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Introduction

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. 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, colour, etc.

Preparing pull requests

To ensure the code quality, MONAI relies on several linting tools (flake8 and its plugins, black, isort), static type analysis tools (mypy, pytype), as well as a set of unit/integration tests.

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

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

Licensing information

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.
Exporting modules

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 __init__.py file.

Unit testing

MONAI tests are located under tests/.

  • The unit test's file name currently follows test_[module_name].py or test_[module_name]_dist.py.
  • The test_[module_name]_dist.py subset of unit tests requires a distributed environment to verify the module with distributed GPU-based computation.
  • The integration test's file name follows test_integration_[workflow_name].py.

A bash script (runtests.sh) is provided to run all tests locally. Please run ./runtests.sh -h to see all options.

To run a particular test, for example tests/test_dice_loss.py:

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 3.8+ build-in functions, torch, numpy, coverage (for reporting code coverages) and parameterized (for organising test cases) packages. If it requires any other external packages, please make sure:

Testing data

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 tests.utils: tests.utils.testing_data_config and tests.utils.download_url_or_skip_test.

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 docs/.

# 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 .rst files in docs/source accordingly.
  • build html documentation locally
  • check the auto-generated documentation (by browsing ./docs/build/html/index.html with a web browser)
  • type make clean in docs/ folder to remove the current build files.

Please type 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 /black so 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:

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 exclude_cases in 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 Signed-off-by line.

Git has a -s (or --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 <yourname@example.org>

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.

Utility functions

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 networks/utils.py. Users are encouraged to use these common routines to improve code readability and reduce the code maintenance burdens.

Notably,

  • monai.module.export decorator can make the module name shorter when importing, for example, import monai.transforms.Spacing is the equivalent of monai.transforms.spatial.array.Spacing if class Spacing defined in file monai/transforms/spatial/array.py is decorated with @export("monai.transforms").

For string definition, f-string is recommended to use over %-print and format-print. So please try to use f-string if you need to define any string object.

Backwards compatibility

MONAI in general follows PyTorch's policy for backward compatibility. Utility functions are provided in monai.utils.deprecated to help migrate from the deprecated to new APIs. The use of these utilities is encouraged. The pull request template contains checkboxes that the contributor should use accordingly to clearly indicate breaking changes.

The process of releasing backwards incompatible API changes is as follows:

  1. discuss the breaking changes during pull requests or in dev meetings with a feature proposal if needed.
  2. add a warning message in the upcoming release (version X.Y), the warning message should include a forecast of removing the deprecated API in:
    1. X+1.0 -- major version X+1 and minor version 0 the next major version if it's a significant change,
    2. X.Y+2 -- major version X and minor version Y+2 (the minor version after the next one), if it's a minor API change.
    3. Note that the versioning policy is similar to PyTorch's approach which does not precisely follow the semantic versioning definition. Major version numbers are instead used to represent major product version (which is currently not planned to be greater than 1), minor version for both compatible and incompatible, and patch version for bug fixes.
    4. when recommending new API to use in place of a deprecated API, the recommended version should provide exact feature-like behaviour otherwise users will have a harder time migrating.
  3. add new test cases by extending the existing unit tests to cover both the deprecated and updated APIs.
  4. collect feedback from the users during the subsequent few releases, and reconsider step 1 if needed.
  5. before each release, review the deprecating APIs and relevant tests, and clean up the removed APIs described in step 2.

Submitting pull requests

All code changes to the dev branch must be done via pull requests.

  1. Create a new ticket or take a known ticket from the issue list.
  2. Check if there's already a branch dedicated to the task.
  3. 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-setup corresponds to issue #19. Ideally, the new branch should be based on the latest dev branch.
  4. Make changes to the branch (use detailed commit messages if possible).
  5. Make sure that new tests cover the changes and the changed codebase passes all tests locally.
  6. Create a new pull request from the task branch to the dev branch, with detailed descriptions of the purpose of this pull request.
  7. Check the CI/CD status of the pull request, make sure all CI/CD tests passed.
  8. Wait for reviews; if there are reviews, make point-to-point responses, make further code changes if needed.
  9. If there are conflicts between the pull request branch and the dev branch, pull the changes from the dev and resolve the conflicts locally.
  10. Reviewer and contributor may have discussions back and forth until all comments addressed.
  11. 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.

  1. Check the CI/CD status of the pull request, make sure all CI/CD tests passed before reviewing (contact the branch owner if needed).
  2. Read carefully the descriptions of the pull request and the files changed, write comments if needed.
  3. Make in-line comments to specific code segments, request for changes if needed.
  4. Review any further code changes until all comments addressed by the contributors.
  5. Comment to trigger /black and/or /integration-test for optional auto code formatting and integration tests.
  6. [Maintainers] Review the changes and comment /build to trigger internal full tests.
  7. Merge the pull request to the dev branch.
  8. Close the corresponding task ticket on the issue list.

Admin tasks

Release a new version

The dev branch's HEAD always corresponds to MONAI docker image's latest tag: projectmonai/monai:latest. The main branch's 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 dev branch 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 releasing/[version number].
  • Rebase releasing/[version number] to 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 releasing/[version number].
  • Upload the packages to PyPI. This could be done manually by twine upload dist/*, given the artifacts are unzipped to the folder dist/.
  • Merge releasing/[version number] to dev, this step must make sure that the tagging commit unchanged on dev.
  • Publish the release note.

Note that the release should be tagged with a PEP440 compliant version number.

If any error occurs during the release process, first check out a new hotfix branch from the releasing/[version number], 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 main branch, make a patch version release following the semantic versioning, for example, releasing/0.1.1. Make sure the releasing/0.1.1 is merged back into both dev and main and all the test pipelines succeed.