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Google Cloud Platform Empowers
TensorFlow and Machine Learning
+Kazunori Sato
@kazunori_279
Kaz Sato
Staff Developer Advocate
Tech Lead for Data & Analytics
Cloud Platform, Google Inc.
What we’ll cover
What is Neural Network and Deep Learning
Machine Intelligence at Google Scale
Cloud Vision API and Speech...
What is Neural Network
and Deep Learning
Neural Network is a function that can learn
xn
> b?
w1
wn
x2
x1
Mimicking the behavior of biological neurons
How do you
classify them?
weights
bias
(threshold)
Programmers would specify the parameters
Let’s see how neural network solves the problem
The computer tries to find
the best parameters
A neuron classifies a data point into two kinds
Gradient Descent: changing the params
gradually to reduce errors
How do you
classify them?
More neurons = More features to extract
Mapping inputs to
a feature space,
classifying with
a hyperplane
From: Neural Networks, Manifolds, and Topology, colah's b...
How about this?
More hidden layers = more hierarchies of features
How about this?
We need to build a Deep Neural Network
From: Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hier...
From: mNeuron: A Matlab Plugin to Visualize Neurons from Deep Models, Donglai Wei et. al.
Machine Intelligence at Google scale
The two big challenges of Deep Learning:
Computing Power and Training Data
Enterprise
Google Cloud is
The Datacenter as a Computer
Jupiter network
10 GbE x 100 K = 1 Pbps
Consolidates servers with
microsec latency
Borg
No VMs, pure containers
10K - 20K nodes per Cell
DC-scale job scheduling
CPUs, mem, disks and IO
Confidential & ProprietaryGoogle Cloud Platform 26
Google Cloud +
Neural Network =
Google Brain
What's the scalability of Google Brain?
"Large Scale Distributed Systems for Training Neural
Networks", NIPS 2015
○ Incept...
Externalizing the power
to developers
Image analysis with pre-trained models
REST API: receives an image and returns a JSON
No Machine Learning skill required
F...
3333
Demo
Pre-trained models. No ML skill required
REST API: receives audio and returns texts
Supports 80+ languages
Streaming or no...
3535
Demo
TensorFlow and Cloud ML
Ready to use Machine
Learning models
Use your own data to
train models
Cloud
Vision API
Cloud
Speech API
Cloud
Translate A...
Google's open source library for
machine intelligence
tensorflow.org launched in Nov 2015
Used by many production ML proje...
# define the network
import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10...
TensorBoard: visualization tool
Portable and Scalable
Training on:
Mac/Windows
GPU server
GPU cluster / Cloud
Running on:
Android, iOS
RasPi
Distributed Training and
Prediction with TensorFlow
Distributed Training with TensorFlow
Data Parallelism
split data,
share model
● CPU/GPU scheduling
● Communications
○ Local, RPC, RDMA
○ 32/16/8 bit quantization
● Cost-based optimization
● Fault tole...
Tensor Processing Unit
ASIC for TensorFlow
Designed by Google
10x better perf / watt
latency and efficiency
bit quantizati...
TPU on Production
RankBrain
AlphaGo
Google Photos
Speech
and more
Fully managed distributed training and prediction
Supports custom TensorFlow graphs
Integrated with Cloud Dataflow and Clo...
Jeff Dean's keynote: YouTube video
Define a custom TensorFlow graph
Training at local: 8.3 hours w/ 1 node
Training at clo...
TensorFlow in the Wild
TensorFlow
powered
Fried Chicken
Nugget Server
From: http://www.rt-net.jp/karaage1/
TensorFlow powered
Cucumber Sorter
From: http://workpiles.com/2016/02/tensorflow-cnn-cucumber/
TV popstar classifier
with 95% accuracy
From: http://memo.sugyan.com/entry/2016/06/14/220624
Discriminative
Localization
From: https://github.com/jazzsaxmafia/Weakly_detector
From: http://otoro.net/
Generative Arts with TensorFlow
Thank you!
Links & Resources
Large Scale Distributed Systems for Training Neural Networks, Jeff Dean and
Oriol Vinals
Cloud Vision AP...
Google Cloud Platform Empowers TensorFlow and Machine Learning
Google Cloud Platform Empowers TensorFlow and Machine Learning
Google Cloud Platform Empowers TensorFlow and Machine Learning
Google Cloud Platform Empowers TensorFlow and Machine Learning
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Google Cloud Platform Empowers TensorFlow and Machine Learning

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Google Cloud Platform Empowers TensorFlow and Machine Learning

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Google Cloud Platform Empowers TensorFlow and Machine Learning

  1. 1. Google Cloud Platform Empowers TensorFlow and Machine Learning
  2. 2. +Kazunori Sato @kazunori_279 Kaz Sato Staff Developer Advocate Tech Lead for Data & Analytics Cloud Platform, Google Inc.
  3. 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. 4. What is Neural Network and Deep Learning
  5. 5. Neural Network is a function that can learn
  6. 6. xn > b? w1 wn x2 x1 Mimicking the behavior of biological neurons
  7. 7. How do you classify them?
  8. 8. weights bias (threshold) Programmers would specify the parameters
  9. 9. Let’s see how neural network solves the problem
  10. 10. The computer tries to find the best parameters A neuron classifies a data point into two kinds
  11. 11. Gradient Descent: changing the params gradually to reduce errors
  12. 12. How do you classify them?
  13. 13. More neurons = More features to extract
  14. 14. Mapping inputs to a feature space, classifying with a hyperplane From: Neural Networks, Manifolds, and Topology, colah's blog
  15. 15. How about this?
  16. 16. More hidden layers = more hierarchies of features
  17. 17. How about this?
  18. 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. 19. From: mNeuron: A Matlab Plugin to Visualize Neurons from Deep Models, Donglai Wei et. al.
  20. 20. Machine Intelligence at Google scale
  21. 21. The two big challenges of Deep Learning: Computing Power and Training Data
  22. 22. Enterprise Google Cloud is The Datacenter as a Computer
  23. 23. Jupiter network 10 GbE x 100 K = 1 Pbps Consolidates servers with microsec latency
  24. 24. Borg No VMs, pure containers 10K - 20K nodes per Cell DC-scale job scheduling CPUs, mem, disks and IO
  25. 25. Confidential & ProprietaryGoogle Cloud Platform 26 Google Cloud + Neural Network = Google Brain
  26. 26. 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
  27. 27. Externalizing the power to developers
  28. 28. 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.
  29. 29. 3333 Demo
  30. 30. 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
  31. 31. 3535 Demo
  32. 32. TensorFlow and Cloud ML
  33. 33. 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
  34. 34. Google's open source library for machine intelligence tensorflow.org launched in Nov 2015 Used by many production ML projects What is TensorFlow?
  35. 35. # 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)
  36. 36. TensorBoard: visualization tool
  37. 37. Portable and Scalable Training on: Mac/Windows GPU server GPU cluster / Cloud Running on: Android, iOS RasPi
  38. 38. Distributed Training and Prediction with TensorFlow
  39. 39. Distributed Training with TensorFlow
  40. 40. Data Parallelism split data, share model
  41. 41. ● CPU/GPU scheduling ● Communications ○ Local, RPC, RDMA ○ 32/16/8 bit quantization ● Cost-based optimization ● Fault tolerance Distributed Training with TensorFlow
  42. 42. Tensor Processing Unit ASIC for TensorFlow Designed by Google 10x better perf / watt latency and efficiency bit quantization
  43. 43. TPU on Production RankBrain AlphaGo Google Photos Speech and more
  44. 44. 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)
  45. 45. 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
  46. 46. TensorFlow in the Wild
  47. 47. TensorFlow powered Fried Chicken Nugget Server From: http://www.rt-net.jp/karaage1/
  48. 48. TensorFlow powered Cucumber Sorter From: http://workpiles.com/2016/02/tensorflow-cnn-cucumber/
  49. 49. TV popstar classifier with 95% accuracy From: http://memo.sugyan.com/entry/2016/06/14/220624
  50. 50. Discriminative Localization From: https://github.com/jazzsaxmafia/Weakly_detector
  51. 51. From: http://otoro.net/ Generative Arts with TensorFlow
  52. 52. Thank you!
  53. 53. 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

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