Google Cloud Platform Empowers TensorFlow and Machine Learning

1. Google Cloud Platform Empowers
TensorFlow and Machine Learning

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 Intelligence at Google Scale
Cloud Vision API and Speech API
TensorFlow and Cloud Machine Learning

4. What is Neural Network
and Deep Learning

5. Neural Network is a function that can learn

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

7. How do you
classify them?

8. weights
bias
(threshold)
Programmers would 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: changing the params
gradually to reduce errors

12. How do you
classify them?

13. More neurons = More features to extract

14. Mapping inputs to
a feature space,
classifying with
a hyperplane
From: Neural Networks, Manifolds, and Topology, colah's blog

15. How about this?

16. More hidden layers = more hierarchies of features

17. How about this?

18. We need to build a Deep Neural Network
From: Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, Honglak Lee et al.

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

20. Machine Intelligence at Google scale

21. The two big challenges of Deep Learning:
Computing Power and Training Data

23. Enterprise
Google Cloud is
The Datacenter as a Computer

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

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

26. Confidential & ProprietaryGoogle Cloud Platform 26
Google Cloud +
Neural Network =
Google Brain

27. 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

30. Externalizing the power
to developers

31. Image analysis with pre-trained models
REST API: receives an image and returns a JSON
No Machine Learning skill required
From $2.50 / 1,000 units (no charge* to try)
General Availability
Cloud Vision API
* You will be charged for Google Cloud Storage and other Google Cloud Platform resources used in your project.

33. 3333
Demo

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

35. 3535
Demo

36. TensorFlow and Cloud ML

37. 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

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

39. # 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)

40. TensorBoard: visualization tool

41. Portable and Scalable
Training on:
Mac/Windows
GPU server
GPU cluster / Cloud
Running on:
Android, iOS
RasPi

42. Distributed Training and
Prediction with TensorFlow

43. Distributed Training with TensorFlow

44. Data Parallelism
split data,
share model

45. ● CPU/GPU scheduling
● Communications
○ Local, RPC, RDMA
○ 32/16/8 bit quantization
● Cost-based optimization
● Fault tolerance
Distributed Training with TensorFlow

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

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

48. 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)

49. Jeff Dean's keynote: YouTube video
Define a custom TensorFlow graph
Training at local: 8.3 hours w/ 1 node
Training at cloud: 32 min w/ 20 nodes (15x faster)
Prediction at cloud at 300 reqs / sec
Cloud ML demo

50. TensorFlow in the Wild

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

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

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

54. Discriminative
Localization
From: https://github.com/jazzsaxmafia/Weakly_detector

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

56. Thank you!

57. 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