method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere