create_estimator_sm.py

from sagemaker.predictor import RealTimePredictor, json_deserializer
import sagemaker
from sagemaker.pytorch import PyTorch, PyTorchModel
from sagemaker.sklearn import SKLearn
role = sagemaker.get_execution_role()

## Class to accept output in JSON format
class Predictor(RealTimePredictor):
    def __init__(self, endpoint_name, sagemaker_session):
        super().__init__(endpoint_name, sagemaker_session=sagemaker_session, serializer=None, 
                         deserializer=json_deserializer, content_type='application/json')
        
model = PyTorch(entry_point='serve.py',
                name="twitter-fake-true-torch",
                role=role,
                framework_version='1.7',
                py_version='py36',
                source_dir='mysrc',
                instance_count=1,
                instance_type='ml.m5.4xlarge', 
                predictor_cls=Predictor)