flageval-serving 0.2.2

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FlagEval Serving

Serving Framework of AI Models for Evaluating on FlagEval Platform.

Installation

pip install --upgrade flageval-serving

Usage

1. Model: of course we have a model that is ready be evaluated, let's assume it lives in the path: /path/to/model;

2. Then we can write our service code, let's put the service code in service.py or './tests/service.py' and take a NLP model as the example:

   from flageval.serving.service import NLPModelService, NLPEvalRequest, NLPEvalResponse, NLPCompletion
   
   
   class DemoService(NLPModelService):
       def global_init(self, model_path: str):
           print("Initial model with path", model_path)
   
       def infer(self, req: NLPEvalRequest) -> NLPEvalResponse:
           return NLPEvalResponse(
               completions=[
                   NLPCompletion(
                       text='Hello, world!',
                       tokens='Hello, world!',
                   ),
               ]
           )
   

3. Finally, we use the flageval-serving command to serve:

   flageval-serving --service service:DemoService dev /path/to/model  # start a development server
   flageval-serving --service service:DemoService run /path/to/model  # start a production server
   

Dockerfile

FlagEval evaluation platform construction image