Grand-challenge is distributed as a set of containers that are defined and linked together in docker-compose.yml. To develop the platform you need to have docker and docker compose running on your system.


  1. Download and install Docker

Note for Windows Users: we only support development using Windows 10 and WSL2. Please ensure that the correct backend is enabled in your docker settings, and run all of the following commands in the wsl shell. At the time of writing, we use Ubuntu 20.04 from the Microsoft store as the default distro. As WSL2 is slow at syncing files between Windows and WSL2 filesystems it is best to checkout the codebase within wsl itself.

The docker compose cycle script below utilizes Docker Buildx. Depending on the steps above, buildx should be installed alongside docker. If the docker compose cycle invocation below crashes on buildx, it is recommended to (re)install the latest version.

  1. Clone the repo

$ git clone
$ cd
  1. Set your local docker group id in your .env file

$ echo DOCKER_GID=`getent group docker | cut -d: -f3` > .env
  1. You can then start the development site by invoking

$ make runserver

The app/ directory is mounted in the containers, werkzeug handles the file monitoring and will restart the process if any changes are detected. You can also kill the server with CTRL+C.

The Development Site

If you follow the installation instructions above you will be able to go to https://gc.localhost to see the development site, this is using a self-signed certificate so you will need to accept the security warning.

The development site will apply all migrations and add a set of fixtures to help you with developing These fixtures include Archives, Reader Studies, Challenges, Algorithms and Workstations. Some default users are created with specific permissions, each user has the same username and password. These users include archive, readerstudy, demo, algorithm and workstation, who have permission to administer the existing fixtures and create new ones.

If you would like to test out the algorithms you can create a simple algorithm that lists its inputs in a results.json file by running

$ make create_io_algorithm

Before you run

$ make runserver

You can also download an Example Algorithm Image from Docker Hub. Save it locally with

$ docker pull grandchallenge/otsu
$ docker save grandchallenge/otsu > otsu.tar

You can then upload otsu.tar as a container image to the local site.

If you would like to generate your own Algorithm or Evaluation containers you can do this using Evalutils, please see the Getting Started with Evalutils documentation.

There is an interactive debugger from django-extensions which will halt on exceptions (see the RunServerPlus documentation), it’s really handy for interactive debugging to place 1/0 in your code as a breakpoint.

Running the Tests

GitHub actions is used to run the test suite on every new commit. You can also run the tests locally by

  1. In a console window make sure the database is running

$ make runserver
  1. Then in a second window run

$ docker compose run --rm celery_worker_evaluation pytest -n 2

Replace 2 with the number of CPUs that you have on your system, this runs the tests in parallel.

If you want to add a new test please add them to the app/tests folder. If you only want to run the tests for a particular app, eg. for teams, you can do

$ docker compose run --rm celery_worker_evaluation pytest -k teams_tests


You will need to install pre-commit so that the code is correctly formatted

$ python3 -m pip install pre-commit

Please do all development on a branch and make a pull request to main, this will need to be reviewed before it is integrated.

We recommend using Pycharm for development.

Running through docker compose

You will need the Professional edition to use the docker compose integration. To set up the environment in Pycharm Professional 2018.1:

  1. File -> Settings -> Project: -> Project Interpreter -> Cog wheel (top right) -> Add -> Docker Compose

  2. Then select the docker server (usually the unix socket, or Docker for Windows)

  3. Set the service to web

  4. Click OK

  5. Set the path mappings:

    1. Local path: <Project root>/app

    2. Remote path: /app

  6. Click OK

Pycharm will then spend some time indexing the packages within the container to help with code completion and inspections. If you edit any files these will be updated on the fly by werkzeug.

PyCharm Configuration

It is recommended to setup django integration to ensure that the code completion, tests and import optimisation works.

  1. Open File -> Settings -> Languages and Frameworks -> Django

  2. Check the Enable Django Support checkbox

  3. Set the project root to <Project root>/app

  4. Set the settings to config/

  5. Check the Do not use the django test runner checkbox

  6. In the settings window navigate to Tools -> Python integrated tools

  7. Under the testing section select pytest as the default test runner

  8. Under the Docstrings section set NumPy as the docstrings format

  9. In the settings window navigate to Editor -> Code Style

  10. Click on the Formatter Control tab and enable Enable formatter markers in comments

  11. In the settings window navigate to Editor -> Code Style -> Python

  12. On the Wrapping and Braces tab set Hard wrap at to 86 and Visual guide to 79

  13. On the Imports tab enable Sort Import Statements, Sort imported names in "from" imports, and Sort plain and "from" imports separately in the same group

  14. Click OK

  15. Install the Flake8 Support plugin so that PyCharm will understand noqa comments. At the time of writing, the plugin is not compatible with PyCharm 2020. You can still install Flake8 as an external tool though. To do so, follow these steps:

    1. Install flake8 pip install flake8

    2. In PyCharm, in the settings window navigate to Tools -> External Tools and add a new one with the following configuration:

      1. Program: file path to flake8.exe you just installed

      2. Arguments: $FilePath$

      3. Working directory: $ProjectFileDir$

  16. In the main window at the top right click the drop down box and then click Edit Configurations...

  17. Click on templates -> Python Tests -> pytest, and enter --reuse-db in the Additional Arguments box and run --rm in the Command and options box under Docker Compose

It is also recommended to install the black extension for code formatting. You can add it as an external tool, following the same instructions as for Flake8 above.

Creating Migrations

If you change a file then you will need to make the corresponding migration files. You can do this with

$ make migrations

or, more explicitly

$ docker compose run --rm --user `id -u` web python makemigrations

add these to git and commit.

Building the documentation

Having built the web container with make runserver you can use this to generate the docs with

$ make docs

This will create the docs in the docs/_build/html directory.

Adding new dependencies

Poetry is used to manage the dependencies of the platform. To add a new dependency use

$ poetry add <whatever>

and then commit the pyproject.toml and poetry.lock. If this is a development dependency then use the --dev flag, see the poetry documentation for more details.

Versions are unpinned in the pyproject.toml file, to update the resolved dependencies use

$ poetry lock

and commit the update poetry.lock. The containers will need to be rebuilt after running these steps, so stop the make runserver process with CTRL+C and restart.

Going to Production

The docker compose file included here is for development only. If you want to run this in a production environment you will need to make several changes, not limited to:

  1. Use gunicorn rather than run runserver_plus to run the web process

  2. Disable mounting of the docker socket

  3. Removing the users that are created by development_fixtures