Machine Intelligence at Google Scale: Tensor Flow and Cloud Machine Learning: The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn’t scale for production service, so you need a Distributed Training on the cloud eventually. Google has been building infrastructure for training the large scale neural network on the cloud for years, and now started to share the technology with external developers. In this session, we will introduce new pre-trained ML services such as Cloud Vision API and Speech API that works without any training. Also, we will look how TensorFlow and Cloud Machine Learning will accelerate custom model training for 10x – 40x with Google’s distributed training infrastructure.

1. Machine Intelligence at Google Scale
ML APIs, TensorFlow and Cloud ML

2. +Kazunori Sato
@kazunori_279
Kaz Sato
Staff Developer Advocate
Tech Lead for Data & Analytics
Cloud Platform, Google Inc.

3. What we’ll cover
What is Neural Network and Deep Learning
Machine Learning use cases at Google services
Externalizing the power with ML APIs
TensorFlow: the open source library for ML
TensorFlow in the Wild
Distributed training and prediction with Cloud ML

4. What is Neural Network
and Deep Learning

5. Neural Network is a function that can learn

6. xn
> b?
w1
wn
x2
x1
Inspired by the behavior of biological neurons

7. How do you
classify them?

8. weights
bias
(threshold)
Programmers need to specify the parameters

9. Let’s see how neural network solves the problem

10. The computer tries to find
the best parameters
A neuron classifies a data point into two kinds

11. Gradient Descent: adjusting the params
gradually to reduce errors
From: Andrew Ng

12. How do you
classify them?

13. What we see What the computer “sees”

14. 28 x 28 gray scale image =
784 numbers

15. input vector
(pixel data)
output vector
(probability)

16. How do you
classify them?

17. More neurons = More features to extract

18. Hidden Layers:
mapping inputs to
a feature space,
classifying with
a hyperplane
From: Neural Networks, Manifolds, and Topology, colah's blog

19. How about this?

20. More hidden layers = More hierarchies of features

21. How about this?

22. We need to go deeper neural network
From: Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, Honglak Lee et al.

23. From: mNeuron: A Matlab Plugin to Visualize Neurons from Deep Models, Donglai Wei et. al.

24. Machine Learning use cases
at Google services

25. 25
signal
for Search ranking,
out of hundreds
improvement
to ranking quality
in 2+ years
#3 #1
Search
machine learning for search engines
RankBrain: a deep neural network for search ranking

27. WaveNet by
Google DeepMind

28. 28
[glacier]
Google Photos
28

29. 29
Smart reply
in Inbox by Gmail
10%
of all responses
sent on mobile

30. http://googleresearch.blogspot.com/2016/05/announcing-syntaxnet-worlds-most.html

31. Saved Data Center cooling energy for 40%
Improved Power Usage Effectiveness (PUE) for 15%

32. 32
Android
Apps
Gmail
Maps
Photos
Speech
Search
Translation
YouTube
and many others ...
Used across products:
2012 2013 2014 2015
Deep Learning usage at Google

33. Externalizing the power
with ML APIs

34. TensorFlow Cloud Machine Learning ML API
Easy-to-Use, for non-ML engineers
Customizable, for Data Scientists
Machine Learning products from Google

35. Image analysis with pre-trained models
No Machine Learning skill required
REST API: receives an image and returns a JSON
General Availability
Cloud Vision API

36. Confidential & ProprietaryGoogle Cloud Platform 36
Faces
Faces, facial landmarks, emotions
OCR
Read and extract text, with
support for > 10 languages
Label
Detect entities from furniture to
transportation
Logos
Identify product logos
Landmarks & Image Properties
Detect landmarks & dominant
color of image
Safe Search
Detect explicit content - adult,
violent, medical and spoof

37. 3737
Demo

38. Pre-trained models. No ML skill required
REST API: receives audio and returns texts
Supports 80+ languages
Streaming or non-streaming
Public Beta - cloud.google.com/speech
Cloud Speech API

39. Confidential & ProprietaryGoogle Cloud Platform 39
Features
Automatic Speech Recognition (ASR)
powered by deep learning neural
networking to power your
applications like voice search or
speech transcription.
Recognizes over 80
languages and variants
with an extensive
vocabulary.
Returns partial
recognition results
immediately, as they
become available.
Filter inappropriate
content in text results.
Audio input can be captured by an application’s
microphone or sent from a pre-recorded audio
file. Multiple audio file formats are supported,
including FLAC, AMR, PCMU and linear-16.
Handles noisy audio from many
environments without requiring
additional noise cancellation.
Audio files can be uploaded in the
request and, in future releases,
integrated with Google Cloud
Storage.
Automatic Speech Recognition Global Vocabulary Inappropriate Content
Filtering
Streaming Recognition
Real-time or Buffered Audio Support Noisy Audio Handling Integrated API

40. 4040
Demo

41. Pre-trained models. No ML skill required
REST API: receives text and returns analysis results
Supports English, Spanish and Japanese
Public Beta - cloud.google.com/natural-language
Cloud Natural Language API

42. Confidential & ProprietaryGoogle Cloud Platform 42
Features
Extract sentence, identify parts of
speech and create dependency parse
trees for each sentence.
Identify entities and label by types such
as person, organization, location, events,
products and media.
Understand the overall sentiment of a
block of text.
Syntax Analysis Entity Recognition
Sentiment Analysis

43. 4343
Demo

44. TensorFlow:
An open source library for
Machine Intelligence

45. Google's open source library for
machine intelligence
tensorflow.org launched in Nov 2015
Used by many production ML projects
What is TensorFlow?

46. Sharing our tools with researchers and developers
around the world
repository
for “machine learning”
category on GitHub
#1
Released in Nov. 2015
From: http://deliprao.com/archives/168

47. Before
Hire Data Scientists
↓
Understand the math model
↓
Impl with programming code
↓
Train with single GPU
↓
Build a GPU cluster
↓
Train with the GPU cluster
↓
Build a prediction server
or Impl mobile/IoT prediction
After
Easy network design and impl
↓
Train with single machine
↓
Train on the cloud
↓
Prediction on the cloud
or mobile/IoT devices
many people
stuck here

48. # define the network
import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
# define a training step
y_ = tf.placeholder(tf.float32, [None, 10])
xent = -tf.reduce_sum(y_*tf.log(y))
step =
tf.train.GradientDescentOptimizer(0.01).minimize(xent)

49. TensorBoard: visualization tool

50. Portable and Scalable
Training on:
Mac/Windows
GPU server
GPU cluster / Cloud
Prediction on:
Android and iOS
RasPi and TPU

51. Distributed Training with TensorFlow

52. TensorFlow in the Wild
(or democratization of deep learning)

53. TensorFlow
powered
Fried Chicken
Nugget Server
From: http://www.rt-net.jp/karaage1/

54. TensorFlow powered Cucumber Sorter
From: http://workpiles.com/2016/02/tensorflow-cnn-cucumber/

55. Autonomous
Driving
of RasPi car
with Inception 3
on TensorFlow
From:
https://github.com/zxzhijia/GoPiGo-Driven-by-Te
nsorflow

56. TV popstar classifier
with 95% accuracy
From: http://memo.sugyan.com/entry/2016/06/14/220624

57. TensorFlow＋
RasPi for
sorting garbages
From:
https://techcrunch.com/2016/09/13/auto-trash-sorts-
garbage-automatically-at-the-techcrunch-disrupt-hacka
thon/

58. TensorFlow +
Drones
for counting trucks
From: http://www.brainpad.co.jp/news/2016/09/02/3454

59. From: http://otoro.net/
Generative Arts with TensorFlow

60. Distributed Training and
Prediction with Cloud ML

61. From: Andrew Ng
The Bigger, The Better

62. The Challenge: Computing Power
DNN requires large training datasets
Large models doesn't fit into a GPU
Requires try-and-errors to find the
best design, configs and params
↓
Need to spend a few days or
weeks to finish a training

63. GPUs run at nanoseconds
GPU cluster needs microsec network

65. Enterprise
Google Cloud is
The Datacenter as a Computer

66. Jupiter network
10 GbE x 100 K = 1 Pbps
Consolidates servers with
microsec latency

67. Borg
No VMs, pure containers
10K - 20K nodes per Cell
DC-scale job scheduling
CPUs, mem, disks and IO

68. Distributed Training
with TensorFlow
by data parallelism
split data,
share model

69. ● CPU/GPU scheduling
● Communications
○ Local, RPC, RDMA
○ 32/16/8 bit quantization
● Cost-based optimization
● Fault tolerance
Distributed Systems for Large Neural Network

70. What's the scalability of Google Brain?
"Large Scale Distributed Systems for Training Neural
Networks", NIPS 2015
○ Inception / ImageNet: 40x with 50 GPUs
○ RankBrain: 300x with 500 nodes

71. Fully managed distributed training and prediction
Supports custom TensorFlow graphs
Integrated with Cloud Dataflow and Cloud Datalab
Limited Preview - cloud.google.com/ml
Cloud Machine Learning (Cloud ML)

72. 7272
Ready to use Machine
Learning models
Use your own data to
train models
Cloud
Vision API
Cloud
Speech API
Cloud
Translate API
Cloud Machine Learning
Develop - Model - Test
Google
BigQuery
Stay
Tuned….
Cloud
Storage
Cloud
Datalab
NEW
Alpha
GA BetaGA
Alpha
GA
GA

73. Tensor Processing Unit
ASIC for TensorFlow
Designed by Google
10x better perf / watt
latency and efficiency
bit quantization

74. TPU on Production
RankBrain
AlphaGo
Google Photos
Speech
and more

75. Thank you!

76. Links & Resources
Large Scale Distributed Systems for Training Neural Networks, Jeff Dean and
Oriol Vinals
Cloud Vision API: cloud.google.com/vision
Cloud Speech API: cloud.google.com/speech
TensorFlow: tensorflow.org
Cloud Machine Learning: cloud.google.com/ml
Cloud Machine Learning: demo video