Contributor Guidelines¶
This document will go through best practices for contributing to this project. We welcome and appreciate contributions or ideas for improvement.
Bug Reports and Feature Requests¶
Before creating a pull request, we ask contributors to please open a bug report or feature request first: issues
We will do our best to monitor and maintain the backlog of issues.
Local Installation for Development¶
For development,
For new features or non-urgent bug fixes, create a branch off of
devFor an urgent hotfix to our production environment, create a branch off of
main
Consult the Branches and Pull Requests and Release Cycles for more details.
Running a local server¶
From the root directory, run:
pip install -e .[dev]
to install the relevant code for development.
We are working on setting up a local dev server to mock airflow responses. In the meantime, it’s assumed that you are able to connect to our development backend. Please reach out to us if you need an airflow account.
To mock airflow responses for job status list:
cd tests/test_server
docker build -t json_server .
docker run -p 8080:3000 json_server
To run uvicorn locally:
# export MSYS_NO_PATHCONV=1 # Uncomment this line if running on Windows
export AIND_METADATA_SERVICE_PROJECT_NAMES_URL='http://aind-metadata-service-dev/project_names'
export AIND_AIRFLOW_SERVICE_URL='http://aind-airflow-service-dev:8080/api/v1/dags/run_list_of_jobs/dagRuns'
# export AIND_AIRFLOW_SERVICE_JOBS_URL='http://aind-airflow-service-dev:8080/api/v1/dags'
export AIND_AIRFLOW_SERVICE_JOBS_URL='http://localhost:8080/transform_and_upload'
export AIND_AIRFLOW_SERVICE_PASSWORD='*****'
export AIND_AIRFLOW_SERVICE_USER='user'
export ENV_NAME='local'
export AWS_DEFAULT_REGION='us-west-2'
export AIND_AIRFLOW_PARAM_PREFIX='/aind/dev/airflow/variables/job_types'
export AIND_SSO_SECRET_NAME='/aind/dev/data_transfer_service/sso/secrets'
uvicorn aind_data_transfer_service.server:app --host 0.0.0.0 --port 5000 --reload
You can now access aind-data-transfer-service at
http://localhost:5000.
Branches and Pull Requests¶
Branch naming conventions¶
Name your branch using the following format:
<type>-<issue_number>-<short_summary>
where:
<type> is one of: - build: Changes that affect the build system
or external dependencies (e.g., pyproject.toml, setup.py) - ci:
Changes to our CI configuration files and scripts (examples:
.github/workflows/ci.yml) - docs: Changes to our documentation -
feat: A new feature - fix: A bug fix - perf: A code change
that improves performance - refactor: A code change that neither
fixes a bug nor adds a feature, but will make the codebase easier to
maintain - test: Adding missing tests or correcting existing tests -
hotfix: An urgent bug fix to our production code
<issue_number> references the GitHub issue this branch will close
<short_summary> is a brief description that shouldn’t be more than 3
words.
Some examples:
feat-12-adds-email-fieldfix-27-corrects-endpointtest-43-updates-server-test
We ask that a separate issue and branch are created if code is added outside the scope of the reference issue.
Commit Messages¶
Please format your commit messages as <type>: <short summary> where
<type> is from the list above and the short summary is one or two
sentences.
Testing and Docstrings¶
We strive for complete code coverage and docstrings, and we also run code format checks.
To run the code format check:
flake8 .
There are some helpful libraries that will automatically format the code and import statements:
black .
and
isort .
Strings that exceed the maximum line length may still need to be formatted manually.
To run the docstring coverage check and report:
interrogate -v .
This project uses NumPy’s docstring format: Numpy docstring standards
Many IDEs can be configured to automatically format docstrings in the NumPy convention.
To run the unit test coverage check and report:
coverage run -m unittest discover && coverage report
To view a more detailed html version of the report, run:
coverage run -m unittest discover && coverage report
coverage html
and then open htmlcov/index.html in a browser.
Pull Requests¶
Pull requests and reviews are required before merging code into this
project. You may open a Draft pull request and ask for a preliminary
review on code that is currently a work-in-progress.
Before requesting a review on a finalized pull request, please verify that the automated checks have passed first.
Release Cycles¶
For this project, we have adopted the Git Flow system. We will strive to release new features and bug fixes on a two week cycle. The rough workflow is:
Hotfixes¶
A
hotfixbranch is created off ofmainA Pull Request into
mainis opened, reviewed, and merged intomainA new
tagwith a patch bump is created, and a newreleaseis deployedThe
mainbranch is merged into all other branches
Feature branches and bug fixes¶
A new branch is created off of
devA Pull Request into
devis opened, reviewed, and merged
Release branch¶
A new branch
release-v{new_tag}is createdDocumentation updates and bug fixes are created off of the
release-v{new_tag}branch.Commits added to the
release-v{new_tag}are also merged intodevOnce ready for release, a Pull Request from
release-v{new_tag}intomainis opened for final reviewA new tag will automatically be generated
Once merged, a new GitHub Release is created manually
Pre-release checklist¶
☐ Increment
__version__inaind_data_transfer_service/__init__.pyfile☐ Run linters, unit tests, and integration tests
☐ Verify code is deployed and tested in test environment
☐ Update examples
☐ Update documentation
Run:
sphinx-apidoc -o docs/source/ src sphinx-build -b html docs/source/ docs/build/html
☐ Update and build UML diagrams
To build UML diagrams locally using a docker container:
docker pull plantuml/plantuml-server docker run -d -p 8080:8080 plantuml/plantuml-server:jetty
Post-release checklist¶
☐ Merge
mainintodevand feature branches☐ Edit release notes if needed
☐ Post announcement