Contribution Guide

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:

  1. Registering an issue
  2. Sending a pull request (PR)
  3. Sending a question to CuPy User Group
  4. 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.

Coding Guidelines

We use PEP8 and a part of OpenStack Style Guidelines related to general coding style as our basic style guidelines.

To check your code, use autopep8 and flake8 command installed by hacking package:

$ pip install autopep8 hacking
$ autopep8 --global-config .pep8 path/to/your/
$ flake8 path/to/your/

To check Cython code, use .flake8.cython configuration file:

$ flake8 --config=.flake8.cython path/to/your/cython/code.pyx

The autopep8 supports automatically correct Python code to conform to the PEP 8 style guide:

$ autopep8 --in-place --global-config .pep8 path/to/your/

The 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 checking.

Note that 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. For example, cupy.cuda.Device is a shortcut of cupy.cuda.device.Device. It is not allowed to use such shortcuts in the ``cupy`` library implementation. Note that you can still use them in tests and examples directories.

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 Guidelines

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 pytest and mock package for testing, so install them before writing your code:

$ pip install pytest 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:

$ pip install -e .


When you modify *.pxd files, before running pip install -e ., you must clean *.cpp and *.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 NVCC='ccache nvcc'.

Once Cython modules are built, you can run unit tests by running the following command at the repository root:

$ python -m pytest

CUDA must be installed to run unit tests.

Some GPU tests require cuDNN to run. In order to skip unit tests that require cuDNN, specify -m='not cudnn' option:

$ python -m pytest path/to/your/ -m='not cudnn'

Some GPU tests involve multiple GPUs. If you want to run GPU tests with insufficient number of GPUs, specify the number of available GPUs to CUPY_TEST_GPU_LIMIT. For example, if you have only one GPU, launch pytest by the following command to skip multi-GPU tests:

$ python -m pytest path/to/gpu/

Tests are put into the tests/cupy_tests and tests/install_tests directories. These have the same structure as that of cupy and 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 tests must end with the _tests suffix.
  • The name of each test script must start with the test_ prefix.

Following this naming convention, you can run all the tests by running the following command at the repository root:

$ python -m pytest

Or you can also specify a root directory to search test scripts from:

$ python -m pytest tests/cupy_tests     # to just run tests of CuPy
$ python -m pytest 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.

There are many examples of unit tests under the tests directory. 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 cupy.testing.attr.gpu:

import unittest
from cupy.testing import attr

class TestMyFunc(unittest.TestCase):

    def test_my_gpu_func(self):

The functions tagged by the gpu decorator are skipped if CUPY_TEST_GPU_LIMIT=0 environment variable is set. We also have the cupy.testing.attr.cudnn decorator to let pytest know that the test depends on cuDNN. The test functions decorated by cudnn are skipped if -m='not cudnn' is given.

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() or 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, Travis-CI automatically checks if your code meets our coding guidelines described above. Since Travis-CI does not support CUDA, we cannot run unit tests automatically. The reviewing process starts after the automatic check passes. Note that reviewers will test your code without the option to check CUDA-related code.

We leverage doctest as well. You can run doctest by typing make doctest at the docs directory:

$ cd docs
$ make doctest