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def _write_saved_model(saved_model_path,
                       trained_checkpoint_prefix,
                       inputs,
                       outputs):
  """Writes SavedModel to disk.
  Args:
    saved_model_path: Path to write SavedModel.
    trained_checkpoint_prefix: path to trained_checkpoint_prefix.
    inputs: The input image tensor to use for detection.
    outputs: A tensor dictionary containing the outputs of a DetectionModel.

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_write_saved_model(saved_model_path, trained_checkpoint_prefix,
                     placeholder_tensor, outputs)

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import tensorflow as tf

# Assuming object detection API is available for use
from object_detection.utils.config_util import create_pipeline_proto_from_configs
from object_detection.utils.config_util import get_configs_from_pipeline_file
import object_detection.exporter

# Configuration for model to be exported
config_pathname = ${Model_configuration}

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# Copyright 2017 The TensorFlow Authors. 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.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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# def _write_frozen_graph(frozen_graph_path, frozen_graph_def):
#   """Writes frozen graph to disk.
#
#   Args:
#     frozen_graph_path: Path to write inference graph.
#     frozen_graph_def: tf.GraphDef holding frozen graph.
#   """
#   with gfile.GFile(frozen_graph_path, 'wb') as f:
#     f.write(frozen_graph_def.SerializeToString())
#   logging.info('%d ops in the final graph.', len(frozen_graph_def.node))

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def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False):
  """Create a TensorProto.
  Args:
    values:         Values to put in the TensorProto.
    dtype:          Optional tensor_pb2 DataType value.
    shape:          List of integers representing the dimensions of tensor.
    verify_shape:   Boolean that enables verification of a shape of values.
  Returns:
    A TensorProto. Depending on the type, it may contain data in the
    "tensor_content" attribute, which is not directly useful to Python programs.

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#!/bin/bash
# add the GPG key for the official Docker repository to the system
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

# add the Docker repository to APT sources
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"

# update the package database with the Docker packages from the newly added repo
sudo apt-get update

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# Create stub
host, port = FLAGS.server.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)

# Create prediction request object
request = predict_pb2.PredictRequest()

# Specify model name (must be the same as when the TensorFlow serving serving was started)
request.model_spec.name = 'obj_det'

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# Create stub
host, port = FLAGS.server.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)

# Create prediction request object
request = predict_pb2.PredictRequest()

# Specify model name (must be the same as when the TensorFlow serving serving was started)
request.model_spec.name = 'obj_det'

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