Nishant Shukla BinRoot

TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

Tutorial examples have C++ code (which I don't know)
Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

	# Copyright 2015 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,

	import os
	from six.moves import urllib
	import zipfile
	import tensorflow as tf
	import collections
	import csv

	class DatasetLoader:
	def __init__(self):
	self.url = 'http://mattmahoney.net/dc/'

	import tensorflow as tf

	x = tf.Variable(2, name='x', dtype=tf.float32)
	log_x = tf.log(x)
	y = tf.square(log_x) + 1

	optimizer = tf.train.GradientDescentOptimizer(0.5)
	train = optimizer.minimize(y)

	init = tf.global_variables_initializer()

	import tensorflow as tf

	# Define a matrix and negate it
	matrix = tf.constant([[1., 2.]])
	negMatrix = tf.neg(matrix)

	# Start the session with a special config passed into the constructor to enable logging
	with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
	# Evaluate negMatrix
	result = sess.run(negMatrix)

	import tensorflow as tf

	# Define an arbitrary matrix
	matrix = tf.constant([[1., 2.]])

	# Run the negation operator on it
	neg_matrix = tf.neg(matrix)

	# Start a session to be able to run operations
	with tf.Session() as sess:

	import tensorflow as tf

	# Define an arbitrary tensor
	x = tf.constant([[1, 2]])

	# Negate the tensor
	neg_x = tf.neg(x)

	# Print the object
	print(neg_x)

	import tensorflow as tf

	# Define a 2x1 matrix
	matrix1 = tf.constant([[1., 2.]])

	# Define a 1x2 matrix
	matrix2 = tf.constant([[1],
	[2]])

	# Define a rank 3 tensor

	import tensorflow as tf
	# We’ll use NumPy matrices in TensorFlow
	import numpy as np

	# Define a 2x2 matrix in 3 different ways
	m1 = [[1.0, 2.0],
	[3.0, 4.0]]
	m2 = np.array([[1.0, 2.0],
	[3.0, 4.0]], dtype=np.float32)
	m3 = tf.constant([[1.0, 2.0],

	import numpy as np
	revenue = np.dot(prices, amounts)