Getting Up and Running Locally With Docker

The steps below will get you up and running with a local development environment. All of these commands assume you are in the root of your generated project.


If you’re new to Docker, please be aware that some resources are cached system-wide and might reappear if you generate a project multiple times with the same name (e.g. this issue with Postgres).


Build the Stack

This can take a while, especially the first time you run this particular command on your development system:

$ docker-compose -f local.yml build

Generally, if you want to emulate production environment use production.yml instead. And this is true for any other actions you might need to perform: whenever a switch is required, just do it!

Run the Stack

This brings up both Django and PostgreSQL. The first time it is run it might take a while to get started, but subsequent runs will occur quickly.

Open a terminal at the project root and run the following for local development:

$ docker-compose -f local.yml up

You can also set the environment variable COMPOSE_FILE pointing to local.yml like this:

$ export COMPOSE_FILE=local.yml

And then run:

$ docker-compose up

To run in a detached (background) mode, just:

$ docker-compose up -d

Execute Management Commands

As with any shell command that we wish to run in our container, this is done using the docker-compose -f local.yml run --rm command:

$ docker-compose -f local.yml run --rm django python migrate
$ docker-compose -f local.yml run --rm django python createsuperuser

Here, django is the target service we are executing the commands against.

(Optionally) Designate your Docker Development Server IP

When DEBUG is set to True, the host is validated against ['localhost', '', '[::1]']. This is adequate when running a virtualenv. For Docker, in the config.settings.local, add your host development server IP to INTERNAL_IPS or ALLOWED_HOSTS if the variable exists.

Configuring the Environment

This is the excerpt from your project’s local.yml:

# ...

    context: .
    dockerfile: ./compose/production/postgres/Dockerfile
    - local_postgres_data:/var/lib/postgresql/data
    - local_postgres_data_backups:/backups
    - ./.envs/.local/.postgres

# ...

The most important thing for us here now is env_file section enlisting ./.envs/.local/.postgres. Generally, the stack’s behavior is governed by a number of environment variables (env(s), for short) residing in envs/, for instance, this is what we generate for you:

├── .local
│   ├── .django
│   └── .postgres
└── .production
    ├── .django
    └── .postgres

By convention, for any service sI in environment e (you know someenv is an environment when there is a someenv.yml file in the project root), given sI requires configuration, a .envs/.e/.sI service configuration file exists.

Consider the aforementioned .envs/.local/.postgres:

# PostgreSQL
# ------------------------------------------------------------------------------
POSTGRES_DB=<your project slug>

The three envs we are presented with here are POSTGRES_DB, POSTGRES_USER, and POSTGRES_PASSWORD (by the way, their values have also been generated for you). You might have figured out already where these definitions will end up; it’s all the same with django service container envs.

One final touch: should you ever need to merge .envs/production/* in a single .env run the

$ python

The .env file will then be created, with all your production envs residing beside each other.

Tips & Tricks

Activate a Docker Machine

This tells our computer that all future commands are specifically for the dev1 machine. Using the eval command we can switch machines as needed.:

$ eval "$(docker-machine env dev1)"



If you are using the following within your code to debug:

import ipdb; ipdb.set_trace()

Then you may need to run the following for it to work as desired:

$ docker-compose -f local.yml run --rm --service-ports django


In order for django-debug-toolbar to work designate your Docker Machine IP with INTERNAL_IPS in


The container_name from the yml file can be used to check on containers with docker commands, for example:

$ docker logs worker
$ docker top worker


When developing locally you can go with MailHog for email testing provided use_mailhog was set to y on setup. To proceed,

  1. make sure mailhog container is up and running;
  2. open up

Celery tasks in local development

When not using docker Celery tasks are set to run in Eager mode, so that a full stack is not needed. When using docker the task scheduler will be used by default.

If you need tasks to be executed on the main thread during development set CELERY_TASK_ALWAYS_EAGER = True in config/settings/

Possible uses could be for testing, or ease of profiling with DJDT.

Celery Flower

Flower is a “real-time monitor and web admin for Celery distributed task queue”.


  • use_docker was set to y on project initialization;
  • use_celery was set to y on project initialization.

By default, it’s enabled both in local and production environments (local.yml and production.yml Docker Compose configs, respectively) through a flower service. For added security, flower requires its clients to provide authentication credentials specified as the corresponding environments’ .envs/.local/.django and .envs/.production/.django CELERY_FLOWER_USER and CELERY_FLOWER_PASSWORD environment variables. Check out localhost:5555 and see for yourself.