Category: Misc

A recent poll during the Edge Computing 101 webinar revealed that many IT professionals interested in edge AI are still just learning the basics about the technology and…

A recent poll during the Edge Computing 101 webinar revealed that many IT professionals interested in edge AI are still just learning the basics about the technology and considerations for production deployments.

One key consideration for production edge AI is how administrators will manage ongoing maintenance for applications and systems post-deployment, sometimes referred to as Day-2 operations. Remote management is critical functionality that enables you to easily manage dozens or even thousands of remote sites.

Remote management is essential for edge AI

The process for bringing an edge AI proof of concept (POC) into a production environment at-scale requires that you have full access to both edge systems and applications at distributed locations.

Without complete and painless access, the ability to progress and scale quickly is limited by the time it takes to manually troubleshoot issues at the remote edge site. That process can be quite time-consuming and expensive as installing and scaling new technology always presents unpredictable issues. 

Traditional VPN connections lack security

After setup, you want to deploy and scale new applications on existing hardware, update existing applications, troubleshoot bugs, and validate new configurations. Having remote management capabilities that are secure is critical, as production deployments contain important data and insights that you will want to keep safe. 

But the traditional process of accessing machines and systems through VPN is not secure enough for the changing security landscape that edge deployments present.

First, most VPN connections do not have the ability to set time limits or restrictions. Administrators could (and often do) forget to close out a VPN session, leaving an avenue open for malicious actors.

Second, VPN connections do not easily provide the access controls needed for securely deploying and managing edge AI given the number of different partners, vendors, contractors, and other actors that might need access to parts of the deployment solution. 

To successfully operate edge deployments, you need remote management features with advanced functionality and security like just-in-time (JIT) access, clearly defined access controls, and timed sessions. 

To ensure this functionality, NVIDIA Fleet Command has two features to provide full remote management of both systems and applications. 

Remote system access

Remote console on Fleet Command provides secure, remote access to systems at the edge without needing physical access to the system or the network. You can view system information or data, navigate directories, view logs, and more.

Having an on-demand remote console eliminates the need for additional ports and traditional VPN connections and provides peace of mind. You’ll know that you can troubleshoot and remediate unexpected problems at remote edge locations. 

Another unique aspect of the remote console on Fleet Command is concurrent remote access to multiple edge nodes in an organization. To ensure the highest security across nodes, Fleet Command infrastructure isolates each of the open nodes in separate sessions and ensures that any issues on one system do not affect other systems. 

Remote application access

In addition to system-level access, you also have access to the applications. Fleet Command remote application access allows for web-based access to applications running on remote edge systems, eliminating the need for manual connection to the system and network through VPN to where applications are running.

Remote application access gives you visibility to the application services, providing full access to all features and functionality of the web applications running on the edge devices. Using remote application access, you can remotely access the application UI and configure applications, ensure that applications are running successfully, and troubleshoot any issues without compromising the security posture of your edge deployments. 

For added security, remote application access also features a configurable time allowance that automatically ends remote access sessions. This greatly simplifies resource management and frees up available remote sessions for other services.

Like remote console, Fleet Command remote application access enables multiple sessions to be open at the same time, so that multiple users from multiple locations can operate simultaneously. 

Secure remote management

A key aspect of remote management on Fleet Command is the security benefits of using these features. Access controls on remote console and remote application access mean that you can grant role-based usage capabilities to partners, customers, contractors, and others, ensuring limited exposure to the solution and network. 

Additionally, both features provide just-in-time (JIT) security, so sessions and privileges are granted by administrators and are time-limited. Time-limited sessions eliminate the possibility of perpetually open VPN sessions that provide backdoor access for malicious actors. 

Get started with remote management

Organizations are increasingly adopting edge AI solutions to power innovative new use cases. With any new technology, new approaches must ensure optimum functionality and safety, especially for production solutions dealing with critical or sensitive data. 

Remote management with Fleet Command provides everything you need to fully access edge systems and applications. It provides a layer of security that traditional VPN connections lack. 

To walk through the entire process of using remote console and remote application access on Fleet Command, see the Remotely Operate Systems and Applications with Fleet Command [NEEDS LINK] demo. 

Try Fleet Command yourself with NVIDIA LaunchPad, a free program that provides short-term access to a large catalog of hands-on labs. You can walk through the entire flow for deploying and managing applications on Fleet Command, including using remote management and other key features. Get started now. 

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Technology company CORSAIR and streaming partner BigCheeseKIT step In the NVIDIA Studio this week. A leader in high-performance gear and systems for gamers, content creators and PC enthusiasts, CORSAIR has integrated NVIDIA Broadcast technologies into its hardware and iCUE software. Similar AI enhancements have also been added to Elgato’s audio and video software, Wave Link and Camera Hub.

The post CORSAIR Integrates NVIDIA Broadcast’s Audio, Video AI Features in iCUE and Elgato Software This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.

Australian animator Marko Matosevic is taking jokes from a children’s school dads’ group and breathing them into animated life with NVIDIA Omniverse, a virtual world simulation and collaboration platform for 3D workflows.

The post Meet the Omnivore: Animator Entertains and Explains With NVIDIA Omniverse appeared first on NVIDIA Blog.

Drug discovery startup Insilico Medicine—alongside researchers from Harvard Medical School, Johns Hopkins School of Medicine, the Mayo Clinic, and others—used AI to identify…

Drug discovery startup Insilico Medicine—alongside researchers from Harvard Medical School, Johns Hopkins School of Medicine, the Mayo Clinic, and others—used AI to identify more than two dozen gene targets related to amyotrophic lateral sclerosis (ALS). The research findings, which included 17 high-confidence and 11 novel therapeutic targets, were recently published in Frontiers in Aging Neuroscience.

Using Insilico’s AI-driven target discovery engine, called PandaOmics, the researchers analyzed massive datasets to discover genes that new drugs could target to improve outcomes for ALS,  also known as Lou Gehrig’s disease. Today, patients typically face an average life expectancy of between two and five years after symptom onset. 

The research team used NVIDIA GPUs to train the deep learning models for target identification. The PandaOmics AI engine uses a combination of omics AI scores, text-based AI scores, financial scores, and more to rank gene targets. 

ALS is a debilitating disease. Patients rapidly lose voluntary muscle movement, affecting the ability to walk, talk, eat, and breathe. The five existing FDA-approved therapies for the disease are unable to halt or reverse this loss of function, which affects more than 700,000 people around the world. 

“The results of this collaborative research effort show what is possible when we bring together human expertise with AI tools to discover new targets for diseases where there is a high unmet need,” said Alex Zhavoronkov, founder and CEO of Insilico Medicine, in a press release. “This is only the beginning.”

Insilico Medicine is a Premier member of NVIDIA Inception, a global program designed to support cutting-edge startups with co-marketing, expertise, and technology. 

AI uncovers new paths to treat untreatable diseases

The research team used Quiver, a distributed graph learning library, to accelerate its AI models on multiple NVIDIA GPUs. They used natural language processing models including BioBERT, GPT, and OPT, as well as text recognition models including PaddleOCR and docTR. 

To help identify the genes related to ALS, the researchers used public datasets as well as data from Answer ALS, a global project with clinical data consisting of 2.6 trillion data points from around 1,000 ALS patients. In a preclinical animal model, the team validated that 18 of the 28 identified gene targets were functionally correlated to ALS—and that in eight of them, suppression would strongly reduce neurodegeneration. 

The researchers are now working to advance some of these targets toward clinical trials for ALS. The targets will be shared on ALS.AI to help accelerate drug discovery.

Earlier this year, Insilico began a Phase 1 clinical trial for an AI-discovered, AI-designed drug to treat pulmonary fibrosis, another fast-progressing, hard-to-treat disease. 

Read more in Insilico Medicine’s press release and Frontiers in Aging Neuroscience article. 

Do you have a startup? Join the NVIDIA Inception global program of over 10,000 technology startups.

Acknowledgments

Featured image courtesy of Insilico Medicine.

Investigate the ultimate truth this GFN Thursday with Loopmancer, now streaming to all members on GeForce NOW. Stuck in a death loop, RTX 3080 and Priority members can search for the truth with RTX ON — including NVIDIA DLSS and ray-traced reflections. Plus, players can enjoy the latest Genshin Impact event with the “Summer Fantasia” Read article >

The post Action on Repeat: GFN Thursday Brings Loopmancer With RTX ON to the Cloud appeared first on NVIDIA Blog.

Over the last decades, organizations of all sizes across the world have flocked to implement video management systems (VMS) that tie together the components of a video network…

Over the last decades, organizations of all sizes across the world have flocked to implement video management systems (VMS) that tie together the components of a video network infrastructure. By allowing businesses to easily capture, record, store, retrieve, view, and analyze video collected from their cameras, VMS can improve their operations, increase visibility, and enhance safety.

 VMS infrastructure is now so pervasive that enterprises can no longer monitor the firehose of video streaming day and night. The growing need for scalable and real-time analysis of video is possibly the greatest driver today of AI in the enterprise. With vast amounts of video data to be analyzed in real time, smart video analytics call for edge AI technology, where the heavy computation executes in the field near sensors like video cameras. 

Organizations across all industries are eager to add AI to their existing VMS to maximize the return on their initial investments and take advantage of this valuable data but, unfortunately, it is a difficult task.

Organizations must partner with an independent software vendor who provides an intelligent video analytics (IVA) application. The vendor must then develop, deploy, manage, and support their own integration for every application that the organization wants to run. It is a painstaking process that requires significant time, energy, and expertise to execute. 

An NVIDIA Metropolis partner themselves, Milestone Systems is a global leader in VMS helping to address this challenge and make it easier for hundreds of other Metropolis IVA partners to expand accessibility to incredibly valuable vision AI applications.

John Madsen, a senior research engineer at Milestone, explains, “When you have thousands of cameras that are recording 24/7, how do you find the relevant data? With AI, our end users can find recorded events in their logs that they want to find in minutes instead of combing through hours and hours of footage. We want to help our end users find the relevant video footage and run live analytics.”  

Introducing AI Bridge

Milestone has embarked on a mission to help their customers get the most out of their existing VMS platforms. The result is Milestone AI Bridge. 

AI Bridge is an API gateway that eases the integration of intelligent video analytics (IVA) applications with the Milestone XProtect VMS. 

How AI Bridge works: 

1. A camera sends video data to the VMS site. 
2. The VMS site is connected to AI Bridge and sends video data back and forth. 
3. AI Bridge connects the video from the VMS site to the GPU-accelerated IVA applications to run AI analytics and generate insights. 
4. The insights are then fed back into the VMS so that actions can be taken based on whatever insight is provided from the AI application. 

With AI Bridge, Milestone users can now instantly integrate third-party AI models into their own video systems. Milestone users are typically application providers or independent software vendors that help organizations create IVA applications. 

To get access to AI Bridge from Milestone, create an account with the NGC catalog. 

AI Bridge in action 

Another NVIDIA Metropolis partner, DataFromSky is using AI Bridge to provide AI solutions for smart parking, traffic control, and retail.

One of their customers, the Køge Nord Train Station, located near Copenhagen, was experiencing large volumes of commuter congestion. For many commuters, a lack of parking spots and traffic congestion can lead to frustration, wasted time, accidents, and even missed trains or buses.

To solve this, DataFromSky built an intelligent parking application that monitors parking lots for occupancy, enables mobile payments, and navigates drivers to empty parking spots. With the addition of AI, each camera installed on the parking lot is able to monitor up to 400 parking spots in real-time. All this results in commuters having smoother and better travel experiences.

Thanks to AI Bridge, DataFromSky is able to integrate AI solutions into their customers’ existing camera infrastructure easily. This results in a significantly faster installation time, especially critical for larger deployments that may span hundreds of cameras.

Bringing AI Bridge to life 

In building AI Bridge, Milestone knew that they needed to work with a partner that had deep roots in the AI community. That is why they chose NVIDIA. 

“Our VMS works on a Windows platform which is very different from the AI community which uses modern software such as Linux, Kubernetes, and Docker,” says Madsen, “Working with NVIDIA allows us to modernize our stack and makes it extremely easy for us to work with the AI community.” 

Milestone leveraged a wide array of NVIDIA AI products to make AI Bridge possible.

• NVIDIA-Certified Systems provide enterprises with optimized hardware to enable quick and efficient video processing and inference that can be scaled across many cameras. 
• The NVIDIA Metropolis platform is an application framework that simplifies the development and scale of IVA applications for connecting to the AI ecosystem. 
• NVIDIA Fleet Command is a managed platform for container orchestration that streamlines the provisioning and deployment of systems and AI applications at the edge.

Milestone leverages Fleet Command to deploy the AI Bridge API remotely onto dozens or even thousands of edge systems within minutes.

“A big challenge is not just the integration, but deploying the analytics on-premises and how you manage it,” added Madsen. “This is why we turned to NVIDIA Fleet Command.”  

Fleet Command also provides a single control plane for IT administrators to securely manage all AI applications through one dashboard. This makes it the ideal way to accelerate deployments, POCs, and edge infrastructure management.  

The use cases of IVA

IVA promises to bring new, intelligent use cases across every industry. Some of the transformational use cases include the following:

• Automating processes
• Improving customer experience
• Responding to emergencies
• Tracking assets
• Improving supply chain efficiency 

Any enterprise interested in driving safety, effectiveness, and efficiency should consider the benefits that edge AI brings to video. For more information, see the countless possibilities that IVA can bring to your business.

You’re invited to connect with NVIDIA experts through a new exclusive series of Ask Me Anything (AMA) sessions. During these live Q&As, members of the NVIDIA Developer Program…

You’re invited to connect with NVIDIA experts through a new exclusive series of Ask Me Anything (AMA) sessions. During these live Q&As, members of the NVIDIA Developer Program can submit questions to our experts, brainstorm about common challenges that developers are facing, and engage in online discussions about NVIDIA technologies. The series will also provide guidance on integrating NVIDIA SDKs.

The AMA series kicks off on July 28 at 10:00 AM, Pacific time. Attendees can get tips on incorporating real-time rendering across their projects from the editors of Ray Tracing Gems II:

Adam Marrs is a principal engineer in the Game Engines and Core Technology
group at NVIDIA. He holds a Ph.D. in computer science and has shipped graphics
code in various AAA games and commercial game engines. He has written for GPU
Zen 2, Ray Tracing Gems, and recently served as the editor-in-chief of Ray Tracing Gems II.

Peter Shirley is a distinguished engineer in the Research group at NVIDIA. He holds a Ph.D. in computer science and has worked in academics, startup companies, and industry. He is the author of several books, including the recent Ray Tracing in One Weekend series.

Ingo Wald is a director of ray tracing at NVIDIA. He holds a Ph.D. in computer science, has a long history of research related to ray tracing in both academia and industry, and is known for authoring and co-authoring various papers and open-source software projects on rendering, visualization, and data structures.

Eric Haines currently works at NVIDIA on interactive ray tracing. He co-authored the books Real-Time Rendering, 4th Edition and An Introduction to Ray Tracing. He edited The Ray Tracing News, and co-founded the Journal of Graphics Tools and the Journal of Computer Graphics Techniques. Most recently, he co-edited Ray Tracing Gems.

Each of these exclusive Q&A sessions will offer the developer community a chance to get answers from experts in real time, along with a forum for collaboration after the event.

To participate, you must be a member of the NVIDIA Developer Program. Sign up if you’re not already a member. Post questions to the dedicated online forum before the event and during the 60-minute live session. 

Mark your calendars for the second AMA in the series scheduled for October 26, 2022. We’ll dive into best practices for building, training, and deploying recommender systems.

Join us at SIGGRAPH Aug. 8-11 to explore how NVIDIA technology is driving innovations in simulation, collaboration, and design across industries.

Join us at SIGGRAPH Aug. 8-11 to explore how NVIDIA technology is driving innovations in simulation, collaboration, and design across industries.

Join this digital conference from August 2-4 to learn how science is being advanced through the work done at Open Hackathons or accelerated using OpenACC.

Join this digital conference from August 2-4 to learn how science is being advanced through the work done at Open Hackathons or accelerated using OpenACC.

A new approach to data The convergence of AI and IoT has shifted the center of gravity for data away from the cloud and to the edge of the network. In retail stores, factories,…

A new approach to data

The convergence of AI and IoT has shifted the center of gravity for data away from the cloud and to the edge of the network. In retail stores, factories, fulfillment centers, and other distributed locations, thousands of sensors are collecting petabytes of data that power insights for innovative AI use cases. Because the most valuable insights are generated at the edge, organizations have quickly adopted new technologies and processes to better capitalize on this new center of gravity.

One of the major technologies adopted is edge computing, the process of bringing the computing power for an application to the same physical location where sensors are collecting information. When this computing method is used to power AI applications at the edge, it’s referred to as edge AI.

To ensure that these edge locations harvesting valuable insights do not exist in isolated silos, organizations are increasingly working to integrate their edge computing solutions into their existing workflows to develop, test, and optimize applications. By having a seamless path from the development process to the deployment process, teams are able to simultaneously have strong visibility into how applications are operating in production environments while also taking advantage of the data and insights collected by the applications at edge locations. 

This process will only become more important as AI models are quickly and constantly retrained and iterated on based on new data collected at edge locations.

Machine learning operations and edge AI

Machine learning operations (MLOps) is a system of processes to streamline the development, deployment, monitoring, and ongoing management of machine learning models. It allows organizations to quickly scale the development process for applications and enables rapid iterations between data science and IT teams. MLOps platforms organize that philosophy into a set of tools that can be used cross-functionally in an organization to speed up the rate of innovation. 

Integrating MLOps platforms and edge computing solutions allows for a seamless and rapid workflow for data scientists and IT teams to collaboratively develop and deploy applications in production environments. With a complete workflow, teams can significantly increase the rate of innovation as they constantly iterate, test, deploy, and retain based on insights and information collected at edge sites. And for organizations diligently working to capitalize on the new data paradigm, innovation is paramount.

Integrating Domino Data Lab and NVIDIA Fleet Command

The Domino Data Lab Enterprise MLOps Platform and NVIDIA Fleet Command are now integrated to provide data scientists and IT teams with a consistent, simplified flow from model development to deployment.

Domino Data Lab provides an enterprise MLOps platform that powers model-driven business to accelerate the development and deployment of data science work while increasing collaboration and governance. It allows data scientists to experiment, research, test, and validate AI models before deploying them into production. 

NVIDIA Fleet Command is a managed platform for container orchestration that streamlines provisioning and deployment of systems and AI applications at the edge. It simplifies the management of distributed computing environments with the scale and resiliency of the cloud, turning every site into a secure, intelligent location.

From development to deployment

The integration with NVIDIA Fleet Command provides Domino Data Lab users an easy avenue to deploy models they are working on to edge locations. The integration bridges the gap between the data scientist team developing applications and IT teams deploying them, allowing both teams access to the entire application lifecycle.  

“The integration with NVIDIA Fleet Command is the last piece in the puzzle to give data scientists access to the complete workflow for developing and deploying AI applications to the edge,” says Thomas Robinson, VP of Strategic Partnerships and Corporate Development at Domino Data Lab. “Full visibility into production deployments is critical for teams to take advantage of the data and insights generated at the edge, ultimately producing better applications faster.”

Data scientists can use the Domino Data MLOps Platform to quickly iterate on models they are working on. Through the same interface, users have the ability to load their new models onto Fleet Command, making them available to deploy to any connected location. Once deployed, administrators have remote access to the applications for monitoring and troubleshooting, providing critical feedback that can be used in the next iteration of the model. 

A data scientist working on a quality inspection application for a beverage manufacturing plant is one example of this integration used in production environments. The application is used to visually catch dents and defects on cans to prevent them from reaching consumers. The challenge is that the packaging on the cans changes frequently as new designs are tested, seasonal products are released, and event-based packages go to market. The application needs to be able to learn new designs quickly and frequently while still maintaining precise levels of success. This requires a high rate of innovation in order to keep up with the frequent changes in packaging. To achieve this, the data scientist uses Domino Data Lab Enterprise MLOps Platform and NVIDIA Fleet Command to create a fast and seamless flow from the development and iteration efforts to the deployment and monitoring efforts. By doing so, they are able to increase the rate of innovation by easily deploying new models with limited disruption in service as products change. Additionally, model monitoring ensures that the data scientist catches any issues with the quality or predictive power of their models. 

Watch an end-to-end demo of model development, deployment, and monitoring in the oil and gas space using Domino and NVIDIA Fleet Command. 

Get started with Domino on NVIDIA Fleet Command

Deploying applications on NVIDIA Fleet Command is currently available to Domino users. The Domino Enterprise MLOps Platform is also accessible on NVIDIA LaunchPad, which provides free short-term access to a catalog of hands-on labs. Quickly test AI initiatives and get practical experience with scaling data science workloads.

Learn more and get started.