- name: mpg-estimator
  kind: RealtimeAPI
  predictor:
    type: python
    path: predictor.py
    models:
      path: s3://cortex-examples/sklearn/mpg-estimator/linreg/

import mlflow.sklearn
import numpy as np
​
​
class PythonPredictor:
    def __init__(self, config, python_client):
        self.client = python_client
​
    def load_model(self, model_path):
        return mlflow.sklearn.load_model(model_path)
​
    def predict(self, payload, query_params):
        model_version = query_params.get("version")
​
        # model_input = ...
​
        model = self.client.get_model(model_version=model_version)
        result = model.predict(model_input)
​
        return {"prediction": result, "model": {"version": model_version}}

- name: text-analyzer
  kind: RealtimeAPI
  predictor:
    type: python
    path: predictor.py
    ...

class PythonPredictor:
    def __init__(self, config):
        self.analyzer = initialize_model("sentiment-analysis")
        self.summarizer = initialize_model("summarization")
​
    def predict(self, query_params, payload):
        model_name = query_params.get("model")
        model_input = payload["text"]
​
        # ...
​
        if model_name == "analyzer":
            results = self.analyzer(model_input)
            predicted_label = postprocess(results)
            return {"label": predicted_label}
        elif model_name == "summarizer":
            results = self.summarizer(model_input)
            predicted_label = postprocess(results)
            return {"label": predicted_label}
        else:
            return JSONResponse({"error": f"unknown model: {model_name}"}, status_code=400)

- name: multi-model-classifier
  kind: RealtimeAPI
  predictor:
    type: tensorflow
    path: predictor.py
    models:
      paths:
        - name: inception
          path: s3://cortex-examples/tensorflow/image-classifier/inception/
        - name: iris
          path: s3://cortex-examples/tensorflow/iris-classifier/nn/
        - name: resnet50
          path: s3://cortex-examples/tensorflow/resnet50/
      ...

class TensorFlowPredictor:
    def __init__(self, tensorflow_client, config):
        self.client = tensorflow_client
​
    def predict(self, payload, query_params):
        model_name = query_params["model"]
        model_input = preprocess(payload["url"])
        results = self.client.predict(model_input, model_name)
        predicted_label = postprocess(results)
        return {"label": predicted_label}

- name: multi-model-classifier
  kind: RealtimeAPI
  predictor:
    type: onnx
    path: predictor.py
    models:
      paths:
        - name: resnet50
          path: s3://cortex-examples/onnx/resnet50/
        - name: mobilenet
          path: s3://cortex-examples/onnx/mobilenet/
        - name: shufflenet
          path: s3://cortex-examples/onnx/shufflenet/
      ...

class ONNXPredictor:
    def __init__(self, onnx_client, config):
        self.client = onnx_client
​
    def predict(self, payload, query_params):
        model_name = query_params["model"]
        model_input = preprocess(payload["url"])
        results = self.client.predict(model_input, model_name)
        predicted_label = postprocess(results)
        return {"label": predicted_label}