iap-messenger 1.5.10

Messaging service package for IA PARC

iap_messenger

The IA Parc inference plugin allows developers to easily integrate their inference pipeline into IA Parc's production module.

Installation

pip install iaparc-inference

Usage

• If your inference pipeline support batching:

  from iap_messenger import MsgListener, Message
  
  # Define a callback to query your inference pipeline
  # To load your model only once it is recommended to use a class:
  class MyModel:
      def __init__(self, model_path: str):
          ## Load your model in pytorch, tensorflow or any other backend
      
      def batch_query(msgs: list[Message]) -> list[Message]:
          ''' execute your pipeline on a batch input
              Note:   "parameters" is an optional argument.
                      It can be used to handle URL's query parameters
                      It's a list of key(string)/value(string) dictionaries
          '''
  
  if __name__ == '__main__':
      # Initiate your model class
      my_model = MyModel("path/to/my/model")
  
      # Initiate IAParc listener
      listener = MsgListener(my_model.batch_query)
      # Start the listener
      listener.run()
  

• If your inference pipeline do not support batching:

  from iap_messenger import MsgListener, Message
  
  # Define a callback to query your inference pipeline
  # To load your model only once it is recommended to use a class:
  class MyModel:
      def __init__(self, model_path: str):
          ## Load your model in pytorch, tensorflow or any other backend
      
      def single_query(msg: Message) -> Message:
          ''' execute your pipeline on a single input
              Note:   "parameters" is an optional argument.
                      It can be used to handle URL's query parameters
                      It's a key(string)/value(string) dictionary
          '''
  
  if __name__ == '__main__':
      # Initiate your model class
      my_model = MyModel("path/to/my/model")
  
      # Initiate IAParc listener
      listener = MsgListener(my_model.single_query, batch=1)  # Note that batch size is forced to 1 here
      # Start the listener
      listener.run()
  

Features

• Dynamic batching
• Autoscalling
• Support both synchronous and asynchronous queries
• Data agnostic

License

This project is licensed under the Apache License Version 2.0 - see the Apache LICENSE file for details.