OUTDATED DOCUMENTATION

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).

Communication¶

Mailing-list¶

We have a public mailing-list that you can e-mail at numba-users@anaconda.com. If you have any questions about contributing to Numba, it is ok to ask them on this mailing-list. You can subscribe and read the archives on Google Groups, and there is also a Gmane mirror allowing NNTP access.

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.

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

Note

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

Note

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__
'0.24.0'

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.

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.

Stability¶

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 Travis CI and Azure to provide public continuous integration information for as many combinations as can be supported by the service. Travis CI automatically tests all pull requests on OS X and Linux, as well as a sampling of different Python and NumPy versions, Azure does the same but also includes Windows. 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.

Documentation¶

The Numba documentation is split over two repositories:

This documentation is in the docs directory inside the Numba repository.
The Numba homepage has its sources in a separate repository at https://github.com/numba/numba-webpage

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.