This document describes the current stable version of Celery (5.4). For development docs, go here.

First steps with Django

Using Celery with Django


Previous versions of Celery required a separate library to work with Django, but since 3.1 this is no longer the case. Django is supported out of the box now so this document only contains a basic way to integrate Celery and Django. You’ll use the same API as non-Django users so you’re recommended to read the First Steps with Celery tutorial first and come back to this tutorial. When you have a working example you can continue to the Next Steps guide.


Celery 5.3.x supports Django 2.2 LTS or newer versions. Please use Celery 5.2.x for versions older than Django 2.2 or Celery 4.4.x if your Django version is older than 1.11.

To use Celery with your Django project you must first define an instance of the Celery library (called an “app”)

If you have a modern Django project layout like:

- proj/
  - proj/

then the recommended way is to create a new proj/proj/ module that defines the Celery instance:



import os

from celery import Celery

# Set the default Django settings module for the 'celery' program.
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'proj.settings')

app = Celery('proj')

# Using a string here means the worker doesn't have to serialize
# the configuration object to child processes.
# - namespace='CELERY' means all celery-related configuration keys
#   should have a `CELERY_` prefix.
app.config_from_object('django.conf:settings', namespace='CELERY')

# Load task modules from all registered Django apps.

@app.task(bind=True, ignore_result=True)
def debug_task(self):
    print(f'Request: {self.request!r}')

Then you need to import this app in your proj/proj/ module. This ensures that the app is loaded when Django starts so that the @shared_task decorator (mentioned later) will use it:


# This will make sure the app is always imported when
# Django starts so that shared_task will use this app.
from .celery import app as celery_app

__all__ = ('celery_app',)

Note that this example project layout is suitable for larger projects, for simple projects you may use a single contained module that defines both the app and tasks, like in the First Steps with Celery tutorial.

Let’s break down what happens in the first module, first, we set the default DJANGO_SETTINGS_MODULE environment variable for the celery command-line program:

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'proj.settings')

You don’t need this line, but it saves you from always passing in the settings module to the celery program. It must always come before creating the app instances, as is what we do next:

app = Celery('proj')

This is our instance of the library, you can have many instances but there’s probably no reason for that when using Django.

We also add the Django settings module as a configuration source for Celery. This means that you don’t have to use multiple configuration files, and instead configure Celery directly from the Django settings; but you can also separate them if wanted.

app.config_from_object('django.conf:settings', namespace='CELERY')

The uppercase name-space means that all Celery configuration options must be specified in uppercase instead of lowercase, and start with CELERY_, so for example the task_always_eager setting becomes CELERY_TASK_ALWAYS_EAGER, and the broker_url setting becomes CELERY_BROKER_URL. This also applies to the workers settings, for instance, the worker_concurrency setting becomes CELERY_WORKER_CONCURRENCY.

For example, a Django project’s configuration file might include:

# Celery Configuration Options
CELERY_TIMEZONE = "Australia/Tasmania"

You can pass the settings object directly instead, but using a string is better since then the worker doesn’t have to serialize the object. The CELERY_ namespace is also optional, but recommended (to prevent overlap with other Django settings).

Next, a common practice for reusable apps is to define all tasks in a separate module, and Celery does have a way to auto-discover these modules:


With the line above Celery will automatically discover tasks from all of your installed apps, following the convention:

- app1/
- app2/

This way you don’t have to manually add the individual modules to the CELERY_IMPORTS setting.

Finally, the debug_task example is a task that dumps its own request information. This is using the new bind=True task option introduced in Celery 3.1 to easily refer to the current task instance.

Using the @shared_task decorator

The tasks you write will probably live in reusable apps, and reusable apps cannot depend on the project itself, so you also cannot import your app instance directly.

The @shared_task decorator lets you create tasks without having any concrete app instance:


# Create your tasks here

from demoapp.models import Widget

from celery import shared_task

def add(x, y):
    return x + y

def mul(x, y):
    return x * y

def xsum(numbers):
    return sum(numbers)

def count_widgets():
    return Widget.objects.count()

def rename_widget(widget_id, name):
    w = Widget.objects.get(id=widget_id) = name

See also

You can find the full source code for the Django example project at:

Trigger tasks at the end of the database transaction

A common pitfall with Django is triggering a task immediately and not wait until the end of the database transaction, which means that the Celery task may run before all changes are persisted to the database. For example:

def create_user(request):
    # Note: simplified example, use a form to validate input
    user = User.objects.create(username=request.POST['username'])
    return HttpResponse('User created')

def send_email(user_pk):
    user = User.objects.get(pk=user_pk)
    # send email ...

In this case, the send_email task could start before the view has committed the transaction to the database, and therefore the task may not be able to find the user.

A common solution is to use Django’s on_commit hook to trigger the task after the transaction has been committed:

- send_email.delay(
+ transaction.on_commit(lambda: send_email.delay(

Added in version 5.4.

Since this is such a common pattern, Celery 5.4 introduced a handy shortcut for this, using a DjangoTask. Instead of calling delay(), you should call delay_on_commit():

- send_email.delay(
+ send_email.delay_on_commit(

This API takes care of wrapping the call into the on_commit hook for you. In rare cases where you want to trigger a task without waiting, the existing delay() API is still available.

This task class should be used automatically if you’ve follow the setup steps above. However, if your app uses a custom task base class, you’ll need inherit from DjangoTask instead of Task to get this behaviour.


django-celery-results - Using the Django ORM/Cache as a result backend

The extension provides result backends using either the Django ORM, or the Django Cache framework.

To use this with your project you need to follow these steps:

  1. Install the library:

    $ pip install django-celery-results
  2. Add django_celery_results to INSTALLED_APPS in your Django project’s


    Note that there is no dash in the module name, only underscores.

  3. Create the Celery database tables by performing a database migrations:

    $ python migrate django_celery_results
  4. Configure Celery to use the backend.

    Assuming you are using Django’s to also configure Celery, add the following settings:

    CELERY_RESULT_BACKEND = 'django-db'

    For the cache backend you can use:

    CELERY_CACHE_BACKEND = 'django-cache'

    We can also use the cache defined in the CACHES setting in django.

    # celery setting.
    CELERY_CACHE_BACKEND = 'default'
    # django setting.
    CACHES = {
        'default': {
            'BACKEND': 'django.core.cache.backends.db.DatabaseCache',
            'LOCATION': 'my_cache_table',

    For additional configuration options, view the Task result backend settings reference.

django-celery-beat - Database-backed Periodic Tasks with Admin interface.

See Using custom scheduler classes for more information.

Starting the worker process

In a production environment you’ll want to run the worker in the background as a daemon - see Daemonization - but for testing and development it is useful to be able to start a worker instance by using the celery worker manage command, much as you’d use Django’s runserver:

$ celery -A proj worker -l INFO

For a complete listing of the command-line options available, use the help command:

$ celery help

Where to go from here

If you want to learn more you should continue to the Next Steps tutorial, and after that you can study the User Guide.