Alison B Lowndes - Fueling the Artificial Intelligence Revolution with Gaming - Codemotion Milan 2017
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This document discusses how gaming technologies like the Unreal Engine are fueling advances in artificial intelligence. It notes that gaming GPUs are well-suited for tasks like AI and deep learning due to their optimization for latency, throughput, and parallel processing. NVIDIA GPUs and frameworks like CUDA, Caffe2, TensorFlow, PyTorch and NVIDIA's Deep Learning SDK are enabling powerful deep learning capabilities. Applications discussed include self-driving cars, healthcare, robotics, reinforcement learning, and more. The document emphasizes that GPU-accelerated AI is being applied across industries and is powering advances in areas like autonomous systems, health, and scientific discovery.
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Alison B Lowndes - Fueling the Artificial Intelligence Revolution with Gaming - Codemotion Milan 2017
- 1. 1 ALISON B LOWNDES AI DevRel | EMEA @alisonblowndes November 2017 FUELING THE AI REVOLUTION WITH GAMING
- 2. 2 The day job AUTOMOTIVE Auto sensors reporting location, problems COMMUNICATIONS Location-based advertising CONSUMER PACKAGED GOODS Sentiment analysis of what’s hot, problems $ FINANCIAL SERVICES Risk & portfolio analysis New products EDUCATION & RESEARCH Experiment sensor analysis HIGH TECHNOLOGY / INDUSTRIAL MFG. Mfg. quality Warranty analysis LIFE SCIENCES MEDIA/ENTERTAINMENT Viewers / advertising effectiveness ON-LINE SERVICES / SOCIAL MEDIA People & career matching HEALTH CARE Patient sensors, monitoring, EHRs OIL & GAS Drilling exploration sensor analysis RETAIL Consumer sentiment TRAVEL & TRANSPORTATION Sensor analysis for optimal traffic flows UTILITIES Smart Meter analysis for network capacity, LAW ENFORCEMENT & DEFENSE Threat analysis - social media monitoring, photo analysis
- 4. An unlikely hero… Unreal Engine 4 © Epic Games
- 5. 5 Gaming GPU Computing VR AI & HPC Self-Driving Cars NVIDIA
- 6. 6
- 7. 7 “the machine equivalent of experience”
- 8. Definitions
- 9. 9 GPU Computing GPUs are latency & throughput optimised (latency = time to do a task, throughput = # of tasks per unit of time) x86
- 10. 10 HOW GPU ACCELERATION WORKS Application Code + GPU CPU 5% of Code Compute-Intensive Functions Rest of Sequential CPU Code
- 12. 12
- 13. 13 Describe the differences between these 2 bikes……..
- 15. 15 HOW DOES IT WORK? Modeled on the Human Brain and Nervous System Untrained Neural Network Model App or Service Featuring Capability INFERENCE Applying this capability to new data NEW DATA Trained Model Optimized for Performance “?” Trained Model New Capability TRAINING Learning a new capability from existing data Deep Learning Framework TRAINING DATASET Suzuki X “Suzuki”
- 16. 16 HOW DOES IT WORK? Trained Model New Capability App or Service Featuring Capability INFERENCE Applying this capability to new data NEW DATA Trained Model Optimized for Performance “?” MV Agusta F3
- 18. 18 Long short-term memory (LSTM) Hochreiter (1991) analysed vanishing gradient “LSTM falls out of this almost naturally” Gates control importance of the corresponding activations Training via backprop unfolded in time LSTM: input gate output gate Long time dependencies are preserved until input gate is closed (-) and forget gate is open (O) forget gate Fig from Vinyals et al, Google April 2015 NIC Generator Fig from Graves, Schmidhuber et al, Supervised Sequence Labelling with RNNs
- 19. 19 ResNets vs Highway Nets (IDSIA) https://arxiv.org/pdf/1612.07771.pdf Klaus Greff, Rupesh K. Srivastava Really great explanation of “representation” Compares the two.. shows for language modelling, translation HN >> RN. Not quite as simple as each layer building a new level of representation from the previous - since removing any layer doesn’t critically disrupt.
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- 21. 21 Capsules & routing with EM, Hinton et al https://arxiv.org/pdf/1710.09829.pdf [NIPS2017]
- 22. 22
- 23. THE NEXT STAGE
- 24. 24 REINFORCEMENT LEARNING & ROBOTS THINK / REASON SENSE / PERCEIVE ACT
- 25. 25 DEEPMIND ALPHAGO 2.0 REMATCH 5-0 AlphaGo Zero learns to play by itself now.
- 27. 27 DeepMind Parkour, July 2017
- 29. 29 Shakir Mohamed and Danilo Rezende, DeepMind, UAI 2017
- 30. 30 A PLETHORA OF HEALTHCARE STORIES Molecular Energetics For Drug Discovery AI for Drug Discovery Medical Decision Making Treatment Outcomes Reducing Cancer Diagnosis Errors by 85% Predicting Toxicology Predicting Growth Problems Image Processing Gene Mutations Detect Colon Polyps Predicting Disease from Medical Records Enabling Detection of Fatty Acid Liver Disease
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- 35. 35 Photorealistic models Interactive physics Collaboration Early access in September THE DREAMS OF SCIENCE FICTION https://nvidianews.nvidia.com/news/welcome-to-the-holodeck-nvidia-s-design-lab-of-the-future
- 36. 36 NVIDIA DGX-1 TRAINED DNN ON NVIDIA JETSON Photoreal graphics Physics AI Multi-user VR ISAAC LAB
- 37. 37
- 38. A massive Deep Learning challenge
- 39. 39 Autonomous vehicles will modernize the $10 trillion transportation industry — making our roads safer and our cities more efficient. NVIDIA DRIVE™ PX is a scalable AI car platform that spans the entire range of autonomous driving. Toyota recently joined some 225 companies around the world that have adopted the NVIDIA DRIVE PX platform for autonomous vehicles. They range from car companies and suppliers, to startups and research organizations. THE BRAIN OF AI CARS
- 41. 41 AI PROCESSOR FOR AUTONOMOUS MACHINES XAVIER 30 TOPS DL 30W Custom ARM64 CPU 512 Core Volta GPU 10 TOPS DL Accelerator General Purpose Architectures Domain Specific Accelerators Energy Efficiency CPU CUDA GPU DLA Volta +
- 42. 42 450+ GPU-ACCELERATED APPLICATIONS All Top 10 HPC Apps Accelerated Gaussian ANSYS Fluent GROMACS Simulia Abaqus NAMD WRF VASP OpenFOAM LS-DYNA AMBER DEFINING THE NEXT GIANT WAVE IN HPC DGX SATURNV Fastest AI supercomputer in Top 500 TITECH TSUBAME 3 Japan’s fastest AI supercomputer Piz Daint Powered by P100’s sets DL scaling record EVERY DEEP LEARNING FRAMEWORK ACCELERATED #1 IN AI & HPC ACCELERATION
- 44. 44 SOFTWARE
- 45. 45 NVIDIA DEEP LEARNING SDK and CUDA developer.nvidia.com/deep-learning-software NVIDIA DEEP LEARNING SOFTWARE PLATFORM TRAINING Training Data Management Model Assessment Trained Neural Network Training Data INFERENCE Embedded Automotive Data center GRE + TensorRT DriveWorks SDK JETPACK SDK
- 46. 46 • C++ with Python API • builds on the original Caffe designed from the ground up to take full advantage of the NVIDIA GPU platform • fast and scalable: multi- GPU and multi-node distributed training • lightweight and portable: designed for mobile and cloud deployment http://caffe2.ai/ https://github.com/caffe2/caffe2 http://caffe2.ai/docs/tutorials https://devblogs.nvidia.com/parallelforall/caffe2- deep-learning-framework-facebook/
- 47. 47 Circa 2000 - Torch7 - 4th (using odd numbers only 1,3,5,7) Web-scale learning in speech, image and video applications Maintained by top researchers including Soumith Chintala - Research Engineer @ Facebook All the goodness of Torch7 with an intuitive Python frontend that focuses on rapid prototyping, readable code & support for a wide variety of deep learning models. https://devblogs.nvidia.com/parallelforall/recursive-neural-networks-pytorch/ http://pytorch.org/tutorials/
- 48. Tofu: Parallelizing Deep Learning Systems with Auto-tiling Memonger: Training Deep Nets with Sublinear Memory Cost MinPy: High Performance System with NumPy Interface FlexibilityEfficiency Portability MX NET + Apache https://github.com/dmlc/ https://github.com/NVIDIA/keras MULTI CORE – MULTI GPU – MULTI NODE https://devblogs.nvidia.com/parallelforall/scaling-keras-training-multiple-gpus/
- 50. May 8-11, 2017 | Silicon Valley CUDA 9 https://devblogs.nvidia.com/parallelforall/cuda-9-features-revealed/
- 51. 51NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE. NVIDIA DEEP LEARNING SDK UPDATE GPU-accelerated DL Primitives Faster training Optimizations for RNNs Leading frameworks support cuDNN 7 Multi-node distributed training (multiple machines) Leading frameworks support Multi-GPU & Multi-node NCCL 2 TensorFlow model reader Object detection INT8 RNNs support High-performance Inference Engine TensorRT 3
- 52. 52 NVIDIA Collective Communications Library (NCCL) Multi-GPU and multi-node collective communication primitives High-performance multi-GPU and multi-node collective communication primitives optimized for NVIDIA GPUs Fast routines for multi-GPU multi-node acceleration that maximizes inter-GPU bandwidth utilization Easy to integrate and MPI compatible. Uses automatic topology detection to scale HPC and deep learning applications over PCIe and NVink Accelerates leading deep learning frameworks such as Caffe2, Microsoft Cognitive Toolkit, MXNet, PyTorch and more Multi-Node: InfiniBand verbs IP Sockets Multi-GPU: NVLink PCIe Automatic Topology Detection github.com/NVIDIA/nccl https://developer.nvidia.com/nccl
- 53. 53 GRAPH ANALYTICS with NVGRAPH developer.nvidia.com/nvgraph GPU Optimized Algorithms Reduced cost & Increased performance Standard formats and primitives Semi-rings, load-balancing Performance Constantly Improving
- 54. 54 WHAT’S NEW IN DIGITS 6? TENSORFLOW SUPPORT NEW PRE-TRAINED MODELS Train TensorFlow Models Interactively with DIGITS Image Classification: VGG-16, ResNet50 Object Detection: DetectNet DIGITS 6 Release Candidate available now on Docker Hub for testing and feedback General availability in September
- 55. 55 WHAT’S NEW IN DEEP LEARNING SOFTWARE TensorRT Deep Learning Inference Engine DeepStream SDK Deep Learning for Video Analytics 36x faster inference enables ubiquitous AND responsive AI High performance video analytics on Tesla platforms https://devblogs.nvidia.com/parallelforall/deploying-deep-learning-nvidia-tensorrt/
- 56. 56 HARDWARE
- 57. 57 SINGLE UNIVERSAL GPU FOR ALL ACCELERATED WORKLOADS 10M Users 40 years of video/day 270M Items sold/day 43% on mobile devices V100 UNIVERSAL GPU BOOSTS ALL ACCELERATED WORKLOADS HPC AI Training AI Inference Virtual Desktop 1.5X Vs P100 3X Vs P100 3X Vs P100 2X Vs M60
- 58. 58 VOLTA: A GIANT LEAP FOR DEEP LEARNING P100 V100 P100 V100 ImagesperSecond ImagesperSecond 2.4x faster 3.7x faster FP32 Tensor Cores FP16 Tensor Cores V100 measured on pre-production hardware. ResNet-50 Training ResNet-50 Inference TensorRT - 7ms Latency
- 59. 59 NEW TENSOR CORE New CUDA TensorOp instructions & data formats 4x4 matrix processing array D[FP32] = A[FP16] * B[FP16] + C[FP32] Optimized for deep learning Activation Inputs Weights Inputs Output Results
- 60. 60 NEW TENSOR CORE New CUDA TensorOp instructions & data formats 4x4 matrix processing array D[FP32] = A[FP16] * B[FP16] + C[FP32] Optimized for deep learning Activation Inputs Weights Inputs Output Results
- 61. 61
- 62. 62 NVIDIA ® DGX-1™ Containerized Applications TF Tuned SW NVIDIA Docker CNTK Tuned SW NVIDIA Docker Caffe2 Tuned SW NVIDIA Docker Pytorch Tuned SW NVIDIA Docker CUDA RTCUDA RTCUDA RTCUDA RT Linux Kernel + CUDA Driver Tuned SW NVIDIA Docker CUDA RT Other Frameworks and Apps. . . THE POWER TO RUN MULTIPLE FRAMEWORKS AT ONCE Container Images portable across new driver versions
- 63. 63 Productivity That Follows You From Desk to Data Center to Cloud Access popular deep learning frameworks, NVIDIA-optimized for maximum performance DGX containers enable easier experimentation and keep base OS clean Develop on DGX Station, scale on DGX-1 or the NVIDIA Cloud 63 EFFORTLESS PRODUCTIVITY
- 64. 64 Registry of Containers, Datasets, and Pre-trained models NVIDIA GPU CLOUD CSPs ANNOUNCING NVIDIA GPU CLOUD Containerized in NVDocker | Optimization across the full stack Always up-to-date | Fully tested and maintained by NVIDIA | In Beta now GPU-accelerated Cloud Platform Optimized for Deep Learning
- 65. 65 PULL CONTAINERDEPLOY IMAGESIGN UP THREE STEPS TO DEEP LEARNING WITH NGC To get an NGC account, go to: www.nvidia.com/ngcsignup Pick your desired framework (TensorFlow, PyTorch, MXNet, etc.), and pull the container into your instance On Amazon EC2, choose a P3 instance and deploy the NVIDIA Volta Deep Learning AMI for NGC
- 66. GPU DEEP LEARNING IS A NEW COMPUTING MODEL DGX-1 Training Inference in the Datacenter Tesla Inference at the Edge Jetson
- 67. WHY AI AT THE EDGE MATTERS LATENCYBANDWIDTH AVAILABILITY 1 billion cameras WW (2020) 10’s of petabytes per day 30 images per second 200ms latency 50% of populated world < 8mbps Bulk of uninhabited world no 3G+ PRIVACY Confidentiality Private cloud or on-premise storage PRIVACY
- 68. Max-Q operating mode (< 7.5 watts) delivers up to 2x energy efficiency vs. Jetson TX1 maximum performance Max- P operating mode (< 15 watts) delivers up to 2x performance vs. Jetson TX1 maximum performance JETSON TX2 EMBEDDED AI SUPERCOMPUTER Advanced AI at the edge JetPack SDK < 7.5 watts full module Up to 2X performance or 2X energy efficiency
- 69. Jetson TX1 Developer Kit reduced to €549/£459 – Jetson TX1/TX2 Developer kits have same price for education JETSON TX2 DEVELOPER KIT €649/£544 Web or retail €350/£300 education
- 70. 70 Available to Instructors Now! developer.nvidia.com/teaching-kits Robotics Teaching Kit with ‘Jet’ - ServoCity D E E P L E A R N I N G
- 71. 71 Sample Code Deep Learning CUDA, Linux4Tegra, ROS Multimedia API MediaComputer Vision Graphics Nsight Developer Tools Jetson Embedded Supercomputer: Advanced GPU, 64-bit CPU, Video CODEC, VIC, ISP JETPACK SDK FOR AI @ THE EDGE TensorRT cuDNN VisionWorks OpenCV Vulkan OpenGL libargus Video API
- 72. 72 Develop and deploy Jetson TX1 and Jetson TX1 Developer Kit
- 73. 73 Wrap Up
- 74. 74 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 www.nvidia.com/dli Hands-on Training for Data Scientists and Software Engineers NVIDIA Deep Learning Institute
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- 77. 77 NVIDIA INCEPTION PROGRAM Accelerates AI startups with a boost of GPU tools, tech and deep learning expertise Startup Qualifications Driving advances in the field of AI Business plan Incorporated Web presence Technology DL startup kit* Pascal Titan X Deep Learning Institute (DLI) credit Connect with a DL tech expert DGX-1 ISV discount* Software release notification Live webinar and office hours *By application Marketing Inclusion in NVIDIA marketing efforts GPU Technology Conference (GTC) discount Emerging Company Summit (ECS) participation+ Marketing kit One-page story template eBook template Inception web badge and banners Social promotion request form Event opportunities list Promotion at industry events GPU ventures+ +By invitation www.nvidia.com/inception
- 78. COME DO YOUR LIFE’S WORK JOIN NVIDIA We are looking for great people at all levels to help us accelerate the next wave of AI-driven computing in Research, Engineering, and Sales and Marketing. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions like artificial intelligence and autonomous cars. Check out our career opportunities: • www.nvidia.com/careers • Reach out to your NVIDIA social network or NVIDIA recruiter at DeepLearningRecruiting@nvidia.com