serve

#!/usr/bin/env python

# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.

# This file implements the hosting solution, which just starts TensorFlow Model Serving.
import subprocess


def start_server():
    print('Starting TensorFlow Serving.')

    # link the log streams to stdout/err so they will be logged to the container logs
    subprocess.check_call(['ln', '-sf', '/dev/stdout', '/var/log/nginx/access.log'])
    subprocess.check_call(['ln', '-sf', '/dev/stderr', '/var/log/nginx/error.log'])

    # start nginx server
    nginx = subprocess.Popen(['nginx', '-c', '/opt/ml/code/nginx.conf'])

    # start TensorFlow Serving
    # https://www.tensorflow.org/serving/api_rest#start_modelserver_with_the_rest_api_endpoint
    # SageMaker copies our model artifact from our Training Job into the /opt/ml/model.
    # https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-code-load-artifacts
    tf_model_server = subprocess.call(['tensorflow_model_server',
                                       '--rest_api_port=8501',
                                       '--model_name=deepctr_model',
                                       '--model_base_path=/opt/ml/model/export/Servo'])


# The main routine just invokes the start function.
if __name__ == '__main__':
    start_server()