Zookeeper as a message transport. for using SQLAlchemy as a result backend (supported). for using the Pyro4 message transport (experimental). for using the SoftLayer Message Queue transport (experimental). greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier. You can verify this by looking you’ll hold on to the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready()

with Celery tutorial. • You can also check out the FAQ. 3 Celery Documentation, Release 3.1.25 4 Chapter 1. Getting Started CHAPTER 2 Contents 2.1 Copyright Celery User Manual by Ask Solem Copyright message transport (experimental), or as a result backend (supported). celery[pyro] for using the Pyro4 message transport (experimental). celery[slmq] for using the SoftLayer Message Queue transport (experimental) greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier, and you can verify that by looking

message transport (experimental), or as a result backend (supported). celery[pyro]: for using the Pyro4 message transport (experimental). celery[slmq]: for using the SoftLayer Message Queue transport (experimental) greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier, and you can verify that by looking you’ll hold on to the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready()

celery[sqlalchemy]: for using SQLAlchemy as a result backend (supported). celery[pyro]: for using the Pyro4 message transport (experimental). celery[slmq]: for using the SoftLayer Message Queue transport (experimental) greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier. You can verify this by looking you’ll hold on to the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready()

Zookeeper as a message transport. for using SQLAlchemy as a result backend (supported). for using the Pyro4 message transport (experimental). for using the SoftLayer Message Queue transport (experimental). greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier. You can verify this by looking you’ll hold on to the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready()

Zookeeper as a message transport. for using SQLAlchemy as a result backend (supported). for using the Pyro4 message transport (experimental). for using the SoftLayer Message Queue transport (experimental). greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier. You can verify this by looking you’ll hold on to the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready()

Zookeeper as a message transport. for using SQLAlchemy as a result backend (supported). for using the Pyro4 message transport (experimental). for using the SoftLayer Message Queue transport (experimental). greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier. You can verify this by looking you’ll hold on to the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready()

Zookeeper as a message transport. for using SQLAlchemy as a result backend (supported). for using the Pyro4 message transport (experimental). for using the SoftLayer Message Queue transport (experimental). greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier. You can verify this by looking you’ll hold on to the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready()

Zookeeper as a message transport. for using SQLAlchemy as a result backend (supported). for using the Pyro4 message transport (experimental). for using the SoftLayer Message Queue transport (experimental). greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier. You can verify this by looking you’ll hold on to the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready()

celery[sqlalchemy]: for using SQLAlchemy as a result backend (supported). celery[pyro]: for using the Pyro4 message transport (experimental). celery[slmq]: for using the SoftLayer Message Queue transport (experimental) greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed by the worker you started earlier, and you can verify that by looking you’ll hold on to the AsyncResult instance returned when you call a task: >>> result = add.delay(4, 4) The ready() method returns whether the task has finished processing or not: >>> result.ready()