Lex Fridman Podcast - Rajat Monga: TensorFlow

Episode Date: June 3, 2019

Rajat Monga is an Engineering Director at Google, leading the TensorFlow team. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations.

The following is a conversation with Rajet Manga. He's an engineering director at Google, leading the TensorFlow team. TensorFlow is an open source library at the center of much of the work going on in the world and deep learning, both the cutting edge research and the large scale application of learning-based approaches. But it's quickly becoming much more than a software library. It's now an ecosystem of tools for the deployment of machine learning in the cloud, on the phone, in the browser, on both generic and specialized hardware,

TPU, GPU, and so on. Plus, there's a big emphasis on growing a passionate community of developers. Roger, Jeff Dean, and a large team of engineers at Google Brain are working to define the future of machine learning with TensorFlow 2.0, which is now in Alpha.

I think the decision to open source TensorFlow is a definitive moment in the tech industry. It showed that open innovation can be successful and inspire many companies to open source their code to publish and in general engage in the open exchange of ideas. This conversation is part of the Artificial Intelligence Podcast. If you enjoy it, subscribe by YouTube iTunes or simply connect with me on Twitter at Lex Friedman spelled F-R-I-D. And now here's my conversation with Roger Manga. You were involved with Google Brain since its start in 2011 with Jeff Dean. It started with disbelief, the proprietary machine learning library and turned into TensorFlow in 2014, the open source library.

So what were the early days of the brain like? What were the goals, the missions? How do you even proceed forward once there's so much possibilities before you? It was interesting back then, you know, when I started or when you were even just talking about it, the idea of deep learning was interesting and intriguing in some ways. It hadn't yet taken off, but it held some promise that it showed some very promising and early results. But it helped some promise that it showed some very promising in early results. I think the idea where Andrew and Jeff had started was,

what if we can take this, what people are doing in research and scale it to what Google has in terms of the computer power. And also put that kind of data together. What does it mean? And so far, the results had been if you scale the computer, scale the data, it does better and would not work. And so that was the first year or two can we prove that out? And we disbelief and we started the first year, we got some early wins, which is always great. What were the wins like? What was the wins where you were? There are some problems to this. This is going to be good. I think there are two early events where one was speech that we collaborated Very closely with the speech research team who was also getting interested in this and the other one was on images where we, you know, The cat paper as we call it that was covered by a lot of folks. And the birth of Google Brain was around neural networks. That was So it was deep learning from the very beginning.

That was the whole mission. So what was in terms of scale, what was the sort of a dream of what this could become? Were there echoes of this open source TensorFlow community that might be brought in? Was there a sense of TPUs? Was there a sense of like machine learning is now going to be at the core of the entire company? It's going to grow into that direction. Yeah, I think so that was interesting and like if I think back to 2012 or 2011 and first was can we scale it in in the year or so we had started scaling it to hundreds and thousands of machines

and in fact we had some runs even going to 10,000 machines and all of those shows great promise in terms of machine learning at Google the good thing was Google's been doing machine learning for a long time Deep learning was new But as we scaled this up, we sure that yes, that was possible and it was going to impact lots of things. Like we started seeing real products wanting to use this. Again, speech was the first. There were image things that photos came out of and then many other products as well. So that was exciting. As we went into with that a couple of years, externally also academia started to,

there was lots of push on, okay, deep learnings interesting, we should be doing more and so on. And so by 2014, we were looking at, okay, this is a big thing, it's going to grow. And not just internally, externally as well. Yes, maybe Google's ahead of where everybody is, but there's a lot to do. So a lot of this start to make sense and come together. So the decision to open source, I was just chatting with Chris Gladner about this. The decision to go open source with TensorFlow, I would say that for me personally, seems to be one of the big seminal moments

in all of software engineering ever. I think that's when a large company like Google decides to take a large project that many lawyers might argue has a lot of IP just decide to go open source of it and then so doing lead the entire world and saying, you know what open innovation is, is a pretty powerful thing and it's okay to do. That was, I mean, that's an incredible, incredible moment in time. So do you remember those discussions happening? Yeah. Are do you remember those discussions happening?

Other open sources should be happening. What was that like? I would say, I think, so the initial idea came from Jeff who was a big, proper end of this. I think it came off of two big things. One was research wise, we were research group. We were putting all our research out there. We were building on all those research and we wanted to push the state of the art forward.

And part of that was to share the research. That's how I think deep learning and machine learning is really growing so fast. So the next step was, okay, now, would software help with that? And it seemed like they were existing a few libraries out there, the Anoping One, Torch being in other and a few others, but they were all done by academia and the level was significantly different. The other one was from a software perspective, Google had done lots of software that we used internally, you know, and we published papers. or that we used internally, you know, and we published papers. Often there was an open source project that came out of that that somebody else picked up

that paper and implemented and they were very successful. Back then it was like, okay, there's her doop which has come off of tech that we've built. We know that tech we've built is way better for a number of different reasons. We've invested a lot of effort in that. Turns out we have Google's out and we are now not really providing our tech, but we are saying, we have Bigtable, which is the original thing.

We are going to now provide HBAS APIs on top of that, which isn't as good, but that's what everybody's used to. So there's this like, can we make something that is better and really just provide helps the community in lots of ways, but it also helps push the right, a good standard forward. So how does cloud fit into that? There's a TensorFlow open source library, and how does the fact that you can use so many of the resources the Google provides and the cloud fit into that strategy. So, TensorFlow itself is open and you can use it anywhere, right?

And we want to make sure that continues to be the case. On Google Cloud, we do make sure that there's lots of integrations with everything else. And we want to make sure that it works really, really well there. So you're leading the TensorFlow effort. Can you're leading the TensorFlow effort. Can you tell me the history and the timeline of TensorFlow project in terms of major design decisions, so like the open source decision, but really,

what to include and not. There's this incredible ecosystem that I'd like to talk about. There's all these parts. But if you just some sample moments that defined what TensorFlow eventually became through it's, I don't know if you were allowed to say history when it's just, but in deep learning everything moves so fast and just a few years is already history. Yes, yes.

So looking back, we were building TensorFlow, I guess we open source state in 2015, November 2015. We started on it in summer of 2014, I guess. And somewhere like three to six late 2014 by then we had decided that, okay, there's a high likelihood we'll open source it. So we started thinking about that and making sure we're heading down that path. At that point, by that point, we had seen a few, you know, lots of different use cases at Google.

So there were things like, okay, yes, you want to run it at large scale in the data center. Yes, we need to support different kind of hardware. We had GPUs at that point. We had our first GPU at that point, or was about to come out roughly around that time. So the design sort of included those. We had started to push on mobile,

so we were running models on mobile at that point. People were customizing code. So we wanted to make sure TensorFlow could support that as well. So that that sort of became part of that overall design. When you say mobile, you mean like pretty complicated algorithms running on the phone. That's correct. So then do you have a model that you deployed on the phone and run it there? Right. So already at that time, there was ideas of running machine learning on the phone.

That's correct. We already had a couple of products that were doing that by then. In those cases, we had basically customized handcrafted code or some internal libraries that we're using. I was actually at Google during this time in a parallel, I guess, universe, but we were using theano and cafe. Yeah. Was there some degree to which you were bouncing? I like trying to see what cafe was offering people, trying to see what theano was offering that you want to make sure you're delivering on whatever that is. Perhaps the Python part of thing, maybe the dead influence any desired

decisions. Totally. So when we built this belief and some of that was in parallel with some of these libraries coming up, I mean, Tiano itself is older, but we were building this belief focused on our internal thing because our systems were very different. By the time we got to this, we looked at a number of libraries that were out there. Tiano, there were folks in the group who had experience with Torch with Lua. There were folks here who had seen CAF, I mean actually, Yang Ching was here as well. There's, what other libraries. I think we looked at a number of things. Might even have looked at Jaina back then and trying to remember

if it was there. In fact, we did discuss ideas around, okay, should we have a graph or not? And they were supporting all these together was definitely, you know, there were key decisions that we wanted. We had seen limitations in our prior disbelief things. A few of them were just in terms of research was moving so fast. We wanted the flexibility. We want the hardware was changing fast. We expected to change that so that those probably were two things. And yeah, I think the flexibility in terms of being able to express all kinds of crazy things

was definitely a big one then. So what the graph decisions that I with moving towards TensorFlow 2.0, there's more, by default, there'll be eager execution. So sort of hiding the graph a little bit, because it's less intuitive in terms of the way people develop and so on. What was that discussion like with in terms of using graphs? It's kind of the the N O A, the same the obvious choice.

So I think where it came from was our like disbelief, had a graph like thing as well. A much more simple, it wasn't a general graph. It was more like a straight line, you know thing. More like where you might think of cafe, I guess, in that sense. But the graph was, and we always cared about the production stuff. Like, even with disbelief, you were deploying a whole bunch of stuff in production. So, graph did come from that when we thought of, okay, should we do that in Python

and we experiment with it some ideas where it looked a lot simpler to use, but not having a graph meant, okay, how do you deploy now? So that was probably what triggered the balance for us and eventually we ended up with a graph. And I guess the question there is, did you? I mean, as a production seems to be the really good thing to focus on, did you even anticipate the other side of it where there could be, what is it, what are the numbers? Something crazy, 41 million downloads. Yep.

I mean, was that even like a possibility in your mind that it would be as popular as it became? So I think we did see a need for this a lot from the research perspective and like early days of deep learning in some is 41 million. No, I don't think I imagined this number then. It seemed like there's a potential future where lots more people would be doing this and how do we enable that. I would say this kind of growth probably started seeing somewhat after the open sourcing where we're like, okay, deep learning is actually growing way faster for a lot of different reasons and we're in just the right place to push on that and leverage

that and deliver on lots of things that people want. So what changed once the open source? Like how this incredible amount of attention from a global population of developers, how do the projects start changing? I don't know you actually remember it during those times. I know looking now there's really good documentation, there's an ecosystem of tools, there's a community there's a blog, there's an ecosystem of tools, there's a community that's applause, a YouTube channel. Yeah. It's very, very community driven. Back then, like a 0.1 version. Is that the version? I think we called it 0.6 or 5 something that I forgot about that. What changed leading into 1.0? It's interesting. You know,

I think we've gone through a few things there. When we started out, when we first came out, people loved the documentation we have. Because it was just a huge step up from everything else because all of those were academic projects people doing, we don't think about documentation. I think what that changed was, instead of deep learning being a research thing,

some people who were just developers could now suddenly take this out and do some interesting things with it, right? Who had no clue what machine learning was before then? And that, I think, really changed how things started to scale up in some ways and pushed on it. Over the next few months, as we looked at, how do we stabilize things? As we look at not just researchers, now we want stability people who aren't deployed things, that's how we start planning for one not oh,

and there are certain needs for that perspective. And so again, documentation comes up, designs more kinds of things to put that together. And so that was exciting to get that to a stage where more and more enterprises wanted to buy in and really get behind that. And I think post 1.0 and you know, with the next few releases, their enterprise adoption also started to take off. I would say between the initial release and 1.0, it was, okay, researchers of course, then a lot of hobbies, similarly, interest people excited about this who started to get on board and then over the 1.x thing, lots of enterprises.

I imagine anything that's below 1.0 gets pressured to be, and Brian's probably wants something that's stable. Exactly. And, uh, do you have a sense now that TensorFlow is state like it feels like deep learning in general is extremely dynamic field. So much is changing. Do you have, uh, and TensorFlow has been growing incredibly? You have a sense of stability at the helm of it? I mean, I know you're in the midst of it. Yeah. It's, I think in the midst of it, it's often easy to forget what an enterprise wants and what some of the people on that side want.

There are still people running models that are three years old, four years old. So inception is still used by tons of people. Even less than 50 is what? Couple of years old now or more, but there are tons of people who use that and they're fine. They don't need the last couple of bits of performance or quality. They want some stability and things that just work. And so there is value in providing that with that kind of stability and making it really simpler, because that allows a lot more people to access it. And then there's the research crowd, which wants, okay, they wanna do these crazy things

exactly like you're saying, right? And not just deep learning in the straight up models that used to be there, they want RNNs, and even RNNs are maybe older, they are transformers now, and now it needs to combine with RL and GANs and so on. So there's definitely that area that, and now it needs to combine with RL and GANS and so on. So there's definitely that area, like the boundary that's shifting

and pushing the state of the art, but I think there's more and more of the past that's much more stable and even stuff that was two, three years old is very, very usable by lots of people. So that makes it, that part makes it a lot easier. So I imagine maybe you can correct me if I'm wrong. One of the biggest use cases is essentially taking something like ResNet50 and doing some kind of transfer learning on a very particular problem

that you have. It's basically probably what majority of the world does. And you want to make that as easy as possible. So I would say for the hobbyist perspective, that's the most common case, right? In fact, the apps on phones and stuff that you'll see, the early ones, that's the most common case. I would say there are a couple of reasons for that. One is that everybody talks about that. It looks great on slides.

Yeah. That's a visual presentation. Yeah, exactly. What enterprises want is that is part of it, but that's not the big thing. Interprises really have data that they want to make predictions on. This is often what they used to do with the people who were doing ML was just regression models, linear regression, logistic regression, linear models, or maybe gradient booster trees and so on. Some of them still benefit from deep learning, but they weren't that that's the bread and butter like the structure data and so on. So depending on the audience you look at, they're a little bit

different. And they just have, I mean, the best of enterprise probably just has a very large data set or deep learning can probably shine. That's correct. Right. And then they, I think the other pieces that they weren't, again, with 2.0 or that the developer summit we put together is the whole TensorFlow extended piece, which is the entire pipeline. They care about stability across doing their entire thing. They want simplicity across the entire thing. I don't need to just train a model.

I need to do that every day again, over and over again. I wonder to which degree you have a role in, I don't know. So I teach a course on deep learning. I have people like lawyers come up to me and say, you know, say one is machine learning going to enter legal, the legal realm. The same thing in all kinds of disciplines, immigration, insurance, often when I see what it boils down to is these companies are often a little bit old school

in the way they organize the day. So the data is just not ready yet, it's not digitized. You also find yourself being in the role of an evangelist for like Let's get organized your data folks and then you'll get the big benefit of TensorFlow Do you get those have those conversations? Yeah, yeah, I you know, I get all kinds of questions there from Okay, what can I what do I need to make this work right? What can I, what do I need to make this work, right? Do we really need deep learning? I mean, there are all these things, I already used this linear model. Why would this help? I don't have enough data, let's say, you know, or

I want to use machine learning, but I have no clue where to start. So, said various, start to all the way to the experts who wise for very specific things. So, it's interesting. Is there a good answer? It boils down to oftentimes digitizing data. So whatever you want automated, whatever data you want to make prediction based on, you have to make sure that it's in an organized form.

Like within the intensive flow ecosystem, there's now you're providing more and more data sets and more and more pre-trained models. Are you finding yourself also the organizer of data sets? Yes, I think with TensorFlow data sets that we just released, that's definitely come up where people want these data sets, can we organize them and can we make that easier? So that's definitely one important thing. The other related thing I would say is I often tell people, you know what, don't think of the most fanciest thing that the US model that you see, make

something very basic work and then you can improve it. There's just lots of things you can do with it. Yeah, start with the basics. Yeah. One of the big things that makes it makes TensorFlow even more accessible was the appearance whenever that happened of Keras, the Keras standard, sort of outside of TensorFlow. I think it was Keras on top of the piano at first only and then Keras became on top of TensorFlow. Do you know when Keras also add TensorFlow as a back end.

Who was the, was it just the community that drove that initially? Do you know if there was discussions, conversations? Yeah, so, Franco started the Keras project before he was at Google. And the first thing was the, I don't remember if that was after TensorFlow was created or before. And then at some point when TensorFlow started becoming popular, there were enough similarities that he decided to create this interface and put TensorFlow's back in.

I believe that might still have been before he joined Google. So I, you know, we were really talking about that. He decided on his own and thought that was interesting and relevant to the community. In fact, I didn't find out about him being at Google until a few months after he was here. He was working on some research ideas and doing kerosene nights and weekends project and stuff.

I wish this thing. So he wasn't like part of the TensorFlow. He didn't join in. He joined research and he was doing some amazing research. He has some papers on that iron research. He's done, he's a great researcher as well. And at some point we realized, oh, he's he's doing this good stuff. People seem to like the API and he's right here. So we talked to him and he said, okay, I don't like come over to your team and work with you for a quarter. Let's make that integration happen. And we talked to his manager

and he said, sure, my quarter's fine. And that quarter's been something like two years now. So he's fully on this. So Cara's got integrated into TensorFlow, he's fully on this. So, Keras got integrated into TensorFlow, like in a deep way. Yeah. And now with 2.0, TensorFlow 2.0, sort of Keras is kind of the recommended way for a beginner to interact with TensorFlow, which makes that initial sort of transfer learning

or the basic use cases, even for enterprise, super simple, right? That's correct. That's right. So what was that decision like? That seems like a, it's kind of a bold decision as well. We did spend a lot of time thinking about that one. We had a bunch of API, some bit by us. There was a parallel layer of API that we were building and when we decided to do kerosene parallel, so they were like, okay, two things that we are looking at. And the first thing we was trying to do is just have them look similar, like be as integrators possible share, all of that stuff. There

were also like three other APIs that others had built over time because we didn't have a standard one. But one of the messages that we keep kept hearing from the community, okay, which one do we use? And they kept seeing like, okay, here's a model in this one. And here's a model in this one, which should I pick? So that's sort of like, okay, we had to address that straight on with 2.0. The whole idea is if you need to simplify, you had to pick one. Based on where we were, if you were like, okay, let's see what's what are the what are the people like. And Keras was clearly

one that lots of people loved. There were lots of great things about it. So pretty settled on that. Organically, that's kind of the best way to do it. It was great. It was surprising. There were less to sort of bring in and Outside, I mean, there was a feeling like Karris might Be almost like a competitor in this a certain kind of a two-tonc of flow and in a sense it became an Empowering element of TensorFlow. That's right. Yeah, it's interesting how you can put two things together Which don't which can can align right in. In this case, I think Francois, the team, and I, you know, a bunch of us have chatted and I think we all want to see the same kind of things.

We all care about making it easier for the huge set of developers out there. And that makes a difference. So Python has a grid of an awesome will until recently help the position of benevolent dictated for life. Right, so there's a huge successful open source project like TensorFlow need one person who makes a final decision. So you did a pretty successful TensorFlow dev summit just now last couple of days. There's clearly a lot of different new features

being incorporated in amazing ecosystem, so on. Who's, uh, how are those design decisions made? Is there, is there a BDFL and TensorFlow and, uh, or is it more distributed and organic. I think it's it's somewhat different, I would say. I've always been involved in the key design directions, but there are lots of things that are distributed where there are a number of people, Martin Wick, being one, who has really driven a lot of our open source stuff, a lot of the APIs, and there are a number of other people who have been pushed and been responsible for different parts of it. We do have regular design reviews over the last year. We've really spent a lot of time opening up to the community and adding

transparency. We're setting more processes in place. So, OFCs, special interest groups, really grow that community and scale that. I think the kind of scale that ecosystem is in, I don't think we could scale with having me as the one-point-of-estation maker. I got it. So, yeah, the growth of that ecosystem, maybe you can talk about it a little bit.

First of all, when I started with Andre Karpathy when he first did come that JS, the fact that you can train in your own network and the browser in JavaScript was incredible. So now TensorFlow.js is really making that a serious, like, a legit thing, a way to operate whether it's in the backend or the front end. Then there's the TensorFlow Extended, like you've mentioned, there's TensorFlow Lite for mobile. And all of it, as far as I can tell, it's really converging towards being able to save models in the same kind of way. You can move around, you can train on the desktop and then move it to mobile and so on. Like that's right. So this is that cohesiveness. So can you maybe give me whatever I missed, a bigger overview of the mission of the ecosystem that's trying to be built and

where is it moving forward? Yeah. So in short, the way I like to think of this is our goals to enable machine learning. And in a couple of ways, you know, one is we have lots of exciting things going on in ML today. We started with deep learning, but we now support a bunch of other algorithms too. So one is to on the research side, keep pushing on the state of the art. Can we, you know, how do we enable researchers to build the next amazing thing? So, Bert came out recently, you know, it's great that people are able to do the new kinds of research. There are lots of, you know, amazing research that happens across the world. So, that's one direction. The other is, how do you take that across all the people outside who want to take that research and do some great things with it and integrate it to build real products, to have a real impact on people.

And so if that's the other axes in some ways. You know, at a high level one way I think about it is there are a crazy number of compute devices across the world. And we often used to think of ML and training and all of this as, okay, something you do either in the workstation or the data center cloud. But we see things running on the phones, we see things running on really tiny chips, and we had some demos in the developer summit. And so the way I think about this ecosystem is,

how do we help get machine learning on every device that has the compute capability? And that continues to grow. And so in some ways, this ecosystem is looked at, you know, various aspects of that and grown over time to cover more of those. And we continue to push the boundaries. In some areas, we've built more tooling and things around that to help you. I mean, the first tool we started was TensorFlow, you want to learn just the training piece, the effects or TensorFlow extended to really do your entire ML pipelines. If you're, you know, care about all that production stuff, but then going to the edge, going to different kinds of things. but then going to the edge, going to different kinds of things. And it's not just us now,

there are places where there are lots of libraries being built on top. So there's some for research, maybe things like TensorFlow agents or TensorFlow probability that started as research things or for researchers for focusing on certain kinds of algorithms, but they also being deployed or used by production folks. And some have come from within Google, just teams across Google who want to do the build these things. Others have come from just the community because there are different pieces that different parts of the community care about. And I see our goal as enabling even that, right? It's not we cannot and won't build

every single thing that just doesn't make sense. But if we can enable others to build the things that they care about and there's a broader community that cares about that and we can help encourage that and that's great. That really helps the entire ecosystem, not just those. One of the big things about 2.0 that we're pushing on is, okay, we have so many different pieces, right? How do we help make all of them work well together? So there are a few key pieces there that we're pushing on, one being the core format and then how we share the models themselves through save modeling, what TensorFlow hub and so on. And a few of the pieces that we really put this together.

I was very skeptical that that's, you know, when TensorFlow JS came out, it didn't seem or deep learning JS. Yeah, that was the first. It seems like technically very difficult project. As a standalone, it's not as difficult, but as a thing that integrates into the ecosystem, it seems very difficult. So, I mean, there's a lot of aspects of this you're making look easy, but and the technical side, how many challenges have to be overcome here?

A lot. And still have to be overcome. That's the question here too. There are lots of steps to it. I can be reiterrated over the last few years so there's a lot we've learned. Often when things come together well, things look easy, and that's exactly the point. It should be easy for the end user, but there are lots of things that go behind that. If I think about still challenges ahead, there are, you know, we have a lot more devices coming on board, for example, from the hardware perspective. How do we make it really easy for these vendors to integrate with

something like TensorFlow, right? So there's a lot of compiler stuff that others are working on, there are things we can do in terms of our APIs and so on that we can do. As we, TensorFlow started as a very monolithic system and to some extent it still is. There are less lots of tools around it, but the core is still pretty large in monolithic. One of the key challenges for us to scale that out

is how do we break that apart with clearer interfaces. It's, you know, in some ways it's software engineering 101, but for a system that's now four years old, I guess, or more, and that's still rapidly evolving and that we're not slowing down with, it's hard to, you know, change and modify and really break apart. It's sort of like, as people say, right? It's like changing the engine with a car running or fixed-printed-fix, that's exactly what we're trying to do. So there's a challenge here, because the downside of so many people

being excited about TensorFlow and becoming to rely on it in many other applications, is that you're kind of responsible, it's the technical debt. You're responsible for previous versions to some degree still working. So when you're trying to innovate, I mean, it's probably easier to just start from scratch

every few months. Absolutely. So do you feel the pain of that? A 2.0 does break some back compatibility, but not too much. It seems like the conversion is pretty straightforward. Do you think that's still important given how quickly deep learning is changing? Can you just the things that you've learned, can you just start over or is there pressure to not? It's a tricky balance. So if it was just a researcher writing a paper who a year later will not look at that code

again, sure, it doesn't matter. There are a lot of production systems that rely on TensorFlow, both at Google and across the world. And people worry about this. I mean, these systems Google and across the world. And people worry about this. I mean, these systems run for a long time. So it is important to keep that compatibility and so on. And yes, it does come with a huge cost. There's a we have to think about a lot of things as we do new things and make new changes. I

think the it's a trade off, right? You can, you might slow certain kinds of things down, but the overall value you're bringing because of that is much bigger because it's not just about breaking the person yesterday. It's also about telling the person tomorrow that, you know what, this is how we do things. We're not going to break you when you come on board

because there are lots of new people who are also going to come on board. You know, one way I like to think about this and I always push the team to think about as well, when you want to do new things, you want to start with a clean slate, design with a clean slate in mind, and then we'll figure out how to make sure all the other things work. And yes, we do make compromises occasionally, but unless you're designed with the cleans later, and not worry about that, you'll never get to a good place. That's brilliant. So even if you're, you are responsible

in the idea stage, when you're thinking of new, to put all that behind you, That's really, really well put. So I have to ask this because a lot of students, developers ask me, how do I feel about PyTorch versus TensorFlow? So I've recently completed, switched my research group to TensorFlow. I wish everybody would just use the same thing

and TensorFlow is as close to that, I believe, as we have. But do you enjoy competition? So TensorFlow is leading in many ways, many dimensions in terms of ecosystem, in terms of number of users, momentum, power, production level, so on. But you know, a lot of researchers are now also using PyTorch. Do you enjoy that kind of competition or do you just ignore it and focus on making TensorFlow the best that it can be? So just like research or anything people are doing, right, it's great to get different kinds of ideas. And when we started with TensorFlow like I was saying earlier, one, it was very important for us to also have production in mind. We didn't want just research, right?

And that's why we chose certain things. Now, PyTorch came along and said, you know what? I only care about research. This is what I'm trying to do. What's the best thing I can do for this? And it started iterating and said, okay, I don't need to worry about graphs. Let me just run things.

I don't care if it's not as fast as it can be, but let me just make this part easy. And there are things you can learn from that, right? They again had the benefit of seeing what had come before, but also exploring certain different kinds of spaces and they had some good things there, building on things like chain error and so on before that. So competition is definitely interesting. It made us, you know, this is an area that we had talked about like I said, you know, very early on. Over time, we had revisited this a couple of times. Should we add this again?

At some point, we said, you know what? Here's, it seems like this can be done well. So, let's try it again. And that's how, you know, we start pushing on eager execution. How do we combine those two together, which is finally come very well together in 2.0 but it took us a while to get all the things together and so on. So let me ask, put another way, I think eager execution is a really powerful thing, those added. You think he wouldn't have been, you know, Muhammad Ali versus your Frazier, right? You think it wouldn't have been added

as quickly if PyTorch wasn't there? It might have taken longer. The longer? Yeah. Yeah. It was, I mean, we had dried some radiance of that before, so I'm sure it would have happened, but it might have taken longer. I'm grateful that TensorFlow Spontan in the way they did. It's doing some incredible work last couple of years. What other things that we didn't talk about, you're looking forward to 2.0 that comes to mind. So we talk about some of the ecosystem stuff, making it easily accessible to carous, e-car execution. Is there other things that we miss?

Yeah, so I would say one is just very two point O is and with all the things that we've talked about, I think as we think beyond that, there are lots of other things that it enables us to do and that we are excited about. So what it's setting us up for, okay, the here are these really clean APIs, we've cleaned up the surface for what the users want, what it also allows us to do a whole bunch of stuff behind the scenes once we're ready with 2.0. So for example, in TensorFlow with graphs and all the things you could do, you could always get a lot of good performance

if you spend the time to tune it. And we've clearly shown that, lots of people that with 2.0 with these APIs where we are, we can give you a lot of performance just with whatever you do. You know, if you're because we see these, it's much cleaner. We know most people are going to do things this way. We can really optimize for that and get a lot of those things out of the box. And it really allows us, you know, both for single machine and distributed and so on, really explore other spaces behind the scenes after, you know, to point out in the future versions as well. So right now the team is really excited about that.

That over time, I think we'll see that. The other piece that I was talking about in terms of just restructuring the monolithic thing into more pieces and making it more modular, I think that's going to be really important for a lot of the other people in the ecosystem, other organizations and so on that wanted to build things. Can you elaborate a little bit what you mean by making TensorFlow more ecosystem more modular? So the way it's organized today is there's one, there are lots of repositories in the TensorFlow organization at GitHub, the core one where we have TensorFlow.

It has the execution engine, it has the key backends for CPUs and GPUs. It has the work to do distributed stuff. And all of these just work together in a single library or binary. There's no way to split them apart easily. I mean, there are some interfaces, but they're not very clean. In a perfect world, you would have clean interfaces where okay, I want to run it on my fancy cluster with some custom networking. Just implement this into that. I mean, we kind of support that, but it's hard for people today.

I think as we are starting to see more interesting things in some of these places, having that clean separation will really start to help. And again, going to the large size of the ecosystem and the different groups involved there, enabling people to evolve and push on things more independently just allows it to scale better. And by people you mean individual developers and organizations? And organizations?

So the hope is that everybody, major Pepsi or something uses major corporations, go to TensorFlow to this kind of. Yeah, if you look at enterprises like Pepsi or these, I mean, all of them are already using TensorFlow. They are not the ones that do the development or changes in the core. Some of them do, but a lot of them don't. I mean, they touch small pieces. There are lots of these, some of them being, let's say hardware vendors who are building their custom hardware and they want their own pieces, or some of them being bigger companies say IBM. I mean, they're involved in some of our special interest groups and they see a lot of users who warn certain things and they want to optimize for that folks like that often. Autonomous vehicle companies perhaps exactly.

Yes. autonomous vehicle companies perhaps. Exactly. Yes. So, yeah, like I mentioned, TensorFlow has been downloaded 41 million times, 50,000 commits, almost 10,000 pull requests, 1800 contributors. So, I'm not sure if you can explain it, but what does it take to build a community like that?

What, in retrospect, what do you think, what is the critical thing that allowed for this growth to happen and how does that growth continue? Yeah. Yeah, that's an interesting question. I had all the answers there, I guess, so you could replicate it. I think there's a, there are a number of things

that need to come together, right? One, you know, just like any new thing, it is about there's a sweet part of timing, what's needed, does it grow with what's needed. So in this case, for example, TensorFlow is not just growing because there is a good tool. It's also growing with the growth of deep learning itself. So those factors come into play. Other than that, though, I think just hearing,

listening to the community, what they're to what they need, being open to, like in terms of external contributions, we've spent a lot of time in making sure we can accept those contributions well, we can help the contributors in adding those, putting the right process in place, getting the right kind of community, welcoming them, and so on. Like over the last year, we've really pushed on transparency. That's important for an open source project. People want to know where things are going, and we're like,

okay, here's a process for you. You can do that here, or our season, so on. So thinking through, there are lots of community aspects that come into that you can really work on. As a small project, it's maybe easy to do because there's like two developers and you can do those. As you grow, putting more of these processes in place, thinking about the documentation, thinking about what two developers get about what kind of tools would they want to use? A lot of these come into play, I think.

So one of the big things I think that feeds the TensorFlow Fire is people building something on TensorFlow and you know, some implement a particular architecture that does something cool and useful. And they put it at that on GitHub. And so it just feeds this growth. Have a sense that with 2.0 and 1.0 that there may be a little bit of a partitioning, like there is a Python 2 and 3, that there will be a code base. And in the older versions of TensorFlow, they will not be as compatible easily or a pretty confident that this kind of conversion is pretty natural and

easy to do. So, we're definitely working hard to make that very easy to do. There's lots of tooling that we talked about at the developer summit this week and we continue to invest in that tooling. It's, you know, when you think of these significant version changes, that's always a risk. And we are really pushing hard to make that transition very, very smooth. I think so at some level, people want to move and they see the value in the new thing. They don't want to move just because it's a new thing. And some people do, but most people want a really good thing. And I think over the next few months,

as people start to see the value, we'll definitely see that shift happening. So I'm pretty excited and confident that we will see people moving. As you said earlier, this field is also moving rapidly, so that'll help because we can do more things, and you know, all the new things will clearly happen in 2.x. So people will have lots of good reasons to move. So what do you think TensorFlow 3.0 looks like? Is there things happening so crazy that even at the end of the CR seems impossible to plan for? Or is it possible to plan for the next five years? plan for, or is it possible to plan for the next five years? I think it's tricky. There are some things that we can expect in terms of, okay,

change, yes, change is going to happen. Are there some going things going to stick around and some things not going to stick around? I would say the basics of deep learning, the, you know, say, convolutional models or the basic kind of things, there'll probably be around in some forms still in five years. Will Aril and Gans stay very likely based on where they are? Will we have new things? Probably, but those are hard to predict. Some, directionally, some things that we can see is you know and things that we're starting to do right with some of our projects right now is just to point

out combining your execution and graphs where we're starting to make it more like just your natural programming language you're not trying to program something else. Similarly with Swift for TensorFlow we're taking that approach can Can you do something roundup, right? So some of those ideas seem like, okay, that's the right direction. In five years, we expect to see more in that area. Other things we don't know is, they'll hardware accelerators be the same. Will we be able to train with four bits instead of 32 bits? we'll be able to train with four bits instead of 32 bits. And I think the TPU side of things is exploring that. I mean, TPUs are already on version three. It seems that the evolution of TPU and TensorFlow

are sort of their co-evolving, almost in terms of both are learning from each other and from the community and from the applications where the biggest benefit is achieved. That's right. You've been trying to sort of with ego with carous to make TensorFlow as accessible and easy to use as possible. What do you think for beginners is the biggest thing they struggle with? Have you encountered that or is basically what carous is solving is that eager? Like we talked about. Yeah, for some of them, like you said, right, the beginners want to just be able to take some image model. They don't care if it's inception or rest net or something else and do some

training or transfer learning on the kind of model. Being able to make that easy is important. So in some ways, if you do that by providing them simple models with say in hub or so on, they don't care about what's inside that box, but they want to be able to use it. So we're pushing on, I think, different levels. If you look at just a component that you get, which has the layers already smushed in, the beginners probably just want that. Then the next step is, okay, look at building layers with carers. If you go out to research, then they are probably writing custom layers themselves or doing their own loops. So there's a whole spectrum there. And then providing the pre-trained models seems to really decrease the time from

you trying to start. So you could basically in a colab notebook achieve what you need. So I basically answering in a collab notebook achieve what you need. So I basically answering my own question because I think what TensorFlow delivered on recently is this trivial for beginners. So I was just wondering if there was other pain points you trying to ease, but I'm not sure there would. No, those are probably the big ones. I mean, I see high schoolers doing a whole bunch of things now, which is pretty amazing. It's both amazing and terrifying. So, yes, in a sense, that when they grow up, it's some incredible ideas will be coming from them. So, there's certainly a technical aspect to your work, but you also have a management aspect to your role with TensorFlow leading the project, large number of developers and people. So, what do you look for in a good team?

What do you think? Google has been at the forefront of exploring what it takes to build a good team. And TensorFlow is one of the most cutting-edge technologies in the world. So, in this context, what do you think makes for a good team? It's definitely something I think a fair bit about. I think the, in terms of, you know,

the team being able to deliver something well, one of the things that's important is a cohesion across the team. So being able to execute together and doing things, it's not an end, like at this scale, an individual engineer can only do so much. There's a lot more that they can do together, even though we have some amazing superstars

across Google and in the team, where there's, you know, often the way I see it is the product of what the team generates is way larger than the whole Lord, you know, often the way I see it is the product of what the team generates is way larger than the whole or, you know, the individual put together. And so how do we have all of them work together, the culture of the team itself? Hiring good people is important, but part of that is it's not just that, okay, we hire bunch of smart people and throw them together and let them do things. It's also people have to care about what they're building, people have to be motivated for the right kind of things. That's often an important factor. And you know finally how do you

put that together with somewhat unified vision of where we want to go. So are we all looking in the same direction or it's going all over? And sometimes it's a mix. Google is a very bottom-up organization in some sense. Also research even more so. And that's how we started. But as we've become this larger product and ecosystem, I think it's also important to combine that well with mix of, okay, here's the direction we want to go in. There is exploration we'll do around that, but let's keep staying in that direction,

not just all over the place. Is there a way you monitor the health of the team? Is there a way you know you did of the team? Sort of like, is there a way you know you did a good job? The team is good. Like, I mean, you're sort of, you're saying nice things, but it's sometimes difficult to determine how aligned. Yes. Because it's not binary. It's not like it's, it's, it's their tensions and complexities and so on. And the other element of is that the mission superstars, you know, there's so much, even a Google, such a large percentage of work is done by individual

superstars too. So there's a, yeah, and sometimes those superstars can be against the dynamic of a team and those, those tensions have, it was that has the, I mean, I'm sure in TensorFlow, it might be a little bit easier because the mission of the project is so sort of beautiful. You're at the cutting edge, that's exciting. Yeah. Have you had struggle with that? Has there been challenges? There are always people challenging in different kinds of ways. That's I think we've been what's good about getting people who care and have the same kind of culture, and that's Google in general, do a large extent, but also like you said, given that the project has

had so many exciting things to do, there's been room for lots of people to do different kinds of things in grow, which does make the problem a bit easier, I guess. And it allows people, depending on what they're doing, if there's room around them, then that's fine. But yes, we do care about whether superstar or not that they need to work well with the team across the world. That's interesting to hear. So it's like superstar or not, the productivity broadly is about the team. Yeah. Yeah. I mean, they might add a lot of value, but if they're importing the team, then that's a problem. So in hiring engineers,

it's so interesting, right, the hiring process, what do you look for? How do you determine a good developer or a good member of a team from just a few minutes or hours together? developer, a good member of a team from just a few minutes or hours together. Again, no magic answers, I'm sure. Yeah, yeah. I mean, Google has a hiding process that we've refined over the last 20 years, I guess, and that you've probably heard and seen a lot about. So we do work with the same hiding process in that that's really helped.

For a mean particular, I would say, in addition to the core technical skills, what does matter is their motivation in what they want to do. Because if that doesn't align well with where we want to go, that's not going to lead to long term success, free the demo or the team. And I think that becomes more important, the more senior the person is, but it's important at every level. Like even the junior most engineer, if they're not motivated to do well at what they're trying to do, however smart they are, it's going to be hard for them to succeed.

Does the Google hiring process touch on that passion? So like trying to determine, because I think as far as I understand, maybe you can speak to it, that the Google hiring process sort of helps the initial like determines the skill set there is your puzzle solving ability, problem solving ability good, but like, I'm not sure, but it seems that the determining whether the person is like fire inside them, that burns to do anything, really, it doesn't really matter. It's just some cool stuff, I'm gonna do it. That, I don't know, is that something that ultimately ends up when they have a conversation with you

or once it gets closer to the team? So one of the things we do have as part of the process is just a culture effect, like part of the interview process itself, in addition to just the technical skills in each engineer or whoever the interviewer is, is supposed to rate the person on the culture and the culture fit with Google and so on. So that is definitely part of the process. Now, there are various kinds of projects and different kinds of things. So there might be variants and if the kind of culture you want there and so on.

And yes, that does very. So for example, TensorFlow's always been a fast moving project. And we want people who are comfortable with that. But at the same time now, for example, we are at a place where we are also very full-fledged product. And we want to make sure things that work really, really work, right? You can't cut corners all the time. So balancing that out and finding the people who are the right fits for those is important. And I think those kind of things do vary a bit across projects and teams and product areas

across Google. And so you'll see some differences there in the final checklist. But a lot of the core culture, it comes along with just the engineering excellence and so on. What is the hardest part of your job? I take you, Pique, I guess. It's fun, I would say. Hard, yes.

I mean, lots of things at different times. I think that does very. So let me clarify that difficult things are fun. Yeah. When you solve them, right? So it's fun in that sense. I think the key to a successful thing across the board and, you know, in this case, it's a large ecosystem now, but even a small product is striking that fine balance across different aspects of it. Sometimes it's how fast you go,

versus how perfect it is. Sometimes it's how do you involve this huge community? Who do you involve? Or do you decide, okay, now it's not a good time to involve them because it's not the right fit. And sometimes it's saying no to certain kinds of things. Those are often the hard decisions. Some of them you make quickly because you don't have the time. Some of them you get time to think about them, but they're always hard. So on both choices are pretty good. It's those decisions.

What about deadlines? Is this defined TensorFlow to be driven by deadlines to a degree that a product might, or is there still a balance to where it's less deadline? You had the dev summit that came together incredibly. Look like there's a lot of moving pieces and so on so that that deadline make people rise to the occasion releasing that's a full 2.0 alpha. Yeah, I'm sure that was done last minute as well. I mean like the up to the up to the last point. Again, you know, it's one of those things that's a, you need to strike the good balance. There's some value that deadlines bring that does bring a sense of urgency to get the right things together instead of, you know, getting the perfect thing out, you need

something that's good in works well. And the team definitely did a great job in putting that together. So it was very amazed and excited by everything how that came together. together. So it was very amazed and excited by everything how that came together. That's it across there. We try not to put artificial deadlines. We focus on key things that are important, figure out what that, how much of it's important. And we are developing in the open, what, you know, internally and externally everything's available to everybody. So you can pick and look at where things are. If you do releases at a regular cadence, so fine, if something doesn't necessarily end up with this month, it'll end up in the next release in the month or two. And that's okay, but we want to get like keep moving as fast as we can in these different areas. Because we can iterate and improve one thing. Sometimes it's okay to put things out that aren't fully ready. If you make sure it's clear, that's okay.

This is experimental, but it's out there if you want to try and give feedback. That's very, very useful. I think that quick cycle and quick iteration is important. That's what we often focus on rather than here's a deadline where you get everything else. Is 2.0 is there pressure to make that stable? Or like for example WordPress 5.0 just came out and there was no pressure to it was a lot of build updates delivered way too late. But and they said okay, but we're going to release a lot of updates really quickly to improve it. But and they said okay, well, but we're gonna release a lot of updates really quickly to improve it

This do you see TensorFlow 2.0 in that same kind of way or is there this pressure to once it hits 2.0 Once you get to the release candidate and then you get to the final that it that's gonna be the the stable thing So it's gonna be stable in Just like when not x was where every API that's there is going to remain and work. It doesn't mean we can't change things in another covers. It doesn't mean we can't add things. So there's still a lot more for us to do and we're going to do have more releases. So in that sense, there's still I don't think we'd be done in like two months when we release this.

I don't know if you can say, but is there, you know, there's not external deadlines for tens of flow 2.0, but is there internal deadlines, the artificial or otherwise that you're trying to set for yourself, or is it whenever it's ready? So we want it to be a great product, right? And that's a big big important piece for us. TensorFlow is already out there. We have 41 million downloads for 1.x. It's pretty good.

It's pretty good. Yeah, exactly. So it's not like a lot of the features that we've really polishing and putting them together are there. We don't have to rush that just because, so in that sense, we want to get it right and really focus on that. That said, we have said sense, we want to get it right and really focus on that. That said, we have said that we are looking to get this out in the next few months, in the next quarter. And, you know, as far as possible, we definitely try to make that happen.

Yeah, my favorite line was Spring is a relative concept. I love it. Yes. I spoke on like a true developer. So, you know, something I'm really interested in and your previous line of work is before TensorFlow, you let a team at Google on Search ads. I think this is like, this is a very interesting topic on every level and a technical level because that their best ads connect people to the things they want and need. Yep. So, and that they're worse, they're just these things that annoy the heck out of you to the point of ruining the entire user experience of whatever you're actually doing.

And so they have a bad rep, I guess. And so, at the, on the other end, so that this connecting users to the thing they need to want is a beautiful opportunity for machine learning to shine. Right. Huge amounts of data that's personalized and you kind of map to the thing they actually won't get annoyed. So what have you learned from this Google that's leading the world in this aspect? What have you learned from that experience?

And what do you think is the future of ads? Take you back to the that. Yes, it's been a while, but I totally agree with what you said. I think the search ads the way it was always looked at and I believe it still is, is it's an extension of what search is trying to do. And the goal is to make the information and make the world's information accessible. That's it's not just information, but it may be products or you know, the things that people care about. And so it's really important for them to align with what the users need. And you know, the in search ads, there's a minimum quality level before

that ad would be shown. If you don't have an ad that hits that quality, but it will not be shown, even if we have it. And, okay, maybe we lose some money there. That's fine. That is really, really important. And I think that that is something I really liked about being there. Advertising is a key part. I mean, as a model, it's been around for ages, right? It's not a new model. It's been adapted to the web and, you know, became a core part of search in many other search engines across the world. I do hope, you know, like I said, there are aspects of ads that are annoying and I go to a website and if it just keeps popping and I did my face not to let me read,

that's gonna be annoying clearly. So I hope we can strike that balance between showing a good ad where it's valuable to the user and provides the monetization to the service. And this might be searched. This might be a website, all of these. They do need the monetization for them to provide that service. But if it's done in a good balance between showing just some random stuff that's distracting versus showing something that's actually valuable.

So do you see it moving forward as to continue being a model that funds businesses like Google? That's a significant revenue stream because that's one of the most exciting things but also limiting things in the internet is nobody wants to pay for anything Yep, and advertisements again coupled at their best are actually really useful and not annoying do you continue? Do you see that continuing and growing and improving or is there DC sort of More Netflix type models where you have to start to pay for content. I think it's a mix.

I think it's going to take a long while for everything to be paid on the Internet, if at all, probably not. I mean, I think there's always going to be things that are sort of monetized with things like ads. But over the last few years, I would say we've definitely seen that transition towards more paid services across the web and people are willing to pay for them because they do see the value. I mean, Netflix is a great example.

I mean, we have YouTube doing things. People pay for the apps they buy. More people I find are willing to pay for newspaper content for the good news websites across the web. That was in the case a few years, even a few years ago, I would say. And I just see that change in myself as well and just lots of people around me. So definitely hopeful that we'll transition

to that mixed model where maybe you get to try something out for free, maybe with ads, but then there's a more clear revenue model that sort of helps go beyond the art. So, speaking of revenue, how is it that a person can use the TPU and a Google call app for free? So what's the... I guess the question is, what's the future of TensorFlow? In terms of empowering, say, a class of 300 students, and they are masked by MIT, what is going to be the future

of them being able to do their homework in TensorFlow? Like, where are they going to train these networks? What's that future look like with TPUs, with cloud services, and so on. I think a number of things that I mean, any tens of flow open source, you can run it wherever you can run. Don your desktop and your desktop

always keep getting more powerful. So maybe you can do more. My phone is like, I don't know how many times more powerful than my first desktop. You'll probably train it on your phone though, yeah. Right, so in that sense, the power you have in your hand is a lot more. Clouds are actually very interesting from say students or courses perspective because they make it very easy to get started. I mean, go lab. The great thing about is go to a website and it just works. No installation needed. Nothing to, you're just there and things are working.

That's really the power of cloud as well. And so I do expect that to grow. Again, collab is a free service. It's great to get started, to play with things, to explore things that said, with free you can only get so much. You be here. So just like we were talking about you know free versus

Yeah, they are there are services you can pay for and get a lot more Great so if I'm a complete beginner interested in machine learning and TensorFlow. What should I do? Probably start with going to our website and playing there Just go to TensorFlow.org and start clicking on things. Yep. Check out tutorials and guides There's stuff you can just click there and go to a collab and do things. No installation needed. You can get started right there. Okay, awesome. Rajit, thank you so much for talking today. Thank you, likes.

Fun is great. Thank you.