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2018 GTC Washington DC
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Browse all available sessions and instructor-led trainings below. Click “Add to Schedule” to build your personal agenda and reserve your place at instructor-led trainings.

Please note:

  • DLI instructor-led workshops must be purchased separately through registration. See GTC Pricing for more information.
  • To attend instructor-led trainings, you must have a Conference & Training pass. You must also reserve your place at each instructor-led training you wish to attend. If you have reserved a place at an instructor-led training, please arrive on time to ensure you do not lose your seat.
  • Attendance to sessions is first come, first served. Please arrive early to the sessions you wish to attend to guarantee entry.

DLIW01 - Fundamentals of Deep Learning for Computer Vision

Prerequisites: Basic technical background

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

In this hands-on course, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to:

  • Implement common deep learning workflows, such as image classification and object detection.
  • Experiment with data, training parameters, network structure, and other strategies to increase performance and capability.
  • Deploy your neural networks to start solving real-world problems.

Upon completion of this workshop, you’ll be able to start solving problems on your own with deep learning.

You will need to purchase a special pass to attend this full-day workshop. See GTC Pricing for more information.

DLI Instructor-Led Workshop Adam Thompson - Senior Solutions Architect, NVIDIA
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DLIW02 - Fundamentals of Accelerated Computing with CUDA C/C++

Prerequisites: Basic C/C++ competency

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world's fastest massively parallel GPUs. Experience C/C++ application acceleration by:

  • Accelerating CPU-only applications to run their latent parallelism on GPUs
  • Utilizing essential CUDA memory management techniques to optimize accelerated applications
  • Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
  • Leveraging command line and visual profiling to guide and check your work

Upon completion of this workshop, you'll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

You will need to purchase a special pass to attend this full-day workshop. See GTC Pricing for more information.

DLI Instructor-Led Workshop Robert Crovella - OEM Technical Enablement Manager, NVIDIA
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DLIW03 - Deep Learning for Healthcare Image Analysis

Prerequisites: Basic familiarity with concepts of deep learning and convolutional neural networks

This hands-on course explores how to apply Convolutional Neural Networks (CNNs) to MRI scans to perform a variety of medical tasks and calculations. You’ll learn how to:

  • Perform image segmentation on MRI images to determine the location of the left ventricle.
  • Calculate ejection fractions by measuring differences between diastole and systole using CNNs applied to MRI scans to detect heart disease.
  • Apply CNNs to MRI scans of LGGs to determine 1p/19q chromosome co-deletion status.

Upon completion of this workshop, you’ll be able to apply CNNs to MRI scans to conduct a variety of medical tasks.

You will need to purchase a special pass to attend this full-day workshop. See GTC Pricing for more information.

DLI Instructor-Led Workshop David Nola - Deep Learning Solutions Architect, Healthcare, NVIDIA
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DLIW04 - Fundamentals of Deep Learning for Natural Language Processing

Prerequisites: Basic experience with neural networks

Explore the latest techniques for understanding textual input using natural language processing (NLP). You’ll learn how to:

  • Convert text to machine understandable representation and classical approaches
  • Implement distributed representations (embeddings) and understand their properties
  • Train Machine Translators from one language to another

Upon completion of this workshop, you’ll be proficient in NLP using embeddings in similar applications.

You will need to purchase a special pass to attend this full-day workshop. See GTC Pricing for more information.

DLI Instructor-Led Workshop Jacqueline Cenci-McGrody - Technical Marketing Engineer, NVIDIA
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DC8101 - AI for Government 101: Presented by Booz Allen Hamilton

Attend this half-day session on Monday, October 22 from 1:00-5:00pm to hear how AI is transforming operations now and how deep learning will influence the future.

Session topics include: "Bootstrap AI Projects within the Government", "Building Blocks of AI, Improving ROI with Computer Vision and Natural Language Processing", "How to effectively recruit and train employees while building your AI capability" and "The Future of AI".

Sponsor Session James Chung, Booz Allen Hamilton
Brad Stone, Booz Allen Hamilton
Seth Clark, Booz Allen Hamilton
Col. Benjamin Ring, National Counterintelligence and Security Center
Josh Elliot - Director of Artificial Intelligence, Booz Allen Hamilton
Cameron Kruse - Lead Technologist, Booz Allen Hamilton
John Larson, Booz Allen Hamilton
Anil Tilbe, Department of Veteran Affairs
Lee Becker, Department of Veteran Affairs
Thomas Beach - Chief Data Strategist, United States Patent & Trademark Office
Jen Arnold, Booz Allen Hamilton
Sean Khozin, FDA
Kirk Borne - Principal Data Scientist, Booz Allen Hamilton
Kenneth Clark
Logan Gibson, Booz Allen Hamilton
Shelly Brown, Booz Allen Hamilton
Kumar Manish, Booz Allen Hamilton
Joseph Campbell, Booz Allen Hamilton
Mike Martoccia, Booz Allen Hamilton
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DC8212 - Today's AI Industrial Revolution - What's Real, What's Next?

Many examples of AI are reported daily that enhance traditional products and services– but the benefits have only just begun to scratch the surface. Entire industries will be transformed and massive benefits will be realized in the next wave of AI deployment. Learn about the next generation of AI and how it will add over 60M jobs and $13 trillion to the global economy if policy makers, businesses and the AI community adopt the right strategies, initiatives and platforms to harness this incredible new technology.

Keynote Ian Buck - VP, Accelerated Computing, NVIDIA
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DC8104 - RAPIDS: Accelerating Data Science End-to-End with GPUs

Numerous Fortune 500 customers experience latency and performance issues in their machine learning and data pipelines. Big data platforms and solutions tried to address these challenges with massive infrastructure scale out. But the cost to scale relative to the volume and velocity of current need is prohibitively expensive. NVIDIA is addressing these challenges with RAPIDS, an end-to-end GPU-accelerated data science software stack for enterprises to explore and integrate AI into their core data driven decision-making processes. Learn how to get started with GPU-accelerated data science and quickly identify opportunities to accelerate machine learning workflows in your organization.

Talk Jeffrey Tseng - Head of Product, AI Infrastructure, NVIDIA
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DC8108 - Deep Learning Demystified What is Deep Learning? In what fields is it useful, and how does it relate to artificial intelligence? During this session, we'll get an understanding of deep learning and why this powerful new technology is getting so much attention. Learn how deep neural networks are trained to perform tasks with super-human accuracy, and the challenges organizations face in adopting this new approach. We'll also cover some of the best practices, software, hardware, and training resources that many organizations are using to overcome these challenges and deliver breakthrough results. Talk Will Ramey - Director, Developer Programs, NVIDIA
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DC8156 - Accelerate Video Analytics Development with DeepStream This talk explores how DeepStream enables developers to create high-stream density applications with deep learning and accelerated multimedia image processing, building IVA solutions at scale. Leverage a heterogeneous concurrent neural network architecture to bring in different deep learning techniques for more intelligent insights. The framework makes it easy to create flexible and intuitive graph-based applications, resulting in highly optimized pipelines for maximum throughput. Talk Saurabh Jain - Business Development, NVIDIA
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DC8165 - Safety by Way of Supercompute The computational complexity of solving for vehicle autonomy is dramatically increased by safety requirements. We will present key tenants of solving for safety via supercompute, including redundancy, diversity, and AI. Talk Neda Cvijetic - Senior Manager, Autonomous Vehicles, NVIDIA
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DC8175 - Accelerating Understanding: The Convergence of HPC and AI in a Post-Moore Law's World Most AI researchers and industry pioneers agree that the wide availability and low cost of highly-efficient and powerful GPUs and accelerated computing parallel programming tools (originally developed to benefit HPC applications) catalyzed the modern revolution in AI/Deep Learning. Now, AI methods and tools are starting to be applied to HPC applications to great effect. This talk will describe an emergent workflow that uses traditional HPC numeric simulations to generate the labeled data sets required to train machine learning algorithms, then employs the resulting AI models to predict the computed results, often with dramatic gains in efficiency, performance, and even accuracy. Some compelling success stories will be shared, and the implications of this new HPC + AI workflow on HPC applications and system architecture in a post-Moore's Law world considered. Talk Steve Oberlin - Chief Technology Officer, Accelerated Computing, NVIDIA
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DC8185 - Artificial Intelligence: How Do We Get It to the Clinic? More than ever, AI and deep learning are a prevalent focus in healthcare. Today there are well over 400 start-ups working on AI for healthcare, and over 70% of new medical imaging research is utilizing AI to improve patient care – reducing the cost of healthcare and making it more widely available. How do we get all of this innovation to the clinic? This panel will delve into the major opportunities for deep learning in healthcare from smart instrumentation to precision AI, the availability and access to data, and discuss standards, regulation and future directions for continued innovation. Panel Matthew DiDonato - Machine Learning Product Manager, Arterys
Abdul Hamid Halabi - Global Business Development Lead, Healthcare & Life Sciences, NVIDIA
Agata Anthony - Regulatory Affairs Executive, GE Healthcare
Elizabeth Jones - Director, Radiology and Imaging Sciences, National Institutes of Health (NIH)
Bakul Patel - Associate Director for Digital Health, Food and Drug Adminsitration (FDA)
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DC8188 - American Leadership Through AI Research The U.S. is the world leader in developing AI technologies, but other countries are catching up. What must the U.S. do to sustain and strengthen its global leadership in AI research and development? What are the challenges and what more can and should be done by industry and the government to advance AI? Panel James Kurose - Assistant Director, National Science Foundation
Howie Choset - Professor of Robotics, Carnegie Mellon University
Missye Brickell - Professional Staff, U.S. Senate Committtee on Commerce, Science and Transportation
Keoki Jackson - CTO, Lockheed Martin
David Luebke - Vice President, Graphics Research, NVIDIA
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DC8207 - High-Performance Input Pipelines for Scalable Deep (Presented by Pure Storage)

Learn how to keep your GPUs fed with data as you train the next-generation of deep learning architectures. As GPU technology continues to advance, the demand for faster data continues to grow. In deep learning, input pipelines are responsible for a complex chain of actions that ultimately feed data into GPU memory: defining how files are read from storage, deserializing them into data structures, pre-processing on a CPU, and copying to the GPU. These pipelines bring together complex hardware systems--including cluster networks, peripheral interconnects, modern CPUs, and storage devices--along with sophisticated software systems to drive the data movement and transformation. In this talk, we present a new benchmark suite for evaluating and tuning input pipelines. We will examine results with TensorFlow's DataSets API on a DGX-1 with V100 and provide guidance on key tuning parameters and diagnostic techniques for improving performance.

Sponsor Session Brian Gold - Founding Member, FlashBlade, Pure Storage
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DC8218 - Best Practices in Designing Balanced AI Systems (Presented by Penguin Computing)

Attendees will learn from how to address the challenges of building AI systems based on the design principles and technologies that have proven successful in Penguin Computing AI deployments for customers in the Top 500. Lessons learned will focus on end-to-end aspects of designing and deploying large scale GPU clusters including datacenter and environmental challenges, network performance and optimization, data pipeline and storage challenges as well as workload orchestration and optimization. Attendees will hear about real life deployments for private organizations and government labs, including those using OCP technology.

Sponsor Session Sid Mair - Sr. Vice President Federal Systems, Penguin Computing
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DC8261 - Accelerating Metadata Across Scalable Storage (Presented by IBM)

Launching today, IBM introduces new software for managing large scale data sets to facilitate both HPC and AI data lifecycles. 

New simulation and AI models are dramatically shifting the traditional data lifecycle, which has shifted from intelligent archiving to continuous review, revision and extraction of growing data sets. The result is greater need to tag, track and report on the metadata to be used to extract training sets, understand usage patterns, and manage storage. 

In this presentation, I will cover new IBM Spectrum software to deliver better metadata management and review case studies of HPC and AI clients who have been testing it. 

Sponsor Session Doug O'Flaherty - Portfolio Marketing, IBM
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DLIS05 - Applying Natural Language Processing on News Data using Deep Learning

Prerequisites: Advanced experience with neural networks and knowledge of financial industry

Learn the fundamentals of natural language processing (NLP) as it applies to the generation of trade signals from real-time news data. In this session, you'll leverage a dataset of news article headlines to:

  • Train neural networks to predict market direction
  • Understand several popular models for text representation including bag-of-words, Word2Vec, GloVe, and Doc2Vec
  • Appreciate the effectiveness of LSTM, CNN, and feed-forward deep neural networks
  • Explore techniques used for improving training times with NVIDIA GPUs and the cuDNN library

Upon completion, you'll be able to apply NLP to generate trade signals from real-time news data. 

Instructor-Led Training Yuval Mazor - Senior Solutions Architect, Deep Learning, NVIDIA
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DLIS09 - Develop Scalable IVA Applications using DeepStream

Prerequisites: Experience with C++

Understanding video requires multi-stream decoding/encoding, scaling, color space conversion, tracking, and multi-stage inference. DeepStream SDK allows developers to focus on core deep learning development, while offering the best system level software optimization and performance. You'll learn how to:

  • Build modular and scalable multi-stream applications using heterogenous concurrent neural networks for video analytics
  • Utilize hardware accelerated multi-media processing and optimized memory management for high stream density and throughput
  • Utilize included sample code and pre-trained models for image classification, understanding, categorization and filtering

Upon completion, you'll know how to create AI-based video analytics applications using DeepStream to transform video into valuable insights.

Instructor-Led Training Nicholas Becker - Deep Learning Solutions Architect, NVIDIA
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DLIS10 - Introduction to Object Detection with TensorFlow

Prerequisites: None

Get started with an introduction to object detection and image segmentation. You'll explore the shift from traditional computer vision techniques to innovative methods based on deep learning and convolution neural networks (CNNs) for object detection. You'll learn how to:

  • Leverage CNNs to iteratively improve performance and expand image understanding capabilities
  • Use Microsoft Common Object in Contexts dataset and the Google object detection API in TensorFlow
  • Determine accuracy vs. performance trade-offs between Single Shot Multibox Detectors (SSDs), Faster R-CNN with residual networks, and Mask R-CNN

Upon completion, you'll understand how to implement object detection networks with the API in TensorFlow.

Instructor-Led Training Jonathan Howe, NVIDIA
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DC8214 - Slurm Workload Management for GPU Systems (Presented by SchedMD LLC)

Learn how to effectively schedule and manage your system workload using Slurm; the free, open source and highly scalable cluster management and job scheduling system for Linux clusters. Slurm is in use today on roughly half of the largest systems in the world servicing a broad spectrum of applications. Slurm developers have been working closely with NVIDIA to provide capabilities specifically focused on the needs of GPU management. This includes a multitude of new options to specify GPU requirements for a job in various ways (GPU count per job, node, socket and/or task), additional resource requirements for allocated GPUs (CPUs and/or memory per GPU), how spawned tasks should be bound to allocated GPUs, and control over GPU frequency and voltage. An introduction to Slurm's design and capabilities will be presented with a focus on managing workloads for GPUs.

Sponsor Session Morris Jette - Developer, SchedMD LLC
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DC8234 - Considerations in Architecting an AI Ready Data Platform (Presented by DDN Storage)

Analytics and AI present a serious challenge to businesses in developing new expertise and transforming data architectures from enterprise-class to AI-ready. AI workloads demand a different approach to managing the data lifecycle. The new AI datacenter must be optimized for ingesting, storing, transforming and optimizing data and feeding that data through hyper-intensive analytics workflows and ultimately, extracting value. Failing fast during experimentation, and scaling successful models quickly to production is vital. Learn how to architect and deploy data platforms with robust and balanced performance for all I/O patterns.

Sponsor Session Kurt Kuckein - Sr. Director, Marketing, DDN Storage
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DC8128 - Deep Learning for Smart Home Monitoring We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home monitoring. We introduce a representation of the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training set to unseen layouts in the test set. We introduce the Agent-in-Place Action dataset to show that our method allows neural network models to generalize significantly better to unseen scenes. Talk Hongcheng Wang - Senior Manager, Technical R&D, Comcast Applied AI Research
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DC8162 - Accelerating Research to Production with PyTorch 1.0 and ONNX (Presented by Facebook)

Facebook's strength in AI innovation comes from its ability to quickly bring cutting-edge research into large scale production using a multi-faceted toolset. Learn how ONNX and PyTorch 1.0 are helping to accelerate the path from research to production by making AI development more seamless and interoperable. We'll share the latest on PyTorch 1.0 and discuss Facebook's initiatives around ethical and responsible AI development.

Sponsor Session Sarah Bird - Technical Program Manager, Facebook
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DC8171 - Training Mixed Precision Neural Networks with Tensor Cores: Theory and Practice Tensor Cores, introduced with the Volta GPU architecture, provide up to 125 teraflops of throughput for operations on IEEE half-precision floats. In the theory portion of this talk we will review the half-precision format, the features of Tensor Cores, and principles for building mixed precision neural networks in any framework. The practice portion will review these principles with examples in PyTorch and show how tools like Apex can automatically convert existing neural networks to use Tensor Cores. This conversion requires no change in model architecture or hyperparameters and has been successfully applied to visual, auditory, and linguistic tasks on multiple frameworks. Talk Christian Sarofeen - Senior Dev Tech Engineer, NVIDIA
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DC8182 - Delivering Safety and Efficiency via Automation in Trucks and Buses This presentation will provide an overview of how automation can help deliver value to bus and truck fleet customers. With the convergence of electrification, connectivity and automation, there is a tremendous push to unlock the value of the technologies to the customer. Attendees will be introduced to the challenges and opportunities in the commercial vehicle industry. Talk Brendan Chan - Engineering Group Lead - ADAS Technology, Navistar, Inc.
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DC8186 - Better Serving Americans with AI

AI's potential cuts across all industries, from agriculture to healthcare to oil and gas and more. But it also can help government agencies be more efficient, better at identifying waste and fraud, and more responsive and convenient for all Americans. This panel will discuss the various AI applications that can make government smarter, and how we get there.

Panel Sunmin Kim - Technology Policy Advisor, Office of U.S. Senator Brian Schatz
Josh Sullivan - Senior Vice President, Booz Allen Hamilton
Austin Carson - Government Relations, NVIDIA
Dominic Delmolino - Managing Director, Chief Technology Officer, Accenture Federal Services
Michael Garris - Founder/Chair, Artificial Intelligence(AI) Community of Interest, National Institute of Standards and Technology
Cameron Chehreh - CTO/Vice President, Pre-Sales Engineering, Dell EMC
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DC8195 - Deep Learning for the Early Detection of Pancreatic Cancer on CT Scanning The talk will focus on our 2 year journey to develop deep learning algorithms for the detection of pancreatic cancer on CT scans. The effort of a multi-disciplinary teams of Computer Scientists, Radiologists, Oncologists, and Pathologists determined that deep learning could potentially change the trajectory of pancreatic cancer survival (currently under 7% at 5 years) by early detection and avoiding what has been documented as an up to 25% false negative rate. The session will address the challenges of creating over 1100 normal studies to train the computer and then over 1500 pancreatic cancer studies to train the algorithms in booth organ segmentation and then tumor detection and analysis We will discuss what successes we have had in lesion detection and what changes remain . We will also discuss the role of Radiomics with deep learning as a way of potentially improving lesion detection and eventually lesion classification , The talk will illustrate the work done with a series of cases studies showing both the successes and challenges of this work. Finally we will address we we see this work going and the opportunities for introducing this into clinical practice. Talk Elliot Fishman - Professor of Radiology, Oncology, Surgery and Urology, Johns Hopkins Hospital
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DC8256 - RAPIDS: The Platform Inside and Out

Learn how RAPIDS and the open source ecosystem are advancing data science. In this session, we will explore RAPIDS, the NEW open source data science platform from NVIDIA. Deep dive into the RAPIDS platform and learn how to get started leveraging the open-source libraries for easier development and enhanced performance data science on GPUs. See the latest engineering work, including benchmarks and demos. Finally, see how customers are benefiting from early primitives and outperforming CPU equivalents.

Talk Joshua Patterson - Director, AI Infrastructure, NVIDIA
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DC8263 - Making AI Mission-Critical (Presented by IBM)

With the changing demands of application workloads, together with the huge growth of data, new ways of approaching how to solve mission-critical problems need to be addressed.  Conventional scale-out systems and methods are often not sufficient.  This session will cover some of the challenges we face along with solutions including innovative hardware architectures combined with a robust Deep Learning software ecosystem to address these challenges.

Sponsor Session Aaron Potler - Distinguished Engineer - U.S. Federal IBM Global Markets - Systems HW Sales, IBM
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DC8265 - Revealing Geospatial Insights with GPU Accelerated Applications

This three part presentation will explore how Radiant Solutions is making it possible to see and understand our changing world by applying computer vision to satellite imagery, enabling interactive terrain analytics, and powering immersive analytics in virtual reality.

Kevin McGee will talk first about machine learning/computer vision, then Ryan Smith will talk about terrain analytics, and Nick Deliman will close with VR.

Talk Nicholas Deliman - Senior Scientist, Radiant Solutions
Ryan Smith - Senior Manager, Radiant Solutions
Kevin McGee - Production Lead, Radiant Solutions
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DC8243 - NVIDIA in the Public Sector: How to Leverage Carahsoft as a Partner (Presented by Carahsoft)

Federal resellers and integrators interested in building their business with NVIDIA are cordially invited to meet with Carahsoft's NVIDIA team at during the GPU Conference. Recently named a government distributor for NVIDIA, Carahsoft offers a broad range of complementary technologies, services, and tools to support our public sector partner ecosystem. Join us to learn more about our proactive sales and marketing campaigns, training opportunities, and contract vehicles, so that you can drive revenue while you're helping your customers apply the power of the GPU for everything from supercomputing, virtual reality, and autonomous machines, to artificial intelligence and deep learning. 

Sponsor Session Michael Shrader - Vice President, Innovative and Intelligence Solutions, Carahsoft
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DC8138 - Training and Inferencing DL Models with NVIDIA GPU and MapR Kubernetes Volume Plugin The audience will learn examples and common practices for using Kubernetes to leverage NVIDIA GPU computing power when building DL models. We use a converged data platform to serve as data infrastructure, providing distributed file system and key-value storage and streams. Kubernetes is an orchestration layer that manages containers to scale out the training and deployment of DL models using heterogeneous GPU clusters. We also leverage the ability to publish and subscribe to streams on the platform to build next-gen applications with DL models, and monitor the model performance and shift of feature distributions. Talk James Scott - Vice President, Enterprise Architecture, MapR
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DC8152 - Accelerated-Node Software Technologies and Applications in the U.S. Department of Energy Exascale Computing Project The vision of the Exascale Computing Project, initiated in 2016 as a formal U.S. Department of Energy project executing through 2022, is to accelerate innovation with exascale simulation and data science solutions. After a brief overview of this, we will give illustrative examples on how the ECP teams are leveraging, exploiting, and advancing accelerated-node software technologies and applications on hardware such as the powerful GPUs provided by NVIDIA. We will summarize best practices and lessons learned from these accelerated-node experiences along with ECP's plans moving into the exascale era, which is on the now near-term horizon. These solutions will enhance U.S. economic competitiveness, change our quality of life, and strengthen our national security. ECP's mission is to deliver exascale-ready applications and solutions that address currently intractable problems of strategic importance and national interest; create and deploy an expanded and vertically integrated software stack on DOE HPC exascale and pre-exascale systems, defining the enduring US exascale ecosystem; and leverage U.S. HPC vendor research activities and products into DOE HPC exascale systems. The project is a joint effort of two DOE programs: the Office of Science Advanced Scientific Computing Research Program and the National Nuclear Security Administration Advanced Simulation and Computing Program. ECP's RD&D activities, which encompass the development of applications, software technologies, and hardware technologies and architectures, is carried out by over 100 small teams of scientists and engineers from the DOE national laboratories, universities, and industries. Talk Doug Kothe - Director, Exascale Computing Project, Oak Ridge National Lab
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DC8173 - AI Powered Distributed Computer Vision Enabling Smart City IoT Platforms Cities are always looking for new ways to maintain high standards of living, better connect with citizens and find ways to save money—all while serving growing populations. As city population densities increase and cities strive to increase walkability and mobility for their citizens, they have a big focus on a holistic approach to traffic safety. As part of their efforts to become smarter, more and more cities are turning to the Internet of Things (IoT) and Machine-to-Machine (M2M) technologies to improve municipal services, create additional sources of revenue, and enable city management in new and creative ways. Talk Andrew Herson - Head of Computer Vision, Verizon
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DC8174 - RAPIDS: Turbocharging the AI Pipeline with Python and Anaconda

The rise of GPU-accelerated data science and AI has come about through a combination of open source innovation and better tooling to support reproducible workflows. However, as the diverse array of deep learning libraries continue to mature, attention is moving to other parts of the AI pipeline, including simulation, ETL, and deployment. In this talk, I'll review open source projects that address these other areas, such as Numba, for implementing custom simulations and data transformations on the GPU, and PyGDF, for GPU accelerated dataframes. I'll discuss how the Anaconda Distribution and its conda packaging system helps data scientists create reproducible environments and deploy models. Finally, I'll talk about how Anaconda Enterprise allows data science teams to collaborate efficiently on GPU-accelerated projects with each other, and supports AI workflows from data exploration all the way to deployment.

Talk Stanley Seibert - Director of Community Innovation, Anaconda
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DC8179 - Simulation: Key to Safe Autonomous Vehicle Development Developers are training deep learning algorithms to understand an autonomous vehicle's surrounding environment and follow the rules of the road, but these algorithms still require testing and validation before the system is ready to drive on its own. Many companies have begun testing vehicles on public roads, gathering driving data and exposing the technology to real world experiences. However, this type of validation on its own can be cumbersome — the Rand Corporation estimates it would take hundreds of millions to hundreds of billions of miles driven to prove an autonomous vehicle can safely drive on its own, which translates to nearly a century of driving. Now with the power of simulation, the deep learning algorithms that act as the brain of the self-driving car can drive millions of miles in the fraction of the time it would take to drive that distance in the real world. Photorealistic simulation can create any range of driving scenarios and test them again and again to verify that the car's AI brain can safely navigate them. This session will demonstrate how virtual reality simulation is being utilized to test and validate self-driving hardware and software systems, accelerating the path to safe deployment of autonomous vehicles. Talk Curtis Beeson - Principal Scientist, NVIDIA
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DC8180 - Deep Learning Genomics An overview of the deep learning genomics space and introduction to NVIDIA's deep learning platform to accelerate the growth of genomics. Talk Johnny Israeli - Manager, Deep Learning and Genomics, NVIDIA
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DC8187 - Transforming Agriculture with AI

Artificial intelligence has opened a new class of efficient technologies to help the American farmer from AI-assisted thinning, weeding and spraying for row crops to automated soft fruit picking. This panel will discuss the latest AI innovations for the farm and the policies that will continue to advance U.S. agriculture through the 21st Century.

Panel Trevor White - Professional Staff, U.S. House Agriculture Committee
Scott Jantz - Lead Electrical Engineer, Harvest CROO Robotics
George Kantor - Senior Systems Scientist, Robotics Institute, Carnegie Mellon University
Rajesh Radhakrishnan - Senior Computer Vision and Machine Learning Engineer, Blue River Technology
Jesse Clayton - Senior Manager, Product Management for Intelligent Machines, NVIDIA
Dionne Toombs - Director for the Office of the Chief Scientist, USDA
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DC8209 - The Role of AI in a VR World (Presented by Booz Allen Hamilton)

The convergence of Artificial Intelligence and Virtual Reality has escalated within the past few years. With techniques like deep learning elevating the efficiency of AI, the government can now approach training simulations for specific missions based on the information provided by AI algorithms. This session will dive into the different initiatives that are needed to empower these emerging technologies and address the benefits that will make the world a safer place.

Sponsor Session Cameron Kruse - Lead Technologist, Booz Allen Hamilton
Andrew Farris - Chief Technologist, Booz Allen Hamilton
Nirmal Mehta - Chief Technologist, Booz Allen Hamilton
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DC8239 - Operationalizing AI for Disaster Response (Presented by Raytheon)

This panel will explore the challenges and opportunities of operationalizing and sustaining artificial intelligence (AI) enabled systems in complex high consequence disaster response and recovery scenarios.    How can the Department of Defense (DoD), Federal Aviation Administration (FAA), National Oceanic and Atmospheric Administration (NOAA) and other agencies leverage AI across the disaster preparation, response and recovery spectrum?  How might AI impact training and procedures?  What are the challenges and opportunities?

Sponsor Session Jana Eggers - CEO, Nara Logics
Shane Zabel - Chief Artificial Intelligence Officer, Raytheon Company
Bilal Zuberi - Partner, Lux Capital
Brady Cline - Vice President, Sales, SpaceKnow
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DC8264 - The Future of Autonomy & Sports

What will the Sport of The Future look like? Join The Drone Racing League (DRL), the global, professional circuit for drone racing, for an engaging discussion on how AI will transform sports. The Star Wars-inspired league recently announced a multi-year partnership with Lockheed Martin to accelerate AI innovation, and will soon launch the premier, global autonomous drone racing platform. Learn how to get involved in DRL's new Artificial Intelligence Robotic Racing (AIRR) Circuit, which will challenge teams of the world's best engineers to design an AI/ML framework, powered by the NVIDIA Jetson platform, capable of flying a drone autonomously through complex, 3D tracks -- all for the chance to win more than $2 million in prize money.

Talk Ryan Gury - Director of Product, Drone Racing League
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DLIS04 - Detection of Anomalies in Financial Transactions using Deep Autoencoder Networks

Prerequisites: Advanced experience with neural networks and knowledge of financial industry

The "unsupervised" and "end-to-end" detection of anomalies in transactional data is one of the long-standing challenges in financial statement audits or fraud investigations. In this session, you'll explore how autoencoder neural network can be trained to detect anomalies by learning a compressed but "lossy" model of regular transactions. You'll learn:

  • Basic concepts, intuition and major building blocks of autoencoder neural networks · How to pre-process financial data in order to learn a model of its characteristics
  • How to design, implement, and train a deep autoencoder network using PyTorch to detect anomalies in large-scale financial data
  • How to interpret and evaluate the network's detection results as well as its reconstruction loss

Upon completion, you'll understand how to train deep autoencoder neural networks to detect anomalies in financial transactions.

Instructor-Led Training Yuval Mazor - Senior Solutions Architect, Deep Learning, NVIDIA
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DLIS08 - Medical Image Classification Using the MedNIST Dataset

Prerequisites: None

Get a hands-on practical introduction to deep learning for radiology and medical imaging. You'll learn how to:

  • Collect, format, and standardize medical image data
  • Architect and train a convolutional neural network (CNN) on a dataset
  • Use the trained model to classify new medical images

Upon completion, you’ll be able to apply CNNs to classify images in a medical imaging dataset.

Instructor-Led Training Cristiana Dinea - Master Instructor, DLI, NVIDIA
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DLIS12 - Introduction to CUDA Python with Numba

Prerequisites: Basic Python and Numpy Competency

Explore an introduction to Numba, a just-in-time function compiler that allows developers to utilize the CUDA platform in their Python applications. You'll learn how to:

  • Decorate Python functions to be compiled by Numba
  • Use Numba to GPU accelerate NumPy ufuncs

Upon completion, you'll be able to use Numba to GPU-accelerate NumPy ufuncs in your Python code, and will be ready to learn how to write custom CUDA kernels in Python.

Instructor-Led Training Robert Crovella - OEM Technical Enablement Manager, NVIDIA
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DC8144 - The Keys to Deploying Self-Driving Cars

Autonomous vehicles can drastically increase the safety of U.S. roads, where federal officials estimate 94% of all traffic accidents are caused by human error. In addition to road safety, AVs can also improve the quality of life, reducing congestion and freeing up valuable time spent in the car. To realize these significant benefits, manufacturers must develop this technology safely and comprehensively. This session will explore how breakthrough technologies like virtual reality simulation and deep learning are helping to accelerate safe development and deployment of AVs. It will also address, how are manufacturers and regulators can work to ensure the rules of the road evolve with autonomous driving technology, and the further benefits that driverless vehicles bring to our everyday lives.

Panel Heidi King - Deputy Administrator, National Highway Traffic Safety Administration (NHTSA)
Melissa Froelich - Chief Counsel, U.S. House Committee on Energy and Commerce
Danny Shapiro - Senior Director, Automotive, NVIDIA
Finch Fulton - Deputy Assistant Secretary for Transportation Policy, U.S. Department of Transportation
Bert Kaufman - Head, Corporate and Regulatory Affairs, Zoox
Brad Stertz - Director, Audi Government Affairs, Audi
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DC8153 - Large-scale AI Deployments with Kubernetes on NVIDIA GPUs

In this session, we'll explore some of the common challenges with scaling-out deep learning training and inference deployment on data centers and public cloud using Kubernetes on NVIDIA GPUs. Through examples, we'll review a typical workflow for AI deployments on Kubernetes. We'll discuss advanced deployment options such as deploying to heterogenous GPU clusters, specifying GPU memory requirements, and analyzing and monitoring GPU utilizations using NVIDIA DCGM, Prometheus and Grafana.

Talk Shashank Prasanna - Sr. Solutions Architect for Autonomous Driving, NVIDIA
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DC8206 - Artificial Intelligence for Pathology: From Discovery to AI-powered Companion Diagnostics Pathologic analysis of patient tissue specimens plays a central role in the field of oncology. Recent advances in artificial intelligence and computer vision offer tremendous potential for discovering new pathologic mechanisms of cancer treatment response and identifying new diagnostics for matching patients and therapies. We will discuss these new advances and their potential for accelerating progress in the diagnosis and treatment of cancer. Talk Aditya Khosla - CTO, PathAI
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DC8208 - Revolutionizing Cyber with AI and Rapids (Presented by Booz Allen Hamilton)

For the first time, cyber defenders have access to technical solutions that can proactively detect and combat future zero-day attacks at the pace of the cyber mission. This is only made possible by augmenting established cyber defenses with NVIDIA’s RAPIDS platform, accelerated GPU hardware, and Artificial Intelligence techniques deployed at the edge and in the data center. In this session, we’ll cover the shortcomings of traditional cyber methods and tools, and how AI is the force multiplier needed to scale an end-to-end cyber capability without having to change your infrastructure. 

Sponsor Session Josh Sullivan - Senior Vice President, Booz Allen Hamilton
Aaron Sant-Miller - Lead Scientist, Booz Allen Hamilton
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DC8213 - Scaling Video Intelligence for Responsive Solutions at the Edge From managing wilderness to urban environments, deploying & integrating visual intelligence is critical but challenging. We will discuss a number of scenarios where deploying deep learning right at the edge solves scalability, networking, and responsiveness constraints. Boulder AI will showcase how deep learning and NVIDIA Jetson accelerated computing enabled cameras has solved key problems in hydroelectric dams , agriculture, and urban traffic systems. This talk will also cover the benefits and challenges of leveraging and analyzing 12 bit high fidelity imagery in diverse and challenging outdoor environments. Talk Dan Connors - Chief Technology Officer and Co-Founder, Boulder AI
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DC8226 - RAPIDS: BlazingDB on Apache GDF - Accelerated ETL for AI Workloads

BlazingDB, the distributed SQL engine on GPUs, will show how we contribute to the Apache GPU Data Frame (GDF) project, and begun to leverage inside BlazingDB. Through the integration of the GDF we have been able to dramatically accelerate our data engine, getting over 10x performance improvements. More importantly, we have built a robust framework to help users bring data from their data lake into GPU accelerated workloads without having to ETL on CPU memory, or separate CPU clusters. Keep everything in the GPU, BlazingDB handles the SQL ETL, and then pyGDF and DaskGDF can take these results to continue machine learning workloads. With the GDF customer workloads can keep the data in the GPU, reduce network and PCIE I/O, dramatically improve ETL heavy GPU workloads, and enable data scientists to run end-to-end data pipelines from the comfort of one GPU server/cluster.

Talk Felipe Aramburu - CTO, Blazing DB
Rodrigo Aramburu - CEO, BlazingDB
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