This document provides an overview of TensorFlow, an open source machine learning framework. It discusses how machine learning systems can become complex with modeling complexity, heterogeneous systems, and distributed systems. It then summarizes key aspects of TensorFlow, including its architecture, platforms, languages, parallelism approaches, algorithms, and tooling. The document emphasizes that TensorFlow handles complexity so users can focus on their machine learning ideas.

1. Deep Learning with TensorFlow
Rajat Monga
Engineering Director, TensorFlow

3. How complex are
Machine Learning Systems?

4. Neural networks

5. Inception (2015)

6. That’s not all...
Input
Parameters
Labels
Prediction Loss
Update

7. Parameters
Labels
Prediction Loss
Update
Input
batch
...
That’s not all...

8. That’s not all...
Input
batch
...
Parameters
Labels
Prediction Loss
Update
Parameter Server

9. That’s not all...
Input
batch
...
Parameters Update
Model
Worker

10. UpdateUpdateUpdate
Model
That’s not all...
Input
batch
...
Parameters Update
Model
Model
Model
...
Parallelize this!
Distribute this!

11. Raspberry
Pi
Android iOS
Portability is a requirement
TPU
GPU
CPU

12. machine learning gets complex quickly
Modeling complexityHeterogenous
System
Distributed
System

14. repository
for “machine learning”
category on GitHub
#1
Released in Nov. 2015

15. 10,000+ commits since Nov, 2015
490+ contributors
1M+ binary downloads
#20 most popular repository on GitHub by stars
Used in ML classes at many universities:
Toronto, Berkeley, Stanford, ...
Some Stats

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

19. machine learning gets complex quickly
Modeling complexityHeterogenous
System
Distributed
System

20. Platforms

21. Languages

22. TensorFlow Architecture
CPU GPU Android iOS ...
C++ front end Python front end ...
Kernels
Bindings + Compound Ops
add mul Print reshape ...
Core TensorFlow Execution System
Ops

23. Ecosystem

24. machine learning gets complex quickly
Modeling complexityHeterogenous
System
Distributed
System

25. Parallelism in Op implementations
MatMul
...
...

26. ...
...
...
...
...MatMul
MatMul
Task Parallelism in DataFlow graph

27. Data Parallelism
MatMul
Input
MatMulParam
...
...MatMul
MatMul
...
...

28. Model Parallelism
MatMul
Matrix
Matrix
MatMul
Split Concat

29. GPU 0 CPU
Add Mul
biases
learning rate
−=...
...
Distribution across Devices

30. GPU 0 CPU
Add Mul
biases
learning rate
−=...
...
Distribution
● 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
−=...
...
Distribution
● 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. Scale across machines

33. machine learning gets complex quickly
Modeling complexityHeterogenous
System
Distributed
System

34. MatMul
Add Relu
biases
weights
input
input = ...
biases = tf.get_variable(’biases’, ...)
weights = tf.get_variable(’weights’, ...)
out = tf.matmul(input, weights)
out = tf.add(out, biases)
out = tf.nn.relu(out)

35. MatMul
Add Relu
biases
weights
input
input = ...
output = tf.contrib.layers.fully_connected(input, ...)

36. Align cognitive model with programming model
Each box is one line of code!

37. TensorFlow contains complete algorithms
Linear{Classifier,Regressor}
DNN{Classifier,Regressor}
DNNLinearCombined{Classifier,Regressor}
SVM
KMeansClustering
GMM
...

38. Simple machine learning
classifier = learn.LinearClassifier(feature_columns=feature_columns,
n_classes=10)
classifier.fit(data, labels, batch_size=100, steps=1000)
classifier.evaluate(eval_data, eval_labels)
Tooling provided for distributed training and evaluation, graphical debugging, and
export to production server (tensorflow/serving).

39. UpdateUpdateUpdate
Model
Remember this?
Input
batch
...
Parameters Update
Model
Model
Model
...
Parallelize this!
Distribute this!

40. TensorFlow handles complexity for you...
...so you can focus on your ideas

41. 41
Ready to use Machine
Learning models
Use your own data to
train models
Cloud
Vision API
Cloud
Translate API
Cloud Machine Learning
Develop - Model - Test
Google
BigQuery
Cloud
Storage
Cloud
Datalab
Beta
GA BetaGA
Beta
GA
GA
Cloud
Natural Language
API
Beta
Cloud
Speech API
TensorFlow

42. Cats

44. AutoTrash http://myplace.frontier.com/~r.bond/cats/cats.htm

45. AutoTrash http://myplace.frontier.com/~r.bond/cats/cats.htm

46. Rajat Monga
@rajatmonga
Thank You!