easy-serve 0.0.1

High level abstraction for deploying simple machine learning models using Flask

Easy Serve

High level abstraction for deploying simple machine learning models using Flask. This project allows you to quickly deploy a small testing model locally as an API service without complicated setup.

Getting started

1. Install the package using pip command: pip install easy_serve
2. Extend your model using EasyServe class.
3. Run server using this command: python -m easy_deploy.serve --class_path PATH_TO_easy_serve_CLASS --class_name YOUR_CUSTOM_easy_serve --port PORT --model_args param1=value1;param2=value2

Example

1. Create a file custom_model.py Here's a complete example of creating and deploying a simple model:

from easy_serve import EasyServe

class TextProcessor(EasyServe):
    def __init__(self, prefix=""):
        self.prefix = prefix
    
    def model_init(self):
        print("Model initialized!")
    
    def preprocessing(self, request):
        return request.json.get('text', '')
    
    def inference(self, text):
        return f"{self.prefix} {text}".strip()
    
    def postprocessing(self, result):
        return {"result": result.upper()}

2. Run the server

python -m easy_serve.server --class_path custom_model --class_name TextProcessor --port 5000 --model_args prefix=Hello

3. Test with curl:

curl -X POST http://localhost:5000/prediction -H "Content-Type: application/json" -d '{"text":"world"}'

Response:

{"result":"HELLO WORLD"}