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Misc

Running Multiple Applications on the Same Edge Devices

Smart spaces are one of the most prolific edge AI use cases. From smart retail stores to autonomous factories, organizations are quick to see the value in this innovative…

Smart spaces are one of the most prolific edge AI use cases. From smart retail stores to autonomous factories, organizations are quick to see the value in this innovative technology. However, building and scaling a smart space requires many different pieces of technology, including multiple applications. Operating multiple applications at edge locations can be tricky.

To do this, organizations may add new hardware to a location so that each application has dedicated compute resources, but the costs associated with purchasing and installing new hardware with every new application can be substantial. Many organizations deploy multiple applications on the same device.

While that is a solution for scale, it can present different challenges.

Many organizations rely on the performance of GPUs to power applications at the edge. Even with high-performance GPU-accelerated systems, having two or more AI applications running concurrently on the same device using time slicing inevitably leads to higher latency with minimal hardware optimization.

With multiple applications running on the same device, the device time-slices the applications in a queue so that applications are run sequentially as opposed to concurrently. There is always a delay in results while the device switches from processing data for one application to another. The amount of delay varies per deployment, but it could be as much as 8ms. That could present serious concerns for applications powering high-speed operations, such as a manufacturing production line. 

Because applications are running sequentially, the GPU is only ever used as much as each individual application needs while it is running. For instance, if there are three applications operating sequentially on a GPU and each application requires 60% of the GPU’s resources, then at any given time less than 60% of the GPU is used. The GPU utilization would be 0% during each context switch.

There are a few ways organizations can avoid time-slicing and better utilize their GPU resources.

NVIDIA Multi-Instance GPU

NVIDIA Multi-Instance GPU (MIG) is a feature that enables you to partition GPUs into multiple instances, each with their own compute cores enabling the full computing power of a GPU. MIG alleviates the issue of applications competing for resources by isolating applications and dedicating resources to each. MIG also allows for better resource optimization and low latency.

By providing up to seven distinct partitions, you can support every workload, from the smallest to the largest, with the exact amount of compute power needed to effectively operate each deployed application.

In addition to performance, MIG provides added security and resilience by dedicating a set of hardware resources for compute, memory, and cache for each instance. MIG provides fault isolation for workloads, where a fault caused by an application running in one instance does not impact applications running on other instances. If one workload fails, all other workloads continue operating uninterrupted since instances and workloads run in parallel while remaining separate and isolated.

MIG works equally well with containers or virtual machines (VMs). When using VMs, it is easy to virtualize GPUs using NVIDIA vGPU, which can be configured to employ either time slicing or MIG.

MIG for edge AI

When deploying edge AI, optimizing for cost, power, and space are all important considerations, especially if you want to replicate to thousands of edge nodes. By allowing organizations to run multiple applications on the same GPU, MIG eliminates the need for installing a dedicated GPU for each workload, significantly reducing resource requirements.

More than resource optimization, MIG helps ensure predictable application performance. Without MIG, different jobs running on the same GPU, such as different AI inference requests, compete for the same resources such as memory and bandwidth. Due to the competition for resources inherent in time slicing, the performance of one application can be affected by activity in another. For edge AI environments, unpredictable performance can have serious consequences.

For example, a computer vision application monitoring a production line to detect product defects has to be able to react instantaneously to its dynamic environment. It must be able to inspect products quickly, and also to communicate with other machinery to stop the production line in the case of a defective product. For safety and efficiency, organizations must know that the AI applications powering their production lines are running correctly and predictably all of the time.

Jobs running simultaneously with different resources result in predictable performance with quality of service and maximum GPU utilization, making MIG an essential addition to every edge deployment.

A GPU is spliced into four MIG instances representing quality inspection, sorting, and safety workload. Each instance has its own dedicated GPU and GPU memory resources.
Figure 1. Each MIG instance can handle an independent workload, optimizing environments where multiple use cases need to operate simultaneously

MIG on NVIDIA Fleet Command

Fleet Command is a cloud service that centrally connects systems at edge locations to securely deploy, manage, and scale AI applications from one dashboard. Purpose-built for edge AI, Fleet Command is the best way to orchestrate AI across hundreds or even thousands of devices. 

From the Fleet Command cloud platform, administrators have complete control over MIG for edge AI deployments with minimal configuration needed. Using MIG on Fleet Command enables you to make resource utilization decisions across hundreds or even thousands of devices with just a few clicks. You can easily add new MIG partitions, scale down existing partitions, and create custom deployments all from one dashboard.

The combination of MIG and Fleet Command provides organizations with all the functionality needed to have full control over edge AI deployments, leading to better used and more effective workloads. For more information about the entire workflow for using MIG on Fleet Command, see the following video.

Video 1. How to Run Multiple Applications on the Same Edge Device with Fleet Command

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

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Categories
Misc

Remotely Operating Systems and Applications at the Edge

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. 

Sign up for Edge AI News to stay up to date with the latest trends, customer use cases, and technical walkthroughs.

Categories
Misc

CORSAIR Integrates NVIDIA Broadcast’s Audio, Video AI Features in iCUE and Elgato Software This Week ‘In the NVIDIA Studio’

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.

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Misc

Meet the Omnivore: Animator Entertains and Explains With NVIDIA Omniverse

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.

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Offsites

Towards Reliability in Deep Learning Systems

Posted by Dustin Tran and Balaji Lakshminarayanan, Research Scientists, Google Research

Deep learning models have made impressive progress in vision, language, and other modalities, particularly with the rise of large-scale pre-training. Such models are most accurate when applied to test data drawn from the same distribution as their training set. However, in practice, the data confronting models in real-world settings rarely match the training distribution. In addition, the models may not be well-suited for applications where predictive performance is only part of the equation. For models to be reliable in deployment, they must be able to accommodate shifts in data distribution and make useful decisions in a broad array of scenarios.

In “Plex: Towards Reliability Using Pre-trained Large Model Extensions”, we present a framework for reliable deep learning as a new perspective about a model’s abilities; this includes a number of concrete tasks and datasets for stress-testing model reliability. We also introduce Plex, a set of pre-trained large model extensions that can be applied to many different architectures. We illustrate the efficacy of Plex in the vision and language domains by applying these extensions to the current state-of-the-art Vision Transformer and T5 models, which results in significant improvement in their reliability. We are also open-sourcing the code to encourage further research into this approach.

Uncertainty — Dog vs. Cat classifier: Plex can say “I don’t know” for inputs that are neither cat nor dog.
Robust Generalization — A naïve model is sensitive to spurious correlations (“destination”), whereas Plex is robust.
Adaptation — Plex can actively choose the data from which it learns to improve performance more quickly.

Framework for Reliability
First, we explore how to understand the reliability of a model in novel scenarios. We posit three general categories of requirements for reliable machine learning (ML) systems: (1) they should accurately report uncertainty about their predictions (“know what they don’t know”); (2) they should generalize robustly to new scenarios (distribution shift); and (3) they should be able to efficiently adapt to new data (adaptation). Importantly, a reliable model should aim to do well in all of these areas simultaneously out-of-the-box, without requiring any customization for individual tasks.

  • Uncertainty reflects the imperfect or unknown information that makes it difficult for a model to make accurate predictions. Predictive uncertainty quantification allows a model to compute optimal decisions and helps practitioners recognize when to trust the model’s predictions, thereby enabling graceful failures when the model is likely to be wrong.
  • Robust Generalization involves an estimate or forecast about an unseen event. We investigate four types of out-of-distribution data: covariate shift (when the input distribution changes between training and application and the output distribution is unchanged), semantic (or class) shift, label uncertainty, and subpopulation shift.
    Types of distribution shift using an illustration of ImageNet dogs.
  • Adaptation refers to probing the model’s abilities over the course of its learning process. Benchmarks typically evaluate on static datasets with pre-defined train-test splits. However, in many applications, we are interested in models that can quickly adapt to new datasets and efficiently learn with as few labeled examples as possible.
Reliability framework. We propose to simultaneously stress-test the “out-of-the-box” model performance (i.e., the predictive distribution) across uncertainty, robust generalization, and adaptation benchmarks, without any customization for individual tasks.

We apply 10 types of tasks to capture the three reliability areas — uncertainty, robust generalization, and adaptation — and to ensure that the tasks measure a diverse set of desirable properties in each area. Together the tasks comprise 40 downstream datasets across vision and natural language modalities: 14 datasets for fine-tuning (including few-shot and active learning–based adaptation) and 26 datasets for out-of-distribution evaluation.

Plex: Pre-trained Large Model Extensions for Vision and Language
To improve reliability, we develop ViT-Plex and T5-Plex, building on large pre-trained models for vision (ViT) and language (T5), respectively. A key feature of Plex is more efficient ensembling based on submodels that each make a prediction that is then aggregated. In addition, Plex swaps each architecture’s linear last layer with a Gaussian process or heteroscedastic layer to better represent predictive uncertainty. These ideas were found to work very well for models trained from scratch at the ImageNet scale. We train the models with varying sizes up to 325 million parameters for vision (ViT-Plex L) and 1 billion parameters for language (T5-Plex L) and pre-training dataset sizes up to 4 billion examples.

The following figure illustrates Plex’s performance on a select set of tasks compared to the existing state-of-the-art. The top-performing model for each task is usually a specialized model that is highly optimized for that problem. Plex achieves new state-of-the-art on many of the 40 datasets. Importantly, Plex achieves strong performance across all tasks using the out-of-the-box model output without requiring any custom designing or tuning for each task.

The largest T5-Plex (top) and ViT-Plex (bottom) models evaluated on a highlighted set of reliability tasks compared to specialized state-of-the-art models. The spokes display different tasks, quantifying metric performance on various datasets.

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The largest T5-Plex (top) and ViT-Plex (bottom) models evaluated on a highlighted set of reliability tasks compared to specialized state-of-the-art models. The spokes display different tasks, quantifying metric performance on various datasets.

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Plex in Action for Different Reliability Tasks
We highlight Plex’s reliability on select tasks below.

Open Set Recognition
We show Plex’s output in the case where the model must defer prediction because the input is one that the model does not support. This task is known as open set recognition. Here, predictive performance is part of a larger decision-making scenario where the model may abstain from making certain predictions. In the following figure, we show structured open set recognition: Plex returns multiple outputs and signals the specific part of the output about which the model is uncertain and is likely out-of-distribution.

Structured open set recognition enables the model to provide nuanced clarifications. Here, T5-Plex L can recognize fine-grained out-of-distribution cases where the request’s vertical (i.e., coarse-level domain of service, such as banking, media, productivity, etc.) and domain are supported but the intent is not.

Label Uncertainty
In real-world datasets, there is often inherent ambiguity behind the ground truth label for each input. For example, this may arise due to human rater ambiguity for a given image. In this case, we’d like the model to capture the full distribution of human perceptual uncertainty. We showcase Plex below on examples from an ImageNet variant we constructed that provides a ground truth label distribution.

Plex for label uncertainty. Using a dataset we construct called ImageNet ReaL-H, ViT-Plex L demonstrates the ability to capture the inherent ambiguity (probability distribution) of image labels.

Active Learning
We examine a large model’s ability to not only learn over a fixed set of data points, but also participate in knowing which data points to learn from in the first place. One such task is known as active learning, where at each training step, the model selects promising inputs among a pool of unlabeled data points on which to train. This procedure assesses an ML model’s label efficiency, where label annotations may be scarce, and so we would like to maximize performance while minimizing the number of labeled data points used. Plex achieves a significant performance improvement over the same model architecture without pre-training. In addition, even with fewer training examples, it also outperforms the state-of-the-art pre-trained method, BASE, which reaches 63% accuracy at 100K examples.

Active learning on ImageNet1K. ViT-Plex L is highly label efficient compared to a baseline that doesn’t leverage pre-training. We also find that active learning’s data acquisition strategy is more effective than uniformly selecting data points at random.

Learn more
Check out our paper here and an upcoming contributed talk about the work at the ICML 2022 pre-training workshop on July 23, 2022. To encourage further research in this direction, we are open-sourcing all code for training and evaluation as part of Uncertainty Baselines. We also provide a demo that shows how to use a ViT-Plex model checkpoint. Layer and method implementations use Edward2.

Acknowledgements
We thank all the co-authors for contributing to the project and paper, including Andreas Kirsch, Clara Huiyi Hu, Du Phan, D. Sculley, Honglin Yuan, Jasper Snoek, Jeremiah Liu, Jie Ren, Joost van Amersfoort, Karan Singhal, Kehang Han, Kelly Buchanan, Kevin Murphy, Mark Collier, Mike Dusenberry, Neil Band, Nithum Thain, Rodolphe Jenatton, Tim G. J. Rudner, Yarin Gal, Zachary Nado, Zelda Mariet, Zi Wang, and Zoubin Ghahramani. We also thank Anusha Ramesh, Ben Adlam, Dilip Krishnan, Ed Chi, Neil Houlsby, Rif A. Saurous, and Sharat Chikkerur for their helpful feedback, and Tom Small and Ajay Nainani for helping with visualizations.

Categories
Misc

Insilico Medicine Identifies Therapeutic Targets for ALS With AI

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

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. 

Flowchart showing the input data for the PandaOmics AI platform and the output results
Figure 1. The PandaOmics AI platform analyzed ALS patient brain samples and other ALS data to identify new gene targets and existing drugs that could be repurposed to treat the disease.

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.

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Misc

Action on Repeat: GFN Thursday Brings Loopmancer With RTX ON to the Cloud

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.

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Misc

Building AI Bridge to Expand Vision AI Adoption to Every Industry

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. 

The image represents how camera sensors feed into VMS sites connect to AI Bridge which then connects to IVA applications.
Figure 1. Relationship between the cameras, VMS site, AI Bridge, the partner application, and where it sits on an NVIDIA EGX server.

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.

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Misc

Ask Me Anything Series: NVIDIA Experts Answer Your Questions Live

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.

Ask Me Anything with the editors of Ray Tracing Gems II on July 28, 2022.

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.

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Misc

Upcoming Event: SIGGRAPH 2022

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.