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).
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.
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 numba.discourse.group Announcements and everyone is welcome to join the discussion. Minutes will be taken and will be posted to the Numba wiki.
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.
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=3.8 llvmlite numpy scipy jinja2 cffi
This installs an environment based on Python 3.8, but you can of course
choose another version supported by Numba. To test additional features,
you may also need to install
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 3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> import llvmlite >>> llvmlite.__version__ '0.35.0'
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.
build_ext command in Numba’s setup also accepts the following
--noopt: This disables optimization when compiling Numba’s CPython extensions, which makes debugging them much easier. Recommended in conjunction with the standard
--werror: Compiles Numba’s CPython extensions with the
--wall: Compiles Numba’s CPython extensions with the
Note that Numba’s CI and the conda recipe for Linux build with the
--wall flags, so any contributions that change the CPython extensions
should be tested with these flags too.
Numba is validated using a test suite comprised of various kind of tests
(unit tests, functional tests). The test suite is written using the
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
--help to get a glimpse at those options.
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:
--failed-firstoption is added to capture the list of failed tests and to re-execute them first:
$ python -m numba.runtests --failed-first -m -v -b
--last-failedoption is used with
--failed-firstto 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
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
NUMBA_USE_TYPEGUARD=1 and use runtests.py from the source root
instead. For example:
$ NUMBA_USE_TYPEGUARD=1 python runtests.py
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
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
conda and then run from the root of the Numba repository:
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
pip install pre-commit
and then running:
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
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
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
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.
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.
Every commit to the master 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:
It’s huge, 9000+ tests.
In part because of 1. and that compilers are pretty involved, the test suite takes a long time to run.
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).
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:
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”.
When public CI runs it:
Looks for files that contain tests that have been altered by the proposed change and runs these on the whole testing matrix.
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
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
runtests.py in the source root as the test runner and set environment
NUMBA_USE_TYPEGUARD=1. For example:
$ NUMBA_USE_TYPEGUARD=1 python runtests.py 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.
The core developers thank everyone for their understanding with the above!
The Numba documentation is split over two repositories:
This documentation is in the
docsdirectory inside the Numba repository.
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 https://numba.pydata.org by using the
gh-pages.py script under
$ python gh-pages.py version # version can be 'dev' or '0.16' etc
then verify the repository under the
gh-pages directory and use
Web site homepage¶
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
git push to update the website.