This document discusses deep learning frameworks Neon and Nervana Cloud. It provides an introduction to deep learning, describing how it uses learned features from multiple levels to make predictions rather than designed features. The document outlines Neon's model design, training workflow, model zoo, Caffe compatibility, and backend interface. It also describes the Nervana Cloud for importing, building, training, and deploying models using Neon on AWS hardware and S3 storage. A demo of the tools is proposed.

1. Proprietary and confidential. Do not distribute.
Deep Learning, neon,
and the Nervana Cloud
Will Constable
March 3 2016
MAKING MACHINES SMARTER.™

2. Agenda
2
Neon
Nervana Cloud
Intro to Deep Learning
Demo

3. deep speech 2
3

4. what is deep learning?
4
Before deep learning:
• Input → designed features → output
• Input → designed features → SVM → output
• Input → learned features → SVM → output
• Input → levels of learned features → output

5. what is deep learning?
5
(Zeiler and Fergus, 2013)

6. what is deep learning?
6
++ +

7. Proprietary and confidential. Do not distribute.
Deep learning techniques
imagnet error rate
7
Source: ImageNet
1: ImageNet top 5 error rate
Error rate1
human performance

8. what is deep learning?
8
No free lunch:
• lots of data
• model design needs intuition
• slow experiments

9. agenda
9
Neon
Nervana Cloud
Intro to Deep Learning
Demo

10. model
Convolution
BatchNorm
ReLu Activation
training workflow
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Fully Connected
Pooling
Softmax
cost
optimizer
Train Set
Validation Set

11. model zoo
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• github.com/nervanazoo/NervanaModelZoo
• model files, parameters GoogLeNetAlexnet
VGG
Deep Residual Net
bAbI Q&A
imdb Sentiment Analysis
Video Activity Detection
Deep Reinforcement Learning
LSTM Image Captioning
Fast-RCNN Object Localization
AllCNN

12. caffe compatibility
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• github.com/NervanaSystems/caffe2neon.git
• Convert caffe model to neon format
> ./decaffeinate.py caffe_model.prototxt caffe_params -o neon_model

13. backend interface
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from neon.backends import gen_backend
be = gen_backend(backend=‘gpu') # backend creation
x0 = be.ones((2, 2), name=‘x0') # buffer allocation
x1 = be.ones((2, 2), name=‘x1')
f_val = be.empty((2, 2))
f = x0 + x1 # op-tree creation
f_val[:] = f # execution
• numpy-like
• consistent cpu/gpu interface
• deferred execution op-tree

14. full stack optimization
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• multithreaded data loader
• hand-assembled kernels
• compound kernel generator

15. release
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neon 1.3
github.com/nervanaSystems/neon

16. winograd convolution
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• 2x faster convolutionAlgorithmicSpeedup
0
0.5
1
1.5
2
winograd cuDNN v4
Efficient Convolution

17. Agenda
17
Neon
Nervana Cloud
Intro to Deep Learning
Demo

18. nervana cloud
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Import Build Train Deploy

19. nervana cloud
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neon deep
learning
framework
S3Nervana HardwareAWS S3TitanX Cluster

20. web UI
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21. ncloud cli
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22. Proprietary and confidential. Do not distribute.
Summary
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• neon 1.3 (download today)
• cloud - contact us (info@nervanasys.com)
• We’re hiring! (nervanasys.com/careers)
will@nervanasys.com

23. Agenda
23
Neon
Nervana Cloud
Intro to Deep Learning
Demo