This repo contains python scripts that are needed to deploy a machine learning model behind gRPC running using asyncio. You'll find the instructions on how to use gRPC to deploy machine learning model, how to build gRPC server and client in Python, how to define gRPC services and messages and how to serialize and deserialize data using protocol buffers.
To get started first you need to install all the necessary libraries grpcio,grpcio-tools,tensorflow,pillow
client.py contains the client code that will send a inference request
server.py contains the server code that will receive the inference request and send the inference reply (the predicted class the prediction confidence)
inference.proto contains the service and the messages between the client and server
inference.py contains the function to load the model and make predictions. The gRPC server will call this function.
After defining the services and messages in the protocol buffer file we generate the client and server code using the grpcio-tools,
python -m grpc_tools.protoc -I . --python_out=. --pyi_out=. --grpc_python_out=. inference.proto
This will generate inference_pb2.py (contains gRPC message definition), inference_pb2_grpc.py (contains gRPC server definition) files.