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
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.
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.
Warp and Blend SDK
Warp and Blend are interfaces exposed in NVAPI for warping (image geometry corrections) and blending (intensity and black level adjustment) a single display output or multiple display outputs. This SDK provides an easy way to bring this functionality to any application with minimal performance impact and no incremental latency.
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.
NVIDIA VRWorks Graphics
VRWorks™ is a comprehensive suite of APIs, libraries, and engines that enable application and headset developers to create amazing virtual reality experiences. VRWorks enables a new level of presence by bringing physically realistic visuals, sound, touch interactions, and simulated environments to virtual reality.
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).
Improve Your VR Image Quality with NVIDIA VRSS
Variable Rate Supersampling (VRSS) expands on Turing’s Variable Rate Shading (VRS) feature to deliver image quality improvements by performing selective supersampling. This can also be selectively engaged only if idle GPU cycles are available. VRSS is completely handled from within the NVIDIA display driver without application developer integration.
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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