STRATA DATA CONFERENCE 2018 · NVIDIA SDK & LIBRARIES INDUSTRY FRAMEWORKS & APPLICATIONS CUSTOMER...
Transcript of STRATA DATA CONFERENCE 2018 · NVIDIA SDK & LIBRARIES INDUSTRY FRAMEWORKS & APPLICATIONS CUSTOMER...
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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: [email protected]
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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