7.1. 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).

7.1.1. Communication¶

7.1.1.1. Mailing-list¶

We have a public mailing-list that you can e-mail at numba-users@continuum.io. 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.

7.1.1.2. 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.

7.1.2. 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 platorm. In this case, please prepend [WIP] to your pull request’s title.

7.1.2.1. 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 Binstar 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:

$ <path_to_miniconda>/conda create -n numbaenv python=3.5 llvmlite numpy

Note

This installs an environment based on Python 3.5, but you can of course choose another version supported by Numba.

To activate the environment for the current shell session:

$ source <path_to_miniconda>/activate numbaenv

Note

Those 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 3.4.2 |Continuum Analytics, Inc.| (default, Oct 21 2014, 17:16:37)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import llvmlite
>>> llvmlite.__version__
'0.2.0-3-g9f60cd1'

7.1.2.2. Building Numba¶

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

$ 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.

7.1.2.3. 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 -l 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
```

7.1.3. Development rules¶

7.1.3.1. 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

7.1.3.2. 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).

7.1.3.3. 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.

7.1.3.4. Platform support¶

Numba is to be kept compatible with Python 2.7, 3.4 and 3.5 under at least Linux, OS X and Windows. Also, Numpy versions 1.7 and upwards are supported.

We don’t expect invidual contributors to test those combinations themselves! Instead, we have a continuous integration platform. Part of the platform is hosted at Travis-CI. Each time you submit a pull request, a corresponding build will be started at Travis-CI and check that Numba builds and tests without any errors. You can expect this to take less than 20 minutes.

Some platforms (such as Windows) cannot be hosted by Travis-CI, and the Numba team has therefore access to a separate platform provided by Continuum, our sponsor. We hope parts of that infrastructure can be made public in the future.

7.1.4. 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

7.1.4.1. Main documentation¶

This documentation is under the docs directory of the Numba repository. It is built with Sphinx, which is available using conda or pip.

To build the documentation, you need the basicstrap theme and its dependencies:

$ pip install sphinxjp.themes.basicstrap
$ pip install sphinxjp.themecore

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.

7.1.4.2. 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.

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