Gaurav Kaila gauravkaila

parameters	data_type	explanation
docker_image_version	int	Version of the docker image created in the previous step (can be found via the VSC IDE)
cpu_cores	int	Number of CPU cores for the Docker container
memory	int	Memory for the Docker container
service_name	string	Deployment service name

parameters	data_type	explanation
model_version	int	Version of the trained model (can be located via the VSC IDE)
pip_packages	list	List of pip packages to be installed
conda_packages	list	List of conda packages to be installed
conda_env_file	string	Name of the conda env file
path_scoring_script	string	Path to prediction script [score.py]
docker_image_name	string	Name of the docker image

parameter	data_type	explanation
experiment_name	string	Name of the experiment to place the current training run under (new experiment is created if no previous experiment with the same name is found)
compute_target_name	string	Name of VM where training will be done (new compute name is created if no previous compute of the same name is found)
vm_size	string	Ability to choose from various available VM configurations (e.g. Standard_D5_v2)
data_folder	string	Name of the folder in the Azure storage is store your training data.
local_directory	string	Path to the training data's local directory
conda_packages	list	List of conda packages required to train your model
script	string	Name of the training script (placed in the current working directory)
model_name	string	Name of trained model to be registered under in the workspace.

parameter	data_type	Azure name
subscription_id	string	Azure subscription id
resource_group	string	Azure resource group
workspace_name	string	Azure machine learning workspace

	# 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'

	# 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'

	#!/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

	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.

	# 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))

	# 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,