HOW TO BUILD A MODERN AI
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- Elinor Marshall
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1 HOW TO BUILD A MODERN AI FOR THE UNKNOWN IN MODERN DATA PURE STORAGE INC.
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3 Official Languages Act (1969/1988) 3 Translation Bureau
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6 DAWN OF 4 TH INDUSTRIAL REVOLUTION BIG DATA, AI DRIVING CHANGE IN EVERY INDUSTRY 1 st Revolution s Steam Power Rural to Industrial 2 nd Revolution Electricity Industrial to Mass Production 3 rd Revolution PC Mass production to Digital 4 th Revolution 2010-now AI, Big Data & IoT Digital to Intelligence 6
7 DATA IS VITAL TO MACHINE LEARNING OBSERVATION BY PROF. ANDREW NG, AI LUMINARY 7
8 VALUABLE DATA STUCK IN NEUTRAL LEGACY, RETROFIT STORAGE BUILT ON SERIAL TECHNOLOGIES, PERFORMANCE GAP GROWING STORAGE TECHNOLOGY NOT KEEPING UP Gap Will Only Grow Worse LEGACY & RETROFIT STORAGE Built on Decade-Old Serial Technology Deep Learning Compute Required 15x in 2 Years Object Translation Layer NFS Software Stack PERFORMANCE GAP Compute Delivered 10x in 2 Years Newer Technologies Retrofitted SAS (Serial Attached SCSI) SATA Disk Emulation Software Decade-old Protocol & SW SSD/Disk Performance Delivered ~Flat in 2 Years
9 STORAGE FOR AI: BOTH A TRUCK AND A RACE CAR 9 CAPACITY LARGE FILES THROUGHPUT SEQUENTIAL ACCESS CONCURRENCY SMALL FILES LATENCY RANDOM ACCESS
10 SOUL OF DGX-1 IS PARALLEL 10
11 SOUL OF FLASHBLADE IS PARALLEL POWERING 75 BLADE-SCALE IN SINGLE IP WITH PURITY FOR FLASHBLADE NATIVE OBJECT NATIVE NFS/SMB KEY VALUE KEY-VALUE DATABASE STORE FOR DISTRIBUTED PARTITIONS BILLIONS & BILLIONS OF OBJECTS blade feature is subject to GA release
12 THREE ESSENTIAL THINGS FOR AI FRAMEWORKS & APPLICATIONS COMPUTE FROM CPU TO GPU SERVERS STORAGE POWER ENTIRE AI PIPELINE 12
13 WIDE RANGE OF NEEDS IN THE PIPELINE SIGNIFICANT CHALLENGE TO LEGACY STORAGE INGEST CLEAN & TRANSFORM EXPLORE TRAIN From sensors, machines, & user generated CPU Servers GPU Server GPU Production Cluster IO PROFILE 13
14 REAL WORLD PIPELINE IN AN AUTONOMOUS CAR COMPANY INGEST CLEAN, LABEL, RESIZE EXPLORE TRAIN INFERENCE IN VIRTUAL WORLD CPU Servers GPU Server GPU Production Cluster GPU Production Cluster 10 S OF PB COLD STORAGE 14
15 AI SYSTEMS DESIGN PATTERNS GOAL IS TO KEEP THE GPUs 100% BUSY FULL TRAINING WORKFLOW decode scale evaluate forwardpropagation update back-propagation I/O CPU GPU BENCHMARK SETUP Setup #1: DGX-1 with 4x Local SSDs Setup #2: DGX-1 with 1x FlashBlade 15
16 RESULT: FLASHBLADE vs LOCAL SSDs TENSORFLOW TRAINING BENCHMARK WITH RESNET-50 TIME TO SOLUTIONS (HOURS) 1.8 Hours to process 10M images 6% Faster 1.7 Hours to process 10M images 16 NVIDIA DGX-1 + Local SSDs NVIDIA DGX-1 + Pure FlashBlade
17 RESULT: 3X FASTER END-TO-END TENSORFLOW TRAINING BENCHMARK WITH RESNET-50 TIME TO SOLUTIONS (HOURS) 3.4 Hours to load 8TB into SSDs 1.8 Hours to process 10M images 305% Faster 1.7 Hours to process 10M images 17 NVIDIA DGX-1 + Local SSDs NVIDIA DGX-1 + Pure FlashBlade
18 ANALYTICS FOR PRODUCTION DATA TUNED FOR EVERYTHING DATA PLATFORM FOR BOTH TRAINING AND INFERENCING WORKLOADS TRAINING ANALYSIS ON INFERENCE DATA DEPLOY TRAINED MODELS 18
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20 MAKING AUTONOMOUS CARS POSSIBLE BY Zenuity, a joint venture of Volvo and Autoliv, aims to build autonomous driving software for production vehicles by They chose to build their deep learning infrastructure with NVIDIA DGX-1 servers and Pure FlashBlade systems to accelerate their AI initiative.
21 AUTONOMOUS VEHICLE SOFTWARE COMPANY 4 NVIDIA DGX-1 DL Training Cluster 2 PURE FLASHBLADE Over 1 PB of training data w/ performance headroom ENTIRE AI PIPELINE ON A SINGLE HUB Preprocessing, Exploring, and Training on FlashBlade 21
22 AI EXPANDED OUR VIEW OF THE WORLD STRUCTURED (BLOCK, DBs, VMs) DBs & Apps UNSTRUCTURED (FILE, OBJECT, KEY-VALUE CONTAINERS) ALL-FLASH ARRAYS VM Farms Tier2 Apps 22
23 FLASHBLADE INDUSTRY S FIRST DATA HUB PURPOSE-BUILT FOR AI & DEEP LEARNING BLADE PURITY SCALE-OUT FABRIC Powerful, Elastic Data Processing & Storage Unit Massively Distributed Software for Limitless Scale Software-defined fabric that scales linearly with more data & clients blade feature is subject to GA release
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