127.0.0.1 localhost myhost myhost.local If you start the rabbitmq server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, which returns the sum of two numbers. Running the celery worker server You now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info 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 at the workers

Calling Tasks Canvas: Designing Workflows Routing Remote Control Timezone Optimization What to do now? Resources Getting Help Bug tracker Wiki Contributing License Version Requirements Celery version 0.1 localhost myhost myhost.local If you start the rabbitmq server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, which returns the sum of two numbers. Running the celery worker server You now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info

127.0.0.1 localhost myhost myhost.local If you start the rabbitmq-server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, returning the sum of two numbers. Running the Celery worker server You can now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info 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 at the worker’s console

Calling Tasks Canvas: Designing Work-flows Routing Remote Control Timezone Optimization What to do now? Resources Getting Help Bug tracker Wiki Contributing License Version Requirements This document 0.1 localhost myhost myhost.local If you start the rabbitmq-server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, returning the sum of two numbers. Running the Celery worker server You can now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info

Calling Tasks Canvas: Designing Work-flows Routing Remote Control Timezone Optimization What to do now? Resources Getting Help Bug tracker Wiki Contributing License Version Requirements This document 0.1 localhost myhost myhost.local If you start the rabbitmq-server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, returning the sum of two numbers. Running the Celery worker server You now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info

127.0.0.1 localhost myhost myhost.local If you start the rabbitmq-server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, returning the sum of two numbers. Running the Celery worker server You now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info 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 at the workers

127.0.0.1 localhost myhost myhost.local If you start the rabbitmq-server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, returning the sum of two numbers. Running the Celery worker server You now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info 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 at the workers

Calling Tasks Canvas: Designing Work-flows Routing Remote Control Timezone Optimization What to do now? Resources Getting Help Bug tracker Wiki Contributing License Version Requirements This document 0.1 localhost myhost myhost.local If you start the rabbitmq-server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, returning the sum of two numbers. Running the Celery worker server You now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info

Calling Tasks Canvas: Designing Work-flows Routing Remote Control Timezone Optimization What to do now? Resources Getting Help Bug tracker Wiki Contributing License Version Requirements This document 0.1 localhost myhost myhost.local If you start the rabbitmq-server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, returning the sum of two numbers. Running the Celery worker server You now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info

127.0.0.1 localhost myhost myhost.local If you start the rabbitmq-server, your rabbit node should now be rabbit@myhost, as verified by rabbitmqctl: $ sudo rabbitmqctl status Status of node rabbit@myhost single task, called add, returning the sum of two numbers. Running the Celery worker server You now run the worker by executing our program with the worker argument: $ celery -A tasks worker --loglevel=info 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 at the workers