Transformer

Introduction
A Machine Learning model takes tensors as inputs and outputs. Often you will want to submit and receive data in other formats, such as text or images. A transformer can be used to encode/decode the data as part of the Inference Service. Additional Transformer examples can be found in the KFServe Documentation.
The transformer in this repository encodes text for the GPT-2 model. You can use it without building by referencing pre-build images in Docker Hub.
Modifying
The transformation logic is implemnted in transformer.py. preprocess is called on data before it is sent to model and postprocess on the result. In the GPT-2 example, the model is saved with signature name predict. This is added via the transformer for correct querying.
class Transformer(kfserving.KFModel):
    def __init__(self, name: str, predictor_host: str):
        super().__init__(name)
        self.predictor_host = predictor_host
        self.encoder = get_encoder()
​
    def preprocess(self, inputs: Dict) -> Dict:
        print (inputs)
        return {'signature_name':'predict','instances': [self.encoder.encode(instance) for instance in inputs['instances']]}
​
    def postprocess(self, inputs: List) -> List:
        return {'predictions': [self.encoder.decode(prediction) for prediction in inputs['predictions']]}
Building
When making modifications you can easily build and upload your own version of the image to Docker Hub.
docker build -t dockerHubAccountName/imagename:0.1 .
docker push dockerHubAccountName/imagename:0.1

PVC Model Serving

S3 Model Serving

Last modified 1yr ago