source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently remotely. Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML, or again – but this time we’ll keep track of the task by holding on to the AsyncResult: >>> result = add.delay(4, 4) >>> result.ready() # returns True if the task has finished processing. False >>> result

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently remotely. Monitoring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serialization Supports Pickle, JSON, YAML, or again – but this time we’ll keep track of the task by holding on to the AsyncResult: >>> result = add.delay(4, 4) >>> result.ready() # returns True if the task has finished processing. False >>> result

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML, or again – but this time we’ll keep track of the task by holding on to the AsyncResult: >>> result = add.delay(4, 4) >>> result.ready() # returns True if the task has finished processing. False >>> result

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML, or please see Result Backends. Now with the result backend configured, let’s execute the task again. This time we’ll hold on to the AsyncResult: >>> result = add.delay(4, 4) Here’s some examples of what you

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Monitoring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serialization Supports Pickle, JSON, YAML, or again – but this time we’ll keep track of the task by holding on to the AsyncResult: >>> result = add.delay(4, 4) >>> result.ready() # returns True if the task has finished processing. False >>> result

open source asynchronous task queue/job queue based on distributed message passing. Focused on real- time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML, or please see Result Backends. Now with the result backend configured, let’s execute the task again. This time we’ll hold on to the AsyncResult: >>> result = add.delay(4, 4) Here’s some examples of what you

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Monitoring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serialization Supports Pickle, JSON, YAML, or please see Result Backends. Now with the result backend configured, let’s execute the task again. This time we’ll hold on to the AsyncResult: >>> result = add.delay(4, 4) Here’s some examples of what you

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently remotely. Monitoring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serialization Supports Pickle, JSON, YAML, or task again, but this time we’ll keep track of the task by keeping the AsyncResult: >>> result = add.delay(4, 4) >>> result.ready() # returns True if the task has finished processing. False >>> result

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently only) Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML, or please see Result Backends. Now with the result backend configured, let’s execute the task again. This time we’ll hold on to the AsyncResult: >>> result = add.delay(4, 4) Here’s some examples of what you

source asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently remotely. Moni- toring You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development. Serial- ization Supports Pickle, JSON, YAML, or task again, but this time we’ll keep track of the task by keeping the AsyncResult: >>> result = add.delay(4, 4) >>> result.ready() # returns True if the task has finished processing. False >>> result