The data science workflow has traditionally been slow and cumbersome when it comes to loading, filtering, and manipulating data, as well as ML training itself. These processes were constrained to slow, CPU-based computing, and resulted in lengthy cycle times impacting data science productivity. NVIDIA RAPIDS delivers GPU-accelerated machine learning and analytics libraries, deployed on NVIDIA GPU-platforms for maximized data science productivi...

Join NVIDIA and partners to hear how language-based AI solutions (natural language processing or NLP) are making an impact in law enforcement and citizen services today. We will share commercially available technologies for off-line real time language translation, speech to text transcription, named entity voice recognition search, audio file search and more. This webinar is hosted by Carahsoft, NVIDIA’s public sector distributor, and De...

Taking advantage of GPU-accelerated computation for Natural Language Processing (NLP) does not have to be a whole new foreign language in itself. New software development kits and pretrained AI models with state-of-the-art performance can kick-start your newest development task or be added into your existing speech and text analytics workflow. NVIDIA and Carahsoft will bring you up to speed on NVIDIA's latest open-source, compute-accelerated N...

Today's siloed data center infrastructures are not capable of meeting the dynamic requirements of AI-driven research and development applications that simply do not care about the sales cycle for servers such as NVMe, NICs, GPUs, and other accelerator technologies. New software-defined architectures, such as composable infrastructure, are providing a way to make the most of valuable hardware resources, eliminating silos for data performance, a...

Today’s siloed data center infrastructures are not capable of meeting the dynamic requirements of AI-driven research and development applications that simply do not care about the sales cycle for servers such as NVMe, NICs, GPUs, and other accelerator technologies. Liqid and NVIDIA eliminate data center silos to accelerate global academic research and development. Join Liqid and NVIDIA for a live webinar on composable disaggregated infra...

The data center is changing and federal agencies are looking for alternative networking solutions to meet the increasing workflow demands of the data center while reducing cost and complexity. Join us to explore the latest Open Networking innovations from NVIDIA Networking (previously Mellanox) and Cumulus. See how you can leverage this cutting-edge technology to increase the efficiency of your mission by accelerating deployment and operation...

Modern technologies have incredible power, but IT staff across government, healthcare and academia still face barriers to efficient operations in the field. Join experts from Red Hat, NVIDIA and Hewlett Packard Enterprise on March 25th to learn how your team can solve data challenges with KubeFrame for AI-Edge, a highly portable solution that enables real-time, AI-driven processing for: Public Safety and Defense Smart Cities Disaster Relief H...

Welcome to the ENCCS/NVIDIA “AI for Science” Bootcamp! This online event will take place during two half-days on March 8-9 2021 and is designed to teach researchers how AI can accelerate HPC simulations by introducing the concepts of Deep Neural Networks, including data pre-processing, and techniques on how to build, compare and improve accuracy of deep learning models. The bootcamp will introduce real-world problems, where partici...

The scale and power of modern artificial intelligence can seem intimidating, but it doesn't have to be. As datacenters have gotten bigger, small edge computers have become more capable and to the point where cutting-edge computer vision techniques can run on devices in the palm of your hand for fewer watts than a lightbulb. Join NVIDIA & Government Acquisitions and Carahsoft for a practical demonstration of video processing at the edge and...

Stencil operations are used widely in HPC applications and pose an optimization challenge on both CPUs and GPUs. On GPUs, fine-tuned optimizations can be formulated using low-level APIs such as CUDA, but many large established codes prefer a portable, higher-level API such as OpenACC. Although OpenACC lacks the fine-tuning of CUDA, it does allow for some tuning through a variety of parallelization constructs and loop directives. Here, we try v...