- The contribution process
- The code reviewing process (for the maintainers)
- Admin tasks (for the maintainers)
Welcome to Project MONAI! We're excited you're here and want to contribute. This documentation is intended for individuals and institutions interested in contributing to MONAI. MONAI is an open-source project and, as such, its success relies on its community of contributors willing to keep improving it. Your contribution will be a valued addition to the code base; we simply ask that you read this page and understand our contribution process, whether you are a seasoned open-source contributor or whether you are a first-time contributor.
Communicate with us
We are happy to talk with you about your needs for MONAI and your ideas for contributing to the project. One way to do this is to create an issue discussing your thoughts. It might be that a very similar feature is under development or already exists, so an issue is a great starting point. If you are looking for an issue to resolve that will help Project MONAI, see the good first issue and Contribution wanted labels.
Does it belong in PyTorch instead of MONAI?
MONAI is part of PyTorch Ecosystem, and mainly based on the PyTorch and Numpy libraries. These libraries implement what we consider to be best practice for general scientific computing and deep learning functionality. MONAI builds on these with a strong focus on medical applications. As such, it is a good idea to consider whether your functionality is medical-application specific or not. General deep learning functionality may be better off in PyTorch; you can find their contribution guidelines here.
The contribution process
Pull request early
We encourage you to create pull requests early. It helps us track the contributions under development, whether they are ready to be merged or not. Change your pull request's title, to begin with
[WIP] and/or create a draft pull request until it is ready for formal review.
Please note that, as per PyTorch, MONAI uses American English spelling. This means classes and variables should be: normalize, visualize, colo
Preparing pull requests
This section highlights all the necessary preparation steps required before sending a pull request. To collaborate efficiently, please read through this section and follow them.
- Checking the coding style
- Licensing information
- Unit testing
- Building documentation
- Signing your work
Checking the coding style
Coding style is checked and enforced by flake8, black, and isort, using a flake8 configuration similar to PyTorch's. Before submitting a pull request, we recommend that all linting should pass, by running the following command locally:
# optionally update the dependencies and dev tools python -m pip install -U pip python -m pip install -U -r requirements-dev.txt # run the linting and type checking tools ./runtests.sh --codeformat # try to fix the coding style errors automatically ./runtests.sh --autofix
All source code files should start with this paragraph:
# Copyright (c) MONAI Consortium # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
If you intend for any variables/functions/classes to be available outside of the file with the edited functionality, then:
- Create or append to the
__all__variable (in the file in which functionality has been added), and
- Add to the
MONAI tests are located under
- The unit test's file name currently follows
test_[module_name]_dist.pysubset of unit tests requires a distributed environment to verify the module with distributed GPU-based computation.
- The integration test's file name follows
A bash script (
runtests.sh) is provided to run all tests locally.
./runtests.sh -h to see all options.
To run a particular test, for example
python -m tests.test_dice_loss
Before submitting a pull request, we recommend that all linting and unit tests should pass, by running the following command locally:
./runtests.sh -f -u --net --coverage
or (for new features that would not break existing functionality):
./runtests.sh --quick --unittests
It is recommended that the new test
test_[module_name].py is constructed by using only
python 3.7+ build-in functions,
coverage (for reporting code coverages) and
parameterized (for organising test cases) packages.
If it requires any other external packages, please make sure:
- the packages are listed in
- the new test
test_[module_name].pyis added to the
./tests/min_tests.pyso that the minimal CI runner will not execute it.
Testing data such as images and binary files should not be placed in the source code repository.
Please deploy them to a reliable file sharing location (the current preferred one is https://github.com/Project-MONAI/MONAI-extra-test-data/releases).
At test time, the URLs within
tests/testing_data/data_config.json are accessible
via the APIs provided in
If it's not tested, it's broken
All new functionality should be accompanied by an appropriate set of tests. MONAI functionality has plenty of unit tests from which you can draw inspiration, and you can reach out to us if you are unsure of how to proceed with testing.
MONAI's code coverage report is available at CodeCov.
Building the documentation
MONAI's documentation is located at
# install the doc-related dependencies pip install --upgrade pip pip install -r docs/requirements.txt # build the docs cd docs/ make html
The above commands build html documentation, they are used to automatically generate https://docs.monai.io.
Before submitting a pull request, it is recommended to:
- edit the relevant
- build html documentation locally
- check the auto-generated documentation (by browsing
./docs/build/html/index.htmlwith a web browser)
docs/folder to remove the current build files.
make help in
docs/ folder for all supported format options.
Automatic code formatting
MONAI provides support of automatic Python code formatting via a customised GitHub action.
This makes the project's Python coding style consistent and reduces maintenance burdens.
Commenting a pull request with
/black triggers the formatting action based on
psf/Black (this is implemented with
slash command dispatch).
Steps for the formatting process:
- After submitting a pull request or push to an existing pull request,
make a comment to the pull request to trigger the formatting action.
The first line of the comment must be
/blackso that it will be interpreted by the comment parser.
- [Auto] The GitHub action tries to format all Python files (using
psf/Black) in the branch and makes a commit under the name "MONAI bot" if there's code change. The actual formatting action is deployed at project-monai/monai-code-formatter.
- [Auto] After the formatting commit, the GitHub action adds an emoji to the comment that triggered the process.
- Repeat the above steps if necessary.
Adding new optional dependencies
In addition to the minimal requirements of PyTorch and Numpy, MONAI's core modules are built optionally based on 3rd-party packages. The current set of dependencies is listed in installing dependencies.
To allow for flexible integration of MONAI with other systems and environments, the optional dependency APIs are always invoked lazily. For example,
from monai.utils import optional_import itk, _ = optional_import("itk", ...) class ITKReader(ImageReader): ... def read(self, ...): return itk.imread(...)
The availability of the external
itk.imread API is not required unless
monai.data.ITKReader.read is called by the user.
Integration tests with minimal requirements are deployed to ensure this strategy.
To add new optional dependencies, please communicate with the core team during pull request reviews, and add the necessary information (at least) to the following files:
- setup.cfg (for package's
- docs/requirements.txt (pip requirements.txt file)
- environment-dev.yml (conda environment file)
- installation.md (documentation)
When writing unit tests that use 3rd-party packages, it is a good practice to always consider an appropriate fallback default behaviour when the packages are not installed in the testing environment. For example:
from monai.utils import optional_import plt, has_matplotlib = optional_import("matplotlib.pyplot") @skipUnless(has_matplotlib, "Matplotlib required") class TestBlendImages(unittest.TestCase):
It skips the test cases when
matplotlib.pyplot APIs are not available.
Alternatively, add the test file name to the
tests/min_tests.py to completely skip the test
cases when running in a minimal setup.
Signing your work
MONAI enforces the Developer Certificate of Origin (DCO) on all pull requests.
All commit messages should contain the
Signed-off-by line with an email address. The GitHub DCO app is deployed on MONAI. The pull request's status will be
failed if commits do not contain a valid
Git has a
--signoff) command-line option to append this automatically to your commit message:
git commit -s -m 'a new commit'
The commit message will be:
a new commit Signed-off-by: Your Name <email@example.com>
Full text of the DCO:
Developer Certificate of Origin Version 1.1 Copyright (C) 2004, 2006 The Linux Foundation and its contributors. 1 Letterman Drive Suite D4700 San Francisco, CA, 94129 Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Developer's Certificate of Origin 1.1 By making a contribution to this project, I certify that: (a) The contribution was created in whole or in part by me and I have the right to submit it under the open source license indicated in the file; or (b) The contribution is based upon previous work that, to the best of my knowledge, is covered under an appropriate open source license and I have the right under that license to submit that work with modifications, whether created in whole or in part by me, under the same open source license (unless I am permitted to submit under a different license), as indicated in the file; or (c) The contribution was provided directly to me by some other person who certified (a), (b) or (c) and I have not modified it. (d) I understand and agree that this project and the contribution are public and that a record of the contribution (including all personal information I submit with it, including my sign-off) is maintained indefinitely and may be redistributed consistent with this project or the open source license(s) involved.
MONAI provides a set of generic utility functions and frequently used routines.
These are located in
monai/utils and in the module folders such as
Users are encouraged to use these common routines to improve code readability and reduce the code maintenance burdens.
monai.module.exportdecorator can make the module name shorter when importing, for example,
import monai.transforms.Spacingis the equivalent of
class Spacingdefined in file
monai/transforms/spatial/array.pyis decorated with
For string definition, f-string is recommended to use over
format-print. So please try to use
f-string if you need to define any string object.
MONAI is currently under active development, and with major version zero (following the Semantic Versioning).
The backwards compatibility of the API is not always guaranteed at this initial development stage.
However, utility functions are provided in the
monai.utils.deprecated modules to help users migrate to the new API.
The use of these functions is encouraged.
Submitting pull requests
All code changes to the dev branch must be done via pull requests.
- Create a new ticket or take a known ticket from the issue list.
- Check if there's already a branch dedicated to the task.
- If the task has not been taken, create a new branch in your fork
of the codebase named
[ticket_id]-[task_name]. For example, branch name
19-ci-pipeline-setupcorresponds to issue #19. Ideally, the new branch should be based on the latest
- Make changes to the branch (use detailed commit messages if possible).
- Make sure that new tests cover the changes and the changed codebase passes all tests locally.
- Create a new pull request from the task branch to the dev branch, with detailed descriptions of the purpose of this pull request.
- Check the CI/CD status of the pull request, make sure all CI/CD tests passed.
- Wait for reviews; if there are reviews, make point-to-point responses, make further code changes if needed.
- If there are conflicts between the pull request branch and the dev branch, pull the changes from the dev and resolve the conflicts locally.
- Reviewer and contributor may have discussions back and forth until all comments addressed.
- Wait for the pull request to be merged.
The code reviewing process
Reviewing pull requests
All code review comments should be specific, constructive, and actionable.
- Check the CI/CD status of the pull request, make sure all CI/CD tests passed before reviewing (contact the branch owner if needed).
- Read carefully the descriptions of the pull request and the files changed, write comments if needed.
- Make in-line comments to specific code segments, request for changes if needed.
- Review any further code changes until all comments addressed by the contributors.
- Comment to trigger
/integration-testfor optional auto code formatting and integration tests.
- [Maintainers] Review the changes and comment
/buildto trigger internal full tests.
- Merge the pull request to the dev branch.
- Close the corresponding task ticket on the issue list.
Release a new version
HEAD always corresponds to MONAI docker image's latest tag:
HEAD always corresponds to the latest MONAI milestone release.
When major features are ready for a milestone, to prepare for a new release:
- Prepare a release note and release checklist.
- Check out or cherry-pick a new branch
releasing/[version number]locally from the
devbranch and push to the codebase.
- Create a release candidate tag, for example,
git tag -a 0.1.0rc1 -m "release candidate 1 of version 0.1.0".
- Push the tag to the codebase, for example,
git push origin 0.1.0rc1. This step will trigger package building and testing. The resultant packages are automatically uploaded to TestPyPI. The packages are also available for downloading as repository's artifacts (e.g. the file at https://github.com/Project-MONAI/MONAI/actions/runs/66570977).
- Check the release test at TestPyPI, download the artifacts when the CI finishes.
- Optionally run the cron testing jobs on
main, make sure all the test pipelines succeed.
- Once the release candidate is verified, tag and push a milestone, for example,
git push origin 0.1.0. The tag must be with the latest commit of
- Upload the packages to PyPI.
This could be done manually by
twine upload dist/*, given the artifacts are unzipped to the folder
dev, this step must make sure that the tagging commit unchanged on
- Publish the release note.
If any error occurs during the release process, first check out a new hotfix branch from the
then make PRs to the
releasing/[version number] to fix the bugs via the regular contribution procedure.
If any error occurs after the release process, first check out a new hotfix branch from the
make a minor version release following the semantic versioning, for example,
Make sure the
releasing/0.1.1 is merged back into both
main and all the test pipelines succeed.