I have money anxiety, man. I'm fixing to share a lot of stuff with you guys, just personal stuff. But I want to be completely transparent with you, right? This is going to be one of those get to know Ricky videos. And I want to do these kind of videos because I want you to know who I really am because I care about you and I want a lifelong relationship with you helping each other, collaborating, doing business, right? Me helping you be more efficient in your business. You helping me by selling more property, you sell more property, you're successful. I still have a business helping more agents be successful. That's what I'm excited about is the future for me and you working together. And that's why I want to do a video like this.  And but yet they have millions or hundreds of millions or billions of dollars. Why? Why do they do this? And it's not true for everyone. It's not true for everyone. Um, but I believe that they have a level of money anxiety, money anxiety, right? It's money anxiety. And this is a real thing. And if you're, you know, even if you're living paycheck to paycheck, that's money anxiety, you're worried if you can pay your bills the next month. And if you have a billion dollars, you're worried about what? You're worried about losing it all. When you, when you get to where you have money, it never goes away.  So I have PTSD from what I went through back when I lost everything back in the 2008 is actually 2005. I saw my last condo in 2005, January. And I saw my first condo coming back in May of 08. So between 2005 and 2008, I sold nothing. And I was literally sleeping in my car. I was eating out of people's refrigerators. I was roofing houses. I was serving tables and I was working on an oil rig in Mississippi every other week. And just years before that, a couple years before that, I was a millionaire. In my early 20s, a millionaire, and I lost it all. Things were going so good. I had hummers, Cadillacs, houses, everything. And then all of a sudden out of nowhere, I get blindsided by the market. It crept up on me and before I knew it, there was nothing I could do. It was too late. And here it was.  And there's nothing you can do about it. It's one of the worst feelings in the world outside of losing a loved one, right? Money isn't everything. Money is not everything. It's not, it's not anything. Money is nothing. But you need it to live. That's the thing. You need it to live. I mean, you know, you could say, well, I don't need money in it. Well, you don't have a home. You don't have a car. You can't get gas.  You do need money to live and you need money to support your family, give them the best life that they could possibly have. So that's where the anxiety comes from. It's not about the money. It's about being able to live. And I've been on both sides of it, not being able to afford the food that I want to eat, not being able to buy gas in my car or pay for a house. I've been there. And so now things are going a million times better than it was back then right before I lost everything.  Where's it coming from? Where's the blindside coming from? What's going to happen? And I had to take a step back and realize what was going on with me. So let me walk you through what was going on with me. And it's been going on with me for years. And I even know that it came back to money anxiety. Oh, man, this has been a breakthrough for me. And I hope it helps you what I'm explaining here. So I wake up. I've been waking up at 5 o'clock every day. Every, I haven't taken a day off in 10 years. Yes, I go on a vacation. Yeah, we'll travel places to speak. Those are work trips. Yes, I take off on the weekends. Yes, at 5 o'clock, I'm done. Yes, yes, yes. Monday through Friday, you know, from really 5 o'clock to 5 o'clock, I'm working. Never took a day off. Never said, oh, I'm just going to lay around today. I'm going to go hang out. I'm going to just watch movies. I'm going to, you know, chill. Never. I haven't taken a day off in 10 years, 20 years. And it comes from the fact that I feel like if I let my foot off the gas for a second, that could be the moment that I lose it all. It's not going to happen. That's just in my mind. I didn't realize I was worried about it until I actually got burned out. So get up at 5 o'clock, go to the gym at 6, working out like a Navy SEAL. By the way, I have lost 20 pounds. I don't know if you can realize that through the video, but from Thanksgiving to now, I've lost 20 pounds. So I'm 222. Now I'm 202. Eating right, taking care of myself, got serious about diet.  And I never could get a hold of it. And I finally said, you know what? I'm going to do this. It's kind of like, it's kind of like when I quit drinking and doing drugs. It was literally 10 years ago from about 20 days from now. It'll be 10 years. And I was addicted. I was addicted to drugs and I couldn't quit. I wanted to, I tried to, couldn't do it. And then one night, I almost died and I said, if I wake up, I'll never touch anything ever again. Well, I woke up, threw it all away, never touched it, never touched it. So we have to hit rock bottom sometimes and we have to get fed up with our current situation until we get fed up with our current situation. We're not going to change our current situation. It's like a lot of people that won't make calls to sell properties as real estate agents.  You're, you're satisfied. You're comfortable where you are until you get uncomfortable enough to make a change. Nothing's going to change.   So 20% of what you do produces 80% of your results. So think about that. You could work just 20% of what you do, still produce 80% of what you do. Now, if you can become efficient on the 80% that doesn't produce, you know, that produces the 20%, you can get to 100% in about half the time that you're spending now. It's the problem is we're spending so much time doing things that don't move the needle. And that's me, right? And I overcompensate by working really hard to, you know, to, I'm chalking that up to, if I work really hard to keep the pedal to the metal, then maybe I can, you know, be in position to fight off whatever happens that comes along to swoop up my well. But the problem is, nothing's going to come through. If I lost everything, if we lost everything, our rental properties pays for our life. And we own those free and clear. We have very little debt at all, right? We owe about 10% of what our home is worth, this home that I'm in right now. I pay cash for rental properties. I've got a little debt out there on a few things. But overall, I don't buy, I don't leverage. I don't buy properties and then leverage those to buy other properties. I don't do that. What I like to do is buy properties, keep them for five, seven years, sell them 1031, that one property into three properties. I like to do that. I don't like to do it the other way around. But nevertheless, the grind.  I'm in here like, I'm just like, it's like, when am I going to live my life? So I became burnout. I became incredibly burnout here recently. I like, it's been going on for a while. It's like, at 9 o'clock at night for years, it's like, I'm dead. I can't even like chill. My brain isn't working. Like it's not, it's not, this doesn't work anymore. So I went to the doctor. I got on testosterone. That's been great. But it hasn't been like the answer. Like it hasn't answered all my problems. I've taken vitamins. I drink more water. All kinds of things. Nothing has really worked, worked. And I started thinking about it and had a long talk with, with my friend Juan. And it's like, he's like, bro, like, if you, you know, if you like, slept in and like went to the gym, like mid-morning and like started working, getting new and your income wouldn't change at all. And he's right. Everything I have is passive. All my income is passive. You know, and I started thinking about everything that I do. And I'm like, oh man, he's right. Why am I living like this? I'm just taking you through the process here of having this money anxiety and what it will do to you and eat you alive. And I don't want it to eat you alive. I want, hopefully, you know, if you live paycheck to paycheck, realize that worrying is not going to solve any problems. If something bad is going to happen, it's going to happen. 95% of the stuff we worry about never happens. The 5% that does, you can work. You can deal with it when it happens. And I didn't live, I haven't been living like that. I didn't even know it. I didn't even know it. So I've just been putting a lot of pressure on myself. You know, why do I answer every single DM? It's because I'm scared I'm going to lose the brand equity that it took me. So I've given my soul to the real estate industry. I don't know if you realize that or not, but I have, tirelessly, coached for free for seven years, helped thousands of agents double and triple their volume, answered every single message, made countless videos, and done countless speeches for free. I pay travel. Like, I've given my soul, my heart and soul to the industry. And for a long, long time, I don't know anybody that's done what I've done as far as just pure manpower and just given back, you know, to the industry. And it's a lot. It's a lot. So I'm just like, this is kind of a testimony. This is me just venting to you and just giving you my thoughts. So again, hopefully it can help you. So I'm here. I'm getting up at five. I'm working out every morning. I come home. I'm just growing, growing, growing until five o'clock. And then I'm dead. Okay. Well, that can only last for so long. All right. That can only last for so long. So back in 2005 and seven and eight, I started reading books. I read about a hundred books over the course of about three years. And then I quit reading books. The knowledge from those books took me to be, to sell 100 properties a year for eight years in a row, took me to be the number one remix agent in Alabama for several years, number one in my MLS for all those eight years. It may, it got me to the point where I wrote a book. It got me to the point where I started doing social media and crushing social media and becoming one of the, one of the, you know, biggest names in North America when it comes to real estate agents. Those hundred books a decade and a half ago got me where I am today. But I've been stuck for several years and I'm just grinding, grinding, grinding, trying to get to the next level, grinding, grinding, grinding, trying to keep my pedal to the metal to make sure that I don't lose everything I worked so hard for. And it's not that I'm, I'm, I'm not only working because I'm trying to like make sure I don't lose it all, but I'm also working hard, always trying to get to the next level. But I've been stuck, been stuck for years, been stuck for a couple years in terms of income, just in terms of personal growth. And it all started to kind of hit me about this and came to the revelation of, of what's going on here. And now, guess what I'm doing? I sleep till six, six, 30, I think it's up to like 6.45 this morning. Oh man, it's been life changing now at, at nine o'clock at night, I'm not dead. I spend an extra hour and a half after I normally go to bed, like from nine to like 1030. I think he even stayed up till 11, which is like big for me, like 42. So I used to go out all night till three or four o'clock in the morning, sleep for two hours and go roof houses at seven o'clock. You should do that every day. All right. But, but now it's different. And the extra hour to two hours I spent last night with my family was euphoric, euphoric. Because my wife's been doing that. My wife's been hanging out with my mother daughter late at night while I'm asleep. You know, we chalk it up to, well, he's the breadwinner. He, I don't see breadwinner, like my wife is the breadwinner. Like she's the one that runs everything. I'm just the face of it. What I'm saying is, is like, you know, I'm just the face of it. What I'm saying is, is like Ricky's the workhorse and he's the one out there really, you know, grinding it and coming up with the ideas and executing on all this stuff. Like, let's let him sleep. But at the end of the day, it's wrong. So now I'm sleeping in, right? And guess what I do? I don't go to my DMs. I don't come in here and start making lists of what I need to do and priorities and everything else. No, no, no, no, no. What I'm saying is, you know, I'm just the face of it. What I do is I go straight to a book. Yeah. And for the last three weeks, I've been reading every day. And let me tell you something. My life has already completely changed because of that. The knowledge that I've gained over the last couple of two or three weeks has been life changing, enormously life changing. The way that I'm talking to people and communicating, the way that I'm carrying myself, the thoughts that I have, the things that trigger certain ideas in my mind is, is on a whole nother level. And I'm just getting started. And I actually went out there and you guys, you know that I'm the originally, right? I'm the original. I'm like, don't pay for coaching.  I went out and wrote a check for $55,000, $55,000. For a coach. And what I've realized, I started charging for coaching back in December and I've been going down that path of understanding it. And what I've realized is that there's a lot of fake coaches, fake gurus out there. There's a lot of people that have not accomplished the things you want to accomplish that are selling courses. They're making a lot of money. They're selling regurgitated information they heard from somewhere else, et cetera. But when you find there are coaches that are the ones, there's the ones out there and the ones, oh, they're worth their weight and goal, whatever they charge it isn't enough. Whatever they charge is not enough. Like for example, I've helped hundreds and we've even tried to add it up. Like somewhere in the thousand plus agents that have doubled and tripled their volume through my free coaching program. And I have testimonials from agents who have made 3, 4, 500 extra thousand dollars. They say, I make 500 extra thousand dollars a year from your advice. Over 10 years, that's $5 million, 20 years, that's $10 million. Like I added $10 million to their life through my advice and that may grow to be even more. I've got agents that will tell me I make an extra one, two. I was talking to an agent today and I was like, I was like, let's just think about it. Do you make an extra like one to 200 extra more just based on what I taught you for free? And they were like, that's, no, that's low. That's low. And you start thinking about this and you start realizing, okay, because back then it fulfilled me to tell somebody some advice, they go execute and make 500,000 bucks. And that used to be, I used to be like, oh my God, this is amazing. And it was. And now, now that it's proven that what I do, that I am one of the ones, now that it's proven there's proof that what I do works and helps people multiply their income, now there's actually a quantifiable price to it. If I help someone make an extra 100,000 a year, is that not worth 10,000 bucks?  It's a million bucks every 10 years you made off of that $10,000 worth of advice. It's worth it. And so I understand this concept and I had to separate the thing that pissed me off about paid coaches is the fake coaches that never had a license, that never really sold property, that never had a license, never sold a property, or the ones that sold 19 properties in their selling courses for $3,000, people that were never top producers. You know, listen, it was envy, it was jealousy, really.   But it was more just like, man, this is unreal, you know, and then I couldn't go out there and sell anything, anytime I tried to offer something, you know, for sale in terms of coaching programs, because I gave everything away for free, you already got everything for free, why are you going to pay? You already got it. I've already helped you double trip your income. What are you going to pay for something for? I've already given it to you. I get it, right? I've kind of brought that on myself. And that's fine. I understand. I understand how I dig myself out of that hole. But the point is, is that I understand the game now and what that charge is really for. So when I paid this man $55,000, I did it in full confidence that I'm going to make a million a year, a million a month, a million, like I'm going to make so much money off of the knowledge that I get from this guy, because he is one of the best in the world at what I want to do. And I'll make so much money from that $55,000 investment. So I did that. So I'm reading, I hired a coach and I'm in a much, much better place. I'm sleeping more. And everything is a complete 180 right now from where it was just a month ago, just a month ago. And I am changing, right? I'm transforming into Ricky 5.0. Reading more, oh my gosh, it has changed my life already. I'm communicating better. I'm helping people more. It's just, it's the quality of life. So anyway, I just wanted to share this. My dad sent me this text. I want to share that with you in just a second. He sent me a devotional, one of those daily devotionals about what we're talking about. But I just wanted to kind of recognize that I know there has to be other people that are going through this, money anxiety. Oh, it's a killer. It will rip you to shreds. And it's ripped me to shreds. And I still have it. It didn't like go away. Like I'm still like worried, but now I'm conscious of the fact that it's going on. I'm conscious of the fact that I have it. And it's a work in progress is what it is. And I've got to, like Juan said, bro, it's time for you to live your life. My dad said, he was like, I've been warned and warned about you, all the money you make. He was like, I've been wondering why you're not in Italy and in Africa and in Morocco. And like, he's like, I don't know why you're not traveling the world and just chilling. Like if I had your money, I would just be hanging loose. I wouldn't be grinding like a government mule every day. So anyway, but that's just who I've always been my whole life. That's all I know. Like in my mind, I'm still the teenager roofing houses trying to make a million dollars, even though I have the millions. In my mind, I don't. I'm still trying to get there. So, but I'll tell you this before I share the devotional that he shared with me. A lot of people think, okay, well, once you make a million, they want to go make 10 million. Once you make 10 million, you want to make 100 million. Once you make 100 million, you want to make a billion. Once you make 1 million, you want to make 2 billion, blah, blah, blah, blah, blah, right? And yes, I get that. I understand where you're coming from when you say that. But for me, what you have to understand is that I always itched for a nicer house. I always itched for a nicer car. I always itched for better life. Better life. And, you know, everywhere we lived, everywhere I lived and I live different places with Carlin, I've always wanted a nicer place, nicer car, etc. In 2020, we bought this house. I haven't had a single moment that I wanted a nicer house. So that did stop. It's not like I just wanted more and more and more and more. I wanted more and more and more until I got to a point and then I was good. I have zero interest in buying another house, moving, not because of interest rates and stuff, just because I don't have that itch. I'm not like, I want something new or nicer, bigger or better. My car, when I got my Tesla, I haven't, you know, I used to want a new truck. Every, I had Ford F-150s. I used to want a new one every year, every year, every year, not every year, every two years, every two years, I would get a new truck. And when I got my Tesla, that stopped. I haven't even had the single inkling of the feeling to want to get a new car or a truck or anything. My wife got an Escalade in 2011, a brand new one. Or maybe it's 22, 21 or 22, and it's 21. And we haven't had a single itch. We used to want another one. She had a Lexus SUV. We wanted another one. We got the Escalade. We haven't had an itch to want anything else. So what I'm saying to you is that, yes, I still have an itch to get to the next level of business and success and make more money. Yes, but I want you to understand it's, it will stop for me. I will get to a level where I'm like, okay, this is where I want to be. Just like my house, just like my car, this is where I want to be. I will get to a level of income where I'm like, okay, this, this feels good. This feels safe. This feels comfortable. This is where I've wanted to be. I don't know what that number is, but I will hit. I didn't know this house was going to be the house that made me stop wanting houses. I didn't know my Tesla was going to be the car that made me stop wanting new cars. I don't know what level of income I'm going to have to hit to where I'm like, okay, I feel great about this. But I know based on what's happened with my car, my house, my wife's car, that it will happen and I will get there. And now I'm just enjoying the process. I want to read this to you before we go. Let's see. Let me scroll up to his text message, share this with you. So we talked about this and like I said, he was like, man, I've been wondering about you is what he said. But he sent me this right after we left each other. We work out every day in the gym together every morning. And even since I switched over to doing later in the mornings, he still comes. So he came to date 10 o'clock. He went yesterday at 1230. So he sent me this.   Your future looks uncertain and feels flimsy, even precarious. That is how it should be. It should be flimsy. It should, it should seem out of whack. Secret things belong to the Lord and future things are secret things. When you try to figure out the future, you're grasping at things that are mine. This, like all forms of worries and active rebellion, doubting my promises to care for you. Okay. Trying to figure out the future, right? You're grasping at things that you don't have any business grasping for, right? Live in the present. Whenever you find yourself worrying about the future, repent and return to me. I will show you the next step forward and the one after that and the one after that. Relax and enjoy the journey. Oh, relax and enjoy the journey in my presence. Trusting me to open up the way before you as you go. And there was a few scriptures in here, Deuteronomy 29, 29. The secret things belong to the Lord or God, but the things reveal belong to us and our children forever that we may follow the words of his law. Luke 12, 25 says, Who of you by worrying can add a single hour to his life since you cannot do this very little thing? Why do you worry about the rest? I will instruct you and teach you in the way you should go. I will counsel you and watch over you. So, I just want to say that one sentence that really hit me. Relax and enjoy the journey. Oh, man. Thank you, Jesus. Thank you, Jesus. We go to church every Sunday. We're part of a really, really incredible church. And we enjoy going to church and being a part of that. But yeah, anyway, look, I just wanted to share this with you. I hope maybe you got something out of this. Maybe you didn't. I felt like, hey, let me let you get to know me a little bit more. There's a lot of things about me you don't know, but over time, I will share those type of things with you. And if you ever have any questions, man, if you guys ever have anything that you want to share with me, you can share with me in confidence. I'm happy to help in any way possible. All right. All right.  We'll talk to you soon. Seem like everybody want to


Welcome to the Red Hat Data Services Office Hour. Maybe we should rename this to All About Data Analytics. Don't be surprised if the name changes. I'm Michelle DePama, Red Hat Principal Solutions Architect, and today I'm hosting a Rhodes partner call with Intel. So remember that Rhodes stands for Red Hat OpenShift Data Science, which is an add-on to our OpenShift Managed Services. But today's focus is Intel's OpenAPI AI Toolkit. So I expect to have time for introductions. We're going to set up the topic, and then we're going to do a deep dive into the toolkit and include a demo, and hopefully have time for Q&A. So I'm here with Audrey and Rachel. Audrey, fellow Red Hatter, could you give us a brief introduction? Of course. Can you hear me okay? I just wanted to... Yes, and we can see you. Lovely. My camera's working now. So hello, everybody. My name is Audrey Resnick. I'm part of the Red Hat Data Sciences team here at Red Hat. I have been with Red Hat for a year, but I've been with the IT industry for over 20 years for full-stack development to data science, and I have been... Oops, sorry. And as a data scientist, I have had the pleasure, or you could say the torment, of developing and deploying AI ML models into production. So in a world of Jupyter Notebooks, VMs, get lab runners, S2I containers, public hybrid cloud, on-prem cloud, et cetera, plus OCP, OpenShift platform. I'm very excited to work with the Red Hat Data Science team and specifically share information on their Red Hat OpenShift Data Science offering with you. Rachel will be introducing herself, and Michelle, I'll ask you just when we're ready, just to let me go ahead and then I'll talk about what I'm talking about today. Okay. Hi, Rachel. Can you introduce yourself? Hello. Yeah. Yeah, so my name is Rachel Oberman. I am a technical consulting engineer here at Intel. What that means is that I work with these product teams for our AI offerings, in particular for the AI and tool kit, which I'll be talking about today to make sure that our products are the best they can be and the customer voice is heard. And in turn, I also work directly and to end with customers either on high-touch engagements or in enabling conferences and trainings such as this. And it's a very exciting job and I'm really excited to talk about what Intel and Red Hat are doing today with Red Hat OpenShift Data Science and the AI kit. Awesome. Okay, so Audrey, I was gonna ask you to kind of set the topic up. So I'm gonna share, I think, Rachel, is this your screen I'm sharing? Yes, I think so. Okay, so I think it's actually, is it a few slides down, but there's sort of an overview and I just, you know, we don't actually have this chat every week. So I think we need to remind people of where we are in our kind of ecosystem. So it would be, hang on, back up one maybe. No. It's our favorite slide. Keep going. This slide that helps me. There we are.  Oh, I, these are the slides I see. Maybe you could share your screen if you have to slide up. Yes, I do. I see it. Nope, I'm sharing your screen right now. So, so Audrey, can you tell us where we are? Is this the slide that you were thinking of speaking to or, oh, did we lose her? We may have lost Audrey. Hang on. I think we're having technical difficulties. I think we're all streaming from our home. So, you know, it's something, you know, when we do these things, it's always, it always happens once in a while for technical difficulties. It does, it does. Okay, so let's see. Hang on. Okay, I'll let her know. We can't hear her. Can't see or hear her at the moment. I'm just going to go back to the title slide right now.  This, the idea was just to kind of set the topic up and see where we are in our ecosystem. But, and then right after that, we were going to go into all your slides anyway. So I think she's going to join. She just dropped off. Okay. All right. So go ahead, Rachel. Yeah, sure. So I think Audrey was supposed to go first and kind of set up the red hat and open Shisha. I should just go ahead and talk about AI kit. And then maybe, maybe we could talk about AI kit and then we could go ahead and talk about red hat open shift and how the two connect together. I think that sounds good. So I'm actually going to go ahead and skip to that part and we'll get back to that good stuff later on. But let's talk about first about the one API AI analytics toolkit and how Intel as a partner is enabling the AI kit in red hat open shift data science. So to understand the one API AI kit, the first thing that you have to understand is what is Intel one API? Essentially, this is a big, big drive by Intel maintained on two specific principles. One is performance. And the other is open source programming across XPU. And I'll talk about what XPU is in just a moment. But essentially with one API, and it's not just in AI libraries, it's not just in Python. It's also built in C++ and Java and so on. It's basically taking all of these low level libraries and all of these beloved libraries and having them built on the C++ and SQL standard and having these all consistent so that you're not dealing with different standards or different compilers and so on and so forth. You're able to mix and match between the libraries and not only that, but also having it be handled on all different types of hardware. So we're not talking about just Intel CPU. We're talking about GPU, PGA and so on and so forth. This XPU where X is like any kind of accelerator that you're trying to run on. And the idea is that once you are coding on CPU, you should be able to port and be able to a code to say like GPU and so on and so forth with much ease rather than having this very differentiated ecosystem, which is really hard to handle. Now, with that being said, what you see on the right is a mix of different things that are all part of the 1API ecosystem. We have the languages encompassing 1API. So we have C++, that's SQL standard and that DPC++ compiler you see there that I mentioned, Fortran and Python. We have libraries that Intel has built and maintained regarding these, such as 1TBB, 1MKL, 1DNN, 1DAL and 1CCL in particular, which have a great importance to the AI kit library. And then we have a media and rendering libraries as well, Embray, OSP, Ray and so on. With 1API, we actually go ahead and package many of these libraries to cater to different personas. So in this case, for example, the AI kit persona is a data scientist or AI developer and I'll talk more about that in a moment. And beyond the libraries, we also provide tools to help with these libraries, such as the DPC++ compatibility tool and DCTune profiler. Now that's everything that's going on with 1API and with this, you're seeing all of these libraries that are built in C++ and I'm sure if you're a data scientist or an analyst or something, you're like, I don't want to learn C++, that's a bit too much. I just want to go ahead and code and take a learn and that's okay because what I'll be talking about today is how we've added these very performance-optimized libraries and we put them underneath these beloved frameworks such as TensorFlow, PyTorch, Psych2Learn and so on and so forth so that in a drop-in acceleration format with less than, I'd say, five lines of code. If that, maybe two, I think is the average. If that, you can actually go ahead and accelerate these beloved libraries to a significant level just by adding a line of code or turning on an environment variable and you don't need to go ahead and overhaul your whole framework or overhaul your whole pipeline to fit a specific framework or to fit a specific hardware. Now with that, let's talk about, that is what's powering underneath the 1API AI Kit libraries and I'll be giving an example later on of how that works as well as a demo but let's talk about what is included as part of the 1API AI Analytics toolkit and what you see on the right is all installed by default when you install the AI Kit operator for Red Hat OpenShift Data Science. So as I mentioned before the 1API AI Analytics toolkit it is the persona, its goal is essentially to accelerate end-to-end machine learning and data analytics pipelines with frameworks and libraries optimized for Intel architecture. So go really fast but also don't compromise the integrity of your pipeline in doing so or don't go insane in trying to fit a specific architecture or a specific framework. You can go ahead and use your favorite frameworks without having to fight with yourself to have your hardware work for you and for this in terms of what this is targeted towards it's targeted towards that scientists, AI researchers and so on and so forth these accelerations in particular. Now in terms of the actual optimizations that you get with the toolkit you get in deep learning realm you get Intel Optimization for TensorFlow and Intel Optimization for PyTorch which are optimized by our one DNN our one deep neural network library from 1API underneath to bring you great accelerations to TensorFlow and PyTorch. We also have Model Zoo for Intel architecture. So if you want actually pre-trained models which are really optimized already by Intel you could go ahead and pull those by Model Zoo. And then we have the Intel Neural Compressor where if you are a user and you typically and you have a great model that's coded and say like instructions that like FP32 and you want to convert it to Int8 for better usage and performance so that you take better memory advantage but you don't want to lose accuracy you could actually play through the Intel Neural Compressor tool and it will go ahead and teach for you what the best way to do that without losing your accuracy. This tool was also previously renamed the Low Position Optimization tool or L-POT for short. Now in terms of machine learning we have Intel Extension for Scikit Learn which plugs in directly with the typical Scikit Learn but when you add two lines of code to it and I'll be showcasing this later you see huge, huge performance benefit and then we have optimizations for ex-divutors included as well. And the data analytics realm and this is really exciting for me personally is that we have Intel distribution of modem and the Omicide back end that we help power and maintain with it with Intel distribution of modem in particular you could think of it as distributed pandas for in a single line code change you could distribute your pandas data frames which typically run on a single core to all available cores. So if you hate waiting for read CSV to run for like hours on end, don't worry this really speeds it up and you don't need to worry about that. And in core Python, as I mentioned pandas we also have Intel optimizations for NumPy and SciPy and Numva that are backed by our 1MKLs library and we are also working on DPPY which is part of that larger XPU XPU drive that I talked about in the beginning and enabling Python across all of your architectures and enabling these frameworks across all different hardware. Now that I've told you everything that's included why should you care about this? Why should you use the 1API AI kit on Red Hat OpenShift Data Science? So besides just accelerating performance maximizing and really maximizing your performance and also giving the opportunity to scale we've actually given features in things such as scikit-learn and scikit-learn where they do not only get this huge, huge performance gain but like say in the case of modem you could also distribute and scale your data now to where the typical library may not handle it. You could also streamline and to end workflows so that all of these components I listed before work seamlessly together so you don't need to worry about you have it all in one place you don't need to worry about one thing not working with the other all of them work seamlessly together and this will help you improve your productivity because if everything works together and you don't need to worry about version controls and it's all packaged together you don't need to worry about like oh do I have to like downgrade my scikit-learn because of this like pandas thing that I'm trying to use you're improving your productivity there and also speeding up development in that because in like say a case where you're trying to have these frameworks work with a specific hardware you're trying to learn that or you have to learn a brand new library to fit your specific framework that takes a lot of time, a lot of hidden costs in terms of learning a new framework or is learning a new hardware or how to optimize it so by being able to skip all that you know you're saving a lot more time just by adding like two lines of code and using the Intel optimization from this toolkit Rachel, we have a question does the Intel distribution of modem come packaged with RAID desk? Yes, correct so the Intel distribution of modem comes with a different back end and it has both a RAID and a desk back end so you can go ahead and choose and you can turn off and on the Omni-Sci back end which is basically we partnered with a company known as Omni-Sci it's a really great partner and they have a library known as the I think it's the Omni-Sci STL library that's running in the back end that brings even more performance here in Intel hardware and you can turn off and on that so you can use a RAID or desk as needed depending on what you want and if you are interested in that's basically you could use either or depending on what you need it's a really great library that's actually helping in the back end of modem do the heavy lifting to distribute across all available cores fun fact so with that we've actually gone ahead and we'll be talking about this more I'll probably come back to this slide when Audrey comes back essentially we've actually enabled all of these great optimizations within the Red Hat OpenShift data science platform so we've actually enabled the AI kit in a friendly JupyterLab interface including a pre-installed environment so you are when you actually go into the JupyterLab and I'll be talking about this later and showing it in real life you actually get three pre-installed conda environments so you could go in and have these optimizations there for you or if you want to go ahead and mix and match or make your own environment you could go ahead and do that as well and create your own environment but those three like beginning environments to help you get started are there for you and then for OpenShift of course this is all available as a Red Hat certified operator and we also have opportunities for inference and deployment with a deep learning framework such as TensorFlow and working with the OpenVINO toolkit which I believe was talked about previously on this podcast for deployment as well now with that I've been talking about how you get these performance benefits and how they're so great and how they actually work but what is the performance benefit that you can actually see and so I'll be showcasing this later in a demo using our XGBoost Dolphi Inference Optimization but as you could see from this diagram you could actually connect and stack these different optimizations together so that you can optimize every single step of the DAS-sign pipeline so starting on the left with the data preprocessing step by using Intel Distribution of Moden and just doing a single line code change you could actually see up to like 38 times faster ETL in the case of like a US Census 2020 workload that we ran ETL being like where you're dropping NANDs and you're cleaning up the data to get it ready for your model training then using Intel Expansion for Psychic Learn on that same use case I think the ridge regression was used in this particular essentially when you use it for both fitting and predicting you could go ahead and see up to 21 times faster fan prediction compared to the stock or unoptimized Psychic Learn by just turning on the simple drop in acceleration for an overall 40% accelerated performance. Now looking at the model training step it's not only about Psychic Learn but I'm gonna talk about it a little more just because I'll be talking about after this slide with Intel Expansion for Psychic Learn as you saw on that use case it was around 21 times faster fit and prediction but you could actually see up to 100 times faster performance or even 200 times I think also in recent benchmarks we've also done over a thousand times faster than a regular stock Psychic Learn through the observations we have for our 1API Data Analytics Library we also compared to our competitors this can be compared to up to 10 times faster compared to Nvidia GPU or up to five times faster compared to running on an AMD CPU now moving now that's all great for data processing and classical now but moving on to the deep learning side of things in terms of model training and just the full disclosure we also have TensorFlow optimizations as well not shown here we have in PyTorch if you use our PyTorch optimizations you can see up to 1.55 times faster DLRM training by using the DS16 instruction set rather than FP32 and that same and those same optimizations if you go ahead and use the DLRM inference with that you can see up to 2.8 times faster using the NT instruction set versus FP32 now in terms of optimizations actually as of 0.81 XTboost we've actually been up streaming our training optimizations to the mainstream XTboost so if you go ahead and use XTboost then you're using the HIST method you already got those going but we have even further optimizations and model inference and that's compared up to 4.5 times faster inference compared to Nvidia GPU if you use that and for TensorFlow if you use the TensorFlow optimizations and quantized inference again that neural compressor tool to convert your model inference from FP32 to INT8 you can see up to 2.8 times faster quantized inference now some of these numbers are really huge and some of these are smaller but let me tell you if that 2.8 times faster is a matter of between hours and minutes you know that could be a huge huge gain for a data scientist they could spend less time waiting around and more time improving their model and getting other things done so it's a really exciting thing to see now with that let's take an example on how a user will go ahead and actually go ahead and use one of these what is some of these optimizations I will begin by talking about Intel exception first like it learns and then I will be given an example on how to use our XTboost and dopper pie optimizations you may be thinking to yourself what is doll for pie but don't worry I'll help you through it you could think of it as basically the Python version of that 1API 1Doll C++ library talked about in the beginning so don't worry about that so in terms of if I am a user I'm using the AI kit operator on Red Hat OpenShift data science how can I actually go ahead and use these optimizations for you so let's say for example I'm trying to use this for a support vector classifier as is in the case of this little small code snippet here what I will want to do is I want to use Intel exception for scikit-learn which is powered by the 1API data analytics library and it's Python API equivalent doll for pie which can also be used directly if you so choose but you could also just go ahead and use the Intel exception for scikit-learn and stay within your regular scikit-learn API so I'm ready I know a bit about how to turn on scikit-learn let's go ahead and do it so I take my original code this regular scikit-learn code and I go ahead and add these two lines here from scikit-learn EX import patch under for scikit-learn and then I turn and then I use the function of scikit-learn what this is telling the computer is hey, anything that could be optimized by Intel exception for scikit-learn and the 1API data analytics library which is one of the most optimized machine learning libraries we have here at Intel please do so now if it is not optimized by the one doll library please just use regular scikit-learn so there's no loss only gain in this case and there's also opportunities where if you don't want to turn on everything that one doll can optimize you could just turn it on for a specific model or whatever you choose and you could also turn this off at any time with the un-patch method so you would just add UN at the end of this and that's all you would need to do and suddenly your SVC fit and predict are really optimized now this already comes pre-installed for you as part of the AI kit operator but if you want to go ahead and try it out as just a component and just do it in like a sandbox environment it is available in the default condos channel the Intel channel condo forge and pip install as well so it's also available there now in terms of the actual what this looks like in terms of an actual speed up compared to the regular scikit-learn you could look at the chart here so on the left is a bunch of popular algorithms such as team fit and predict if you ran in force and so on and so forth we even optimized train test split a very popular processing method used in the scikit-learn library and all we've done is taken our code and we've just gone ahead and added those two lines of code and this is how much SVM you could see so in some cases as in the case of support vector classifying predict on the MNIST MN ISP data set you could see up to 200 times faster so that is like really like the power and the kind of performance thing you could see by just adding that simple drop in acceleration there's no other further change is needed in order to get that acceleration once you use this patch method everything is turned on for you again it's very flexible where if you want to turn it off at any time you can do that or if you want to go ahead and or if you want to go ahead and just do a specific algorithm you could do that as well but you know the results of being able to actually go ahead and use this is very very huge and you know you could think that all I did was add two lines of code and using the AI kit operator and this is what I'm able to do which is a really really huge thing you know if you think about you know having to change your whole workload to try and get this performance gauge is being able to get this in two lines of code and not having to deal with C++ or anything that you would typically need to do now before I move on to the next bit and then going on to the demo I want I I as I explained the this intelligence for psychic learning is powered by the one doll library the one EPI data analytics library what we've done is that that library is coded in C++ and we've gone ahead and put it into a Python friendly library known as doll for pie so it has a Python equivalent so you can think of it as a cake where there's one doll underneath C++ then there's doll for pie which is the Python equivalent and then on top you have Intel sessions for psychic learn but that doll for pie library has more capability than just just accelerating the psychic learn in Intel sessions for psychic learn it also has optimization such as distributing linear regression and and other algorithms found in a psychic learn so if you actually want to go and distribute or use online streaming mode you could actually use the doll for pie library directly this is also included in the AI kit operator but we only recommend that if you really need to it's a very friendly library if you know psychic learns it's I'm not I'm not lying this is a very easy library to pick up but besides that it also has accelerations for other libraries that we've included such as XGBoost and like GBM so if I'm a user and I have this great optimized X library that's using those upstream uh treating optimizations that we already upstream to the regular XGBoost but I want more I want to optimize in my XGBoost inference well you can do that you by using it and by converting it into something that doll for pie can read and then running it in with doll for pie and I'll be demonstrating that in just a moment in the AI kit operator for red hat openshift data science or road so if I have gone ahead I finished the training part of my XGBoost model and this capability is also available for the light GBM library but we're going to be talking about XGBoost for right now I've done I'm ready to go ahead and use inference what I can do is basically take that pre-trained model here I can import doll for pie which again is already imported for you and it's available as part of the AI kit operator and I will just go ahead and call this and say get GPT model from XGBoost and say hey please convert this model into something that doll for pie can read and then afterward I'm just going to go ahead and call the gray and boosting prediction as needed with this which is saying hey convert this this is something that doll for pie can read and then go ahead and actually go ahead and use the gray and boosting classification prediction which I will usually use in XGBoost and just use it in the doll for pie one and what this is doing is that is taking advantage of the doll for pie optimization for gray and boosting inference what this means is more efficient model representation and memory I'm taking advantage of the AVX 512 instruction set and just better cache locality usage and what this will happen is that you see this huge huge performance increase compared if you were just using the regular XGBoost inference call you see in this case up to 20 times faster speed when you're using these two lines of code to go ahead and convert your model and there is no actually loss when you actually go ahead and do that so again just adding these just doing these simple little code changes can prove really really profitable if you are looking for a better time usage and better cost usage for this so being able to do this at 23 times performance boost is really huge now with that let's actually go ahead and demonstrate what this actually looks like on the AI kit for Red Hat OpenShift data science and then we will try to go back to Audrey if possible so I'm actually going to stop sharing my screen for a moment and then I am going to reshare with the demo and show off how what this what AI kit looks like on a Red Hat OpenShift data science AKA wrote and let me move my screen and there we go now I'll go ahead and share okay all set I see it okay you can see my screen okay awesome so here we are in the Red Hat OpenShift data science platform so as you can see here we are in the applications tab here which is basically if I have gone ahead and installed the Red Hat OpenShift data science operator and I have gone ahead and launched into it if I've also installed other partner software that Red Hat has collaborated with such as Intel I will also show up here so in this case I have already pre-installed the Red Hat OneAPI AI kit container as well as the OpenBino toolkit which is also available on Red Hat OpenShift data science so if I go ahead and play the video I'm probably just going to go ahead and wave my cursor around to say look it's there now besides showing what's enabled on the Red Hat OpenShift data science platform there's also all these great resources and optional applications that you could go ahead and learn about so in this case I took the AI kit application here and it will come up with information on what is AI kit as well as how to install the operator so all of that is available there and besides that there's also resources available from Red Hat and it's partner it's partner software so if I actually go ahead and I think I'm going to go ahead and search AI you will actually see AI kit pop up here and it'll come up with a documentation to help you get started now after all of that is done say I've gone ahead and I finished reading the documentation it's amazing I have it installed I'm really excited about using AI kit let's go ahead and launch it now one thing I want to disclose is that there for the sake of this demo it's not going to show just based on how it was recorded but typically this would show a spawner screen with all the available containers that you could select and when you go ahead and pick the AI kit tile AI kit will already be pre-selected so you just have to go ahead and do an extra thing where you click AI kit and launch and then it will show to where it's where it's going to now which is the Jupiter lab environment of AI kit where you could do a lot of great development using our optimizations and pre-loaded conda environment so if I go ahead and stop the screen here what you'll see on the left is a couple of things first of all we have the models.git which is where the models do with those pre-trained models for our deep learning libraries are located we have the Bunny PI samples folder as we have actually already gone ahead and giving you samples to help users get started there's also more samples that are linked in this welcome page I'm looking at but we'll talk about that later and we'll also be showing off one of these samples today as a demo and then there is also the AI kit welcome page so the AI kit as you can tell as I talked about in the beginning there are a lot of a lot of optimizations here and there and so you may be wondering like well where are they in these pre-trained environments and you know what if I want more information so I would definitely recommend if you install the AI kit operator to get started with the AI kit welcome page as you can see here it just basically is an intro to AI kit as well as environment package information like what is where so in the state for the scikit-learn xtbooth and modem environment which if I do not highlight maybe it'll yeah it will go ahead and show intel modem intel testing for scikit-learn which we just talked about in intel distribution of modem including our NumPy sci-fi optimizations and intel optimized xtbooth and if you go ahead and click on each of these links that are a little bit stated right now it will go ahead and take you to more resources on that now if that's not enough for you we also have provided more resources for you to get started such as access to the AI kit website AI kit code samples that include more code samples than what is included here as well as the AI kit release notes that you can get more information on that so I'm going to go ahead and do that and if I am a so if I've gone ahead and finished the welcome welcome page review you will see when you actually click that plus tab to start a new notebook you will see these again these preconfigured environments so in the intel pie torch and quantization tool you'll get the neural compressor and intel pie torch optimization in intel scikit-learn xtbooth and modem as I just mentioned you will get those kinds of optimization and intel tensorflow and quantization environment you will get the tensorflow optimizations and the neural compressor as well so besides having these available for the samples you can go ahead and launch an experiment in those condit environments or if you want to create your own environment you could go ahead and do that for for you and also has a terminal access which if I recall correctly it's going to show off here so I'm going to go ahead and highlight over that and the reason that this demo is recorded just as a full disclosure is that you'll see in a moment that it takes a while for the unoptimized xtbooth inference to run so I don't want it waiting around for that to run so that's why it's that's why it is it is taking a that is why it's recorded so again there's these resources for you so say I've read this and like okay I'm going to get started let's go to the samples now there are three samples if I pause this here we have a one use case using the intel distribution modem and the intel extension for scikit-learn optimization we have the intel optimizations for a tensorflow and the lpot tool that is now renamed to the neural compressor and then we have our intel optimized xtbooth sample using our xtbooth training optimizations and that are upstreamed and the dolphin pie inference method that I talked about right before going into this demo so we are actually going to go ahead and show off those xtbooth optimizations and that dolphin pie in print and I will go ahead and click into that notebook so as you can see here this is the the sample and what is going on is that this is an end to end sample the goal of this sample is that you're a data scientist essentially and you are trying to use the Higgs data set to basically predict if a particle is going to produce a Higgs Bason process or it's not going to so if it produces it it will mark it as a will classify it as a one if it does not we're going to classify it as a zero and we're going to analyze and predict using the information on the particle features and functions of those features that's all you need to know you don't need to know any physics just basically if it's if it's a Higgs Bason it's going to produce a one if it's not it's going to do a zero and this is based on the information from the data set or the particle features so as any data science work flow first starts we'll go ahead and start by importing and organizing the data so we're going to go ahead and do that here you can see I've already imported Higgs and Delfar Pi and we're just going to go ahead and pull the model and turn test split now I'm actually going to go ahead and run through this very rapidly so that it can run while I'm explaining it so we've already explained that first part so it's going to go ahead and pause there and in this case I'm only using that the Higgs data is pretty vast so we're only using the first 100,000 rows in this case but we will still be able to see a performance speed up with that now once I am done with importing and organizing the data I'm going to go ahead and train the model so as you could see here I am using the XGBoost classifier from the package and I'm using these different parameters as you could see from tree methods the Higgs parameter is marked meaning that we are taking advantage of those Intel optimizations that are upstream to the XGBoost library and let me tell you this is going to take a bit of time to run just to train the model but I've run it without the optimizations before and it takes it takes quite a long time like I think in this case it takes two minutes to train the model and I'm going to go ahead and skip ahead to that part but in most cases in many cases you know compared to the unoptimized because it is a huge performance gain so I'm going to go ahead and pause and wait here and I will go ahead and skip to where it is going to finish training and it should be right now yes give it a moment there we go so after it is done training it will go ahead and just put it down and say hey I'm done training here's your future model object and after that we're ready to do some prediction now maybe I'm kind of a skeptic and I'm like well is the Dolphi prediction faster let's go ahead and try it out so what we're going to go ahead and do first is that we're going to try it without the XGBoost without the Dolphi optimizations for a gradient boosting first and we're just going to do a typical XGBoost prediction and add a timer for that and as well as an error count because even if the Dolphi turns out faster is it going to be as accurate now after that I'm going to actually go ahead and run the Dolphi prediction which is again using those two lines to get the to take this model that we just trained and convert it into something Dolphi can read and after that we are going to go ahead and run the prediction object and go ahead and use that model that we just convert to Dolphi and run that here now and then we're also going to go ahead and compare the accuracy and use the timer there so we are actually going to go ahead and run those now I think they already pre ran at this point so as you can see here I'm just highlighting these are the lines that are running to give that performance speed up and let's go ahead and see the results so this is the printout of the comparison of the if I go back and pause it one moment so if I go here this is the regular XGBoost inference results and this is the a Delphi Pi gradient boosting inference results using the same exact model and as you can see here the both have a very both have the same error count and the same accuracy score and the prediction time in this again this smaller data set for the sake of the sample the XGBoost prediction time took around like 20 25 seconds and the Delphi Pi inference time took around 0.06 seconds and if we go ahead and actually visualize that you can see here that this is approximately a 3.47 times speed up just on that little data set so again that huge performance gain that can really scale up as your a data set size your training model gets very complex now that's great look at accuracy and the great thing about these Delphi Pi inference optimizations which are again those like two little simple line converter changes is that there's no accuracy difference compared to the typical XGBoost stock inference accuracy so you could go ahead and get a huge performance gain by just adding those two lines of code and after that just using the same accuracy as you keep coding for you so that's just a bit of an example of using the AI kit of what you can see the kind of optimization gain that you could see with the AI kit operator on a red hat open shift data science so with that we've just finished talking about AI kit some of the great performance optimizations and how they actually look like in code as well as a demo of what it looks like on the red hat open shift data science platform now I think I'm going to turn it over to Audrey to go ahead and talk more about the red hat open shift data science platform and how excited to have an AI kit operator on this is so let me stop sharing my screen and is Audrey are you going to go ahead and share your screen or should I reshare these slides oh do we how's your connectivity okay it's okay I will maybe I should share yes yeah yeah okay yeah so I will go ahead and share on slide two and we will talk about how what is where you can run the AI kit optimizations with a red hat so let me go ahead and present and Audrey whenever you're ready force mute oh no sorry I couldn't see quite what it said hang on Audrey it said I'm mute her I'm mute her oh my she's not muted no she's there sorry no I you're as green as Rachel is no huh oh no maybe maybe try and plugging your headphones maybe it's an audio setting thing because I had to set my default differently for this no nothing huh I'm looking at my settings hang on there's I don't think this there's no more for me to to do for a guest it's sort of your okay I'll I'll put you in the background I'll bring you back okay let's see I'm rude hang on and she's back we're all learning our new platform any luck no nothing so um so I think with this I think uh with this we've actually there was a great presentation that Audrey has already done with uh Ryan Loney on the uh Openvino toolkit powered by red hat OpenShift Datasign so if you want to go ahead and learn more about the uh how to get the AI kit how to what is red hat OpenShift Datasign and what the AI kit operator looks like on it you already got the second uh part since you already know you know all the AI kit optimizations that I ran through but uh just because of technical difficulties again we're we're streaming from our home so this can happen from time to time um yeah I think it might be good to go ahead and look at those red hat OpenShift Datasign resources um but just from my personal experience with red hat OpenShift Datasign it's a really uh it's a really uh great platform um that really helps you yeah I know Audrey's like it's awesome that's awesome it's an awesome product and all that so um but uh red hat OpenShift Datasign is basically this really great open hybrid cloud platform and really is powered and focused on uh AI datasign to basically help you get rid of that really complex infrastructure set up and give power back to the uh datasign test and once you are using the red hat OpenShift Datasign platform you could take that you know no more infrastructures or hassle with cloud or hassle with Kubernetes or anything like that and then you could couple that with the power of AI kit where you no longer have these like have to overhaul your whole pipeline to uh go ahead and fit a certain hardware or infrastructure so really couples together really giving power back to the datasign test and I'm just going to go ahead and flash the features of the red hat OpenShift Datasign so that users can read but again really great hybrid cloud for the cloud service that you can use on different vendors and really speed up your datasign workflow and I'm going to go ahead and flash the infrastructure of all the great partners that red hat is partnering with to bring these optimizations on behalf of Audrey I hope I hope I'm giving it justice I'm getting some stuff so I think I'm giving it some justice so in terms of the partners you know the one API AI kit sits up here with the customer I mean I see software and Intel is Intel AI kit is a partner software so I'm actually going to go ahead and skip to the end here and represent and so if you want to learn more about the AI kit and how to sell red hat ownership datasign here are a couple of resources that will also be available for you after this presentation so if you want to go ahead and download it I definitely recommend cooking this link which will link you to the red hat marketplace where the AI kit operator can be installed and beyond that just some notices and disclaimers about the numbers that I showed but I really encourage you to go ahead and try out the AI kit operator on red hat ownership datasign today so what's that I'm going to stop sharing my screen well I want to thank you that was really it was really impressive actually and I'm not a data scientist so I didn't have any questions to ask but I was impressed and Audrey I want to thank you for trying so hard to join I will have to and of course I want to thank our listeners for bearing with us as we go through all of our new technical issues on this on our new platform and everything so I don't see any questions at this time so I'm going to go ahead and wrap up even though we're a few minutes early and I will do my best to make sure that we promote these these demos and videos that we've done on our product page so anyone who wants to come in later can just go look at the roads product page and find them so again thank you both I really really appreciate it anyway have a good day everybody take care bye


What's going on guys got Kyle here with lane sports cards and I am ripping one box of 2022 tops chrome platinum anniversary baseball This one is going to be for michael br Good luck. Michael. Let's see what we got here some plat Yes matt hit a pluto matt hit a pluto otani It was sick. I think he might have hit a judge too. I think he hit otani and judge We have it on our instagram peter um We posted the otani um Pluto a couple days ago. He hit it release day. I believe Let's do some platinum got two boxes platinum coming right after this one as well the klingons I don't know where the klingons are from I can't contest that. I got no idea Julio pluto. I heard about that one evil. I heard about that one I think I saw it on uh, I think I saw that one on ebay. Um shouldn't be pee You might be one of the last ones though. I'm going until three Um, let me see. You shouldn't you shouldn't be late though. I should be able to get you going Yeah, no problem peat. Probably gonna be one of the last ones though. Probably closer to three here about an hour Yeah, I think we're gonna be full here guys. Um, at least on what I got now About 10 boxes to go And no problem peat appreciate you getting an order in Always good to see you sir. Hope you're having a good weekend with the fam Nice labor day weekend Roddy platinum anniversary for Michael Okay, going we got jake cousins on the refractor Is that a kyle t-shirt? I got my own t-shirt basically basically We got auto nice one here. It's rookie auto brison stott On the blue number to 99 very nice auto there Get one auto per box not a bad one brison stott on the blue to 99 Philly's rookie It's actually um brine fantana Brine fantana Very nice thought there to start us off We've got david right This one is to 299 This shirt's got bits of real panther in it david right for the mets aqua to 299 rookie It's nice one, but uh bobby wit on the rookie refractor One of the better rookies out of this set man He's been picking it up the last half of the season It's been crushing it. There's a x-fractor one pair box the hawk andre dawson Gavin lux This one is to 100 red atomic is going to be the pizza man Mike piazza red atomic number 200 For the mets factor is cal ripkin righty second half of the stack. We've got blue johnny bench This one is blue pattern to 199 for the reds Another bobby wit base rookie now refractor crony worth chrome zone Padres There's a rookie joe perez. This one is a fuchsia atomic to 100 Joe perez astros rookie Zach Thompson refractor the last half here orange It is orange the 25 orange wave jim bunny Phillies Nice orange 20 at 25 on the throwback Throwback Phillies logo is kind of cool actually like that style Cooper Hummel refractor rookie dbecks And nice onio cruise now on the yellow lava of rookie On that one's the 250 the ray wave Nice onio 250 bobby wit refractor two not a bad box here for rookies and cranky Alrighty there mr. Michael that will do it Thank you, sir for the one box of platinum some nice hits there the bryce and stott auto numbered of Onio cruise really nice. We'll get those right out to you, sir


Surgical research has made great strides in the past century, but its application in lower and middle-income countries is limited due to various barriers. These include social and cultural factors, lack of infrastructure, financial constraints, ethical considerations, and personal limitations. However, these obstacles can be overcome through concerted efforts to ensure that all people have access to the same level of medical care as those in developed nations. This article was authored by Nadim Ahmed Siddiqui, Muhammad Anish Reyes, Rehan Nasir Khan, and others.


Hey, everyone. I'm Andrew Cravino with PoA Network. I'm going to be talking about an app we've been building called BlockScout. BlockScout is an open-source EVM blockchain explorer. It's currently live at blockscout.com. If you'd like to contribute to the project, you can do so at github.com.poa.network. slash blockscout. All right, so we support eight networks currently. Most importantly, Ethereum Mainnet. We also support the three test nets of Ethereum, Coven, Robson, and Rinkbee. Rinkbee will go live early next week. We had to make some customizations there. We also support Ethereum Classic, PoA Core, PoA Soquel, and one of our hard spoons of Ethereum Mainnet, XdiChain. We support Parity, Geth, and Gnash. We are working on the internal transactions for Geth, so that should be enabled late next week. So why we built BlockScout? As a sidechain, we don't have access to EtherScan. Everybody knows and loves EtherScan, so we needed to change this. We're friendly to fork sidechains, private networks, and we wanted to allow users of the Ethereum ecosystem to spin up their own full-featured BlockExplorer connected to their nodes, so they can check the balances of their addresses. We thought it was very important for decentralization. Here are some features of BlockScout.  The exciting things happen when you get into BlockRiorgs. We have ERC20 and ERC721 token support. We have contract verification. You're also able to query those verified smart contracts directly from BlockScout. Real-time UI, API, so we have an EtherScan-compatible RESTful API, so you could port your application from using EtherScan's API to using BlockScout, if you chose to. And really exciting, we just added yesterday to master GraphQL, some GraphQL API endpoints, which also includes some webhooks. We'll be building more on top of that in the next couple of weeks as well. For our development stack, we're using Elixir on the Erlang VM with a Postgres database. Erlang is great for running fault-tolerant systems. So some other apps that use Erlang were WhatsApp and Heroku. So we thought it was a great choice for BlockScout. I want to talk a little bit about where we're heading and what we're doing right now. We just launched a couple of weeks ago, BlockScout. So we're still indexing Ethereum mainnet, and we wanted to provide better user experience, such as faster page load speeds, faster indexing times. When a block is mined, we want it immediately to be available and imported into BlockScout. So we're working on that. A lot of it right now is we're de-dossing our own nodes. So we're building up more nodes on the back end so we can hit it a little harder. We're reduced by 90% because we keep de-dossing our node with all the data on mainnet. So we're working on that right now. As far as our indexer, we've had many user requests to define a set of blocks on any EVM chain and just index those. It's good for analyzing certain sets of data. We're also, we have our best engineer working on guest internal transactions. So that should be available late next week. And as I mentioned before, faster indexing times. BlockScout comes with many customizable features. But if you're not familiar with the app, it's difficult to find in the files where these customization settings are. So we're building a back end admin panel. Actually, the admin panel is actually done. But over the next couple of weeks, we'll be including all these configuration settings, such as if you deploy BlockScout, you can change the CSS colors, the logo. You're able to adjust how much memory you want the application to use. It'll just shrink any of the processes that are too large. It's just a small portion of the available configuration settings you'll be able to change. We'll also be offering data export. So you'll be able to download your transactions, tokens, your coin, and token balance history. So you'll be able to do that very soon to CSV. Some other things we're excited about is we have our GraphQL. We think it'll be great for the Ethereum ecosystem to be able to utilize webhooks and other type of endpoints and use our data to build some great applications. We'll also have continued development around ERC721 data attributes. So users can analyze ERC721 non-funtable tokens better right on the blockchain. And finally, right now we're working on input data and logs decoding them by the contract ABI. So that should be finished late next week. But I also want to thank ETH Prize for their both monetary contribution and their community support. If you guys have any questions, I'll be around after and be happy to answer them for you. Thank you.