The document discusses updates and new features in TensorFlow 2.0 and related projects. It summarizes that TensorFlow 2.0 provides simpler APIs inspired by Keras, eager execution by default, distribution strategies for distributed training, and improved documentation. It also discusses TensorFlow Extended (TFX) for end-to-end machine learning pipelines, TensorFlow.js for machine learning in JavaScript, TensorFlow Lite for mobile and embedded devices, and new projects like TensorFlow Federated.

1. Ray Hilton - @rayh Eliiza - @EliizaAI

2. About Us

3. Data Science
Machine Learning
Artificial Intelligence
Data Engineering
Data Ops

4. Video Multi-Classification
Audio Transcription
Predicting Renewable Energy Generation
Natural Language Processing
Operational Forecasting
Sports Data Analysis
Anomaly Detection

5. 12 8 1.5
YearsCountriesPeople

6. 33% 60% 0%
Female
Engineers
Female Data
Scientists
Academic
Background

7. @EliizaAI
info@eliiza.com.au

8. #TFDevSummit2019

10. Sunnyvale, CA 6-7 March

11. ~400 people

12. Adobe, Apple, Twitter, Microsoft,
Nike, TWiML, Small
Consultancies, Research
Institutions, Google (obviously)

13. TF2

14. Simpler APIs

15. # TensorFlow 1.X
outputs = session.run(
f(placeholder),
feed_dict={placeholder: input}
)
# TensorFlow 2.0
outputs = f(input)
Simpler APIs

16. from keras.models import Sequential
from keras.layers import Dense
model = Sequential([
Dense(64, activation='relu')),
Dense(64, activation='relu')),
Dense(10, activation='softmax'))
])
Keras API

17. import tensorflow as tf
from tensorflow.keras.layers import Dense
model = tf.keras.Sequential([
Dense(64, activation='relu')),
Dense(64, activation='relu')),
Dense(10, activation='softmax'))
])
TensorFlow 💖 Keras

18. Eager & Lazy Execution

19. x = [[2.]]
m = tf.matmul(x, x)
print("hello, {}".format(m))
>> hello, [[4.]]
Eager Execution
👏

20. @tf.function
def square_if_positive(x):
x = x * x if x > 0 else x = 0
return x
square_if_positive(tensor)
AutoGraph

21. Distribution Strategies

22. model = tf.keras.Sequential(...)
model.compile(loss='mse', optimizer='sgd').
model.fit(dataset, epochs=2)
model.evaluate(dataset)
Strategy Scopes

23. strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = tf.keras.Sequential(...)
model.compile(loss='mse', optimizer='sgd').
model.fit(dataset, epochs=2)
model.evaluate(dataset)
Multiple GPUs on One Machine

24. strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
with strategy.scope():
model = tf.keras.Sequential(...)
model.compile(loss='mse', optimizer='sgd').
model.fit(dataset, epochs=2)
model.evaluate(dataset)
Multiple GPUs Across Multiple Machines

25. strategy = tf.distribute.experimental.TPUStrategy()
with strategy.scope():
model = tf.keras.Sequential(...)
model.compile(loss='mse', optimizer='sgd').
model.fit(dataset, epochs=2)
model.evaluate(dataset)
Train on TPUs

26. strategy = tf.distribute.experimental.ParameterServerStrategy()
with strategy.scope():
model = tf.keras.Sequential(...)
model.compile(loss='mse', optimizer='sgd').
model.fit(dataset, epochs=2)
model.evaluate(dataset)
Train using Parameter Servers

27. TF2
● Better documentation
● Broken up the monolith - take what you need
● Reduced complexity (deleted stuff)
● Better API (hello keras)
● Eager Execution by default
● … but lazily executed functions
● Strategy Scopes for distributing work

28. TFX

29. What is TFX

30. What does TFX provide?
● TensorFlow Data Validation (TFDV)
● TensorFlow Transform (TFT)
● TensorFlow Model Analysis (TFMA)
● TensorFlow Metadata (TFMD)
● ML Metadata (MLMD)

31. Data Validation

33. Transform

35. Transform: Preprocessing Example

36. Model Analysis

37. Evaluator

38. Metadata

40. TFJS

41. What is it?
● Complete JS implementation of TF
● No drivers
● Interactive
● Local Training & Inference (privacy)
● Uses WebGL to accelerate linear algebra (See TFJS-core)

42. Versatile
● Browser
● Server-Side (Node JS)
● Desktop (via Electron, etc)
● Mobile (😮)

43. Browser

44. <script
src="https://cdn.jsdelivr.net/npm/@tensorfl
ow/tfjs@1.0.0/dist/tf.min.js"></script>
Quick Start: Browser

45. Body Pix

49. Node JS

50. import * as tf from ‘tfjs-node’;
Quick Start: Node

51. import * as tf from ‘tfjs-node-gpu’;
Quick Start: Node + CUDA

52. TFLite

53. Quantizing

55. Selective Ops

56. $ tflite_convert
--output_file=/tmp/foo.tflite
--graph_def_file=/tmp/foo.pb
--input_arrays=input
--output_arrays=Reshape_1
--target_ops=TFLITE_BUILTINS,SELECT_TF_OPS
Selective Ops

58. GPU Delegate

59. // NEW: Prepare GPU delegate.
GpuDelegate delegate = new GpuDelegate();
Interpreter.Options options = (new
Interpreter.Options()).addDelegate(delegate);
// Set up interpreter.
Interpreter interpreter = new Interpreter(model, options);
// Run inference.
writeToInputTensor(inputTensor);
interpreter.run(inputTensor, outputTensor);
readFromOutputTensor(outputTensor);
delegate.close();
GPU Delegate in Android

60. Swift API!

61. import TensorFlowLite
let interpreter = try Interpreter(
modelPath: modelPath,
options: options)
try interpreter.invoke()
let outputTensor = try interpreter.output(at: 0)
Swift API

62. Questions?

63. Thanks 🙏
References: elz.ai/tf2
Ray Hilton - @rayh Eliiza - @EliizaAI

64. Quickly mention...

65. tf.data & tf.datasets

66. SparkFun

67. Coral Edge TPU

68. Coral Edge TPU

69. Federated TensorFlow

70. Mesh TensorFlow

71. Swift for TensorFlow

72. Thanks (Again)
References: elz.ai/tf2
Ray Hilton - @rayh Eliiza - @EliizaAI