Posts in category: nVidia AI

With the holiday season comes many joys for GeForce NOW members.

This month, RTX 3080 membership preorders are activating in Europe.

Plus, we’ve made a list — and checked it twice. In total, 20 new games are joining the GeForce NOW library in December. This week, the list of nine games streaming on GFN Thursday includes new releases like Chorus, Icarus and Ruined King: A League of Legends Story.

The Next Generation of Cloud Gaming Arrives in Europe

Starting today, gamers in Europe who preordered a six-month GeForce NOW RTX 3080 membership will have their accounts enabled with the new tier of service. Rollouts for accounts will continue until all requests have been fulfilled.

A GeForce NOW RTX 3080 membership means streaming from the world’s most powerful gaming supercomputer, the GeForce NOW SuperPOD. RTX 3080 members enjoy a dedicated, high-performance cloud gaming rig, streaming at up to 1440p resolution and 120 frames per second on PCs and Macs, and 4K HDR at 60 FPS on SHIELD TV, with ultra-low latency rivaling many local gaming experiences.

Players can power up their gaming experience with a six-month RTX 3080 membership for $99.99, pending availability. The membership comes with higher resolutions, lower latency and the longest gaming session length — clocking in at eight hours — on top of the most control over in-game settings.

Enjoy the GeForce NOW library of over 1,100 games and 100 free-to-play titles with the kick of RTX 3080 streaming across your devices. For more information about RTX 3080 memberships, check out our membership FAQ.

Preorders are still available in Europe and North America.

Decked Out in December

December kicks off with 20 great games joining GeForce NOW this month, including some out-of-this-world additions.

Enter a dark new universe, teeming with mystery and conflict in Chorus. Join Nara, once the Circle’s deadliest warrior, now their most wanted fugitive, on her mission to destroy the dark cult that created her. Take her sentient ship, Forsaken, on a quest for redemption across the galaxy and beyond the boundaries of reality as they fight to unite resistance forces against the Circle.

Survive the savage alien wilderness of Icarus, a planet once destined to be a second Earth, now humanity’s greatest mistake. Drop from the safety of the orbital space station to explore, harvest, craft and hunt while seeking your fortune from exotic matter that can be found on the abandoned, deadly planet. Make sure to return to orbit before time runs out — those that get left behind are lost forever.

Rise against ruin in Ruined King: A League of Legends Story. Unite a party of League of Legends Champions, explore the port city of Bilgewater and set sail for the Shadow Isles to uncover the secrets of the deadly Black Mist in this turn-based RPG.

Brave the far corners of space in Chorus and Icarus, and lead legends in Ruined King: A League of Legends Story alongside the nine new games ready to stream this GFN Thursday.

Releasing this week:

• Chorus (new game launch on Steam and Epic Games Store, December 3)
• Icarus (new game launch on Steam, December 3)
• MXGP 2021 – The Official Motocross Videogame (new game launch on Steam)
• Propnight (new game launch on Steam)
• Wartales (new game launch on Steam)
• Dead by Daylight (free on Epic Games Store)
• Hextech Mayhem: A League of Legends Story (Steam and Epic Games Store)
• Ruined King: A League of Legends Story (Steam and Epic Games Store)
• Timberborn (Steam and Epic Games Store)

Also coming in December:

• A-Train: All Aboard! Tourism (new game launch on Steam)
• Monopoly Madness (new game launch on Ubisoft Connect)
• Syberia: The World Before (new game launch on Steam and Epic Games Store)
• White Shadows (new game launch on Steam)
• BattleBeasts (Steam)
• FOREWARNED (Steam)
• Operencia: The Stolen Sun (Steam)
• Super Magbot (Steam)
• Tannenberg (Steam and Epic Games Store)
• Untitled Goose Game (Epic Games Store)
• Wargroove (Steam)

We make every effort to launch games on GeForce NOW as close to their release as possible, but, in some instances, games may not be available immediately.

More Fun From November

In addition to the 17 games announced to arrive in November, members can check out the following extra games that made it to the cloud last month:

• Assassin’s Creed: Syndicate Gold Edition (Ubisoft Connect)
• Combat Mission Cold War (Steam)
• Fate Seeker II (Steam)
• Jurassic World Evolution 2 (Steam and Epic Games Store)
• Myth of Empires (Steam)
• theHunter: Call of the Wild (Epic Games Store)
• World of Tanks (Steam)

Unfortunately, a few games that we planned to release last month did not make it:

• Bakery Simulator (Steam), new launch date
• STORY OF SEASON: Pioneers of Olive Town (Steam), coming to GeForce NOW in the near future

With these new additions arriving just in time for the holidays, we’ve got a question for you about your gaming wish list:

your holiday wish list, but there’s only room for 1 game you can stream in the cloud

which one would it be?

— NVIDIA GeForce NOW (@NVIDIAGFN) December 1, 2021

Share your answers on Twitter or in the comments below.

First responders don’t have time on their side.

Whether fires, search-and-rescue missions or medical emergencies, their challenges are usually dangerous and time-sensitive.

Using autonomous technology, Zurich-based Fotokite is developing a system to help first responders save lives and increase public safety.

Fotokite Sigma is a fully autonomous tethered drone, built with the NVIDIA Jetson platform, that drastically improves the situational awareness for first responders, who would otherwise have to rely on manned helicopters to get an aerial perspective.

Tethered to a ground station, either in a transportable case or attached to an emergency vehicle, Fotokite Sigma requires no skilled drone pilot, taking no one away from the scene.

Supported by the compute power of the NVIDIA Jetson platform in the grounded base, Fotokite Sigma covers the vast majority of situations where first responders need an aerial perspective during an emergency. Whether it’s an aerial search for someone off the side of a road, a quick look at a rooftop for hotspots or getting eyes above an active fire to track progress and plan resources, Sigma employs computer vision to send information directly to a tablet, with photogrammetry capabilities and real-time situational awareness.

Fotokite is a member of NVIDIA Inception, a program that offers go-to-market support, expertise and technology assistance for startups working in AI, data science and high performance computing.

Fighting Fire With Data

Firefighters depend on accurate, timely information to help them make important situational decisions.

Fotokite Sigma’s thermal camera can determine where a fire is, as well as where the safest location to enter or exit a structure would be. It can highlight hotspots that need attention and guide firefighters on whether their water is hitting the right areas, even through heavy smoke or with limited visibility at night.

Once the fire is under control, Sigma can monitor the area for potential flare-ups, so firefighters can prioritize resources to act quickly and efficiently.

“Everything from autonomous flight and real-time data delivery to the user interface and real-time streaming is made as simple as pushing a button, which means first responders can focus on saving lives and keeping people safe,” said Chris McCall, CEO of Fotokite.

Fire departments across the U.S. and Europe are using Fotokite Sigma, in both major cities and rural areas.

“The next area of focus for us is increasing the situational awareness and decision-making power in an emergency situation,” said McCall. “Using NVIDIA technology, we can easily introduce new capabilities to our systems.”

In addition to rolling out availability of Sigma across more geographies, Fotokite is working with partners to deliver data in real time, something that might have previously taken several hours to accomplish.

Providing a 3D render of an active emergency situation, tracking first responders, and supplying other intelligent data layers, for example, could be invaluable to first responders, helping them visualize a scene as it unfolds.

Learn more about how NVIDIA partners Lockheed Martin and OroraTech are using accelerated computing technology to fight wildfires.  

Learn more about NVIDIA Inception and the NVIDIA Jetson platform. Watch public sector sessions from GTC on demand. 

Look who just set new speed records for training AI models fast: Dell Technologies, Inspur, Supermicro and — in its debut on the MLPerf benchmarks — Azure, all using NVIDIA AI.

Our platform set records across all eight popular workloads in the MLPerf training 1.1 results announced today.

NVIDIA A100 Tensor Core GPUs delivered the best normalized per-chip performance. They scaled with NVIDIA InfiniBand networking and our software stack to deliver the fastest time to train on Selene, our in-house AI supercomputer based on the modular NVIDIA DGX SuperPOD.

A Cloud Sails to the Top

When it comes to training AI models, Azure’s NDm A100 v4 instance is the fastest on the planet, according to the latest results. It ran every test in the latest round and scaled up to 2,048 A100 GPUs.

Azure showed not only great performance, but great performance that’s available for anyone to rent and use today, in six regions across the U.S.

AI training is a big job that requires big iron. And we want users to train models at record speed with the service or system of their choice.

That’s why we’re enabling NVIDIA AI with products for cloud services, co-location services, corporations and scientific computing centers, too.

Server Makers Flex Their Muscles

Among OEMs, Inspur set the most records in single-node performance with its eight-way GPU systems, the NF5688M6 and the liquid-cooled NF5488A5. Dell and Supermicro set records on four-way A100 GPU systems.

A total of 10 NVIDIA partners submitted results in the round, eight OEMs and two cloud-service providers. They made up more than 90 percent of all submissions.

This is the fifth and strongest showing to date for the NVIDIA ecosystem in training tests from MLPerf.

Our partners do this work because they know MLPerf is the only industry-standard, peer-reviewed benchmark for AI training and inference. It’s a valuable tool for customers evaluating AI platforms and vendors.

Servers Certified for Speed

Baidu PaddlePaddle, Dell Technologies, Fujitsu, GIGABYTE, Hewlett Packard Enterprise, Inspur, Lenovo and Supermicro submitted results in local data centers, running jobs on both single and multiple nodes.

Nearly all our OEM partners ran tests on NVIDIA-Certified Systems, servers we validate for enterprise customers who want accelerated computing.

The range of submissions shows the breadth and maturity of an NVIDIA platform that provides optimal solutions for businesses working at any scale.

Both Fast and Flexible

NVIDIA AI was the only platform participants used to make submissions across all benchmarks and use cases, demonstrating versatility as well as high performance. Systems that are both fast and flexible provide the productivity customers need to speed their work.

The training benchmarks cover eight of today’s most popular AI workloads and scenarios — computer vision, natural language processing, recommendation systems, reinforcement learning and more.

MLPerf’s tests are transparent and objective, so users can rely on the results to make informed buying decisions. The industry benchmarking group, formed in May 2018, is backed by dozens of industry leaders including Alibaba, Arm, Google, Intel and NVIDIA.

20x Speedups in Three Years

Looking back, the numbers show performance gains on our A100 GPUs of over 5x in just the last 18 months. That’s thanks to continuous innovations in software, the lion’s share of our work these days.

NVIDIA’s performance has increased more than 20x since the MLPerf tests debuted three years ago. That massive speedup is a result of the advances we make across our full-stack offering of GPUs, networks, systems and software.

Constantly Improving Software

Our latest advances came from multiple software improvements.

For example, using a new class of memory copy operations, we achieved 2.5x faster operations on the 3D-UNet benchmark for medical imaging.

Thanks to ways you can fine-tune GPUs for parallel processing, we realized a 10 percent speed up on the Mask R-CNN test for object detection and a 27 percent boost for recommender systems. We simply overlapped independent operations, a technique that’s especially powerful for jobs that run across many GPUs.

We expanded our use of CUDA graphs to minimize communication with the host CPU. That brought a 6 percent performance gain on the ResNet-50 benchmark for image classification.

And we implemented two new techniques on NCCL, our library that optimizes communications among GPUs. That accelerated results up to 5 percent on large language models like BERT.

Leverage Our Hard Work

All the software we used is available from the MLPerf repository, so everyone can get our world-class results. We continuously fold these optimizations into containers available on NGC, our software hub for GPU applications.

It’s part of a full-stack platform, proven in the latest industry benchmarks, and available from a variety of partners to tackle real AI jobs today.

Leonardo da Vinci’s portrait of Jesus, known as Salvator Mundi, was sold at a British auction for nearly half a billion dollars in 2017, making it the most expensive painting ever to change hands.

However, even art history experts were skeptical about whether the work was an original of the master rather than one of his many protégés.

Steven Frank is a partner at the law firm Morgan Lewis, specializing in intellectual property and commercial technology law. He’s also half of the husband-wife team that used convolutional neural networks to determine that this painting was likely an authentic da Vinci.

He spoke with NVIDIA AI Podcast host Noah Kravitz about working with his wife, Andrea Frank, a professional curator of art images, to authenticate artistic masterpieces with AI’s help.

Key Points From This Episode:

• Authenticating art is a great challenge, as the characteristics of a painting that distinguish one artist’s work from another’s are very subtle. Determining if a piece is authentic requires an extremely fine analysis of a painting’s highly detailed variants.
• Using large datasets, the Franks trained convolutional neural networks to examine small, manageable segments of masterpieces to analyze and classify their artists’ patterns, down to their brush strokes. The model determined that the Salvator Mundi painting sold five years ago is likely the real work of da Vinci.

Tweetables:

AI might sometimes “be wrong, but it will always be objective, if you train it properly.” — Steven Frank [10:48]

“The most fascinating thing about AI research these days is that you can do cutting-edge AI research on an inexpensive PC … as long as it has an NVIDIA GPU.” — Steven Frank [22:43]

You Might Also Like:

Researchers Chris Downum and Leszek Pawlowicz Use Deep Learning to Accelerate Archaeology

Researchers in the Department of Anthropology at Northern Arizona University are using GPU-based deep learning algorithms to categorize sherds — tiny fragments of ancient pottery.

Wild Things: NVIDIA’s Sifei Liu Talks 3D Reconstructions of Endangered Species

Endangered species can be challenging to study, as they are elusive and the very act of observing them can disrupt their lives. Now, scientists can take a closer look at endangered species by studying AI-generated 3D representations of them.

Metaspectral’s‌ ‌Migel‌ ‌Tissera‌ ‌on‌ ‌AI-Based‌ ‌Data‌ ‌Management‌

‌Moondust‌,‌ ‌minerals‌ ‌and‌ ‌soil‌ ‌types‌ ‌are‌ ‌some‌ ‌of‌ ‌the‌ ‌materials‌ ‌that‌ ‌can‌ ‌be‌ ‌quickly‌ ‌identified‌ ‌and‌ ‌analyzed‌ ‌with‌ ‌AI‌,‌‌ ‌based‌ ‌on‌ their ‌images‌.‌ ‌Migel‌ ‌Tissera‌ ‌is‌ ‌co-founder‌ ‌and‌ ‌CTO‌ ‌of‌ ‌Metaspectral,‌ ‌a‌ ‌Vancouver-based‌ ‌startup‌ ‌that‌ ‌provides‌ ‌an‌ ‌AI-based‌ ‌data‌ ‌management‌ ‌and‌ ‌analysis‌ ‌platform‌ ‌for‌ ‌ultra-high-resolution‌ ‌images.‌ ‌

Subscribe to the AI Podcast

Get the AI Podcast through iTunes, Google Podcasts, Google Play, Castbox, DoggCatcher, Overcast, PlayerFM, Pocket Casts, Podbay, PodBean, PodCruncher, PodKicker, Soundcloud, Spotify, Stitcher and TuneIn. If your favorite isn’t listed here, drop us a note.

Make the AI Podcast Better

Have a few minutes to spare? Fill out this listener survey. Your answers will help us make a better podcast.

Carol Song is opening a door for researchers to advance science on Anvil, Purdue University’s new AI-ready supercomputer, an opportunity she couldn’t have imagined as a teenager in China.

“I grew up in a tumultuous time when, unless you had unusual circumstances, the only option for high school grads was to work alongside farmers or factory workers, then suddenly I was told I could go to college,” said Song, now the project director of Anvil.

And not just any college. Her scores on a national entrance exam opened the door to Tsinghua University, home to China’s most prestigious engineering school.

Along the way, someone told her computers would be big, so she signed up for computer science before she had ever seen a computer. She learned soon enough.

“We were building hardware from the ground up, designing microinstructions and logic circuits, so I got to understand computers from the inside out,” she said.

Easing Access to Supercomputers

Skip forward a few years to grad school at the University of Illinois when another big door opened.

While working in distributed systems, she was hired as one of the first programmers at the National Center for Supercomputing Applications,  one of the first sites in a U.S. program funding supercomputers that researchers shared.

To make the systems more accessible, she helped develop alternatives to the crude editing tools of the day that displayed one line of a program at a time. And she helped pioneering researchers like Michael Norman create visualizations of their work.

GPUs Add AI to HPC

In 2005, she joined Purdue, where she has helped manage nearly three dozen research projects representing more than $60 million in grants as a senior research scientist in the university’s supercomputing center.

“All that helped when we started defining Anvil. I see researchers’ pain points when they are getting on a new system,” said Song.

Anvil links 1,000 Dell EMC PowerEdge C6525 server nodes with 2,000 of the latest AMD x86 CPUs and 64 NVIDIA A100 Tensor Core GPUs on a NVIDIA Quantum InfiniBand network to handle traditional HPC and new AI workloads.

The system, built by Dell Technologies, will deliver 5.3 petaflops and half a million GPU cycles per year to tens of thousands of researchers across the U.S. working on the National Science Foundation’s XSEDE network.

Anvil Forges Desktop, Cloud Links

To harness that power, Anvil supports interactive user interfaces as well as the batch jobs that are traditional in high performance computing.

“Researchers can use their favorite tools like Jupyter notebooks and remote desktop interfaces so the cluster can look just like in their daily work environment,” she said.

Anvil will also support links to Microsoft Azure, so researchers can access its large datasets and commercial cloud-computing muscle. “It’s an innovative part of this system that will let researchers experiment with creating workflows that span research and commercial environments,” Song said.

Fighting COVID, Exploring AI

More than 30 research teams have already signed up to be early users of Anvil.

One team will apply deep learning to medical images to improve diagnosis of respiratory diseases including COVID-19. Another will build causal and logical check points into neural networks to explore why deep learning delivers excellent results.

“We’ll support a lot of GPU-specific tools like NGC containers for accelerated applications, and as with every new system, users can ask for additional toolkits and libraries they want,” she said.

The Anvil team aims to invite industry collaborations to test new ideas using up to 10 percent of the system’s capacity. “It’s a discretionary use we want to apply strategically to enable projects that wouldn’t happen without such resources,” she said.

Opening Doors for Science and Inclusion

Early users are working on Anvil today and the system will be available for all users in about a month.

Anvil’s opening day has a special significance for Song, one of the few women to act as a lead manager for a national supercomputer site.

“I’ve been fortunate to be in environments where I’ve always been encouraged to do my best and given opportunities,” she said.

“Around the industry and the research computing community there still aren’t a lot of women in leadership roles, so it’s an ongoing effort and there’s a lot of room to do better, but I’m also very enthusiastic about mentoring women to help them get into this field,” she added.

Purdue’s research computing group shares Song’s enthusiasm about getting women into supercomputing. It’s home to one of the first chapters of the international Women in High-Performance Computing organization.

Purdue’s Women in HPC chapter sent an all-female team to a student cluster competition at SC18. It also hosts outside speakers, provides travel support to attend conferences and connects students and early career professionals to experienced mentors like Song.

Pictured at top: Carol Song, Anvil’s principal investigator (PI) and project director along with Anvil co-PIs (from left) Rajesh Kalyanam, Xiao Zhu and Preston Smith. 

Whether facilitating cancer screenings, cutting down on false positives, or improving tumor identification and treatment planning, AI is a powerful agent for healthcare innovation and acceleration.

Yet, despite its promise, integrating AI into actual solutions can challenge many IT organizations.

The Netherlands Cancer Institute (NKI), one of the world’s top-rated cancer research and treatment centers, is using the NVIDIA AI Enterprise software suite to test AI workloads on higher-precision 3D cancer scans than are commonly used today.

NKI’s AI model was previously trained on lower-resolution images. But with the higher memory capacity offered by NVIDIA AI Enterprise, its researchers could instead use high-resolution images for training. This improvement helps clinicians better target the size and location of a tumor every time a patient receives treatment.

The NVIDIA AI Enterprise suite that NKI deployed is designed to optimize the development and deployment of AI. It’s certified and supported by NVIDIA to enable hospitals, researchers and IT professionals to run AI workloads on mainstream servers with VMware vSphere in their on-prem data centers and private clouds.

Delivering treatments on virtualized infrastructure means hospitals and research institutions can use the same tools they already work with on existing applications. This helps maximize their investments while making innovations in care more affordable and accessible.

Speeding Breakthroughs in Healthcare Research 

NKI had gotten off to a quick start with its project on NVIDIA AI Enterprise by using NVIDIA LaunchPad.

The LaunchPad program provides immediate access to optimized software running on accelerated infrastructure to help customers prototype and test data science and AI workloads. This month, the program was extended to nine Equinix locations worldwide.

The NVIDIA AI Enterprise software suite, available in LaunchPad, makes it possible to run advanced AI workloads on mainstream accelerated servers with VMware vSphere, including systems from Dell Technologies, Hewlett Packard Enterprise, Lenovo and many others.

Rhino Health, a federated learning platform powered by NVIDIA FLARE, is available today through NVIDIA AI Enterprise, making it easy for any hospital to leverage Federated learning for AI development and validation. Other organizations, like The American College of Radiology’s AI LAB, are also planning to use the NVIDIA AI Enterprise software.

Researchers at NKI used NVIDIA AI Enterprise, running on the HPE Synergy, a composable software system from Hewlett Packard Enterprise, to build deep learning models by combining the massive 2D and 3D data sources and AI to pinpoint the location of tumors before each radiotherapy treatment session. 

“Doctors could use this solution as an alternative to CT scans on day of treatment to optimize the treatment plan to validate the radiotherapy plan,” said Jonas Teuwen, group leader at the Netherlands Cancer Institute.

Using NVIDIA AI Enterprise, Teuwen’s team in Amsterdam ran their workloads on NVIDIA A100 80GB GPUs in a server hosted in Silicon Valley. Their convolutional neural network was built in less than three months and was trained on less than 300 clinical lung CT scans that were then reconstructed and generalized to head and neck data.

In the future, NKI researchers also hope to translate this work to potential use cases in interventional radiology to repair arteries in cardiac surgeries and dental surgery implants.

Accelerating Hospital AI Deployment With NVIDIA AI Enterprise

NVIDIA AI Enterprise simplifies the AI rollout experience for organizations who host a variety of healthcare and operations applications on virtualized infrastructure. It enables IT administrators to run AI applications like Vyasa and iCAD alongside core hospital applications, streamlining the workflow in an environment they’re already familiar with.

Compute resources can be adjusted with just a few clicks, giving hospitals the ability to transform care for both patients and healthcare providers.

Vyasa, a provider of deep learning analysis tools for healthcare and life sciences, uses NVIDIA AI Enterprise to build applications that can search unstructured content such as patient care records. With the software, Vyasa can develop their deep learning applications faster and help dive through unstructured data and PDFs to assess which patients are at a higher risk. It identifies those who haven’t been in for a check-up in more than a year, and can refine for additional risk factors like age and race.

“NVIDIA AI Enterprise has reduced our deployment times by half thanks to rapid provisioning of platform requirements that eliminate the need to manually download and integrate software packages,” said Frans Lawaetz, CIO at Vyasa. 

Radiologists use iCAD’s innovative ProFound AI software to assist with reading mammograms. These AI solutions help identify cancer earlier, categorize breast density, and accurately assess short-term personalized breast cancer risk based on each woman’s screening mammogram. Running advanced workloads with VMware vSphere is important for iCAD’s healthcare customers as they can easily integrate their data intensive applications into any hospital infrastructure.

A host of other software makers, like the American College of Radiology’s AI LAB and Rhino Health, with its federated learning platform, have begun validating their software on NVIDIA AI Enterprise to ease deployment by integrating into a common healthcare IT infrastructure.

The ability for NVIDIA AI Enterprise to unify the data center for healthcare organizations has sparked the creation of an ecosystem with NVIDIA technology at its heart. The common NVIDIA and VMware infrastructure benefits software vendors and healthcare organizations alike by making the deployment and management of these solutions much easier.

For many healthcare IT and software companies, integrating AI into hospital environments is a top priority. Many NVIDIA Inception partners will be testing the ease of deploying their offerings on NVIDIA AI Enterprise in these types of environments. They include ​​Aidence, Arterys, contextflow, ImageBiopsy Lab, InformAI, MD.ai, methinks.ai, RADLogics, Sciberia, Subtle Medical and VUNO.

NVIDIA Inception is a program that offers go-to-market support, expertise and technology for AI, data science and HPC startups.

Qualified enterprises can apply to experience NVIDIA AI Enterprise in curated, no-cost labs offered on NVIDIA LaunchPad.

Hear more about NVIDIA’s work in healthcare by tuning in to my special address on Nov. 29 at RSNA, the Radiological Society of North America’s annual meeting.

Main image shows how NVIDIA AI Enterprise allows hospital IT administrators to run AI applications alongside core hospital applications, like iCAD Profound AI Software for mammograms.

NVIDIA is making it easier than ever for researchers to harness federated learning by open-sourcing NVIDIA FLARE, a software development kit that helps distributed parties collaborate to develop more generalizable AI models.

Federated learning is a privacy-preserving technique that’s particularly beneficial in cases where data is sparse, confidential or lacks diversity. But it’s also useful for large datasets, which can be biased by an organization’s data collection methods, or by patient or customer demographics.

NVIDIA FLARE — short for Federated Learning Application Runtime Environment — is the engine underlying NVIDIA Clara Train’s federated learning software, which has been used for AI applications in medical imaging, genetic analysis, oncology and COVID-19 research. The SDK allows researchers and data scientists to adapt their existing machine learning and deep learning workflows to a distributed paradigm.

Making NVIDIA FLARE open source will better empower cutting-edge AI in almost any industry by giving researchers and platform developers more tools to customize their federated learning solutions.

With the SDK, researchers can choose among different federated learning architectures, tailoring their approach for domain-specific applications. And platform developers can use NVIDIA FLARE to provide customers with the distributed infrastructure required to build a multi-party collaboration application.

Flexible Federated Learning Workflows for Multiple Industries 

Federated learning participants work together to train or evaluate AI models without having to pool or exchange each group’s proprietary datasets. NVIDIA FLARE provides different distributed architectures that accomplish this, including peer-to-peer, cyclic and server-client approaches, among others.

Using the server-client technique, where learned model parameters from each participant are sent to a common server and aggregated into a global model, NVIDIA has led federated learning projects that help segment pancreatic tumors, classify breast density in mammograms to inform breast cancer risk, and predict oxygen needs for COVID patients.

The server-client architecture was also used for two federated learning collaborations using NVIDIA FLARE: NVIDIA worked with Roche Digital Pathology researchers to run a successful internal simulation using whole slide images for classification, and with Netherlands-based  Erasmus Medical Center for an AI application that identifies genetic variants associated with schizophrenia cases.

But not every federated learning application is suited to the server-client approach. By supporting additional architectures, NVIDIA FLARE will make federated learning accessible to a wider range of applications. Potential use cases include helping energy companies analyze seismic and wellbore data, manufacturers optimize factory operations and financial firms improve fraud detection models.

NVIDIA FLARE Integrates With Healthcare AI Platforms

NVIDIA FLARE can integrate with existing AI initiatives, including the open-source MONAI framework for medical imaging.

“Open-sourcing NVIDIA FLARE to accelerate federated learning research is especially important in the healthcare sector, where access to multi-institutional datasets is crucial, yet concerns around patient privacy can limit the ability to share data,” said Dr. Jayashree Kalapathy, associate professor of radiology at Harvard Medical School and leader of the MONAI community’s federated learning working group. “We are excited to contribute to NVIDIA FLARE and continue the integration with MONAI to push the frontiers of medical imaging research.”

NVIDIA FLARE will also be used to power federated learning solutions at: 

• American College of Radiology (ACR): The medical society has worked with NVIDIA on federated learning studies that apply AI to radiology images for breast cancer and COVID-19 applications. It plans to distribute NVIDIA FLARE in the ACR AI-LAB, a software platform that is available to the society’s tens of thousands of members.
• Flywheel: The company’s Flywheel Exchange platform enables users to access and share data and algorithms for biomedical research, manage federated projects for analysis and training, and choose their preferred federated learning solution — including NVIDIA FLARE.
• Taiwan Web Service Corporation: The company offers a GPU-powered MLOps platform that enables customers to run federated learning based on NVIDIA FLARE. Five medical imaging projects are currently being conducted on the company’s private cluster, each with several participating hospitals.
• Rhino Health: The partner and member of the NVIDIA Inception program has integrated NVIDIA FLARE into its federated learning solution, which is helping researchers at Massachusetts General Hospital develop an AI model that more accurately diagnoses brain aneurysms, and experts at the National Cancer Institute’s Early Detection Research Network develop and validate medical imaging AI models that identify early signs of pancreatic cancer.

“To collaborate effectively and efficiently, healthcare researchers need a common platform for AI development without the risk of breaching patient privacy,” said Dr. Ittai Dayan, founder of Rhino Health. “Rhino Health’s ‘Federated Learning as a Platform’ solution, built with NVIDIA FLARE, will be a useful tool to help accelerate the impact of healthcare AI.”

Get started with federated learning by downloading NVIDIA FLARE. Hear more about NVIDIA’s work in healthcare by tuning in to a special address on Nov. 29 at 6 p.m. CT by David Niewolny, director of healthcare business development at NVIDIA, at RSNA, the Radiological Society of North America’s annual meeting.

Subscribe to NVIDIA healthcare news here. 

Happy Thanksgiving, members. It’s a very special GFN Thursday.

As the official kickoff to what’s sure to be a busy holiday season for our members around the globe, this week’s GFN Thursday brings a few reminders of the joys of PC gaming in the cloud.

Plus, kick back for the holiday with four new games coming to the GeForce NOW library this week.

Game Away the Holiday

The holidays are often spent celebrating with extended family — which is great, until Aunt Petunia starts trying to teach you cross-stitch or Grandpa Harold begins another one of his fishing trip stories. If you need a break from the relatives, get your gaming in, powered by the cloud.

With GeForce NOW, nearly any device can become a GeForce gaming rig. Grab Uncle Buck’s Chromebook and get a few rounds of Apex Legends in, or check in with Star-Lord and the crew from your mobile device in Marvel’s Guardians of the Galaxy. You can even squad up on some Macbooks with your cousins for a few Destiny 2 raids at the kid’s table, where we know the real fun is.

How about escaping for a bit to a tropical jungle? For a limited time, get a copy of Crysis Remastered free with the purchase of a six-month Priority membership or the new GeForce NOW RTX 3080 membership. Terms and conditions apply.

GeForce NOW members can experience the first game in the Crysis series — or 1,000+ more games — across nearly all of their devices, turning even a Mac or a mobile device into the ultimate gaming rig. It’s the perfect way to keep the gaming going after pumpkin pie is served.

The Gift of Gaming

The easiest upgrade in PC gaming makes a perfect gift for gamers.

GeForce NOW Priority Membership digital gift cards are now available in 2-month, 6-month or 12-month options. Give the gift of powerful PC gaming to a special someone who uses a low-powered device, a budding gamer using a Mac, or a squadmate who’s gaming on the go.

Gift cards can be redeemed on an existing GeForce NOW account or added to a new one. Existing Founders and Priority members will have the number of months added to their accounts.

Eat, Play and Be Merry

Between bites of stuffing and mashed potatoes, members can look for the following games joining the GeForce NOW library:

• Fate Seeker II (day-and-date release on Steam, Nov. 23)
• theHunter: Call of the Wild (day-and-date release on Epic Games Store, Nov. 25)
• Ghostrunner (Epic Games Store)
• Legion TD 2 (Steam)

We make every effort to launch games on GeForce NOW as close to their release as possible, but, in some instances, games may not be available immediately.

We initially planned to add Farming Simulator 2022 to GeForce NOW in November, but discovered an issue during our onboarding process. We hope to add the game in the coming weeks.

Whether you’re celebrating Thanksgiving or just looking forward to a gaming-filled weekend, tell us what you’re thankful for on Twitter or in the comments below.

A picture worth a thousand words now takes just three or four words to create, thanks to GauGAN2, the latest version of NVIDIA Research’s wildly popular AI painting demo.

The deep learning model behind GauGAN allows anyone to channel their imagination into photorealistic masterpieces — and it’s easier than ever. Simply type a phrase like “sunset at a beach” and AI generates the scene in real time. Add an additional adjective like “sunset at a rocky beach,” or swap “sunset” to “afternoon” or “rainy day” and the model, based on generative adversarial networks, instantly modifies the picture.

With the press of a button, users can generate a segmentation map, a high-level outline that shows the location of objects in the scene. From there, they can switch to drawing, tweaking the scene with rough sketches using labels like sky, tree, rock and river, allowing the smart paintbrush to incorporate these doodles into stunning images.

The new GauGAN2 text-to-image feature can now be experienced on NVIDIA AI Demos, where visitors to the site can experience AI through the latest demos from NVIDIA Research. With the versatility of text prompts and sketches, GauGAN2 lets users create and customize scenes more quickly and with finer control.

An AI of Few Words

GauGAN2 combines segmentation mapping, inpainting and text-to-image generation in a single model, making it a powerful tool to create photorealistic art with a mix of words and drawings.

The demo is one of the first to combine multiple modalities — text, semantic segmentation, sketch and style — within a single GAN framework. This makes it faster and easier to turn an artist’s vision into a high-quality AI-generated image.

Rather than needing to draw out every element of an imagined scene, users can enter a brief phrase to quickly generate the key features and theme of an image, such as a snow-capped mountain range. This starting point can then be customized with sketches to make a specific mountain taller or add a couple trees in the foreground, or clouds in the sky.

It doesn’t just create realistic images — artists can also use the demo to depict otherworldly landscapes.

Imagine for instance, recreating a landscape from the iconic planet of Tatooine in the Star Wars franchise, which has two suns. All that’s needed is the text “desert hills sun” to create a starting point, after which users can quickly sketch in a second sun.

It’s an iterative process, where every word the user types into the text box adds more to the AI-created image.

The AI model behind GauGAN2 was trained on 10 million high-quality landscape images using the NVIDIA Selene supercomputer, an NVIDIA DGX SuperPOD system that’s among the world’s 10 most powerful supercomputers. The researchers used a neural network that learns the connection between words and the visuals they correspond to like “winter,” “foggy” or “rainbow.”

Compared to state-of-the-art models specifically for text-to-image or segmentation map-to-image applications, the neural network behind GauGAN2 produces a greater variety and higher quality of images.

The GauGAN2 research demo illustrates the future possibilities for powerful image-generation tools for artists. One example is the NVIDIA Canvas app, which is based on GauGAN technology and available to download for anyone with an NVIDIA RTX GPU.

NVIDIA Research has more than 200 scientists around the globe, focused on areas including AI, computer vision, self-driving cars, robotics and graphics. Learn more about their work.

You don’t need a private plane to be at the forefront of personal travel.

Electric automaker Xpeng took the wraps off the G9 SUV this week at the international Auto Guangzhou show in China. The intelligent, software-defined vehicle is built on the high-performance compute of NVIDIA DRIVE Orin and delivers AI capabilities that are continuously upgraded with each over-the-air update.

The new flagship SUV debuts Xpeng’s centralized electronic and electrical architecture and Xpilot 4.0 advanced driver assistance system for a seamless driving experience. The G9 is also compatible with the next-generation “X-Power” superchargers for charging up to 124 miles in 5 minutes.

The Xpeng G9 and its fellow EVs are elevating the driving experience with intelligent features that are always at the cutting edge.

Intelligence at the Edge

The G9 is intelligently designed from the inside out.

The SUV is the first to be equipped with Xpilot 4.0, an AI-assisted driving system capable of address-to-address automated driving, including valet parking.

Xpilot 4.0 is built on two NVIDIA DRIVE Orin systems-on-a-chip (SoC), achieving 508 trillion operations per second (TOPS). It uses an 8-million-pixel front-view camera and 2.9-million-pixel side-view cameras that cover front, rear, left and right views, as well as a highly integrated and expandable domain controller.

This technology is incorporated into a centralized compute architecture for a streamlined design, powerful performance and seamless upgrades.

Charging Ahead

The G9 is designed for the international market, bringing software-defined innovation to roads around the world.

It incorporates new signature details, such as daytime running lights designed to make a sharp-eyed impression. Four daytime running lights at the top and bottom of the headlights form the Xpeng logo. These headlights also include discrete lidar sensors, merging cutting-edge technology with an elegant exterior.

In addition to fast charging, the electric SUV meets global sustainability requirements as well as NCAP five-star safety standards. The G9 is scheduled to officially launch in China in the third quarter of 2022, with plans to expand to global markets soon after.

The intelligent EV joined a growing lineup of software-defined vehicles powered by NVIDIA DRIVE that are transforming the way the world moves.Also on the Auto Guangzhou showfloor until the event closes on Nov. 28 are the Human Horizons HiPhi Z Digital-GT, NIO ET7 and SAIC’s IM Motors all-electric lineup, displaying the depth and diversity of the NVIDIA DRIVE Orin ecosystem.