6.1. 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).
We have a public mailing-list that you can e-mail at email@example.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.
188.8.131.52. 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.
6.1.2. 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.
184.108.40.206. 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=3.4 llvmlite numpy
This installs an environment based on Python 3.4, 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
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'
220.127.116.11. Building Numba¶
For a quick development workaround, 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.
18.104.22.168. 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
The various test modules are inside the
numba/tests directory. There
are two entry points to run the test suite:
if you want to run the whole test suite (which will take a couple of minutes), call the
runtests.pyscript; in particular, the
-mflag will parallelize the test suite into several processes:
$ python runtests.py -m
if you want to run an individual test module, invoke it as a python module, for example:
$ python -m numba.tests.test_closure
Both the global test runner
runtests.py and individual modules allow you
to pass various options to influence test running and report. Pass
--help to get a glimpse at those options.
6.1.3. Development rules¶
22.214.171.124. 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
126.96.36.199. 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).
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.
188.8.131.52. Platform support¶
Numba is to be kept compatible with Python 2.6, 2.7, 3.3 and 3.4 under at least Linux, OS X and Windows. Also, Numpy versions 1.6 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.
The numba documentation is split over two repositories:
- This documentation is in the
docsdirectory inside the Numba repository.
- The Numba homepage has its sources in a separate repository at https://github.com/numba/numba-webpage
184.108.40.206. Main documentation¶
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
$ python gh-pages.py version # version can be 'dev' or '0.16' etc
then verify the repository under the
gh-pages directory and use
220.127.116.11. 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.