Deploy the model on Sagemaker CLTG

deploy_Sagemaker_CLTG.py

from sagemaker.predictor import RealTimePredictor
from sagemaker.pytorch import PyTorchModel

class StringPredictor(RealTimePredictor):
    def __init__(self, endpoint_name, sagemaker_session):
        super(StringPredictor, self).__init__(endpoint_name, sagemaker_session, content_type='text/plain')
# Create a model in Sagemaker 
model = PyTorchModel(model_data=estimator.model_data,
                     role = role,
                     framework_version='0.4.0',
                     entry_point='predict.py',
                     source_dir='serve',
                     predictor_cls=StringPredictor)
# Deploy the model on a compute instance                 
predictor = model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')