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PRs welcome!

  • Consider starting a discussion to see if there's interest in what you want to do.
  • Submit PRs from feature branches on forks to the develop branch.
  • Ensure PRs pass all CI checks.
  • Maintain test coverage at 100%.




This project uses Hatch for dependency management and packaging.



Hatch can be installed with Homebrew or pipx.

Install project with all dependencies: hatch env create.

Key commands

# Basic usage:
hatch env create  # create virtual environment and install dependencies
hatch env find  # show path to virtual environment
hatch env show  # show info about available virtual environments
hatch run COMMAND  # run a command within the virtual environment
hatch shell  # activate the virtual environment, like source venv/bin/activate
hatch version  # list or update version of this package
export HATCH_ENV_TYPE_VIRTUAL_PATH=.venv  # install virtualenvs into .venv

Running the development server

The easiest way to get started is to run the development server locally with the VSCode debugger. The debugger config is stored in launch.json. After installing the virtual environment as described above, start the debugger. Uvicorn enables hot-reloading and addition of debug breakpoints while the server is running. The Microsoft VSCode Python extension also offers a FastAPI debugger configuration, added in version 2020.12.0, which has been customized and included in launch.json. To use it, simply select the FastAPI config and start the debugger.

As explained in the VSCode docs, if developing on Linux, note that non-root users may not be able to expose ports less than 1024.

Testing with pytest


Docker basics

Expand this details element for more useful Docker commands.
# Log in with Docker Hub credentials to pull images
docker login
# List images
docker images
# List running containers: can also use `docker container ls`
docker ps
# View logs for the most recently started container
docker logs -f $(docker ps -q -n 1)
# View logs for all running containers
docker logs -f $(docker ps -aq)
# Inspect a container (web in this example) and return the IP Address
docker inspect web | grep IPAddress
# Stop a container
docker stop # container hash
# Stop all running containers
docker stop $(docker ps -aq)
# Remove a downloaded image
docker image rm # image hash or name
# Remove a container
docker container rm # container hash
# Prune images
docker image prune
# Prune stopped containers (completely wipes them and resets their state)
docker container prune
# Prune everything
docker system prune
# Open a shell in the most recently started container (like SSH)
docker exec -it $(docker ps -q -n 1) /bin/bash
# Or, connect as root:
docker exec -u 0 -it $(docker ps -q -n 1) /bin/bash
# Copy file to/from container:
docker cp [container_name]:/path/to/file destination.file

Building development images

Note that Docker builds use BuildKit. See the BuildKit docs and Docker docs.

To build the Docker images for each stage:

git clone

cd inboard


docker build . --rm --target base -t localhost/br3ndonland/inboard:base && \
docker build . --rm --target fastapi -t localhost/br3ndonland/inboard:fastapi && \
docker build . --rm --target starlette -t localhost/br3ndonland/inboard:starlette

Running development containers

# Run Docker container with Uvicorn and reloading
cd inboard

docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" \
  -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
  -e "LOG_LEVEL=debug" \
  -e "PROCESS_MANAGER=uvicorn" \
  -e "WITH_RELOAD=true" \
  -v $(pwd)/inboard:/app/inboard localhost/br3ndonland/inboard:base

docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" \
  -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
  -e "LOG_LEVEL=debug" \
  -e "PROCESS_MANAGER=uvicorn" \
  -e "WITH_RELOAD=true" \
  -v $(pwd)/inboard:/app/inboard localhost/br3ndonland/inboard:fastapi

docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" \
  -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
  -e "LOG_LEVEL=debug" \
  -e "PROCESS_MANAGER=uvicorn" \
  -e "WITH_RELOAD=true" \
  -v $(pwd)/inboard:/app/inboard localhost/br3ndonland/inboard:starlette

# Run Docker container with Gunicorn and Uvicorn
docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" \
  -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" \
  -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" \
  -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \

# Test HTTP Basic auth when running the FastAPI or Starlette images:
http :80/status -a test_user:r4ndom_bUt_memorable

Change the port numbers to run multiple containers simultaneously (-p 81:80).

Code quality

Running code quality checks

Code quality checks can be run using the Hatch scripts in pyproject.toml.

  • Check: hatch run check
  • Format: hatch run format

Code style

  • Python code is formatted with Ruff. Ruff configuration is stored in pyproject.toml.
  • Other web code (JSON, Markdown, YAML) is formatted with Prettier.

Static type checking

  • To learn type annotation basics, see the Python typing module docs, Python type annotations how-to, the Real Python type checking tutorial, and this gist.
  • Type annotations are not used at runtime. The standard library typing module includes a TYPE_CHECKING constant that is False at runtime, but True when conducting static type checking prior to runtime. Type imports are included under if TYPE_CHECKING: conditions so that they are not imported at runtime. These conditions are ignored when calculating test coverage.
  • Type annotations can be provided inline or in separate stub files. Much of the Python standard library is annotated with stubs. For example, the Python standard library logging.config module uses type stubs. The typeshed types for the logging.config module are used solely for type-checking usage of the logging.config module itself. They cannot be imported and used to type annotate other modules.
  • The standard library typing module includes a NoReturn type. This would seem useful for unreachable code, including functions that do not return a value, such as test functions. Unfortunately mypy reports an error when using NoReturn, "Implicit return in function which does not return (misc)." To avoid headaches from the opaque "misc" category of mypy errors, these functions are annotated as returning None.
  • Mypy is used for type-checking. Mypy configuration is included in pyproject.toml.
  • Mypy strict mode is enabled. Strict includes --no-explicit-reexport (implicit_reexport = false), which means that objects imported into a module will not be re-exported for import into other modules. Imports can be made into explicit exports with the syntax from module import x as x (i.e., changing from import logging to import logging as logging), or by including imports in __all__. This explicit import syntax can be confusing. Another option is to apply mypy overrides to any modules that need to leverage implicit exports.

Spell check

Spell check is performed with CSpell. The CSpell command is included in the Hatch script for code quality checks (hatch run check).

GitHub Actions workflows

GitHub Actions is a continuous integration/continuous deployment (CI/CD) service that runs on GitHub repos. It replaces other services like Travis CI. Actions are grouped into workflows and stored in .github/workflows. See Getting the Gist of GitHub Actions for more info.



  • The default branch is develop.
  • PRs should be merged into develop. Head branches are deleted automatically after PRs are merged.
  • The only merges to main should be fast-forward merges from develop.
  • Branch protection is enabled on develop and main.
    • develop:
      • Require signed commits
      • Include administrators
      • Allow force pushes
    • main:
      • Require signed commits
      • Include administrators
      • Do not allow force pushes
      • Require status checks to pass before merging (commits must have previously been pushed to develop and passed all checks)


  • Each minor version release of FastAPI or Uvicorn should get its own corresponding minor version release of inboard. FastAPI depends on Starlette, so any updates to the Starlette version required by FastAPI should be included with updates to the FastAPI version.
  • To create a release:
    • Bump the version number in inboard.__version__ with hatch version and commit the changes to develop.
      • Follow SemVer guidelines when choosing a version number. Note that PEP 440 Python version specifiers and SemVer version specifiers differ, particularly with regard to specifying prereleases. Use syntax compatible with both.
      • The PEP 440 default (like 1.0.0a0) is different from SemVer. Hatch and PyPI will use this syntax by default.
      • An alternative form of the Python prerelease syntax permitted in PEP 440 (like 1.0.0-alpha.0) is compatible with SemVer, and this form should be used when tagging releases. As Hatch uses PEP 440 syntax by default, prerelease versions need to be written directly into inboard.__version__.
      • Examples of acceptable tag names: 1.0.0, 1.0.0-alpha.0, 1.0.0-beta.1
    • Push to develop and verify all CI checks pass.
    • Fast-forward merge to main, push, and verify all CI checks pass.
    • Create an annotated and signed Git tag.
      • List PRs and commits in the tag message:
        git log --pretty=format:"- %s (%h)" \
          "$(git describe --abbrev=0 --tags)"..HEAD
      • Omit the leading v (use 1.0.0 instead of v1.0.0)
      • Example: git tag -a -s 1.0.0
    • Push the tag. GitHub Actions will build and push the Python package and Docker images, and open a PR to update the changelog.
    • Squash and merge the changelog PR, removing any Co-authored-by trailers before merging.


Documentation is built with Material for MkDocs, deployed on Vercel, and available at and

Vercel build configuration:

  • Build command: python3 -m pip install 'mkdocs-material>=9,<10' && mkdocs build --site-dir public. The version of mkdocs-material installed on Vercel is independent of the version listed in pyproject.toml. If the version of mkdocs-material is updated in pyproject.toml, it must also be updated in the Vercel build configuration.
  • Output directory: public (default)

Vercel site configuration is specified in vercel.json.