Developer Resources for the Public Sector
A hub of news, SDKs, technical resources, and more for developers working in the public sector.
App Frameworks and SDKs
High Performance Computing (HPC)
The NVIDIA HPC SDK is a comprehensive toolbox for GPU-accelerated HPC modeling and simulation applications.
The NVIDIA RAPIDS™ suite of open-source software libraries, which includes the RAPIDS Accelerator for Apache Spark, makes it possible to execute end-to-end data pipelines for analytics, machine learning, and data visualization entirely on GPUs.
NVIDIA® CUDA-X™, built on top of NVIDIA CUDA®, is a collection of libraries, tools, and technologies that delivers dramatically higher performance—compared to CPU-only alternatives—across multiple application domains, from AI to HPC.
Speech and Computer Vision
NVIDIA Jarvis is an SDK for building and deploying AI applications that fuse vision, speech, and other sensors. It offers a complete workflow to build, train, and deploy GPU-accelerated AI systems that can use visual cues, such as gestures and gaze, with speech in context.
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Intelligent Video Analytics (IVA)
NVIDIA Transfer Learning Toolkit is a Python-based AI toolkit for building high-performance, IVA-based applications such as video analytics, logistics, smart cities, access control, and more.
NVIDIA Omniverse™ is a powerful, multi-GPU, real-time simulation and collaboration platform for 3D production pipelines based on Pixar's Universal Scene Description and NVIDIA RTX™.
The NVIDIA DriveWorks SDK is the foundation for all autonomous vehicle (AV) software development. It provides an extensive set of fundamental AV capabilities, including processing modules, tools, and frameworks that are required for advanced AV development.
NVIDIA Aerial Developer Kit
The NVIDIA Aerial™ Developer Kit facilitates the development and deployment of the virtual radio access network (vRAN) stack, increasing performance of wireless communication systems and compute power for massive multiple-in and multiple-out (MIMO)-based applications.
Find Your Application-Specific Resource
Grounded Language Learning in Robotics, Presented by Booz Allen Hamilton
Research Scientist Luke Richards will outline work BAH and the University of Maryland, Baltimore County have done in furthering the ability of robotic agents to learn natural language, and how NVIDIA is critical for advancing research in the intersection of grounded language learning and robotics.
Edge Computing for Intelligent Analytics
Presented by the Air Force Research Lab, at GTC 2020, learn about the new edge computing technologies used to overcome the limitations of the conventional computing architectures, to enable the fast adoption of AI and machine learning-based real-time big data analytics and fusion capabilities onto Air Force’s tactical intelligence platforms.
Developing State-of-the-art Computer Vision Solutions for Federal Applications
Presented by SFL Scientific at GTC 2020, learn how state-of-the-art computer vision has become the vital backbone in examining large volumes of data in real time. Explore how to validate machine and deep learning models, optimize their performance long-term, and integrate the output into the sensitive workflows and production pipelines.
Securely Accessing and Enabling ArcGIS Remotely
In this GTC 2020 session, learn how the GIS visualization and back-end deep learning workloads can be deployed on common GPU-accelerated infrastructure – leading to increased server utilization, employee productivity, and lower costs.
Jump-start AI Training with NGC Pretrained Models On-Premises and in the Cloud
Explore how NGC™ simplifies and accelerates building AI solutions with deep learning framework containers, pretrained models, and model scripts for training.
How to Build an Intelligent Robot Dog
Learn how the modular and easy-to-use navigation stack of the NVIDIA Isaac SDK™ accelerates the development of mobile robots. The SDK includes support for quadruped robots and adds to the other robot platforms supported by the development kit.
Detecting Rotated Objects Using the NVIDIA Object Detection Toolkit (ODTK)
Learn how to use ODTK to detect rotated objects in your own dataset. It’s easy to train, validate, operationalize, and serve models with maximum efficiency using GPU resources.
Building Intelligent Video Analytics Apps Using NVIDIA DeepStream 5.0
The DeepStream SDK is a scalable framework for building high-performance, managed IVA applications for the edge. Learn how DeepStream enables the development of AI applications across industries, from smart cities, architecture, and retail to manufacturing and healthcare.
Cyber-AI Networking for Supercomputing Security and Predictive Maintenance
Learn about the NVIDIA Mellanox UFM Cyber-AI platform that combines enhanced and real-time network telemetry with AI-powered cyber intelligence and analytics to discover operation anomalies and predict network failures for preventive maintenance.
Deep Learning & Predictive Analytics for Platform Sustainment Panel
Learn about the lessons and best practices for applying AI to mission critical systems. Hear how AI-led industrial inspection, predictive maintenance, logistics, NLP and Conversational AI technologies are transforming the sustainment landscape while meeting the desired cost reduction objectives.
Anomaly Detection on Aircraft Sensor Data Using Deep Learning
Learn the benefits of using autoencoders to achieve dimension reduction in a clustering problem and how long short-term memory (LSTM)-based neural networks can be applied to detect anomalies in an unsupervised way.
Predictive Analytics for Fleet Maintenance
Presented by LMI at GTC 2020, learn about predictive analytics methodologies for fleet maintenance. This session covers how machine learning methodologies support the holistic predictive modeling approach.
Automatic Defect Inspection Using the NVIDIA End-to-End Deep Learning Platform
Learn about the problems with traditional quality inspection and how deep learning can address and solve these industrial inspection tasks.
Rapid Prototyping on NVIDIA Jetson Platforms with MATLAB
Explore how you can prototype and deploy deep learning algorithms on hardware like the NVIDIA Jetson Nano™ Developer Kit with MATLAB.
Building a Multi-Camera Media Server for AI Processing on the NVIDIA Jetson Platform
Learn how to build a simple, real-time multi-camera media server for AI processing on the NVIDIA Jetson™ platform. By using GStreamer Daemon (GstD), GstInterpipe, and the NVIDIA DeepStream SDK, you can develop a scalable and robust prototype that captures video from several different sources.
Introducing Jetson Xavier NX, the World’s Smallest AI Supercomputer
Explore how NVIDIA Jetson Xavier™ NX allows you to deploy next-generation autonomous systems and intelligent edge devices that require high-performance AI and complex deep neural networks (DNNs) in a small, low-power footprint—mobile robots, drones, smart cameras, portable medical equipment, embedded Internet of Things (IoT) systems, and more.
Trojan Detection Evaluation: Finding Hidden Behavior in AI Models
Explore the TrojAI program, a collaboration between NIST, IAPRA, and JHU/APL which hopes to combat Trojan attacks. Learn about the dataset generation, testing infrastructure, and a baseline detection method within the TrojAI program in this GTC 2020 session.
Unleashing Cyber AI Reasoning
Learn how Johns Hopkins University Applied Physics Lab is tackling the challenge of labeling cyber data, and moving beyond defensive sensing to reasoning and autonomous behaviors. See techniques like creating autonomous agents that can act at machine speed.
Cyber ML Anomaly Detection, Presented by Lockheed Martin
Applying GPU acceleration to anomaly detection enables the building blocks for accelerated cyber detection. This GTC session provides an initial step for constructing a GPU-accelerated cyber detection hierarchy for critical flight control systems.
Building a Next-generation Cyber Flyaway Kit, Presented by Booz Allen Hamilton
Booz Allen and NVIDIA are building tera-scale GPU compute into a portable flyaway kit with next-gen, AI-based incident response capabilities. Join Will Badart and Project Manager JC Sullivan as they share their experiences working in this unique problem space and look to the future of GPU-powered AI for cybersecurity.
How to Build Domain-Specific Automatic Speech Recognition Models on GPUs
Learn about NVIDIA NeMo™, the domain-specific automatic speech recognition (ASR) application that lets you train or fine-tune pre-trained (acoustic and language) ASR models with your own data.
Fast Spectral Graph Partitioning on GPUs
Learn how graph partitioning can be used in the numerical solution of partial differential equations (PDEs) to perform more efficient sparse matrix-vector multiplications and how graph clustering can be used to identify communities in social networks and for cybersecurity.
Securing and Accelerating Cloud Computing Platforms with NVIDIA BlueField-2 DPUs
The cloud computing era has opened a host of cybersecurity challenges. Learn how BlueField-2 DPUs allow cloud service providers, telecom operators and enterprises to build high-performing, efficient, and secure cloud infrastructures.
AI from Data Center to the EDGE, Presented by PNY Technologies
Access an end-to-end AI portfolio of solutions from the Data Center to the EDGE. This GTC 2020 session presents the key elements to identifying opportunities for deep learning solutions for data science workflows.
Integrating Custom Sensors Using NVIDIA DriveWorks
Learn how to implement and use the sensor plug-ins for different sensor types such as radar, lidar, and camera and how these plug-ins make it possible to implement the transport and protocol layers necessary to communicate with the sensor.
NVIDIA DRIVE AGX Solutions for Scalable Autonomous Vehicle Development
Learn about NVIDIA’s unique suite of system-on-chip (SoC), GPU, and smart network computational and acceleration options for flexible autonomous vehicle development and how the latest products can be used in a vehicle computer architecture.
Real Time and Dynamic Risk Assessments for Autonomous Vehicles
Learn how to incorporate high fidelity or HD map data and real time traffic data, such as speeds and congestion patterns, into risk assessments, particularly for ADAS and highly autonomous vehicle operation.
Improving Computer Vision with NVIDIA A100 GPUs
Learn the new features of the NVIDIA A100 Tensor Core GPU that make it a powerhouse for computer vision workloads. Two recent computer vision NVIDIA Research projects are also showcased, demonstrating how they benefit from the A100’s compute power and performance.
Introducing NVIDIA Jarvis: A Framework for GPU-Accelerated Conversational AI Applications
NVIDIA Jarvis is an end-to-end framework for building conversational AI applications. Explore its GPU-optimized services for automatic speech recognition (ASR), natural language understanding (NLU), text to speech (TTS), and computer vision, which use state-of-the-art deep learning models.
Creating an Object Detection Pipeline for GPUs
Take a look at the object detection architecture, how to accelerate the training and deployment of an object detection pipeline for GPUs, and a brief look under-the-hood at the optimizations we employed
Autonomous Vehicle Radar Perception in 360 Degrees
Explore an autonomous vehicle system that provides actionable objects 360 degrees around the vehicle, enabling enhanced sensor fusion and functional redundancy to camera and lidar systems for safe autonomous planning and control.
The NVIDIA RTX platform fuses ray tracing, deep learning and rasterization to fundamentally transform the creative process for content creators and developers through the NVIDIA Turing GPU architecture and support for industry leading tools and APIs.
NVIDIA CloudXR SDK
CloudXR is NVIDIA's solution for streaming virtual reality (VR), augmented reality (AR), and mixed reality (MR) content from any OpenVR XR application on a remote server—cloud, data center, or edge.
Synthetic Data Generation with Active Learning Workflows for Radar
Learn how physics-based simulation, parallel model generation, and active learning techniques can help developers overcome challenges like physical realism of simulations, domain transfer, and generating data with sufficient diversity or entropy.
Best Practices and Tools for Training & Simulation
Explore the wide range of GPU-accelerated tools and SDKs that can be leveraged for generating more realistic high-performance synthetic environments in this GTC 2020 session. Learn some best practices for getting the most out of the GPU for training and simulation use cases.
Training and Simulation Applications with Project Anywhere
Project Anywhere is a cloud-based demo that allows you to get high-fidelity imagery from any distance, explore high-resolution 3D terrain and build data in real time from any device. Project Anywhere is deployed on the strength of Cesium 3D Tiles, Microsoft Azure, NVIDIA GPUs, and Unreal Engine.
Best Practices: Using NVIDIA RTX Ray Tracing
Learn about the actionable insights and practical tips for developers working on ray tracing. You’ll get a broad picture on what kind of solutions lead to performance increases, how to build and manage ray-tracing acceleration structures, and more.
Deploying Real-time Object Detection Models
Take a look at how the Isaac SDK can be used to generate synthetic datasets from simulation and then use this data to fine-tune an object detection deep neural network (DNN) using the NVIDIA Transfer Learning Toolkit (TLT).
Optimizing Video Memory Usage with the NVDECODE API and NVIDIA Video Codec SDK
This blog demonstrates which decoder configuration parameters impact the video memory usage and how to configure them optimally. Note that this post assumes basic familiarity with the NVDECODE API.
NVIDIA DEEP LEARNING INSTITUTE
The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems. Training is available as self-paced, online courses or in-person, instructor-led workshops.
Getting Started with DeepStream for Video Analytics on Jetson Nano
In this online course, you’ll learn how to:
- Set up your Jetson Nano and (optional) camera
- Build end-to-end DeepStream pipelines to convert raw video input into insightful annotated video output
- Configure multiple video streams simultaneously
Getting Started with AI on Jetson Nano
In this online course, you'll learn how to:
- Set up your Jetson Nano and camera
- Collect image data for classification models
- Annotate image data for regression models
- Train a neural network on your data to create your own models
- Run inference on the Jetson Nano with the models you create
NVIDIA Public Sector News
NVIDIA Researchers Introduce Robot That Adapts to Different Terrains
This robotics framework combines model-based control and reinforcement learning to adaptively and automatically change contact sequences in real time.
NVIDIA Aerial Developer Kit: Jumpstart 5G vRAN Development
This Aerial Developer Kit provides access to in-built vRAN test vectors included as part of the Aerial SDK. Simplify time-series analysis of sensor data and cyber-threat hunting, as well as increase the compute power for radar and sonar phased-array applications.
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