This is a guide for all contributions to CuPy. The development of CuPy is running on the official repository at GitHub. Anyone that wants to register an issue or to send a pull request should read through this document.
Classification of Contributions¶
There are several ways to contribute to CuPy community:
- Registering an issue
- Sending a pull request (PR)
- Sending a question to CuPy User Group
- Writing a post about CuPy
This document mainly focuses on 1 and 2, though other contributions are also appreciated.
Release and Milestone¶
We are using GitHub Flow as our basic working process. In particular, we are using the master branch for our development, and releases are made as tags.
Releases are classified into three groups: major, minor, and revision. This classification is based on following criteria:
- Major update contains disruptive changes that break the backward compatibility.
- Minor update contains additions and extensions to the APIs keeping the supported backward compatibility.
- Revision update contains improvements on the API implementations without changing any API specification.
The release classification is reflected into the version number x.y.z, where x, y, and z corresponds to major, minor, and revision updates, respectively.
We set a milestone for an upcoming release. The milestone is of name ‘vX.Y.Z’, where the version number represents a revision release at the outset. If at least one feature PR is merged in the period, we rename the milestone to represent a minor release (see the next section for the PR types).
See also API Compatibility Policy.
Issues and PRs¶
Issues and PRs are classified into following categories:
- Bug: bug reports (issues) and bug fixes (PRs)
- Enhancement: implementation improvements without breaking the interface
- Feature: feature requests (issues) and their implementations (PRs)
- NoCompat: disrupts backward compatibility
- Test: test fixes and updates
- Document: document fixes and improvements
- Example: fixes and improvements on the examples
- Install: fixes installation script
- Contribution-Welcome: issues that we request for contribution (only issues are categorized to this)
- Other: other issues and PRs
Issues and PRs are labeled by these categories. This classification is often reflected into its corresponding release category: Feature issues/PRs are contained into minor/major releases and NoCompat issues/PRs are contained into major releases, while other issues/PRs can be contained into any releases including revision ones.
On registering an issue, write precise explanations on what you want CuPy to be. Bug reports must include necessary and sufficient conditions to reproduce the bugs. Feature requests must include what you want to do (and why you want to do, if needed). You can contain your thoughts on how to realize it into the feature requests, though what part is most important for discussions.
If you have a question on usages of CuPy, it is highly recommended to send a post to CuPy User Group instead of the issue tracker. The issue tracker is not a place to share knowledge on practices. We may redirect question issues to CuPy User Group.
If you can write code to fix an issue, send a PR to the master branch. Before writing your code for PRs, read through the Coding Guidelines. The description of any PR must contain a precise explanation of what and how you want to do; it is the first documentation of your code for developers, a very important part of your PR.
Once you send a PR, it is automatically tested on Travis CI for Linux and Mac OS X, and on AppVeyor for Windows. Your PR need to pass at least the test for Linux on Travis CI. After the automatic test passes, some of the core developers will start reviewing your code. Note that this automatic PR test only includes CPU tests.
We are also running continuous integration with GPU tests for the master branch. Since this service is running on our internal server, we do not use it for automatic PR tests to keep the server secure.
Even if your code is not complete, you can send a pull request as a work-in-progress PR by putting the
[WIP] prefix to the PR title.
If you write a precise explanation about the PR, core developers and other contributors can join the discussion about how to proceed the PR.
To check your code, use
flake8 command installed by
$ pip install autopep8 hacking $ autopep8 --global-config .pep8 path/to/your/code.py $ flake8 path/to/your/code.py
To check Cython code, use
.flake8.cython configuration file:
$ flake8 --config=.flake8.cython path/to/your/cython/code.pyx
autopep8 supports automatically correct Python code to conform to the PEP 8 style guide:
$ autopep8 --in-place --global-config .pep8 path/to/your/code.py
flake8 command lets you know the part of your code not obeying our style guidelines.
Before sending a pull request, be sure to check that your code passes the
flake8 command is not perfect.
It does not check some of the style guidelines.
Here is a (not-complete) list of the rules that
flake8 cannot check.
- Relative imports are prohibited. [H304]
- Importing non-module symbols is prohibited.
- Import statements must be organized into three parts: standard libraries, third-party libraries, and internal imports. [H306]
In addition, we restrict the usage of shortcut symbols in our code base.
They are symbols imported by packages and sub-packages of
cupy.cuda.Device is a shortcut of
It is not allowed to use such shortcuts in the ``cupy`` library implementation.
Note that you can still use them in
Once you send a pull request, your coding style is automatically checked by Travis-CI. The reviewing process starts after the check passes.
The CuPy is designed based on NumPy’s API design. CuPy’s source code and documents contain the original NumPy ones. Please note the followings when writing the document.
- In order to identify overlapping parts, it is preferable to add some remarks that this document is just copied or altered from the original one. It is also preferable to briefly explain the specification of the function in a short paragraph, and refer to the corresponding function in NumPy so that users can read the detailed document. However, it is possible to include a complete copy of the document with such a remark if users cannot summarize in such a way.
- If a function in CuPy only implements a limited amount of features in the original one, users should explicitly describe only what is implemented in the document.
Testing is one of the most important part of your code. You must test your code by unit tests following our testing guidelines. Note that we are using the nose package and the mock package for testing, so install nose and mock before writing your code:
$ pip install nose mock
In order to run unit tests at the repository root, you first have to build Cython files in place by running the following command:
$ python setup.py develop
When you modify
*.pxd files, before running
python setup.py develop, you must clean
*.so files once with the following command, because Cython does not automatically rebuild those files nicely:
$ git clean -fdx
It’s not officially supported, but you can use ccache to reduce compilation time. On Ubuntu 16.04, you can set up as follows:
$ sudo apt-get install ccache $ export PATH=/usr/lib/ccache:$PATH
See ccache for details.
If you want to use ccache for nvcc, please install ccache v3.3 or later.
You also need to set environment variable
Once the Cython modules are built, you can run unit tests simply by running
nosetests command at the repository root:
It requires CUDA by default.
In order to run unit tests that do not require CUDA, pass
--attr='!gpu' option to the
$ nosetests path/to/your/test.py --attr='!gpu'
Some GPU tests involve multiple GPUs.
If you want to run GPU tests with insufficient number of GPUs, specify the number of available GPUs by
N is a concrete integer.
For example, if you have only one GPU, launch
nosetests by the following command to skip multi-GPU tests:
$ nosetests path/to/gpu/test.py --eval-attr='gpu<2'
Tests are put into the
These have the same structure as that of
install directories, respectively.
In order to enable test runner to find test scripts correctly, we are using special naming convention for the test subdirectories and the test scripts.
- The name of each subdirectory of
testsmust end with the
- The name of each test script must start with the
Following this naming convention, you can run all the tests by just typing
nosetests at the repository root:
Or you can also specify a root directory to search test scripts from:
$ nosetests tests/cupy_tests # to just run tests of CuPy $ nosetests tests/install_tests # to just run tests of installation modules
If you modify the code related to existing unit tests, you must run appropriate commands.
CuPy tests include type-exhaustive test functions which take long time to execute. If you are running tests on a multi-core machine, you can parallelize the tests by following options:
$ nosetests --processes=12 --process-timeout=1000 tests/cupy_tests
The magic numbers can be modified for your usage.
Note that some tests require many CUDA compilations, which require a bit long time.
process-timeout option, the timeout is set shorter, causing timeout failures for many test cases.
There are many examples of unit tests under the
They simply use the
unittest package of the standard library.
Even if your patch includes GPU-related code, your tests should not fail without GPU capability.
Test functions that require CUDA must be tagged by the
import unittest from cupy.testing import attr class TestMyFunc(unittest.TestCase): ... @attr.gpu def test_my_gpu_func(self): ...
The functions tagged by the
gpu decorator are skipped if
--attr='!gpu' is given.
We also have the
cupy.testing.attr.cudnn decorator to let
nosetests know that the test depends on cuDNN.
The test functions decorated by
gpu must not depend on multiple GPUs.
In order to write tests for multiple GPUs, use
cupy.testing.attr.multi_gpu() decorators instead:
import unittest from cupy.testing import attr class TestMyFunc(unittest.TestCase): ... @attr.multi_gpu(2) # specify the number of required GPUs here def test_my_two_gpu_func(self): ...
Once you send a pull request, your code is automatically tested by Travis-CI with –attr=’!gpu,!slow’ option. Since Travis-CI does not support CUDA, we cannot check your CUDA-related code automatically. The reviewing process starts after the test passes. Note that reviewers will test your code without the option to check CUDA-related code.
Some of numerically unstable tests might cause errors irrelevant to your changes. In such a case, we ignore the failures and go on to the review process, so do not worry about it.
We leverage doctest as well. You can run doctest by typing
make doctest at the
$ cd docs $ make doctest