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.
The NVIDIA DeepStream SDK delivers a complete streaming analytics toolkit for AI-based video and image understanding, as well as multi-sensor processing. DeepStream is an integral part of NVIDIA Metropolis, the platform for building end-to-end services and solutions for smart cities, transforming pixels and sensor data into actionable insights.
Browse by Resource Type
Accelerated Computing Teaching Kit for University Educators
Explore the newest version of the Accelerated Computing Teaching Kit: a comprehensive set of academic labs, university teaching material, and e-book for use in introductory and advanced parallel programming courses.
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
Learn an efficient model parallel approach by making only a few targeted modifications to existing PyTorch transformer implementations.
Teaching Accelerated Data Science in the Classroom
Learn how a novel yet reproducible approach to teaching accelerated data-science topics and skills to graduate students was used in a course at the Georgia Institute of Technology.
Accelerating AI Training with MLPerf Containers and Models from NVIDIA NGC
By Akhil Docca and Vinh Nguyen | July 29, 2020
Deploying Real-time Object Detection Models with the NVIDIA Issac SDK and NVIDIA Transfer Learning Toolkit
By Divya Bhaskara and Heron Ordonez | July 23, 2020
Accelerating Deep Learning Research in Medical Imaging Using MONAI
By Wenqi Li, Guotai Wang, and Wentao Zhu | July 8, 2020
Defining the Next Wave of GPU-Powered Research
Learn how a new AI computing cluster built using NVIDIA DGX-2™ systems at Oregon State University is enabling researchers to accelerate their work and publish cutting-edge advancements.
Bringing GPU Computing to the Classroom
NVIDIA Teaching Kits lower the barrier of incorporating AI and GPU computing in coursework. Listen to NVIDIA’s higher education leadership and partners discuss opportunities for online training, certification, and cloud access to GPUs for teachers and students.
How to Become an Ambassador for Deep Learning
Join NVIDIA’s higher education leadership and academic partners to learn how to get involved with the NVIDIA Deep Learning Institute (DLI) University Ambassador Program.
Find Your Application-Specific Resource
Mission-Critical Decisions for Route Planning with NVIDIA RAPIDS
Discover how RAPIDS, NVIDIA’s GPU-accelerated data science software stack, helps accelerate route replanning for civilian and military disaster response assets under dynamic risk conditions, among other federal mission sets.
Accelerating Disaster Response with NVIDIA Inference
Explore how to train and deploy deep learning models through real applications, such as disaster relief operations, that run anywhere, from the tactical edge to the cloud.
DeepStream: An SDK to Improve Video Analytics
Learn how the NVIDIA DeepStream SDK can accelerate disaster response by streamlining applications such as analytics, intelligent traffic control, automated optical inspection, object tracking, and web content filtering.
Damage Detection and Disaster Response Using AI and GIS
Learn the end-to-end workflow for damage detection and disaster response using Esri’s ArcGIS AI capabilities and NVIDIA GPUs. With the increased accessibility of drone and satellite imagery, the complete automation of damaged structure detection is possible.
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.
Applications of AI for Predictive Maintenance
In this Deep Learning Institute (DLI) workshop, developers will learn how to identify anomalies and failures in time series data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions.
NVIDIA Index 3D Volumetric Visualization Framework
NVIDIA IndeX® is a 3D volumetric interactive visualization SDK that allows you to visualize and interact with massive datasets, make modifications, and navigate to the most pertinent parts of data, all in real time to gather better insights faster.
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.
AI-Enabled Predictive Maintenance
In this GTC DC 2019 session, Lockheed Martin presented the technical challenges and use cases involved in the sustainment of high-value equipment such as helicopters, jets, and radar systems.
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.
Optimizing CyberSecurity Workflows with RAPIDS Using CLX
Learn how NVIDIA CLX’s (“clicks”) collection of cybersecurity applications, primitives, and building blocks allows you to create customized AI tailored for your environment. Built on RAPIDS, CLX utilizes GPU compute to accelerate common network and applications tasks and enable the creation of cybersecurity workflows.
Applications of AI for Anomaly Detection
AI models can be trained and deployed to automatically analyze datasets, define “normal behavior,” and identify breaches in patterns quickly and effectively. Learn how these models can be used to predict future anomalies.
GraphDefense: Toward Robust Large-Scale Graph Convolutional Network
In this GTC poster session, the researchers were inspired by previous works on adversarial defense for deep neural networks, especially adversarial training algorithms. They proposed a method called GraphDefense to defend against adversarial perturbations.
Revolutionizing Wireless Security and Threat Detection at the Edge Using Deep Learning
In this GTC DC 2019 session, DeepSig showed how the components of their platform are linked as microservices using gRPC and Kubernetes. Through use cases, they demonstrated how their AI platform with its intuitive, user-friendly interface improves analyst and customer performance.
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.
Developing a Camera Pipeline Using NVIDIA DriveWorks
Explore how to develop camera image processing software on the NVIDIA DriveWorks SDK, including DriveWorks image basics, low-level computer vision modules, and feature tracking and DNN samples.
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.
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
Lockheed Martin and USC to Launch Jetson-Based Nanosatellite for Scientific Research Into Orbit
In a joint collaboration with the University of Southern California, Lockheed Martin announced plans to launch an AI, GPU-accelerated nanosatellite into orbit.
Introducing NVIDIA Jarvis: A Framework for GPU-Accelerated Conversational AI Applications
Announcing NVIDIA Jarvis, an end-to-end framework for building conversational AI applications. It includes GPU-optimized services for ASR, NLU, TTS, and computer vision that use state-of-the-art deep learning models.
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