message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call

message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call

message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call

message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call

message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call

message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call

message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call

message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call

message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call

message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in of task names to their actual functions, called the task registry. Whenever you define a task, that task will also be added to the local registry: >>> @app.task ... def add(x, y): ... return x + y >>> only do the following: 1. Create a logical clock instance, used for events. 2. Create the task registry. 3. Set itself as the current app (but not if the set_as_current argument was disabled) 4. Call