Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimensional

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As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.

Tecnologia

Anima Anandkumar
MODERN ML:
DEEP, DISTRIBUTED,
MULTI-DIMENSIONAL

3
MOORE’S LAW: A SUPERCHARGED LAW
 More than a billion
operations per image.
 NVIDIA GPUs enable
parallel operations.
 Enables Large-Scale AI.
COMPUTE INFRASTRUCTURE FOR AI: GPU

4
DISTRIBUTED TRAINING INVOLVES COMPUTATION & COMMUNICATION
Parameter
server
GPU 1 GPU 2
With 1/2 data With 1/2 data

5
DISTRIBUTED TRAINING INVOLVES COMPUTATION & COMMUNICATION
Parameter
server
GPU 1 GPU 2
With 1/2 data With 1/2 data
Compress?
Compress?
Compress?

6
DISTRIBUTED TRAINING BY MAJORITY VOTE
Parameter
server
GPU 1
GPU 2
GPU 3
sign(g)
sign(g)
sign(g)
Parameter
server
GPU 1
GPU 2
GPU 3
sign [sum(sign(g))]
Jeremy Bernstein, Jiawei Zhao, Kamyar Azzizadenesheli, Yu-Xiang Wang, A

7
SIGNSGD PROVIDES “FREE LUNCH"
Throughput gain with almost same accuracy
P3.2x machines on AWS, Resnet50 on imagenet

8
SIGNSGD ACROSS DOMAINS AND ARCHITECTURES
Huge throughput gain!

9
SIGNSGD IS BYZANTINE FAULT TOLERANT
SignSGD is robust

10
TAKE-AWAYS FOR SIGN-SGD
• Convergence even under biased gradients and noise.
• Faster convergence than SGD in theory and in practice.
• For distributed training, similar variance reduction as SGD.
• In practice, similar accuracy but with far less communication.
https://github.com/PermiJW/signSGD-with-Majority-Vote
Pytorch code at

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TENSORS:
MULTI-DIMENSIONAL PROCESSING
Image: 3 dimensions
Width * Height * Channels
Video: 4 dimensions
Width * Height * Channels * Time

13
OPERATIONS ON TENSORS: TENSOR CONTRACTION

14
DEEP NEURAL NETS: TRANSFORMING TENSORS

15
DEEP TENSORIZED NETWORKS
Jean Kossaifi, Zack Chase Lipton, Aran Khanna, Tommaso Furlanello, A
Pytorch notebook: https://github.com/JeanKossaifi/tensorly-notebooks

16
SPACE SAVING IN DEEP TENSORIZED NETWORKS

17
T E N S O R L Y : H I G H - L E V E L A P I F O R T E N S O R
A L G E B R A
• Python programming
• User-friendly API
• Multiple backends:
flexible + scalable
• Example notebooks in
repository

18
TENSORS:
TOPIC DETECTION IN TEXT
Co-occurrence
of word triplets Topic 1 Topic 2
STORM
WORLD SERIES
AUSTRALIA
STOCK MARKET
WASHINGTON
HEALTH
CRISIS
MACHINE
LEARNING
LIBRARY OF
NEWS ARTICLES
Amazon
Comprehend
LIST OF TOPICS

19
UNSUPERVISED LEARNING OF TOPIC MODELS THROUGH TENSOR METHODS
Justice
Educatio
n
Sports
Topics

20
TENSOR-BASED LDA TRAINING IS FASTER
• Mallet is an open-source framework for topic modeling
• Benchmarks on AWS SageMaker Platform
• Bulit into AWS Comprehend NLP service.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
5 10 15 20 25 30 50 75 100
Timeinminutes
Number of Topics
Training time for NYTimes
Spectral Time(minutes) Mallet Time (minutes)
0.00
50.00
100.00
150.00
200.00
250.00
5 10 15 20 25 50 100
Timeinminutes
Number of Topics
Training time for PubMed
Spectral Time (minutes) Mallet Time (minutes)
8 million documents
22x faster on average 12x faster on average
300000 documents

A New Vision for Autonomy
Center for Autonomous Systems and Technologies

24NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
RESEARCH LEADERS AT NVIDIA
Robotics
Dieter Fox
Learning &
Perception
Jan KautzBill Dally Dave Luebke Alex Keller Aaron Lefohn
Graphics
Steve Keckler Dave Nellans Mike O’Connor
ArchitectureProgramming
Michael Garland
VLSI
Brucek Khailany
Circuits
Tom Gray
Networks
Larry Dennison
Chief
Scientist
Computer
vision Core ML
Sanja Fidler Me !
Applied
research
Bryan Catanzaro

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Nearly half of all small businesses fail within their first 5 years. However, AI-driven solutions can help solve complex economic problems for consumers and small businesses like missed bill payments, insufficient capital, overinvestment in fixed assets, and more. Ashok Srivastava discusses technology's role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world. Leveraging technology enablers like deep learning, natural language processing, and automated reasoning and combining with a delightful end-user experience and sophisticated UX, Intuit is using technology to help its users have more confidence in their financial management.

Ashok Srivastava at AI Frontiers : Using AI to Solve Complex Economic Problems

AI Frontiers

Rohit Tripathi at AI Frontiers : Using intelligent connectivity and AI to tra...

AI Frontiers

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