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 ##Some more options :
 
-Easy-Job is asynchronous task runner for django , it means you can run a function without waiting for the function to finish .
-
-*Notice : current design of easy-job depends on django framework but in the near future we intend to break this dependency*
-
-
-###How to setup Easy-Job in a django project
-
-three simple steps are required to make setup easy-job in your django project :
-####1. first open your settings file and add the following:
-
-        EASY_JOB = {
-            "easy_job_logger": "easy_job",  # the logger name which easy_job itself will be using
-            "workers": {}
-        }
-    the inside workers you need to define your workers.
-        .. code-block:: python
-
-        # inside workers dictionary
-        EASY_JOB = {
-                    "easy_job_logger": "easy_job",  # the logger name which easy_job itself will be using
-                    "workers": {
-                        "worker1": {
-                                "initializer": "easy_job.workers.rabbitmq.RabbitMQInitializer",
-                                "count": 3,
-                                "logger_name": "sample_worker",
-                                "result_backend": {
-                                    "type": "Log",
-                                    "options": {
-                                        "logger_name": "sample_worker",
-                                        "log_level": logging.DEBUG
-                                    }
-                                },
-                                "options": {
-                                    'queue_name': 'sample_works2',
-                                    "serialization_method": "pickle",
-                                    "rabbitmq_configs": {
-                                        "connection_pool_configs": {
-                                            "max_size": 10,
-                                            "max_overflow": 10,
-                                            "timeout": 10,
-                                            "recycle": 3600,
-                                            "stale": 45
-                                        },
-                                        "connection_parameters": {
-                                            "host": "127.0.0.1"
-                                        }
-                                    }
-                                }
-                        },
-                    }
-        }
-
-let me explain what these configurations are from top  :
-* worker1: worker name
-    worker name is a custom name which you specify for the worker , later you will use this name to send
-    tasks to this particular worker.
-* initializer: worker initializer class path
-    the dot.path to worker initializer class, you can use one of :
-    `easy_job.workers.rabbitmq.RabbitMQInitializer` or `easy_job.workers.mpqueue.MPQueueInitializer`
-    or you can create your own initializer
-* count :
-    how many worker of this type you need
-* logger: name of logger for this worker
-    name of the logger which workers will send their logs to it (it's different than result log)
-* result_backend:
-if you like to store the result of each task you can set result backend but generally result backend is optional.
-to configure your result backend you need to provide
-    * result_backend_class :
-            type of result backend ,
-                you can use `poseidon_async.result_backends.log.LogResultBackend` or create your
-            own result backend .
-    * options:
-            options for the selected result backend , for example a Log result backend needs log_level and logger_name
-            but later another result backend may need other options
-* options:
-depending on the type of worker you chose ,it may have special options and configurations you can provide them in
-this dictionary.
-in this particular example which we are using RabbitMQ the following options are available:
-    * queue_name : name of rabbitMQ queue to use for transferring messages between main process and workers
-    * serialization_method : method of serialization , could be :code:`json` or *pickle*
-    * rabbitmq_configs : configurations related to rabbitmq , following is an example configuration:
-```
-{
-    "connection_pool_configs": {
-        "max_size": 10,
-        "max_overflow": 10,
-        "timeout": 10,
-        "recycle": 3600,
-        "stale": 45
-    },
-    "connection_parameters": {
-        "host": "127.0.0.1"
-    }
-}
-```
-####2. call init() in wsgi.py
-open your wsgi.py file and add the following:
-```
-import easy_job as easy_job
-easy_job.init()
-```
-this little code will initialize easy-job and create all your workers .
-
-####3.run your tasks
-somewhere in your project you can run your tasks:
-
-```
-import easy_job
-runner = easy_job.getRunner("my_worker") # the worker name you want to send your tasks
-runner.run(
-        "path.to.your.function",
-        args=('unnamed','parameters','here'),
-        kwargs={'named':'parameters'}
-)
-```
-
-that's it
-
-###Some more options :
-
 ####Specifying retry policy when running tasks :
 easy-job used retrying package to perform retrying on failure , if you intend to use this feature you should call `run()`
 function with `retry_policy` parameter , like this :