First you need to install pipenv, it will handle the virtual environment creation for the project in order to sandbox our Python environment, as well as manage the dependencies installation, among other things.

For running dependent services, you’ll need Docker (>=18.06.1) and Docker Compose (>=1.22.0), in order get things up and running quickly but also to make sure that the same versions and configuration for these services is used, independent of your OS.

Start all the dependent services using docker-compose (this will start PostgreSQL, Elasticsearch 6, RabbitMQ, Redis, Kibana and Flower):

$ docker-compose up -d


Make sure you have enough virtual memory for Elasticsearch in Docker:

# Linux
$ sysctl -w vm.max_map_count=262144

# macOS
$ screen ~/Library/Containers/com.docker.docker/Data/com.docker.driver.amd64-linux/tty
linut00001:~# sysctl -w vm.max_map_count=262144

Next, bootstrap the instance (this will install all Python dependencies):

$ ./scripts/bootstrap

Next, create the database tables and search indices:

$ ./scripts/setup


Start the webserver and the celery worker:

$ ./scripts/server

Start a Python shell:

$ ./scripts/console


In order to upgrade an existing instance simply run:

$ ./scripts/update


Run the test suite via the provided script:

$ ./


You can build the documentation with:

$ pipenv run build_sphinx

Production environment

You can simulate a full production environment using the docker-compose.full.yml. You can start it like this:

$ docker-compose -f docker-compose.full.yml up -d

In addition to the normal docker-compose.yml, this one will start:

  • HAProxy (load balancer)

  • Nginx (web frontend)

  • uWSGI (application container)

  • Celery (background task worker)

As done for local development, you will also have to run the initial setup script inside the running container:

$ docker-compose -f docker-compose.full.yml run --rm web ./scripts/setup