-commit run

which should run any autoformatting on your code and tell you about any errors it couldn't fix automatically. You may also install black integration into your text editor to format code automatically.

If you have already committed files before setting up the pre-commit hook with pre-commit install, you can fix everything up using pre-commit run --all-files. You need to make the fixing commit yourself after that.

Some of the hooks only run on CI by default, but you can invoke them by running with the --hook-stage manual argument.

Troubleshooting the Installation

If you do not see that your Jupyter Server is not running on dev mode, it's possible that you are running other instances of Jupyter Server. You can try the following steps:

  1. Uninstall all instances of the jupyter_server package. These include any installations you made using pip or conda
  2. Run python3 -m pip install -e . in the jupyter_server repository to install the jupyter_server from there
  3. Run npm run build to make sure the Javascript and CSS are updated and compiled
  4. Launch with python3 -m jupyter_server --port 8989, and check that the browser is pointing to localhost:8989 (rather than the default 8888). You don't necessarily have to launch with port 8989, as long as you use a port that is neither the default nor in use, then it should be fine.
  5. Verify the installation with the steps in the previous section.

Running Tests

Install dependencies:

pip install -e .[test]
pip install -e examples/simple  # to test the examples

To run the Python tests, use:

pytest
pytest examples/simple  # to test the examples

Building the Docs

To build the documentation you'll need Sphinx, pandoc and a few other packages.

To install (and activate) a conda environment named server_docs containing all the necessary packages (except pandoc), use:

conda env create -f docs/environment.yml
source activate server_docs  # Linux and OS X
activate server_docs         # Windows

If you want to install the necessary packages with pip instead:

pip install -r docs/doc-requirements.txt

Once you have installed the required packages, you can build the docs with:

cd docs
make html

After that, the generated HTML files will be available at build/html/index.html. You may view the docs in your browser.

You can automatically check if all hyperlinks are still valid:

make linkcheck

Windows users can find make.bat in the docs folder.

You should also have a look at the Project Jupyter Documentation Guide.