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:

Weekly Meetings

The core Numba developers have a weekly video conference to discuss roadmap, feature planning, and outstanding issues. These meetings are entirely public, details are posted on Announcements and everyone is welcome to join the discussion. 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. Read more about the Numba dependencies here: numba-source-install-check.

When working with a source checkout of Numba you will also need a development build of llvmlite. These are available from the numba/label/dev channel on

To create an environment with the required dependencies, noting the use of the double-colon syntax (numba/label/dev::llvmlite) to install the latest development version of the llvmlite library:

$ conda create -n numbaenv python=3.10 numba/label/dev::llvmlite numpy scipy jinja2 cffi


This installs an environment based on Python 3.10, 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. Check the dependency list above for details.

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 3.10.3 (main, Mar 28 2022, 04:26:28) [Clang 12.0.0 ] 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
$ cd numba
$ python 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 ./ 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

To enable runtime type-checking, set the environment variable NUMBA_USE_TYPEGUARD=1 and use from the source root instead. For example:


See also: Debugging the Test Suite

Running coverage

Coverage reports can be produced using To record coverage info for the test suite, run:

coverage run -m numba.runtests <runtests args>

Next, combine coverage files (potentially for multiple runs) with:

coverage combine

The combined output can be transformed into various report formats - see the coverage CLI usage reference. For example, to produce an HTML report, run:

coverage html

Following this command, the report can be viewed by opening htmlcov/index.html.

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 and release notes (if necessary)

  • test coverage

Policy on large scale changes to code formatting

Please note that pull requests making large scale changes to format the code base are in general not accepted. Such changes often increase the likelihood of merge conflicts for other pull requests, which inevitably take time and resources to resolve. They also require a lot of effort to check as Numba aims to compile code that is valid even if it is not ideal. For example, in a test of operator.eq:

if x == None: # Valid code, even if the recommended form is `if x is None:`

This tests Numba’s compilation of comparison with None, and therefore should not be changed, even though most style checkers will suggest it should.

This policy has been adopted by the core developers so as to try and make best use of limited resources. Whilst it would be great to have an extremely tidy code base, priority is given to fixes and features over code formatting changes.

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 targeting. 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.

Release Notes

Pull Requests that add significant user-facing modifications may need to be mentioned in the release notes. To add a release note, a short .rst file needs creating containing a summary of the change and it needs to be placed in docs/upcoming_changes. The file docs/upcoming_changes/README.rst details the format and file naming conventions.


The repository’s main 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 main branch is automatically tested on all of the platforms Numba supports. This includes ARMv8, POWER8, 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 too and all its 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 (PR) changes CUDA code or will affect the CUDA target, it needs to be run on gpuCI. This can be triggered by one of the Numba maintainers commenting run gpuCI tests on the PR discussion. This runs the CUDA testsuite with various CUDA toolkit versions on Linux, to provide some initial confidence in the correctness of the changes with respect to CUDA. Following approval, the PR will also be run on Numba’s build farm to test other configurations with CUDA (including Windows, which is not tested by gpuCI).

If the PR is not CUDA-related but makes 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!

Type annotation and runtime type checking

Numba is slowly gaining type annotations. To facilitate the review of pull requests that are incrementally adding type annotations, the test suite uses typeguard to perform runtime type checking. This helps verify the validity of type annotations.

To enable runtime type checking in the test suite, users can use in the source root as the test runner and set environment variable NUMBA_USE_TYPEGUARD=1. For example:

$ NUMBA_USE_TYPEGUARD=1 python numba.tests

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!

Why is my pull request/issue seemingly being ignored?

Numba is an open source project and like many similar projects it has limited resources. As a result, it is unfortunately necessary for the core developers to associate a priority with issues/pull requests (PR). A great way to move your issue/PR up the priority queue is to help out somewhere else in the project so as to free up core developer time. Examples of ways to help:

  • Perform an initial review on a PR. This often doesn’t require compiler engineering knowledge and just involves checking that the proposed patch is of good quality, fixes the problem/implements the feature, is well tested and documented.

  • Debug an issue, there are numerous issues which “need triage” which essentially involves debugging the reported problem. Even if you cannot get right to the bottom of a problem, leaving notes about what was discovered for someone else is also helpful.

  • Answer questions/provide help for users on discourse and/or

The core developers thank everyone for their understanding 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, numpydoc and the sphinx-rtd-theme.

To install all dependencies for building the documentation, use:

$ conda install sphinx numpydoc sphinx_rtd_theme

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

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

Web site homepage

The Numba homepage on can be fetched from here: