Ward Eldred — Solution ArchitectSeptember 13, 2019

STRATA DATA CONFERENCE 2018ASSESSING DL PROJECT FEASIBILITY & NEEDS

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ABOUT ME

Deep Learning Solution Architect @ NVIDIA

Architect HPC and AI/DL Solutions

DLI Instructor

Teach Introduction to Computer Vision DLI Courses

Latest Hobby

Building Robotic Car with my son

Ward Eldred: weldred@nvidia.com

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DEEP LEARNING IS SWEEPING ACROSS INDUSTRIES

Internet Services

Image/Video classification

Speech recognition

Natural language processing

Medicine

Cancer cell detection

Diabetic grading

Drug discovery

Media & Entertainment

Video captioning

Content based search

Real time translation

Security & Defense

Face recognition

Video surveillance

Cyber security

Autonomous Machines

Pedestrian detection

Lane tracking

Recognize traffic signs

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NVIDIA

GAMING VR AI & HPC SELF-DRIVING CARS

GPU COMPUTING

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NVIDIA’S DEEP LEARNING ECOSYSTEMIt’s Not Just the GPU

NVIDIA SDK & LIBRARIES

INDUSTRY FRAMEWORKS & APPLICATIONS

CUSTOMER USECASES

SUPERCOMPUTING

+550 Applications

CUDA

NCCL cuDNN TensorRTcuBLAS DeepStreamcuSPARSEcuFFT

Amber

NAMDLAMMPS

CHROMA

ENTERPRISE APPLICATIONSCONSUMER INTERNET

ManufacturingHealthcare EngineeringSpeech Translate Recommender Molecular Simulations

WeatherForecasting

SeismicMapping

cuRAND

TESLA GPUs & SYSTEMS

SYSTEM OEM CLOUDTESLA GPU NVIDIA HGXNVIDIA DGX FAMILY

https://aws.amazon.com/canada/

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GPU COMPUTING AT THE HEART OF AINew Advancements Leapfrog Moore’s Law

Performance Beyond Moore’s Law Big Bang of Modern AI

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DEEP LEARNING

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DEEP LEARNING TRAINING WORKFLOWMuch of the Work is Prior to Actual Training

Update Model & Hyperparametersand Retrain

Days to Months Hours to Months

Data

Collection

Data

Preparation

Data

Labeling

Train

Model

Review

Results

Use

Model

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“BIG 4” OF SUCCESSFUL DL PROJECTS

Data Science Team Use Case Data Platform

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DATA SCIENCE TEAM DL EXPERIENCE?Pick Projects at the Proper Level

New To DLLeverage well-tested, common models

Ensure you have lots of “clean” data to train

Moderate ExperienceStart with well-tested, common models

Experiment with modifying models to improve accuracy

Significant ExperienceStart with well-tested, common models

or build your own

Experiment with modifying models to improve accuracy

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USE CASE: COMPUTER VISION (CNN)Great Place to Start

Character Recognition - MNIST Digital Pathology Marketing

Inventory Management Infrastructure Inspection Autonomous Vehicles

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USE CASE: COMPUTER VISION (CNN)Common Tasks and Models

Image ClassificationAlexNet

ResNet

VGG

Inception

Image Classification +LocalizationSliding Window

RCNN

Object DetectionRCNN

SSD

YOLO

R-FCN

Image SegmentationU-NET

DeepLab

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USE CASE: DATA CLASSIFICATION (AE)Very Common Problem — Large Amounts of Unlabeled Data

Source: Google images - https://rpubs.com/cyobero/k-means

Data Classification(KNN and K-Means Clustering)

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USE CASE: ANOMALY DETECTION (AE / LSTM)Protect Yourself From Abnormal Behavior

Cybersecurity Predictive Maintenance Fraud Prevention

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USE CASE: RECOMMENDATION ENGINE (AE/FCN)Improve User Experience and Increase Product Attach

AdvertisingFintechMediaTravelSocial Networks

E-Commerce Real Estate Transportation Food Delivery

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USE CASE: TIME SERIES ANALYSIS (RNN)Leveraging The Past To Predict The Future

Weather Prediction Finance Patient Health

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USE CASE: SENTIMENT ANALYSIS (NLP)Making Better Decisions and Increasing Customer Satisfaction

Stock Market Customer Support Social Media Customer Sentiment

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USE CASE: MODELS CREATING DATA (GAN)Improving Models Through Generated Data

Cancer Detection Autonomous VehiclesSynthetic Photographs

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IT’S ALL ABOUT THE DATA…

It depends ☺

Key factors

More complex NN models require more data

Need training data to cover entire distribution

More dimensionality requires more data

Preprocessing can improve dimensionality

Use initial training tests with different sizes to predict required training data set size

How Much Data do I Need ?

Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., ... & Zhou, Y. (2017).

Deep Learning Scaling is Predictable, Empirically. arXiv preprint arXiv:1712.00409.

Target accuracy

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IT’S ALL ABOUT THE DATA…What if I don’t have Enough Data ?

Synthetic Data Model Complexity Transfer Learning Public Data Set

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OPTIMIZED DEEP LEARNING PLATFORM

Optimized, Flexible Compute Platform

Optimized, Pre-built DL Frameworks

Simple Scheduling &Service Management

Training to Simplify Getting Started

We Want Data Scientists Performing Data Science

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PURPOSE-BUILT AI SUPERCOMPUTERS

AI WORKSTATION AI DATA CENTER

Universal SW for Deep Learning

Predictable execution across platforms

Pervasive reach

NGC DL SOFTWARE STACK

The Essential Instrument for AI Research

DGX-1

The Personal AI Supercomputer

DGX Station

The World’s Most Powerful AI System for the Most Complex AI Challenges

DGX-2

© 2018 NetApp, Inc. All rights reserved. NetApp Confidential – Limited Use Only

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NVIDIA GPU CLOUD (NGC)Optimized, Pre-Built Deep Learning Framework Containers

Discover 35 Optimized Containers

Run Anywhere with Maximum Performance

Deploy Applications In Minutes, Not Days

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POWERFUL SCHEDULING AND SERVICE MGMT“DeepOps” Solution and “DGX Pod” Architecture

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NVIDIA DEEP LEARNING INSTITUTE

Training organizations and individuals to solve challenging problems using Deep Learning

On-site workshops and online courses presented by certified experts

Covering complete workflows for proven application use cases. Image classification, object detection, natural language processing, recommendation systems, and more

Hands-on Training for Data Scientists and Software Engineers

http://www.nvidia.com/dli

http://courses.nvidia.com/

www.nvidia.com/dli

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NEXT STEPS

• Sign Up For NGC - http://ngc.nvidia.com

• Check out of our DLI and Online Courses – http://courses.nvidia.com

• Pick A Deep Learning Project To Start Growing Your Team

• Identify A Business Use Case

• Research Published Papers And Available Sample Networks (“Stand On The Shoulders Of Giants”)

• Leverage Pre-Built Models at “Model Zoo”

• Engage your Local NVIDIA and NPN Partners Teams

• We are “Advocates” for your Deep Learning Success

http://ngc.nvidia.com/http://courses.nvidia.com/

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UPCOMING SESSIONS

• 01:15 – 01:55 “Simplifying AI Infrastructure: Lessons in Scaling a DL Enterprise”Darrin Johnson (NVIDIA)

• 02:05 – 02:45 “Kubernets on GPUs”Michael Balint (NVIDIA)

• 04:35 – 05:15 “GPU accelerated analytics and machine learning ecosystems”Alen Capalik (FASTDATA.io), Jim McHugh (NVIDIA),SriSatish Ambati (H2O.ai), Tim Delisle (Datalogue)

• 05:25 – 06:05 “Accelerate AI with Synthetic Data Using Generative Adversarial Networks”Renee Yao (NVIDIA)

Wednesday, September 12th