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Large-Scale Deep Learning
With TensorFlow
Jeff Dean
Google Brain team
g.co/brain
In collaboration with many other people at Google
We can now store and perform computation on large datasets
MapReduce
We can now store and perform computation on large datasets
But what we really want is not just raw data,
but computer systems that understand this data
MapReduce
What do I mean by understanding?
What do I mean by understanding?
What do I mean by understanding?
Neural Networks
“cat”
● A powerful class of machine learning model
● Modern reincarnation of artificial neural networks
● Collection of simple, trainable mathematical functions
What is Deep Learning?
“cat”
● Loosely based on
(what little) we know
about the brain
What is Deep Learning?
What is Deep Learning?
● Each neuron is connected to a
small subset of other neurons.
● Based on what it sees,
it decides what it wants to say.
● Neurons learn to cooperate
to accomplish the task.
Commonalities with real brains:
What is Deep Learning?
Each neuron implements a
relatively simple mathematical
function.
But the composition of 106
- 109
such functions is surprisingly
powerful.
ConvNets
Important Property of Neural Networks
Results get better with
more data +
bigger models +
more computation
(Better algorithms, new insights and improved
techniques always help, too!)
Growing Use of Deep Learning at Google
Android
Apps
drug discovery
Gmail
Image understanding
Maps
Natural language
understanding
Photos
Robotics research
Speech
Translation
YouTube
… many others ...
Across many
products/areas:
# of directories containing model description files
Open, standard software for
general machine learning
Great for Deep Learning in
particular
First released Nov 2015
Apache 2.0 license
http://tensorflow.org/
and
https://github.com/tensorflow/tensorflow
Strong External Adoption
GitHub Launch Nov. 2015
GitHub Launch Sep. 2013
GitHub Launch Jan. 2012
GitHub Launch Jan. 2008
50,000+ binary installs in 72 hours, 500,000+ since Nov, 2015
Most forks of any GitHub repo in 2015, despite only being available starting in Nov, 2015
(source: http://donnemartin.com/viz/pages/2015)
Motivations
● DistBelief (our 1st system) was scalable and good for
production deployment, but not as flexible as we
wanted for research purposes
● Better understanding of problem space allowed us to
make some dramatic simplifications
● Define a standard way of expressing machine learning
ideas and computations
● Short circuit the MapReduce/Hadoop inefficiency
http://tensorflow.org/
http://tensorflow.org/
TensorFlow: Expressing High-Level ML Computations
● Core in C++
○ Very low overhead
Core TensorFlow Execution System
CPU GPU Android iOS ...
TensorFlow: Expressing High-Level ML Computations
● Core in C++
○ Very low overhead
● Different front ends for specifying/driving the computation
○ Python and C++ today, easy to add more
Core TensorFlow Execution System
CPU GPU Android iOS ...
TensorFlow: Expressing High-Level ML Computations
● Core in C++
○ Very low overhead
● Different front ends for specifying/driving the computation
○ Python and C++ today, easy to add more
Core TensorFlow Execution System
CPU GPU Android iOS ...
C++ front end Python front end ...
● Build a graph computing a neural net inference.
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
x = tf.placeholder("float", shape=[None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
Example TensorFlow fragment
● Automatically add ops to calculate symbolic gradients
of variables w.r.t. loss function.
● Apply these gradients with an optimization algorithm
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
opt = tf.train.GradientDescentOptimizer(0.01)
train_op = opt.minimize(cross_entropy)
Python API for Machine Learning
● Launch the graph and run the training ops in a loop
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
Python API for Machine Learning
MatMul
Add Relu
biases
weights
examples
labels
Xent
Graph of Nodes, also called Operations or ops.
Computation is a dataflow graph
with tensors
MatMul
Add Relu
biases
weights
examples
labels
Xent
Edges are N-dimensional arrays: Tensors
Computation is a dataflow graph
with state
Add Mul
biases
...
learning rate
−=...
'Biases' is a variable −= updates biasesSome ops compute gradients
Computation is a dataflow graph
GPU 0 CPU
Add Mul
biases
learning rate
Assign
Sub
...
...
distributedComputation is a dataflow graph
GPU 0 CPU
Add Mul
biases
learning rate
Assign
Sub
...
...
Assign Devices to Ops
● TensorFlow inserts Send/Recv Ops to transport tensors across devices
● Recv ops pull data from Send ops
Send Recv
GPU 0 CPU
Add Mul
biases
learning rate
Assign
Sub
...
...
Assign Devices to Ops
● TensorFlow inserts Send/Recv Ops to transport tensors across devices
● Recv ops pull data from Send ops
Send Recv
Send Recv
Send
Recv Send
Recv
phones single machines (CPU and/or GPUs) …
distributed systems of 100s
of machines and/or GPU cards
Automatically Runs on Variety of Platforms
custom ML hardware
What are some ways that
deep learning is having
a significant impact at Google?
“How cold is
it outside?”
Deep
Recurrent
Neural Network
Acoustic Input Text Output
Reduced word errors by more than 30%
Speech Recognition
Google Research Blog - August 2012, August 2015
“ocean”
Deep
Convolutional
Neural Network
Your Photo
Automatic Tag
Search personal photos without tags.
Google Photos Search
Google Research Blog - June 2013
Google Photos Search
Google Photos Search
“Wow. The new Google photo search is a bit insane. I didn’t tag those… :)”
We have tons of vision problems
Image search, StreetView, Satellite Imagery,
Translation, Robotics, Self-driving Cars,
www.google.com/sunroof
“Seeing” Go
April 1, 2009: April Fool’s Day joke
Nov 5, 2015: Launched Real Product
Feb 1, 2016: >10% of mobile Inbox replies
Smart Reply
Small Feed-
Forward
Neural Network
Incoming Email
Activate
Smart Reply?
yes/no
Smart Reply
Google Research Blog
- Nov 2015
Small Feed-
Forward
Neural Network
Incoming Email
Activate
Smart Reply?
Deep Recurrent
Neural Network
Generated Replies
yes/no
Smart Reply
Google Research Blog
- Nov 2015
Combined Vision + Translation
Model: A close up of a child
holding a stuffed animal.
Human: A young girl asleep on
the sofa cuddling a stuffed
bear.
Model: A baby is asleep next to
a teddy bear.
Image Captions Research
Experiment Turnaround Time and Research Productivity
● Minutes, Hours:
○ Interactive research! Instant gratification!
● 1-4 days
○ Tolerable
○ Interactivity replaced by running many experiments in parallel
● 1-4 weeks
○ High value experiments only
○ Progress stalls
● >1 month
○ Don’t even try
Image Model Training Time
Hours
10 GPUs
50 GPUs
1 GPU
Hours
2.6 hours vs. 79.3 hours (30.5X)
10 GPUs
50 GPUs
1 GPU
Image Model Training Time
How Can You Get Started with Machine Learning?
Four ways, with varying complexity:
(1) Use a Cloud-based API (Vision, Speech, etc.)
(2) Run your own pretrained model
(3) Use an existing model architecture, and
retrain it or fine tune on your dataset
(4) Develop your own machine learning models
for new problems
More
flexible,
but more
effort
required
(1) Use Cloud-based APIs
cloud.google.com/translate
cloud.google.com/speech
cloud.google.com/vision
cloud.google.com/text
(1) Use Cloud-based APIs
cloud.google.com/translate
cloud.google.com/speech
cloud.google.com/vision
cloud.google.com/text
Google Cloud Vision API
https://cloud.google.com/vision/
(2) Using a Pre-trained Image Model yourself with TensorFlow
www.tensorflow.org/tutorials/image_recognition/index.html
For training your own models (3 & 4), two choices:
cloud.google.com/ml
or
Run open-source release on your own physical machines or virtual
machines in a cloud hosting environment
(3) Training a Model on Your Own Image Data
www.tensorflow.org/versions/master/how_tos/image_retraining/index.html
(4) Develop your own machine learning models
https://www.tensorflow.org/versions/master/get_started/basic_usage.html
Example: Combining Vision with Robotics
“Deep Learning for Robots: Learning
from Large-Scale Interaction”, Google
Research Blog, March, 2016
“Learning Hand-Eye Coordination for
Robotic Grasping with Deep Learning
and Large-Scale Data Collection”,
Sergey Levine, Peter Pastor, Alex
Krizhevsky, & Deirdre Quillen,
Arxiv, arxiv.org/abs/1603.02199
https://www.youtube.com/watch?v=iaF43Ze1oeI
What Does the Future Hold?
Deep learning usage will continue to grow and accelerate:
● Across more and more fields and problems:
○ robotics, self-driving vehicles, ...
○ health care
○ video understanding
○ dialogue systems
○ personal assistance
○ ...
Conclusions
Deep neural networks are making significant strides in
understanding:
In speech, vision, language, search, …
If you’re not considering how to use deep neural nets to solve
your vision or understanding problems, you almost certainly
should be
Further Reading
● Dean, et al., Large Scale Distributed Deep Networks, NIPS 2012, research.google.
com/archive/large_deep_networks_nips2012.html.
● TensorFlow white paper, tensorflow.org/whitepaper2015.pdf (clickable links in bibliography)
● TensorFlow: A System for Large-Scale Machine Learning, http://arxiv.org/abs/1605.08695
tensorflow.org
research.google.com/people/jeff
We’re Hiring! See g.co/brain
Questions?

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Large Scale Deep Learning with TensorFlow

  • 1. Large-Scale Deep Learning With TensorFlow Jeff Dean Google Brain team g.co/brain In collaboration with many other people at Google
  • 2. We can now store and perform computation on large datasets MapReduce
  • 3. We can now store and perform computation on large datasets But what we really want is not just raw data, but computer systems that understand this data MapReduce
  • 4. What do I mean by understanding?
  • 5. What do I mean by understanding?
  • 6. What do I mean by understanding?
  • 8. “cat” ● A powerful class of machine learning model ● Modern reincarnation of artificial neural networks ● Collection of simple, trainable mathematical functions What is Deep Learning?
  • 9. “cat” ● Loosely based on (what little) we know about the brain What is Deep Learning?
  • 10. What is Deep Learning? ● Each neuron is connected to a small subset of other neurons. ● Based on what it sees, it decides what it wants to say. ● Neurons learn to cooperate to accomplish the task. Commonalities with real brains:
  • 11. What is Deep Learning? Each neuron implements a relatively simple mathematical function. But the composition of 106 - 109 such functions is surprisingly powerful.
  • 13. Important Property of Neural Networks Results get better with more data + bigger models + more computation (Better algorithms, new insights and improved techniques always help, too!)
  • 14. Growing Use of Deep Learning at Google Android Apps drug discovery Gmail Image understanding Maps Natural language understanding Photos Robotics research Speech Translation YouTube … many others ... Across many products/areas: # of directories containing model description files
  • 15. Open, standard software for general machine learning Great for Deep Learning in particular First released Nov 2015 Apache 2.0 license http://tensorflow.org/ and https://github.com/tensorflow/tensorflow
  • 16. Strong External Adoption GitHub Launch Nov. 2015 GitHub Launch Sep. 2013 GitHub Launch Jan. 2012 GitHub Launch Jan. 2008 50,000+ binary installs in 72 hours, 500,000+ since Nov, 2015 Most forks of any GitHub repo in 2015, despite only being available starting in Nov, 2015 (source: http://donnemartin.com/viz/pages/2015)
  • 17. Motivations ● DistBelief (our 1st system) was scalable and good for production deployment, but not as flexible as we wanted for research purposes ● Better understanding of problem space allowed us to make some dramatic simplifications ● Define a standard way of expressing machine learning ideas and computations ● Short circuit the MapReduce/Hadoop inefficiency
  • 20. TensorFlow: Expressing High-Level ML Computations ● Core in C++ ○ Very low overhead Core TensorFlow Execution System CPU GPU Android iOS ...
  • 21. TensorFlow: Expressing High-Level ML Computations ● Core in C++ ○ Very low overhead ● Different front ends for specifying/driving the computation ○ Python and C++ today, easy to add more Core TensorFlow Execution System CPU GPU Android iOS ...
  • 22. TensorFlow: Expressing High-Level ML Computations ● Core in C++ ○ Very low overhead ● Different front ends for specifying/driving the computation ○ Python and C++ today, easy to add more Core TensorFlow Execution System CPU GPU Android iOS ... C++ front end Python front end ...
  • 23. ● Build a graph computing a neural net inference. import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data', one_hot=True) x = tf.placeholder("float", shape=[None, 784]) W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b) Example TensorFlow fragment
  • 24. ● Automatically add ops to calculate symbolic gradients of variables w.r.t. loss function. ● Apply these gradients with an optimization algorithm y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = -tf.reduce_sum(y_*tf.log(y)) opt = tf.train.GradientDescentOptimizer(0.01) train_op = opt.minimize(cross_entropy) Python API for Machine Learning
  • 25. ● Launch the graph and run the training ops in a loop init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) Python API for Machine Learning
  • 26. MatMul Add Relu biases weights examples labels Xent Graph of Nodes, also called Operations or ops. Computation is a dataflow graph
  • 27. with tensors MatMul Add Relu biases weights examples labels Xent Edges are N-dimensional arrays: Tensors Computation is a dataflow graph
  • 28. with state Add Mul biases ... learning rate −=... 'Biases' is a variable −= updates biasesSome ops compute gradients Computation is a dataflow graph
  • 29. GPU 0 CPU Add Mul biases learning rate Assign Sub ... ... distributedComputation is a dataflow graph
  • 30. GPU 0 CPU Add Mul biases learning rate Assign Sub ... ... Assign Devices to Ops ● TensorFlow inserts Send/Recv Ops to transport tensors across devices ● Recv ops pull data from Send ops Send Recv
  • 31. GPU 0 CPU Add Mul biases learning rate Assign Sub ... ... Assign Devices to Ops ● TensorFlow inserts Send/Recv Ops to transport tensors across devices ● Recv ops pull data from Send ops Send Recv Send Recv Send Recv Send Recv
  • 32. phones single machines (CPU and/or GPUs) … distributed systems of 100s of machines and/or GPU cards Automatically Runs on Variety of Platforms custom ML hardware
  • 33. What are some ways that deep learning is having a significant impact at Google?
  • 34. “How cold is it outside?” Deep Recurrent Neural Network Acoustic Input Text Output Reduced word errors by more than 30% Speech Recognition Google Research Blog - August 2012, August 2015
  • 35. “ocean” Deep Convolutional Neural Network Your Photo Automatic Tag Search personal photos without tags. Google Photos Search Google Research Blog - June 2013
  • 37. Google Photos Search “Wow. The new Google photo search is a bit insane. I didn’t tag those… :)”
  • 38. We have tons of vision problems Image search, StreetView, Satellite Imagery, Translation, Robotics, Self-driving Cars, www.google.com/sunroof
  • 40. April 1, 2009: April Fool’s Day joke Nov 5, 2015: Launched Real Product Feb 1, 2016: >10% of mobile Inbox replies Smart Reply
  • 41. Small Feed- Forward Neural Network Incoming Email Activate Smart Reply? yes/no Smart Reply Google Research Blog - Nov 2015
  • 42. Small Feed- Forward Neural Network Incoming Email Activate Smart Reply? Deep Recurrent Neural Network Generated Replies yes/no Smart Reply Google Research Blog - Nov 2015
  • 43. Combined Vision + Translation
  • 44. Model: A close up of a child holding a stuffed animal. Human: A young girl asleep on the sofa cuddling a stuffed bear. Model: A baby is asleep next to a teddy bear. Image Captions Research
  • 45.
  • 46. Experiment Turnaround Time and Research Productivity ● Minutes, Hours: ○ Interactive research! Instant gratification! ● 1-4 days ○ Tolerable ○ Interactivity replaced by running many experiments in parallel ● 1-4 weeks ○ High value experiments only ○ Progress stalls ● >1 month ○ Don’t even try
  • 47.
  • 48. Image Model Training Time Hours 10 GPUs 50 GPUs 1 GPU
  • 49. Hours 2.6 hours vs. 79.3 hours (30.5X) 10 GPUs 50 GPUs 1 GPU Image Model Training Time
  • 50. How Can You Get Started with Machine Learning? Four ways, with varying complexity: (1) Use a Cloud-based API (Vision, Speech, etc.) (2) Run your own pretrained model (3) Use an existing model architecture, and retrain it or fine tune on your dataset (4) Develop your own machine learning models for new problems More flexible, but more effort required
  • 51. (1) Use Cloud-based APIs cloud.google.com/translate cloud.google.com/speech cloud.google.com/vision cloud.google.com/text
  • 52. (1) Use Cloud-based APIs cloud.google.com/translate cloud.google.com/speech cloud.google.com/vision cloud.google.com/text
  • 53. Google Cloud Vision API https://cloud.google.com/vision/
  • 54. (2) Using a Pre-trained Image Model yourself with TensorFlow www.tensorflow.org/tutorials/image_recognition/index.html
  • 55. For training your own models (3 & 4), two choices: cloud.google.com/ml or Run open-source release on your own physical machines or virtual machines in a cloud hosting environment
  • 56. (3) Training a Model on Your Own Image Data www.tensorflow.org/versions/master/how_tos/image_retraining/index.html
  • 57. (4) Develop your own machine learning models https://www.tensorflow.org/versions/master/get_started/basic_usage.html
  • 58. Example: Combining Vision with Robotics “Deep Learning for Robots: Learning from Large-Scale Interaction”, Google Research Blog, March, 2016 “Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection”, Sergey Levine, Peter Pastor, Alex Krizhevsky, & Deirdre Quillen, Arxiv, arxiv.org/abs/1603.02199 https://www.youtube.com/watch?v=iaF43Ze1oeI
  • 59. What Does the Future Hold? Deep learning usage will continue to grow and accelerate: ● Across more and more fields and problems: ○ robotics, self-driving vehicles, ... ○ health care ○ video understanding ○ dialogue systems ○ personal assistance ○ ...
  • 60. Conclusions Deep neural networks are making significant strides in understanding: In speech, vision, language, search, … If you’re not considering how to use deep neural nets to solve your vision or understanding problems, you almost certainly should be
  • 61. Further Reading ● Dean, et al., Large Scale Distributed Deep Networks, NIPS 2012, research.google. com/archive/large_deep_networks_nips2012.html. ● TensorFlow white paper, tensorflow.org/whitepaper2015.pdf (clickable links in bibliography) ● TensorFlow: A System for Large-Scale Machine Learning, http://arxiv.org/abs/1605.08695 tensorflow.org research.google.com/people/jeff We’re Hiring! See g.co/brain Questions?