You are viewing archived documentation from the old Numba documentation site. The current documentation is located at https://numba.readthedocs.io.

Contributing to Numba

We welcome people who want to make contributions to Numba, big or small! Even simple documentation improvements are encouraged. If you have questions, don’t hesitate to ask them (see below).


Real-time Chat

Numba uses Gitter for public real-time chat. To help improve the signal-to-noise ratio, we have two channels:

  • numba/numba: General Numba discussion, questions, and debugging help.
  • numba/numba-dev: Discussion of PRs, planning, release coordination, etc.

Both channels are public, but we may ask that discussions on numba-dev move to the numba channel. This is simply to ensure that numba-dev is easy for core developers to keep up with.

Note that the Github issue tracker is the best place to report bugs. Bug reports in chat are difficult to track and likely to be lost.


Numba uses Discourse as a forum for longer running threads such as design discussions and roadmap planning. There are various categories available and it can be reached at: numba.discourse.group.


We have a public mailing-list that you can e-mail at numba-users@anaconda.com. You can subscribe and read the archives on Google Groups.

Weekly Meetings

The core Numba developers have a weekly video conference to discuss roadmap, feature planning, and outstanding issues. These meetings are invite only, but minutes will be taken and will be posted to the Numba wiki.

Bug tracker

We use the Github issue tracker to track both bug reports and feature requests. If you report an issue, please include specifics:

  • what you are trying to do;
  • which operating system you have and which version of Numba you are running;
  • how Numba is misbehaving, e.g. the full error traceback, or the unexpected results you are getting;
  • as far as possible, a code snippet that allows full reproduction of your problem.

Getting set up

If you want to contribute, we recommend you fork our Github repository, then create a branch representing your work. When your work is ready, you should submit it as a pull request from the Github interface.

If you want, you can submit a pull request even when you haven’t finished working. This can be useful to gather feedback, or to stress your changes against the continuous integration platform. In this case, please prepend [WIP] to your pull request’s title.

Build environment

Numba has a number of dependencies (mostly NumPy and llvmlite) with non-trivial build instructions. Unless you want to build those dependencies yourself, we recommend you use conda to create a dedicated development environment and install precompiled versions of those dependencies there.

First add the Anaconda Cloud numba channel so as to get development builds of the llvmlite library:

$ conda config --add channels numba

Then create an environment with the right dependencies:

$ conda create -n numbaenv python=3.6 llvmlite numpy scipy jinja2 cffi


This installs an environment based on Python 3.6, but you can of course choose another version supported by Numba. To test additional features, you may also need to install tbb and/or llvm-openmp and intel-openmp.

To activate the environment for the current shell session:

$ conda activate numbaenv


These instructions are for a standard Linux shell. You may need to adapt them for other platforms.

Once the environment is activated, you have a dedicated Python with the required dependencies:

$ python
Python 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:07:29)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import llvmlite
>>> llvmlite.__version__

Building Numba

For a convenient development workflow, we recommend you build Numba inside its source checkout:

$ git clone git://github.com/numba/numba.git
$ cd numba
$ python setup.py build_ext --inplace

This assumes you have a working C compiler and runtime on your development system. You will have to run this command again whenever you modify C files inside the Numba source tree.

The build_ext command in Numba’s setup also accepts the following arguments:

  • --noopt: This disables optimization when compiling Numba’s CPython extensions, which makes debugging them much easier. Recommended in conjunction with the standard build_ext option --debug.
  • --werror: Compiles Numba’s CPython extensions with the -Werror flag.
  • --wall: Compiles Numba’s CPython extensions with the -Wall flag.

Note that Numba’s CI and the conda recipe for Linux build with the --werror and --wall flags, so any contributions that change the CPython extensions should be tested with these flags too.

Running tests

Numba is validated using a test suite comprised of various kind of tests (unit tests, functional tests). The test suite is written using the standard unittest framework.

The tests can be executed via python -m numba.runtests. If you are running Numba from a source checkout, you can type ./runtests.py as a shortcut. Various options are supported to influence test running and reporting. Pass -h or --help to get a glimpse at those options. Examples:

  • to list all available tests:

    $ python -m numba.runtests -l
  • to list tests from a specific (sub-)suite:

    $ python -m numba.runtests -l numba.tests.test_usecases
  • to run those tests:

    $ python -m numba.runtests numba.tests.test_usecases
  • to run all tests in parallel, using multiple sub-processes:

    $ python -m numba.runtests -m
  • For a detailed list of all options:

    $ python -m numba.runtests -h

The numba test suite can take a long time to complete. When you want to avoid the long wait, it is useful to focus on the failing tests first with the following test runner options:

  • The --failed-first option is added to capture the list of failed tests and to re-execute them first:

    $ python -m numba.runtests --failed-first -m -v -b
  • The --last-failed option is used with --failed-first to execute the previously failed tests only:

    $ python -m numba.runtests --last-failed -m -v -b

When debugging, it is useful to turn on logging. Numba logs using the standard logging module. One can use the standard ways (i.e. logging.basicConfig) to configure the logging behavior. To enable logging in the test runner, there is a --log flag for convenience:

$ python -m numba.runtests --log

Development rules

Code reviews

Any non-trivial change should go through a code review by one or several of the core developers. The recommended process is to submit a pull request on github.

A code review should try to assess the following criteria:

  • general design and correctness
  • code structure and maintainability
  • coding conventions
  • docstrings, comments
  • test coverage

Coding conventions

All Python code should follow PEP 8. Our C code doesn’t have a well-defined coding style (would it be nice to follow PEP 7?). Code and documentation should generally fit within 80 columns, for maximum readability with all existing tools (such as code review UIs).

Numba uses Flake8 to ensure a consistent Python code format throughout the project. flake8 can be installed with pip or conda and then run from the root of the Numba repository:

flake8 numba

Optionally, you may wish to setup pre-commit hooks to automatically run flake8 when you make a git commit. This can be done by installing pre-commit:

pip install pre-commit

and then running:

pre-commit install

from the root of the Numba repository. Now flake8 will be run each time you commit changes. You can skip this check with git commit --no-verify.

Numba has started the process of using type hints in its code base. This will be a gradual process of extending the number of files that use type hints, as well as going from voluntary to mandatory type hints for new features. Mypy is used for automated static checking.

At the moment, only certain files are checked by mypy. The list can be found in mypy.ini. When making changes to those files, it is necessary to add the required type hints such that mypy tests will pass. Only in exceptional circumstances should type: ignore comments be used.

If you are contributing a new feature, we encourage you to use type hints, even if the file is not currently in the checklist. If you want to contribute type hints to enable a new file to be in the checklist, please add the file to the files variable in mypy.ini, and decide what level of compliance you are targetting. Level 3 is basic static checks, while levels 2 and 1 represent stricter checking. The levels are described in details in mypy.ini.

There is potential for confusion between the Numba module typing and Python built-in module typing used for type hints, as well as between Numba types—such as Dict or Literal—and typing types of the same name. To mitigate the risk of confusion we use a naming convention by which objects of the built-in typing module are imported with an pt prefix. For example, typing.Dict is imported as from typing import Dict as ptDict.


The repository’s master branch is expected to be stable at all times. This translates into the fact that the test suite passes without errors on all supported platforms (see below). This also means that a pull request also needs to pass the test suite before it is merged in.

Platform support

Every commit to the master branch is automatically tested on all of the platforms Numba supports. This includes ARMv7, ARMv8, POWER8, as well as both AMD and NVIDIA GPUs. The build system however is internal to Anaconda, so we also use Azure to provide public continuous integration information for as many combinations as can be supported by the service. Azure CI automatically tests all pull requests on Windows, OS X and Linux, as well as a sampling of different Python and NumPy versions. If you see problems on platforms you are unfamiliar with, feel free to ask for help in your pull request. The Numba core developers can help diagnose cross-platform compatibility issues. Also see the continuous integration section on how public CI is implemented.

Continuous integration testing

The Numba test suite causes CI systems a lot of grief:

  1. It’s huge, 9000+ tests.
  2. In part because of 1. and that compilers are pretty involved, the test suite takes a long time to run.
  3. There’s sections of the test suite that are deliberately designed to stress systems almost to the point of failure (tests which concurrently compile and execute with threads and fork processes etc).
  4. The combination of things that Numba has to test well exceeds the capacity of any public CI system, (Python versions x NumPy versions x Operating systems x Architectures x feature libraries (e.g. SVML) x threading backends (e.g. OpenMP, TBB)) and then there’s CUDA and ROCm too and all their version variants.

As a result of the above, public CI is implemented as follows:

  1. The combination of OS x Python x NumPy x Various Features in the testing matrix is designed to give a good indicative result for whether “this pull request is probably ok”.
  2. When public CI runs it:
    1. Looks for files that contain tests that have been altered by the proposed change and runs these on the whole testing matrix.
    2. Runs a subset of the test suite on each part of the testing matrix. i.e. slice the test suite up by the number of combinations in the testing matrix and each combination runs one chunk. This is done for speed, because public CI cannot cope with the load else.

If a pull request is changing CUDA or ROCm code (which cannot be tested on Public CI as there’s no hardware) or it is making changes to something that the core developers consider risky, then it will also be run on the Numba farm just to make sure. The Numba project’s private build and test farm will actually exercise all the applicable tests on all the combinations noted above on real hardware!

Things that help with pull requests

Even with the mitigating design above public CI can get overloaded which causes a backlog of builds. It’s therefore really helpful when opening pull requests if you can limit the frequency of pushing changes. Ideally, please squash commits to reduce the number of patches and/or push as infrequently as possible. Also, once a pull request review has started, please don’t rebase/force push/squash or do anything that rewrites history of the reviewed code as GitHub cannot track this and it makes it very hard for reviewers to see what has changed.

The core developers thank everyone for their cooperation with the above!


The Numba documentation is split over two repositories:

Main documentation

This documentation is under the docs directory of the Numba repository. It is built with Sphinx and numpydoc, which are available using conda or pip; i.e. conda install sphinx numpydoc.

To build the documentation, you need the bootstrap theme:

$ pip install sphinx_bootstrap_theme

You can edit the source files under docs/source/, after which you can build and check the documentation:

$ make html
$ open _build/html/index.html

Core developers can upload this documentation to the Numba website at http://numba.pydata.org by using the gh-pages.py script under docs:

$ python gh-pages.py version  # version can be 'dev' or '0.16' etc

then verify the repository under the gh-pages directory and use git push.

Web site homepage

The Numba homepage on http://numba.pydata.org can be fetched from here: https://github.com/numba/numba-webpage

After pushing documentation to a new version, core developers will want to update the website. Some notable files:

  • index.rst # Update main page
  • _templates/sidebar_versions.html # Update sidebar links
  • doc.rst # Update after adding a new version for numba docs
  • download.rst # Updata after uploading new numba version to pypi

After updating run:

$ make html

and check out _build/html/index.html. To push updates to the Web site:

$ python _scripts/gh-pages.py

then verify the repository under the gh-pages directory. Make sure the CNAME file is present and contains a single line for numba.pydata.org. Finally, use git push to update the website.