Node Congress

Node Congress 2021

Lightning Talks, Day 2, Node Congress

Hey, welcome back everyone. We are going to go into a nice Q&A time with our lightning talk speakers. But unfortunately, I forgot to mention this before, but Avital and Jonathan are not able to join us. As you can see, they're from Israel. So that means it's now, I think, like 2 or 3 a.m. for them. So they decided that it was not a good time for them to join. But we do have Shivaay and Alejandro with us. So would you join me on stage, please? Hi, everyone. Hey, thanks for joining us. So great for this thanks talks. English is not my first language, so forgive me. I would like to remind the audience that they can ask questions. Alejandro and Shivaay, we were just in the backstage discussing some things about TensorFlow. You were having a nice discussion. So maybe you would like to share this with the audience? Sure, absolutely. So I was just describing my overall experience and how I got started with TensorFlow. So as any kind of a newbie machine learning sort of interest, I got interested in machine learning very quite early in my journey in my computer science engineering. And I basically started off with a few basic algorithms. But of course, like as you go deeper into things like neural networks, there you get introduced to things like TensorFlow. And what I found was that TensorFlow is a really powerful tool when it comes to a lot of different neural network and image classification, object detection based algorithms. And with that kind of an experience, I got the opportunity to be working with the TensorFlow organization in Google coding as a mentor. And that sort of led me to working a lot with TensorFlow.js. And one of my projects that I made in TensorFlow.js actually got featured on the official TensorFlow channel on YouTube. And that just had like a great impact in my life. And with that, I just wanted to spread a lot more about TensorFlow.js because it is a very wonderful tool for those people who are coming in from these JavaScript background, but they also want to include things like machine learning their projects. Because normally, if you want to get like machine learning based projects into your web application, you would have to learn Python, deploy your machine learning models separately on a separate server, and then get that done. But with TensorFlow.js, it's very easy to just have a few lines of code, and you have a completely off the grill running machine learning based web application at your hands. So that is the sort of the inspiration that led to me working on TensorFlow.js. Cool. And can you share also a little bit about your developer story? Like how long have you been developing? We've discussed this in the start of the program today. What was your answer? When did you start professionally programming? So I started professionally programming in my first year of my college. So I graduated last year from my undergrad degree. So it's been almost three, three and a half years, ever since I've been working in quite a lot of different internships, full time roles as well. So I mean, and with TensorFlow.js, it's been almost more than an year right now at this point of time that I've been using TensorFlow.js as my primary tool. Cool. And what about you, Alain? So my first experience was with a microchip, PIC16F, something like that, with assembly language. And it was during high school. It was a very hard introduction to programming, but you will also get to do a lot of things with hardware, which was very interesting. Then later on that made me change into careers and go study computer science. Yeah. You even switched the whole studies because you liked that so much. Yeah, yeah, definitely. I always thought I would pursue electrical engineering. And yeah, after having a rough first experience with programming, I changed into computer science. Yeah. That sounds familiar. I actually was in school to be an electrician and I really liked it, working with my hands. But then I had to do an internship and it was around February in the Netherlands. So it was this time, yeah. And it was really cold and I had to work in construction and like put down cables and stuff in houses that were not finished yet. So without windows and doors and anything. So yeah. And then I was like, no, this is too cold for me. I'm not going to do this job. And then I became a developer. Yeah. Don't get me wrong. I still like all the topics on robotics, electrical engineering. I'm so passionate about that. But yeah, programming turned out to be a wiser choice in my case. Yeah. Yeah, I can imagine. We have a question from Juan Gonzalez to Shivay. Do you know about examples of cool and useful things that have done with TensorFlow? Sure. Yeah. So as we know that machine learning has a wide variety of applications in all different fields of life. So you will see TensorFlow being used in a lot of different applications in the field of healthcare. So I myself made like a personal AI based physiotherapist assistant that can actually give you guidance on if you're making certain poses or like whatever body exercises that you're doing, if you're doing it correctly or not. Then we are using TensorFlow for things like detecting cancer in skin diseases, right? To be able to detect whether it's cancer. Then using to detect different types of malaria. And then again, TensorFlow was actually used to detect the coronavirus pandemic because a lot of different x rays of the test were taken in and there were like TensorFlow was used over there to find out, okay, whether this particular person can have the coronavirus because a lot of the coronavirus patients had developed certain kinds of issues in their lungs, which could be detected with these x rays using TensorFlow. So it has a wide variety of applications and that is why it really is just, you know, you might be interested in any kind of a field, whether it's like linguistics, robotics, natural language, right? It could be education and it can be used anywhere. That's the magic of TensorFlow. Yeah. Yeah. Finding patterns, basically. Yeah. That's actually, I consulted a company that is using AI. I don't know, except the AI team is a different team and because of the pandemic and working from home, I don't interact with them a lot. I don't know if they're using TensorFlow, but we are detecting early stage lung cancer also with, well, AI. So it could be with TensorFlow basically. And yeah, that's really cool. Like this, I didn't even know that that was possible. So I've been working there for three months now and every day that I feel really proud to be doing these kinds of things. Next question is from Kerry Cool Dude. How can Node improve the TensorFlow backend in terms of performance? Absolutely. So see, one of the biggest reasons why we would want to use TensorFlow.js with Node is that we always, we know that Node has the just-in-time compiler, right? So that makes the performance boost of using TensorFlow.js because again, like TensorFlow.js is essentially running machine learning models in JavaScript, right? And machine learning models, they require a certain amount of competition power to sort of train themselves on any kind of an input data that you give to it. And using the just-in-time compiler that dramatically improves the performance because with your server side that you have set up, like if you have like a dedicated server setup, you can also use things like dedicated GPUs and dedicated CPUs for the application that you're running. So even those have bindings with the native Node compilation that takes place and that improves, drastically improves the performance of your Node.js based machine learning applications. So using the built-in just-in-time compiler and then giving the ability for using external servers with these dedicated GPUs that have a direct combination within the Node.js bindings. That enables us to put up very quick performing machine learning applications. Super cool. Next question is from Alexey. Can we run TensorFlow on Raspberry Pi 4 or will it require more powerful hardware? So TensorFlow can be run on like Raspberry Pi. So you can also use it with Node.js because with Node.js, you have like specific bindings for Raspberry Pi running via Node. If someone has configured Raspberry Pi via Node server, they can also run that and you can natively run these machine learning TensorFlow based models directly on Node on these Raspberry Pi machines. And if you have a very light machine, then we have something called the TensorFlow Lite that allows you to run very basic models directly on the system itself, on these very low powered devices. There could be mobile phones, it could be something like a very, like even Raspberry Pi 1, 2 as well. So even those are supported in TF Lite. And you can just, if it doesn't run that fast, you just have to wait longer. Probably yes. But most of these models are optimized for these resources. But keeping in mind that you cannot run like, you know, models that are going to take you hours and days to actually train, we have to be considerate about that. But it runs, allows you to run most of the off the shelf models directly on these platforms. And if you don't have a powerful machine, then you can always rent a virtual machine, of course, that just spin up, run your model, close it, and then will not be that expensive. And like, again, optimizations are always being made to the current models to make them more efficient for low powered devices as well. And that is why TensorFlow.js is a great way to also utilize because it runs off the shelf on your browser or on your backend without a lot of competition required. Really cool stuff. I'm going to make a little plug right now, because we have some interest now in TensorFlow. And for the people that are watching right now and want to learn more about TensorFlow, Git Nation, the organization behind Node Congress, it's actually been organizing MLConf EU, I think around in November. And if you have to Git Nation multi-pass, you can watch all those talks in retrospect. You can just go Google MLConf EU and find a lot more great content from the talks we had back then. So now we have time for one more question, and it's from Communus. I wonder if you can use TensorFlow.js to analyze the Node.js internals. Yes. So basically, if you want to use so again, this is a really good question. But essentially, what we are doing is that we're evaluating the performance of our Node.js applications. And how basically we can use the performance of the Node.js to enhance these machine learning models. So definitely, we have these internal tools that have been dedicatedly built to evaluate the performance of your TensorFlow models directly. So yes, that's definitely possible. Cool. Cool. All right. Well, guys, that's all we have time for right now. So I would like to thank you for joining us and hope to see you again soon. All right. Thank you, Mehdi. Thank you, Shibai. Talk to you soon. Bye-bye. Bye-bye.


Alejandro Oviedo

Shivay Lamba

Panel Discussion: Node.js in the Cloud

Ali Spittel

Eran Hammer

Ruben Casas

When troubleshooting complex Node.js applications recollecting information about the network, filesystem or external dependencies is always useful. Even when working with a new codebase insights on how the application is built, which asynchronous calls are being used or how often. In this talk I want to introduce "instrument", a tool I've created for this which is also Open Source. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Instrumenting Node.js Internals

Hey everyone, before we continue, I wanted first to thank all the organizers and volunteers that made this happen, dedicated their time, and also gave me the opportunity to be presenting to you today. My talk is about troubleshooting Node.js applications. Before that, a little introduction about myself. I'm Alejandro. I'm from Buenos Aires, Argentina. I work as the backend engineer at Xteam, and I also contribute with a couple of communities, both locally and some other that are international.

So into the topic of this talk, say you have a Node.js application that is running, but something is not working. There is no exception. You don't have locks. Where would you start? What I would try to do is to tear apart the parts of the application so it's easier to pinpoint where exactly a problem is. Say it's a server, I would check the bootstrap part of the process, a breakpoint right after it initializes to see if the error happens before that or after that. Oftentimes I would be in the process of troubleshooting an application, and I would think I have no idea why that is happening. I would be just like this dog. So a lot of times this would also happen on my development environment where I have full control, full access, and also should have a good observability about what is happening. This last part was one of the motivations that I had to work on a tool that would practically no changes to your application could allow improved visibility and potentially turn yourself into Ironman like in this scene. Viewing a lot of useful information at once. I will go over a few specific examples that I could think of on a short demo. On the scenario number one, imagine you have a script or an application that is running through Node, we want to be able to tell if it's going over the network and if that's the case, to collect more information about what URLs it's using and possibly more information on that. So we will go on to my demo. For this, let's say we want to try to do that with NPM. And we will try to retrieve the latest version for a package, in this case, Mocha. We get the result 802.1. Say we wanted to do that with this tool, with NPM. We can require instrument pass the same arguments that we were passing before. And first of all, we will see the same output that before 802.1.
And after that, a short summary for the tool. In this case, for these two native modules that are enabled, HTTP and HTTPS. Those two are specifically configured here in the modules list. You can add another ones like FS, require, and others as well. And this configuration is added on the instrument.config.js part. So we can see that this is going to registry.npmjs.org. We see that the method is get. And the headers is region, NPM, NCI, and whatnot. So, all I had to do to require that tool is to add as a development dependency. For another example, say you're a refactoring HTTP server and you want to identify which files are importing module A.js. The module is only being loaded dynamically by a specific route in your server. And in this case, it would be practically impossible to do this task with the static analysis. You would have to instrument your application or your server to get that information. So by adding the require module in the list we previously saw, you could get that information using this tool as well. A little caveat on this and a recommendation if you should be running these on other environments that are not your local environments, and the answer is no, definitely no. There is a performance overhead on adding this instrumentation. And I'm sure there are a few bugs that you will find if you test this on your own. So it's not recommended to do so. You can find this tool on GitHub and see the code. See an open issue, pull request. And if you have further questions on how this is being done or other technical questions, we will be holding a Q&A session or feel free to send them over via social media, Twitter. That's everything that I have for you today. Thank you very much.