With the growth of AI applications being deployed at the edge, IT organizations are looking at the best way to deploy and manage their edge computing systems and software.
NVIDIA Fleet Command brings secure edge AI to enterprises of any size by transforming NVIDIA-Certified Systems into secure edge appliances and connecting them to the cloud in minutes. In the cloud, you can deploy and manage applications from the NGC Catalog or your NGC private registry, update system software over the air, and manage systems remotely with nothing but a browser and internet connection.
To help organizations test the benefits of Fleet Command, you can test the product using NVIDIA LaunchPad. Through curated labs, LaunchPad gives you access to dedicated hardware and Fleet Command software so you can walk through the entire process of deploying and managing an AI application at the edge.
In this post, I walk you through the Fleet Command trial on LaunchPad including details about who should apply, how long it takes to complete the curated lab experience, and next steps.
Who should try Fleet Command?
Fleet Command is designed for IT and OT professionals who are responsible for managing AI applications at multiple edge locations. The simplicity of the product allows it to be used by professionals of any skill level with ease.
The curated lab walks through the deployment of a demo application. For those with an imminent edge project, the demo application can be used to test the features of Fleet Command but full testing onsite is still necessary.
The Fleet Command lab experience is designed for deployment and management of AI applications at the edge. NVIDIA LaunchPad offers other labs for management of training environments with NVIDIA Base Command, and NVIDIA AI Enterprise for streamlined development and deployment of AI from the enterprise data center.
What does the Fleet Command curated lab include?
In this trial, you act as a Fleet Command administrator deploying a computer vision application for counting cars at an intersection. The whole trial should take about an hour.
Access Fleet Command in NGC
Fleet Command can be accessed from anywhere through NGC, the GPU-optimized software hub for AI, allowing administrators to remotely manage edge locations, systems, and applications.
Administrators automatically have Fleet Command added to the NGC console.
Create an edge location
A location in Fleet Command represents a real-world location where physical systems are installed. In the lab, you create one edge location, but customers can manage thousands of locations in production.
To add a new location, choose Add Location and fill in the details. Choose the latest version available.
Figure 1. Add a location to be managed by NVIDIA Fleet Command
Add an edge system
Next, add a system to the location, which represents the physical system at the edge. Completing this step generates a code that you can use to securely provision the server onsite with the Fleet Command operating stack. You then select the location just created and choose Add System.
Figure 2. Add edge systems to a location
Add the system name and description to complete the process.
After a system is added to a location, you get a generated activation code that is used to pair Fleet Command to the physical system onsite.
Figure 3. Activation code generated to connect system in Fleet Command to the edge server
Connect Fleet Command to the LaunchPad server
NVIDIA LaunchPad provides a system console to access the server associated with the trial. Follow the prompts to complete installation. After initial setup, the system prompts for the activation code generated from creating a system in Fleet Command.
Figure 4. Pair the edge server to Fleet Command
When the activation code is entered, the system finalizes pairing with Fleet Command. A check box in the Fleet Command user interface shows you that the server is running and ready to be remotely managed.
Figure 5. Complete pairing of the edge server, which can now be controlled in Fleet Command
Deploy an AI application
Now that the local installer has the system paired to Fleet Command, you can deploy an AI application. Applications can be hosted on your NGC private registry, or directly on the NGC Catalog.
Figure 6. Add application from NGC to the location
AI applications are deployed using Helm charts, which are used to define, install, and upgrade Kubernetes applications. Choose Add Application and enter the information in the prompt.
Now that the application is ready in Fleet Command, it can be deployed onto one or many systems. Create a deployment, making sure to check the box enabling application access, by selecting the location and application that you created.
Figure 7. Create a deployment
Now the application is deployed on the server and you can view the application running on the sample video data in the trial application.
Figure 8. The computer vision application for counting cars is in production
That’s it. I’ve now walked you through the end-to-end process of connecting to physical systems at the edge, creating a deployment, and pushing an AI application to that edge server. In less than an hour, the trial goes from disconnected, remote systems to fully managed, secure, remote edge environments.
Next steps
Fleet Command is a powerful tool for simplifying management of edge computing infrastructure without compromising on security, flexibility, or scale. To understand if Fleet Command is the right tool for managing your edge AI infrastructure, register for your NVIDIA LaunchPad trial.
Hi. So I wanted to ask if it is possible to create a speech to text using a dialect in the Philippines. I would only be using simple words of the dialect.
I’m working on a project called Edify, a digital classroom app. We’re looking for people who are good with TensorFlow for a project. If you want to work with us, our hiring process is simple. We don’t care about your Education, where you worked or anything similar.
I want to see what you know and the best way to demonstrate is to show. So, head on over to https://edify.ws/club/10 for a quick tutorial on how this works.
We are looking for 3 things:
What have you done? #tag_it and share a project.
How have you done this? Explain.
Why have you done this? Explain.
I reckon that if you’re good, you will have no trouble showing it to anyone. If you want, you can also share this challenge with some friends who might also be interested, #ShowWhatYouKnow.
For the 14th consecutive year, each Academy Award nominee for the Best Visual Effects used NVIDIA technologies. The 94th annual Academy Awards ceremony, taking place Sunday, March 27, has five nominees in the running: Dune Free Guy No Time to Die Shang-Chi and the Legend of the Ten Rings Spider-Man: No Way Home NVIDIA has Read article >
I am trying to develop an application using TensorFlow Lite for Microcontrollers and I was wondering if TensorFlow Lite Micro is compatible with any arduino board? The tensorflow page lists only the Nano BLE Sense board.
Moreover where could I find a detailed list of all the platforms/processors supported by the above framework?
At Omniverse Developer Day, deep-dive into panels and expert-led breakout sessions to learn how to build, sell, and distribute your own 3D tools. Happening at GTC March 21-24.
NVIDIA Omniverse continues to transform workflows for developers, researchers, and creators around the world—and there is still more to come. Experience what else Omniverse has in store at NVIDIA GTC, which kicks off March 21.
We’re introducing exclusive events, sessions, and other resources to showcase how developers can use Omniverse to build next level applications and tools for virtual worlds. There’s a session for anyone interested in learning more about the platform. Dive into technical learning or face-to-face breakout sessions during Omniverse Developer Days. Or connect with the community in Omniverse User Groups.
Register now for free, and join us to see how the Omniverse community is building the future of virtual worlds.
Explore Exciting Sessions on Omniverse Developer Days
Omniverse Developer Days will showcase the many ways users and developers can build extensions and apps on the platform. Hear from Omniverse engineering leaders and industry experts—including featured speakers from Pixar, Ubisoft, Autodesk, Adobe, Unity, Epic Games, and Walt Disney Studios—as they share new insights about 3D virtual world building, simulation, rendering, and more.
Multiple sessions, ranging from technical tutorials to business-focused topics, are available for developers and users. Register now and take an unparalleled deep dive into developing applications for virtual worlds.
Also, don’t miss the keynote by NVIDIA CEO Jensen Huang on March 22 at 8 am PST. The Omniverse Community team will host a special Keynote Community Hangout on Discord, where attendees can watch the keynote live and chat.
Make Virtual Connections in the Omniverse User Group
Join us at the upcoming NVIDIA Omniverse User Group, a special event hosted by lead engineers, designers, and artists. The free, virtual event is open to all developers, researchers, creators, students, and industry professionals interested in learning more about the platform.
Attendees will have a behind-the-scenes look at the latest news and updates about the groundbreaking platform. Get a sneak peek at what’s next on the Omniverse roadmap, learn how NVIDIA is supporting the artist community, and check out the newest resources available to help users build custom extensions and applications.
Can’t wait for GTC to get started? Check out the Omniverse Developer Resource Center, which provides users with all the information they need to familiarize themselves with the platform.
Dive into new features such as Omniverse Code, a new app that serves as an IDE for developers and power users. With Omniverse Code, developers can quickly become familiar with the platform while building extensions, apps, or microservices.
Informative sessions on automated cyberattack prevention, power grid management from the edge, optimizing network storage, edge AI-on-5G, and addressing malware with data science.
Looking for different topic areas? Keep an eye out for our other posts!
Join us at GTC, March 21-24, to explore the latest technology and research across AI, computer vision, data science, robotics, and more!
With over 900 options to choose from, our NVIDIA experts put together some can’t-miss sessions to help get you started:
Join Dorit Dor, one of the leading personas of the global cyber security market, and get the most elaborate and professional introduction for AI use cases in the cyber security arena.
Malware is a significant issue for IT in today’s distributed work environment. Join this session to learn how Mandiant helps the issue of malware using data science.
Hear from Marissa Hummon, author of 11 technical publications on power grid management, winner of the Edison Award and Grid Edge Innovation Award, and Top Women in Energy honoree. Hummon will share how accelerated computing through AI and ML can deliver greater value to utilities and customers while unlocking new opportunities for clean-energy companies and third-party developers.
2021 saw massive growth in the demand for edge computing driven by the pandemic and for the need for more efficient business processes. Justin Boitano will talk about the key advances in the IoT, 5G, and edge AI that are helping enterprises digitally transform their business.
In the decade leading up to 2030, technological developments and commercial use of AI and 5G will transform the enterprise landscape and accelerate economic growth. Mavenir and NVIDIA are partnering to deliver flexible, efficient, reliable, and secure AI applications over 5G networks. This session showcases the vertical blueprint of AI-on-5G for computer vision that will drive digital transformation across industries.
NVIDIA AI Enterprise accelerates the process for creating modern applications driven by deep learning and AI. Our experts will reveal how they simplify and streamline processes to make it easier to build powerful applications. This leads to faster execution of GPU-accelerated data preparation, machine learning training, and inference applications.
With enormous 50TB datasets and complex data engineering tasks, data-driven personalization is an insurmountable challenge for AT&T’s data science team. Chris Vo from AT&T will talk about how their team designed and tested various experiments, which ultimately resulted in improved content recommendation and classification, all while reducing infrastructure costs.
Large-scale Machine Learning with Snowflake and RAPIDS Nick Becker, Engineering Manager, RAPIDS, NVIDIA Miles Adkins, Senior Partner Sales Engineer, AI & ML, Snowflake Subhan Ali, Senior Developer Relations Manager, NVIDIA Moselle Freitas, Technology Alliances Director, Snowflake Ayush Dattagupta, Software Engineer, NVIDIA
The modern machine learning workflow faces two major bottlenecks: 1) moving large amounts of data is difficult, and 2) scaling out with this data is even harder. In this session, you’ll learn from the NVIDIA RAPIDS and Snowflake teams about how the combination of Snowflake and RAPIDS + Dask enables a supercharged workflow, where data can move easily and the models trained on that data can run in a fraction of the time.
Hear from Microsoft Azure cloud computing services backend developer and author of over 25 papers, Jithin Jose. Jose and Gilad Shainer discuss how HPC and AI have evolved to use multitenant, isolation, and congestion control to help improve performance in cloud-native supercomputing.
Project Monterey rearchitects VMware Cloud Foundation from the hardware up to support new requirements for modern applications. In this session learn how to leverage accelerators to improve the performance of offloaded tasks while freeing CPU cycles for core application workloads.
NVMe is more than faster flash storage. It enables vastly more efficient transport of data between storage systems and servers. But how do you achieve the full potential of NVMe? In this session, Rob Davis looks at new and modern approaches to optimizing networked storage solutions while introducing intelligent use cases that improve efficiency and performance.
NVIDIA is accelerating the field of genomics and drug discovery with the help of GPUs. We sit down with the lab lead to learn more about their work.
The following post provides a deep dive into some of the accomplishments and current focus of drug discovery and genomics work by NVIDIA. A leader in innovations within healthcare and life sciences, NVIDIA is looking to add AI, deep learning, simulation, and drug discovery researchers and engineers to the team. If what you read aligns with your career goals please review the current job postings.
NVIDIA is tapping into the latest technology as it pairs high-performance computing (HPC) with genome and drug discovery research. As genomic testing becomes more mainstream, the amount of data that requires analysis has increased. Drug discovery has also entered a new era of research, as AI and deep learning open the door to discovering thousands of new compounds that serve as the base of drug discovery.
NVIDIA researchers and engineers, like group-lead Johnny Israeli, are supercharging genomics and drug discovery research. Developing software like NVIDIA Clara Parabricks, which is a GPU-accelerated computational genomics application framework that delivers end-to-end analysis workflows for whole genomes, exomes, cancer genomes, and RNA sequencing data. Leading NVIDIA Research content marketing, I sat down with Johnny to learn more about what he does with his group.
Nathan Horrocks
Hey Johnny, it’s great to finally connect with you. Let’s jump right in. I wanted to ask, given that NVIDIA is a tech company, how does working in your group here differ from working at a biotech company?
Johnny Israeli
Hey Nate, thank you for reaching out. There are a couple of ways to think about the differences. Oftentimes in biotech, there is a very specific technology goal or problem. You use whichever technology, or combination of technologies, to solve that problem. You may be married to the problem or the goal, but not so married to the type of technology you might use. Here we pursue products that leverage our expertise in accelerated computing and AI technologies and have more flexibility in terms of our goals for our products.
For example, a few years ago we worked on genomics, but we didn’t build any kind of product for drug discovery. Today, we are building a product for that specific area. The reason for that is that drug discovery as a field is changing. We see an opportunity for us to pursue a new goal. So I would say we have a track record of chasing new opportunities as they become available to our unique positioning and our unique skill set.
Nathan Horrocks
Could you give me an example of a unique opportunity that differentiates you from a traditional biotech company?
Johnny Israeli
I would highlight the intensity of our AI-oriented work in drug discovery. Quite a few companies in the drug discovery space work with AI but the level and focus of investment may be different. For biotech companies, AI is one of several technology options in a broader technology arsenal to pursue drug discovery programs.
At NVIDIA, we know that we are uniquely positioned to do a great job with AI and accelerated computing. So we’re incentivized to invest in this work with greater intensity and focus than most other companies can, both because of our positioning and because of our scale. So engineers and scientists interested in the intersection of AI and drug discovery, and parallel computing would find our areas of work interesting.
Nathan Horrocks
You mentioned your work on genomics, could you tell me how your past work in genomics is impacting your current work in drug discovery?
Johnny Israeli
The drug discovery space is multidisciplinary and it’s a long and complicated process. At the very early stage of the drug discovery process is the stage of target identification. Most of the drug discovery work out there is what’s called target-based drug discovery workflows, where you figure out what is the target, the protein target to go after, and then develop a drug.
Figure 1. NVIDIA Clara annotates the data analyzed by a sequencer.
Our genomics work contributes greatly to the target identification problem. You can build these genome-wide data sets across many individuals and then analyze them to figure out which mutations are associated with different kinds of diseases. By identifying these mutations and analyzing them, we can then figure out protein targets that are relevant for a given disease. And then build out the rest of the drug discovery workflow from there.
So we use our software called NVIDIA Clara Parabricks to map data from genomics instruments, identify genomic variants, and annotate them. By simplifying these genomics workflows into push-button software solutions and accelerating that software, we are reducing the time and cost to generate large-scale genomics datasets. These large-scale genomics datasets across many individuals are then used to identify protein targets that can impact disease outcomes, and the structures of those proteins are used with our NVIDIA Clara Discovery software to generate and simulate drug compounds and their interaction with those proteins.
Nathan Horrocks
So you’re using Clara Parabricks to fuel protein identification in genomics and then using Clara Discovery to simulate compounds that could potentially be used as a drug?
Johnny Israeli
Exactly, in the context of drug discovery, we help figure out the most promising compounds for a given drug discovery program, and this is something we are really excited about. We started around a year and a half ago looking into drug discovery. We announced at GTC– in the fall of 2020 I believe– that we were going to build this software called NVIDIA Clara Discovery. That it would be an NVIDIA framework for all things pertaining to computational drug discovery. And that’s where there is all this cutting-edge work happening, and where we are actually looking to hire at the moment.
Nathan Horrocks
Do you want to dive into that? If we’re looking for engineers and researchers in this area, they might find it interesting to know more about what work you are focusing on.
Johnny Israeli
Absolutely, yeah. Drug discovery is a long, complicated process involving multiple disciplines. When you think about computational drug discovery, there are three dynamics taking place that could reshape the industry from a computational standpoint. Those three dynamics are what you are trying to do at the core of the computational drug discovery loop. You have a protein –a target– that you want to impact, you have a compound, which is potentially a drug to be developed. Then given a compound and the protein structure you can do all kinds of simulations. You are trying to predict if it would be a useful interaction.
Figure 2. A user can generate compounds with a simple click using NVIDIA Clara
Traditionally you would have a database of these compounds. All kinds of companies are cataloging and producing these databases, and there are billions of compounds today. Then you have the world of protein structures, which is produced by a whole bunch of groups doing structural biology work.
Now, three things are happening that we think could reshape everything. First is the breakthrough work by DeepMind and other groups in the form of AlphaFold and so on. We’re now using deep learning to predict protein structure. So if that’s true, we’re going to have many more protein structures to work within the coming years than we have had up to this point. That is dynamic number one.
Dynamic number two is through our work here in Clara Discovery, and also others in the industry, we are building the capability to generate compounds. Imagine using deep learning—not so different from StyleGAN and Gaugan—that can generate a seemingly infinite number of generated images. Turns out you can generate all kinds of compounds as well. We have software with a graphical user interface where you click and compounds come out. So that means in the coming years as this capability matures, we’re going to have a million X more compounds than before. Before we had a billion and in another few years, we’ll have a million billion compounds to work with. And that’s still scratching the surface because the number of potential compounds out there in the universe of such compounds could be 10 to the 60.That’s dynamic number two.
So the first dynamic is happening at large within the industry and NVIDIA is enabling it. For dynamic number two, we’re building a product for that. We have Clara Discovery and we have a specific workflow and a technology we are using called MegaMolBART.
MegaMolBART is the adoption of Megatron, which was initially developed for natural language processing (NLP) at scale, and we repurposed Megatron for the language of chemistry because there is a way to represent molecules using a string format. So you can repurpose all this NLP technology, and the same technology that is bringing Megatron to market is the same technology powering this part of our drug discovery work. It’s the same piece of software called NeMoMegatron.
Figure 3. Accelerating Drug Discovery with Clara Discovery’s MegaMolBart
Dynamic number three is if you have a million more compounds than before, and you have tens more protein structures than before, then the combination that you want to simulate is millions more than ever before.
Now, simulation, as we know it computationally, can be a very intensive problem. In fact, one of the early use cases of CUDA was in molecular dynamics and scientific computing in this kind of simulation. But the question is how, how do you enable a million X more of it? We are building a team to figure out that simulation capability and we are hiring experts in molecular dynamics, force field development, high-performance computing, and deep learning applications to simulation. We are also hiring cheminformatics experts, deep learning researchers, and engineers to advance our technologies for compound generation and interaction with proteins using AI.
And I think that captures what we do here. It is a unique group, in that we push products out and also have scope for product-driven research. We work extensively with engineering groups across the company to leverage technologies that can advance these products, and we collaborate with a variety of research groups to leverage AI breakthroughs across the company.
Nathan Horrocks
Would you expand on what you just said? What do you mean it differs from other NVIDIA research areas?
Johnny Israeli
I would say most research labs have more flexibility than we have in terms of the kind of research we’re pursuing. Our organization keeps a healthy balance between engineering and research so that we can ship products but also have the bandwidth to pursue innovative opportunities. But that does mean that our research goals or research agenda may be somewhat constrained by the objectives of the product in a way that the typical research lab might not be constrained. In a typical academic lab or even an industry research group, I would expect more flexibility, but it’s a tradeoff. It’s a tradeoff between flexibility and the intense focus that is needed to ship a software product.
Nathan Horrocks
That’s what I was going to ask. What value is there for a researcher then to want to join your group?
Johnny Israeli
Great question. I would say we tend to attract researchers who are interested in innovative research and are passionate about making sure that their research has a business impact. And for those individuals this tradeoff makes sense. They are willing to constrain and focus the research as needed to have that kind of business impact that they desire.
Nathan Horrocks
So their research is more focused on improving Clara Discovery and Clara Megamolbart?
Johnny Israeli
That’s correct. So we need to align the research activities with the product goals.
Nathan Horrocks
You’ve mentioned that the larger portion of your work involves engineers, how knowledgeable do you think these engineers need to be in biotechnology?
Johnny Israeli
A great question. I find that many up from the engineering background learn this on the job. What’s more important is not so much the knowledge of the industry, but genuine interest. We have multiple examples here of engineers who may be studied some of this stuff in college, or they just read about some of the stuff and they have the right engineering background.
And you know, a year or two years in they know their industry really well because they work with our partners and collaborators. So I would say interest matters most.
Nathan Horrocks
I remember you mentioning at the beginning anyone interested in the intersection of AI, simulation, and drug discovery would find this work interesting.
Johnny Israeli
Exactly. This is exciting and incredibly challenging work, and we are just scratching the surface. I am looking forward to what the next few years will bring as we dive deeper into NVIDIA Clara and its potential to contribute to the biotech community.
Additional Resources
If you are interested in learning more about NVIDIA Genomics, check out our Genomics page.
To stay informed about new research being done at NVIDIA, visit NVIDIA Research.
Try Fleet Command for free on NVIDIA LaunchPad. 
At Omniverse Developer Day, deep-dive into panels and expert-led breakout sessions to learn how to build, sell, and distribute your own 3D tools. Happening at GTC March 21-24.
Informative sessions on automated cyberattack prevention, power grid management from the edge, optimizing network storage, edge AI-on-5G, and addressing malware with data science.
NVIDIA is accelerating the field of genomics and drug discovery with the help of GPUs. We sit down with the lab lead to learn more about their work.
