Chainer Contribution Guide¶
This is a guide for all contributions to Chainer. The development of Chainer 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 Chainer community:
- Registering an issue
- Sending a pull request (PR)
- Sending a question to Chainer User Group
- Open-sourcing an external example
- Writing a post about Chainer
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:
- A major release contains catastrophic changes on the interface that may break existing user codes.
- A minor release contains additions and modifications on the interface. It may break some existing user codes, though they must be fixed by small efforts.
- A revision release contains changes that does not affect the documented interface. It mainly consists of bug fixes, implementation improvements, and test/document/example updates.
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 sets milestones for some future releases. A milestone for a revision release is set right after the last release. On the other hand, a milestone for a minor or major release is set four weeks prior to its due.
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)
- Test: test fixes and updates
- Document: document fixes and improvements
- Example: fixes and improvements on the examples
- 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, while other issues/PRs can be contained into any releases including revision ones.
On registering an issue, write precise explanations on what you want Chainer 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 Chainer, it is highly recommended to send a post to Chainer 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 Chainer User Group.
If you can write codes to fix an issue, send a PR to the master branch. Before writing your codes 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 codes for developers, a very important part of your PR.
Once you send a PR, it is automatically tested on Travis CI. After the automatic test passes, some of the core developers will start reviewing your codes. Note that this automatic PR test only includes CPU tests.
We are also running continuous integrations 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 codes are 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.
Before checking your code, you can use automatic formatter to set appropriate spacing, etc.
We recommend you to install the
isort packages, and run the following commands:
$ pyformat -i path/to/your/code.py $ isort path/to/your/code.py
Note that these formatters do not cover all part of the style guidelines.
To check your code, use
flake8 command installed by
$ pip install hacking $ flake8 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 subpackages of
chainer.Variable is a shortcut of
It is not allowed to use such shortcuts in the ``chainer`` library implementation.
Note that you can still use them in
Also note that you should use shortcut names of CuPy APIs in Chainer implementation.
Once you send a pull request, your coding style is automatically checked by Travis-CI. The reviewing process starts after the check passes.
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 codes:
$ pip install nose mock
In order to run unittests at the repository root, you first have to build Cython files in place by running the following command:
$ python setup.py develop
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 command:
$ 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 --attr='gpu<2'
Tests are put into the
These have the same structure as that of
cupy 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/chainer_tests # to just run tests of Chainer $ nosetests tests/cupy_tests # to just run tests of CuPy
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
chainer.testing.attr.gpu decorator (or
cupy.testing.attr.gpu for testing CuPy APIs):
import unittest from chainer.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
chainer.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 chainer.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’ 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.