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WEBVTT | |
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The following is a conversation with Rajat Manga. | |
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He's an engineering director at Google, | |
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leading the TensorFlow team. | |
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TensorFlow is an open source library | |
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at the center of much of the work going on in the world | |
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in deep learning, both the cutting edge research | |
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and the large scale application of learning based approaches. | |
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But it's quickly becoming much more | |
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than a software library. | |
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It's now an ecosystem of tools for the deployment | |
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of machine learning in the cloud, on the phone, | |
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in the browser, on both generic and specialized hardware. | |
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TPU, GPU, and so on. | |
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Plus, there's a big emphasis on growing | |
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a passionate community of developers. | |
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Rajat, Jeff Dean, and a large team of engineers at Google | |
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Brain are working to define the future of machine learning | |
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with TensorFlow 2.0, which is now in alpha. | |
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I think the decision to open source TensorFlow | |
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is a definitive moment in the tech industry. | |
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It showed that open innovation can be successful | |
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and inspire many companies to open source their code, | |
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to publish, and in general engage in the open exchange | |
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of ideas. | |
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This conversation is part of the artificial intelligence | |
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podcast. | |
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If you enjoy it, subscribe on YouTube, iTunes, | |
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or simply connect with me on Twitter | |
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at Lex Friedman, spelled FRID. | |
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And now, here's my conversation with Rajat Manga. | |
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You were involved with Google Brain since its start in 2011 | |
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with Jeff Dean. | |
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It started with disbelief, the proprietary machine learning | |
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library, and turned into TensorFlow 2014, | |
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the open source library. | |
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So what were the early days of Google Brain like? | |
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What were the goals, the missions? | |
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How do you even proceed forward once there's | |
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so much possibilities before you? | |
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It was interesting back then when I started, | |
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or when you were even just talking about it. | |
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The idea of deep learning was interesting | |
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and intriguing in some ways. | |
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It hadn't yet taken off, but it held some promise. | |
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It had shown some very promising and early results. | |
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I think the idea where Andrew and Jeff had started | |
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was what if we can take this, what people are doing in research, | |
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and scale it to what Google has in terms of the compute power, | |
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and also put that kind of data together, what does it mean? | |
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And so far, the results had been if you scale the computer, | |
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scale the data, it does better, and would that work? | |
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And so that was the first year or two. | |
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Can we prove that outright? | |
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And with disbelief, when we started the first year, | |
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we got some early wins, which is always great. | |
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What were the wins like? | |
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What was the wins where there are some problems to this? | |
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This is going to be good. | |
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I think the two early wins were one was speech | |
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that we collaborated very closely with the speech research | |
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team, who was also getting interested in this. | |
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And the other one was on images where | |
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the cat paper, as we call it, that was covered by a lot of folks. | |
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And the birth of Google Brain was around neural networks. | |
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So it was deep learning from the very beginning. | |
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That was the whole mission. | |
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So in terms of scale, what was the dream | |
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of what this could become? | |
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Were there echoes of this open source TensorFlow community | |
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that might be brought in? | |
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Was there a sense of TPUs? | |
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Was there a sense of machine learning | |
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is now going to be at the core of the entire company? | |
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Is going to grow into that direction? | |
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Yeah, I think so that was interesting. | |
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And if I think back to 2012 or 2011, | |
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and first was can we scale it in the year or so, | |
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we had started scaling it to hundreds and thousands | |
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of machines. | |
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In fact, we had some runs even going to 10,000 machines. | |
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And all of those shows great promise. | |
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In terms of machine learning at Google, | |
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the good thing was Google's been doing machine learning | |
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for a long time. | |
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Deep learning was new. | |
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But as we scale this up, we showed that, yes, that was | |
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possible, and it was going to impact lots of things. | |
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Like, we started seeing real products wanting to use this. | |
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Again, speech was the first. | |
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There were image things that photos came out of | |
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in many other products as well. | |
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So that was exciting. | |
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As we went into with that a couple of years, | |
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externally also academia started to, | |
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there was lots of push on, OK, deep learning's | |
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interesting, we should be doing more, and so on. | |
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And so by 2014, we were looking at, OK, this is a big thing. | |
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It's going to grow. | |
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And not just internally, externally as well. | |
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Yes, maybe Google's ahead of where everybody is, | |
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but there's a lot to do. | |
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So a lot of this start to make sense and come together. | |
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So the decision to open source, I was just chatting with Chris | |
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Flattner about this, the decision to go open source | |
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with TensorFlow, I would say for me personally, | |
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seems to be one of the big seminal moments in all | |
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of software engineering ever. | |
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I think that when a large company like Google | |
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decides to take a large project that many lawyers might argue | |
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has a lot of IP, just decide to go open source with it. | |
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And in so doing, lead the entire world in saying, | |
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you know what, open innovation is a pretty powerful thing. | |
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And it's OK to do. | |
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That was, I mean, that's an incredible moment in time. | |
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So do you remember those discussions happening? | |
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Are there open source should be happening? | |
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What was that like? | |
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I would say, I think, so the initial idea came from Jeff, | |
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who was a big proponent of this. | |
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I think it came off of two big things. | |
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One was research wise, we were a research group. | |
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We were putting all our research out there if you wanted to. | |
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We were building on other's research, | |
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and we wanted to push the state of the art forward. | |
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And part of that was to share the research. | |
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That's how I think deep learning and machine learning | |
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has really grown so fast. | |
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So the next step was, OK, now word software | |
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help for that. | |
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And it seemed like they were existing a few libraries | |
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out there, Tiano being one, Torch being another, | |
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and a few others. | |
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But they were all done by academia, | |
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and so the level was significantly different. | |
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The other one was, from a software perspective, | |
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Google had done lots of software that we used internally. | |
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And we published papers. | |
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Often there was an open source project | |
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that came out of that, that somebody else | |
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picked up that paper and implemented, | |
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and they were very successful. | |
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Back then, it was like, OK, there's | |
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Hadoop, which has come off of tech that we've built. | |
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We know that tech we've built is way better | |
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for a number of different reasons. | |
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We've invested a lot of effort in that. | |
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And turns out, we have Google Cloud, | |
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and we are now not really providing our tech, | |
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but we are saying, OK, we have Bigtable, which | |
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is the original thing. | |
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We are going to now provide HBase APIs on top of that, which | |
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isn't as good, but that's what everybody's used to. | |
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So there's like, can we make something that is better | |
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and really just provide? | |
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Helps the community in lots of ways, | |
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but it also helps push the right, a good standard forward. | |
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So how does Cloud fit into that? | |
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There's a TensorFlow open source library. | |
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And how does the fact that you can | |
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use so many of the resources that Google provides | |
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and the Cloud fit into that strategy? | |
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So TensorFlow itself is open, and you can use it anywhere. | |
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And we want to make sure that continues to be the case. | |
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On Google Cloud, we do make sure that there's | |
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lots of integrations with everything else, | |
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and we want to make sure that it works really, really well there. | |
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You're leading the TensorFlow effort. | |
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Can you tell me the history and the timeline of TensorFlow | |
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project in terms of major design decisions, | |
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like the open source decision, but really, what to include | |
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and not? | |
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There's this incredible ecosystem that I'd | |
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like to talk about, there's all these parts. | |
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But if you just some sample moments that | |
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defined what TensorFlow eventually became through its, | |
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I don't know if you were allowed to say history when it's just, | |
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but in deep learning, everything moves so fast | |
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in just a few years, it's already history. | |
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Yes, yes. | |
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So looking back, we were building TensorFlow. | |
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I guess we open sourced it in 2015, November 2015. | |
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We started on it in summer of 2014, I guess. | |
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And somewhere like three to six late 2014, | |
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by then we had decided that, OK, there's | |
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a high likelihood we'll open source it. | |
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So we started thinking about that and making sure | |
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that we're heading down that path. | |
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At that point, by that point, we'd | |
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seen a few lots of different use cases at Google. | |
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So there were things like, OK, yes, | |
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you want to run in at large scale in the data center. | |
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Yes, we need to support different kind of hardware. | |
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We had GPUs at that point. | |
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We had our first GPU at that point | |
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or was about to come out roughly around that time. | |
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So the design included those. | |
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We had started to push on mobile. | |
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So we were running models on mobile. | |
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At that point, people were customizing code. | |
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So we wanted to make sure TensorFlow could support that | |
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as well so that that became part of that overall | |
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design. | |
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When you say mobile, you mean like pretty complicated | |
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algorithms of running on the phone? | |
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That's correct. | |
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So when you have a model that you | |
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deploy on the phone and run it there, right? | |
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So already at that time, there was ideas of running machine | |
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learning on the phone. | |
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That's correct. | |
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We already had a couple of products | |
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that were doing that by then. | |
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And in those cases, we had basically | |
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customized handcrafted code or some internal libraries | |
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that we're using. | |
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So I was actually at Google during this time in a parallel, | |
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I guess, universe. | |
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But we were using Theano and CAFE. | |
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Was there some degree to which you were bouncing, | |
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like trying to see what CAFE was offering people, | |
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trying to see what Theano was offering | |
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that you want to make sure you're delivering on whatever that | |
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is, perhaps the Python part of thing. | |
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Maybe did that influence any design decisions? | |
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Totally. | |
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So when we built this belief, and some of that | |
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was in parallel with some of these libraries | |
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coming up, I mean, Theano itself is older. | |
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But we were building this belief focused on our internal thing | |
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because our systems were very different. | |
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By the time we got to this, we looked | |
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at a number of libraries that were out there. | |
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Theano, there were folks in the group | |
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who had experience with Torch, with Lua. | |
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There were folks here who had seen CAFE. | |
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I mean, actually, Yang Cheng was here as well. | |
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There's what other libraries? | |
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I think we looked at a number of things. | |
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Might even have looked at Jane and her back then. | |
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I'm trying to remember if it was there. | |
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In fact, yeah, we did discuss ideas around, OK, | |
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should we have a graph or not? | |
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And they were supporting all these together | |
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was definitely, you know, there were key decisions | |
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that we wanted. | |
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We had seen limitations in our prior disbelief things. | |
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A few of them were just in terms of research | |
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was moving so fast. | |
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We wanted the flexibility. | |
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We want the hardware was changing fast. | |
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We expected to change that so that those probably were two | |
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things. | |
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And yeah, I think the flexibility in terms | |
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of being able to express all kinds of crazy things | |
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was definitely a big one then. | |
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So what the graph decisions, though, | |
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with moving towards TensorFlow 2.0, there's more, | |
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by default, there'll be eager execution. | |
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So sort of hiding the graph a little bit | |
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because it's less intuitive in terms of the way | |
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people develop and so on. | |
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What was that discussion like with in terms of using graphs? | |
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It seemed it's kind of the theano way. | |
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Did it seem the obvious choice? | |
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So I think where it came from was our disbelief, | |
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had a graph like thing as well. | |
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It wasn't a general graph. | |
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It was more like a straight line thing. | |
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More like what you might think of Cafe, | |
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I guess, in that sense. | |
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And we always cared about the production stuff. | |
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Even with disbelief, we were deploying a whole bunch of stuff | |
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in production. | |
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So graph did come from that when we thought of, OK, | |
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should we do that in Python and we experimented with some ideas | |
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where it looked a lot simpler to use, | |
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but not having a graph meant, OK, how do you deploy now? | |
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So that was probably what tilted the balance for us. | |
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And eventually, we ended up with the graph. | |
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And I guess the question there is, did you? | |
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I mean, production seems to be the really good thing to focus on. | |
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But did you even anticipate the other side of it | |
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where there could be, what is it? | |
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What are the numbers? | |
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Something crazy, 41 million downloads? | |
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Yep. | |
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I mean, was that even like a possibility in your mind | |
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that it would be as popular as it became? | |
13:19.120 --> 13:24.880 | |
So I think we did see a need for this a lot | |
13:24.880 --> 13:30.000 | |
from the research perspective and early days of deep learning | |
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in some ways. | |
13:32.280 --> 13:33.040 | |
41 million? | |
13:33.040 --> 13:37.640 | |
No, I don't think I imagine this number then. | |
13:37.640 --> 13:42.760 | |
It seemed like there's a potential future where lots more people | |
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would be doing this. | |
13:43.760 --> 13:45.640 | |
And how do we enable that? | |
13:45.640 --> 13:49.560 | |
I would say this kind of growth, I probably | |
13:49.560 --> 13:53.680 | |
started seeing somewhat after the open sourcing where it was | |
13:53.680 --> 13:56.240 | |
like, OK, deep learning is actually | |
13:56.240 --> 13:59.200 | |
growing way faster for a lot of different reasons. | |
13:59.200 --> 14:02.720 | |
And we are in just the right place to push on that | |
14:02.720 --> 14:06.040 | |
and leverage that and deliver on lots of things | |
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that people want. | |
14:07.440 --> 14:09.760 | |
So what changed once the open source? | |
14:09.760 --> 14:13.320 | |
Like how this incredible amount of attention | |
14:13.320 --> 14:16.120 | |
from a global population of developers, | |
14:16.120 --> 14:18.200 | |
how did the projects start changing? | |
14:18.200 --> 14:21.880 | |
I don't even actually remember it during those times. | |
14:21.880 --> 14:24.560 | |
I know looking now, there's really good documentation. | |
14:24.560 --> 14:26.560 | |
There's an ecosystem of tools. | |
14:26.560 --> 14:31.080 | |
There's a YouTube channel now. | |
14:31.080 --> 14:33.760 | |
It's very community driven. | |
14:33.760 --> 14:38.800 | |
Back then, I guess 0.1 version. | |
14:38.800 --> 14:39.760 | |
Is that the version? | |
14:39.760 --> 14:42.680 | |
I think we called it 0.6 or 5, something like that. | |
14:42.680 --> 14:43.720 | |
Something like that. | |
14:43.720 --> 14:47.200 | |
What changed leading into 1.0? | |
14:47.200 --> 14:48.480 | |
It's interesting. | |
14:48.480 --> 14:51.640 | |
I think we've gone through a few things there. | |
14:51.640 --> 14:53.680 | |
When we started out, when we first came out, | |
14:53.680 --> 14:56.080 | |
people loved the documentation we have. | |
14:56.080 --> 14:58.800 | |
Because it was just a huge step up from everything else. | |
14:58.800 --> 15:01.920 | |
Because all of those were academic projects, people | |
15:01.920 --> 15:04.560 | |
don't think about documentation. | |
15:04.560 --> 15:08.040 | |
I think what that changed was instead of deep learning | |
15:08.040 --> 15:12.560 | |
being a research thing, some people who were just developers | |
15:12.560 --> 15:15.080 | |
could now suddenly take this out and do | |
15:15.080 --> 15:16.920 | |
some interesting things with it. | |
15:16.920 --> 15:20.720 | |
Who had no clue what machine learning was before then. | |
15:20.720 --> 15:22.520 | |
And that, I think, really changed | |
15:22.520 --> 15:27.880 | |
how things started to scale up in some ways and pushed on it. | |
15:27.880 --> 15:30.400 | |
Over the next few months, as we looked at, | |
15:30.400 --> 15:31.960 | |
how do we stabilize things? | |
15:31.960 --> 15:33.840 | |
As we look at not just researchers, | |
15:33.840 --> 15:34.880 | |
now we want stability. | |
15:34.880 --> 15:36.480 | |
People want to deploy things. | |
15:36.480 --> 15:38.960 | |
That's how we started planning for 1.0. | |
15:38.960 --> 15:42.240 | |
And there are certain needs for that perspective. | |
15:42.240 --> 15:45.320 | |
And so, again, documentation comes up, | |
15:45.320 --> 15:49.480 | |
designs, more kinds of things to put that together. | |
15:49.480 --> 15:53.120 | |
And so that was exciting to get that to a stage where | |
15:53.120 --> 15:56.400 | |
more and more enterprises wanted to buy in and really | |
15:56.400 --> 15:58.720 | |
get behind that. | |
15:58.720 --> 16:02.640 | |
And I think post 1.0 and with the next few releases, | |
16:02.640 --> 16:05.240 | |
their enterprise adoption also started to take off. | |
16:05.240 --> 16:08.000 | |
I would say between the initial release and 1.0, | |
16:08.000 --> 16:11.000 | |
it was, OK, researchers, of course. | |
16:11.000 --> 16:13.720 | |
Then a lot of hobbies and early interest, | |
16:13.720 --> 16:15.920 | |
people excited about this who started to get on board. | |
16:15.920 --> 16:19.000 | |
And then over the 1.x thing, lots of enterprises. | |
16:19.000 --> 16:23.760 | |
I imagine anything that's below 1.0 | |
16:23.760 --> 16:27.160 | |
gets pressured to be enterprise problem or something | |
16:27.160 --> 16:28.000 | |
that's stable. | |
16:28.000 --> 16:28.800 | |
Exactly. | |
16:28.800 --> 16:33.360 | |
And do you have a sense now that TensorFlow is stable? | |
16:33.360 --> 16:35.520 | |
It feels like deep learning, in general, | |
16:35.520 --> 16:37.800 | |
is extremely dynamic field. | |
16:37.800 --> 16:39.680 | |
So much is changing. | |
16:39.680 --> 16:43.400 | |
Do you have a, and TensorFlow has been growing incredibly. | |
16:43.400 --> 16:46.720 | |
Do you have a sense of stability at the helm of this? | |
16:46.720 --> 16:48.360 | |
I mean, I know you're in the midst of it. | |
16:48.360 --> 16:50.360 | |
Yeah. | |
16:50.360 --> 16:54.000 | |
I think in the midst of it, it's often easy to forget what | |
16:54.000 --> 16:58.160 | |
an enterprise wants and what some of the people on that side | |
16:58.160 --> 16:58.760 | |
want. | |
16:58.760 --> 17:00.360 | |
There are still people running models | |
17:00.360 --> 17:02.640 | |
that are three years old, four years old. | |
17:02.640 --> 17:06.000 | |
So inception is still used by tons of people. | |
17:06.000 --> 17:08.880 | |
Even less than 50 is what, a couple of years old now or more. | |
17:08.880 --> 17:12.200 | |
But there are tons of people who use that, and they're fine. | |
17:12.200 --> 17:16.200 | |
They don't need the last couple of bits of performance or quality. | |
17:16.200 --> 17:19.600 | |
They want some stability in things that just work. | |
17:19.600 --> 17:22.720 | |
And so there is value in providing that with that kind | |
17:22.720 --> 17:25.160 | |
of stability and making it really simpler, | |
17:25.160 --> 17:27.800 | |
because that allows a lot more people to access it. | |
17:27.800 --> 17:31.640 | |
And then there's the research crowd, which wants, OK, | |
17:31.640 --> 17:33.680 | |
they want to do these crazy things exactly like you're | |
17:33.680 --> 17:37.000 | |
saying, not just deep learning in the straight up models | |
17:37.000 --> 17:38.400 | |
that used to be there. | |
17:38.400 --> 17:41.920 | |
They want RNNs, and even RNNs are maybe old. | |
17:41.920 --> 17:45.520 | |
They are transformers now, and now it | |
17:45.520 --> 17:48.720 | |
needs to combine with RL and GANs and so on. | |
17:48.720 --> 17:52.160 | |
So there's definitely that area, the boundary that's | |
17:52.160 --> 17:55.120 | |
shifting and pushing the state of the art. | |
17:55.120 --> 17:57.120 | |
But I think there's more and more of the past | |
17:57.120 --> 17:59.680 | |
that's much more stable. | |
17:59.680 --> 18:02.680 | |
And even stuff that was two, three years old | |
18:02.680 --> 18:04.920 | |
is very, very usable by lots of people. | |
18:04.920 --> 18:07.440 | |
So that part makes it a lot easier. | |
18:07.440 --> 18:09.800 | |
So I imagine maybe you can correct me if I'm wrong. | |
18:09.800 --> 18:12.440 | |
One of the biggest use cases is essentially | |
18:12.440 --> 18:15.160 | |
taking something like ResNet 50 and doing | |
18:15.160 --> 18:18.520 | |
some kind of transfer learning on a very particular problem | |
18:18.520 --> 18:19.600 | |
that you have. | |
18:19.600 --> 18:24.480 | |
It's basically probably what majority of the world does. | |
18:24.480 --> 18:27.040 | |
And you want to make that as easy as possible. | |
18:27.040 --> 18:30.400 | |
So I would say, for the hobbyist perspective, | |
18:30.400 --> 18:32.800 | |
that's the most common case. | |
18:32.800 --> 18:34.800 | |
In fact, the apps on phones and stuff | |
18:34.800 --> 18:37.680 | |
that you'll see, the early ones, that's the most common case. | |
18:37.680 --> 18:40.320 | |
I would say there are a couple of reasons for that. | |
18:40.320 --> 18:44.400 | |
One is that everybody talks about that. | |
18:44.400 --> 18:46.120 | |
It looks great on slides. | |
18:46.120 --> 18:48.120 | |
That's a great presentation. | |
18:48.120 --> 18:50.040 | |
Exactly. | |
18:50.040 --> 18:53.120 | |
What enterprises want is that is part of it, | |
18:53.120 --> 18:54.480 | |
but that's not the big thing. | |
18:54.480 --> 18:56.760 | |
Enterprises really have data that they | |
18:56.760 --> 18:58.040 | |
want to make predictions on. | |
18:58.040 --> 19:01.160 | |
This is often what they used to do with the people who | |
19:01.160 --> 19:03.600 | |
were doing ML was just regression models, | |
19:03.600 --> 19:06.440 | |
linear regression, logistic regression, linear models, | |
19:06.440 --> 19:09.800 | |
or maybe gradient booster trees and so on. | |
19:09.800 --> 19:11.760 | |
Some of them still benefit from deep learning, | |
19:11.760 --> 19:14.440 | |
but they weren't that that's the bread and butter, | |
19:14.440 --> 19:16.280 | |
like the structured data and so on. | |
19:16.280 --> 19:18.200 | |
So depending on the audience you look at, | |
19:18.200 --> 19:19.520 | |
they're a little bit different. | |
19:19.520 --> 19:23.320 | |
And they just have, I mean, the best of enterprise | |
19:23.320 --> 19:26.480 | |
probably just has a very large data set | |
19:26.480 --> 19:28.640 | |
where deep learning can probably shine. | |
19:28.640 --> 19:29.360 | |
That's correct. | |
19:29.360 --> 19:30.320 | |
That's right. | |
19:30.320 --> 19:32.240 | |
And then I think the other pieces | |
19:32.240 --> 19:34.560 | |
that they wanted, again, to point out | |
19:34.560 --> 19:36.400 | |
that the developer summit we put together | |
19:36.400 --> 19:38.200 | |
is that the whole TensorFlow Extended | |
19:38.200 --> 19:40.600 | |
piece, which is the entire pipeline, | |
19:40.600 --> 19:43.560 | |
they care about stability across doing their entire thing. | |
19:43.560 --> 19:46.200 | |
They want simplicity across the entire thing. | |
19:46.200 --> 19:47.680 | |
I don't need to just train a model. | |
19:47.680 --> 19:51.280 | |
I need to do that every day again, over and over again. | |
19:51.280 --> 19:54.720 | |
I wonder to which degree you have a role in, I don't know. | |
19:54.720 --> 19:57.040 | |
So I teach a course on deep learning. | |
19:57.040 --> 20:01.320 | |
I have people like lawyers come up to me and say, | |
20:01.320 --> 20:04.200 | |
when is machine learning going to enter legal, | |
20:04.200 --> 20:05.560 | |
the legal realm? | |
20:05.560 --> 20:11.720 | |
The same thing in all kinds of disciplines, immigration, | |
20:11.720 --> 20:13.800 | |
insurance. | |
20:13.800 --> 20:17.400 | |
Often when I see what it boils down to is these companies | |
20:17.400 --> 20:19.760 | |
are often a little bit old school in the way | |
20:19.760 --> 20:20.840 | |
they organize the data. | |
20:20.840 --> 20:22.800 | |
So the data is just not ready yet. | |
20:22.800 --> 20:24.040 | |
It's not digitized. | |
20:24.040 --> 20:28.160 | |
Do you also find yourself being in the role of an evangelist | |
20:28.160 --> 20:33.040 | |
for let's organize your data, folks, | |
20:33.040 --> 20:35.440 | |
and then you'll get the big benefit of TensorFlow? | |
20:35.440 --> 20:38.000 | |
Do you have those conversations? | |
20:38.000 --> 20:45.160 | |
Yeah, I get all kinds of questions there from, OK, | |
20:45.160 --> 20:49.000 | |
what do I need to make this work, right? | |
20:49.000 --> 20:50.800 | |
Do we really need deep learning? | |
20:50.800 --> 20:52.240 | |
I mean, there are all these things. | |
20:52.240 --> 20:54.000 | |
I already used this linear model. | |
20:54.000 --> 20:55.160 | |
Why would this help? | |
20:55.160 --> 20:57.160 | |
I don't have enough data, let's say. | |
20:57.160 --> 20:59.960 | |
Or I want to use machine learning, | |
20:59.960 --> 21:01.760 | |
but I have no clue where to start. | |
21:01.760 --> 21:04.920 | |
So it's a great start to all the way to the experts | |
21:04.920 --> 21:08.520 | |
who wise were very specific things, so it's interesting. | |
21:08.520 --> 21:09.600 | |
Is there a good answer? | |
21:09.600 --> 21:12.480 | |
It boils down to oftentimes digitizing data. | |
21:12.480 --> 21:15.240 | |
So whatever you want automated, whatever data | |
21:15.240 --> 21:17.480 | |
you want to make prediction based on, | |
21:17.480 --> 21:21.240 | |
you have to make sure that it's in an organized form. | |
21:21.240 --> 21:23.920 | |
Like with an intensive flow ecosystem, | |
21:23.920 --> 21:26.080 | |
there's now you're providing more and more data | |
21:26.080 --> 21:28.960 | |
sets and more and more pretrained models. | |
21:28.960 --> 21:32.400 | |
Are you finding yourself also the organizer of data sets? | |
21:32.400 --> 21:34.480 | |
Yes, I think with TensorFlow data sets | |
21:34.480 --> 21:38.360 | |
that we just released, that's definitely come up where people | |
21:38.360 --> 21:39.200 | |
want these data sets. | |
21:39.200 --> 21:41.560 | |
Can we organize them and can we make that easier? | |
21:41.560 --> 21:45.320 | |
So that's definitely one important thing. | |
21:45.320 --> 21:47.680 | |
The other related thing I would say is I often tell people, | |
21:47.680 --> 21:50.960 | |
you know what, don't think of the most fanciest thing | |
21:50.960 --> 21:53.320 | |
that the newest model that you see. | |
21:53.320 --> 21:55.480 | |
Make something very basic work, and then | |
21:55.480 --> 21:56.360 | |
you can improve it. | |
21:56.360 --> 21:58.840 | |
There's just lots of things you can do with it. | |
21:58.840 --> 22:00.080 | |
Yeah, start with the basics. | |
22:00.080 --> 22:00.580 | |
Sure. | |
22:00.580 --> 22:03.760 | |
One of the big things that makes TensorFlow even more | |
22:03.760 --> 22:06.440 | |
accessible was the appearance, whenever | |
22:06.440 --> 22:12.400 | |
that happened, of Keras, the Keras standard outside of TensorFlow. | |
22:12.400 --> 22:18.200 | |
I think it was Keras on top of Tiano at first only, | |
22:18.200 --> 22:22.480 | |
and then Keras became on top of TensorFlow. | |
22:22.480 --> 22:29.840 | |
Do you know when Keras chose to also add TensorFlow as a back end, | |
22:29.840 --> 22:33.960 | |
who was it just the community that drove that initially? | |
22:33.960 --> 22:37.000 | |
Do you know if there was discussions, conversations? | |
22:37.000 --> 22:40.920 | |
Yeah, so Franco started the Keras project | |
22:40.920 --> 22:44.560 | |
before he was at Google, and the first thing was Tiano. | |
22:44.560 --> 22:47.120 | |
I don't remember if that was after TensorFlow | |
22:47.120 --> 22:49.640 | |
was created or way before. | |
22:49.640 --> 22:52.000 | |
And then at some point, when TensorFlow | |
22:52.000 --> 22:54.160 | |
started becoming popular, there were enough similarities | |
22:54.160 --> 22:56.320 | |
that he decided to create this interface | |
22:56.320 --> 22:59.200 | |
and put TensorFlow as a back end. | |
22:59.200 --> 23:03.320 | |
I believe that might still have been before he joined Google. | |
23:03.320 --> 23:06.720 | |
So we weren't really talking about that. | |
23:06.720 --> 23:09.720 | |
He decided on his own and thought that was interesting | |
23:09.720 --> 23:12.760 | |
and relevant to the community. | |
23:12.760 --> 23:17.080 | |
In fact, I didn't find out about him being at Google | |
23:17.080 --> 23:19.680 | |
until a few months after he was here. | |
23:19.680 --> 23:21.840 | |
He was working on some research ideas. | |
23:21.840 --> 23:24.480 | |
And doing Keras and his nights and weekends project and stuff. | |
23:24.480 --> 23:25.280 | |
I wish this thing. | |
23:25.280 --> 23:28.480 | |
So he wasn't part of the TensorFlow. | |
23:28.480 --> 23:29.680 | |
He didn't join initially. | |
23:29.680 --> 23:32.240 | |
He joined research, and he was doing some amazing research. | |
23:32.240 --> 23:35.440 | |
He has some papers on that and research. | |
23:35.440 --> 23:38.400 | |
He's a great researcher as well. | |
23:38.400 --> 23:42.400 | |
And at some point, we realized, oh, he's doing this good stuff. | |
23:42.400 --> 23:45.480 | |
People seem to like the API, and he's right here. | |
23:45.480 --> 23:48.280 | |
So we talked to him, and he said, OK, | |
23:48.280 --> 23:50.600 | |
why don't I come over to your team | |
23:50.600 --> 23:52.800 | |
and work with you for a quarter? | |
23:52.800 --> 23:55.440 | |
And let's make that integration happen. | |
23:55.440 --> 23:57.200 | |
And we talked to his manager, and he said, sure, | |
23:57.200 --> 23:59.720 | |
what, quarter's fine. | |
23:59.720 --> 24:03.320 | |
And that quarter's been something like two years now. | |
24:03.320 --> 24:05.040 | |
So he's fully on this. | |
24:05.040 --> 24:12.000 | |
So Keras got integrated into TensorFlow in a deep way. | |
24:12.000 --> 24:15.920 | |
And now with TensorFlow 2.0, Keras | |
24:15.920 --> 24:19.400 | |
is kind of the recommended way for a beginner | |
24:19.400 --> 24:21.960 | |
to interact with TensorFlow, which | |
24:21.960 --> 24:24.640 | |
makes that initial sort of transfer learning | |
24:24.640 --> 24:28.040 | |
or the basic use cases, even for an enterprise, | |
24:28.040 --> 24:29.320 | |
super simple, right? | |
24:29.320 --> 24:29.920 | |
That's correct. | |
24:29.920 --> 24:30.440 | |
That's right. | |
24:30.440 --> 24:32.040 | |
So what was that decision like? | |
24:32.040 --> 24:38.640 | |
That seems like it's kind of a bold decision as well. | |
24:38.640 --> 24:41.200 | |
We did spend a lot of time thinking about that one. | |
24:41.200 --> 24:46.000 | |
We had a bunch of APIs some bit by us. | |
24:46.000 --> 24:48.760 | |
There was a parallel layers API that we were building | |
24:48.760 --> 24:51.560 | |
and when we decided to do Keras in parallel, | |
24:51.560 --> 24:54.400 | |
so they were like, OK, two things that we are looking at. | |
24:54.400 --> 24:55.960 | |
And the first thing we was trying to do | |
24:55.960 --> 25:00.080 | |
is just have them look similar, be as integrated as possible, | |
25:00.080 --> 25:02.200 | |
share all of that stuff. | |
25:02.200 --> 25:05.800 | |
There were also three other APIs that others had built over time | |
25:05.800 --> 25:09.000 | |
because we didn't have a standard one. | |
25:09.000 --> 25:12.080 | |
But one of the messages that we kept hearing from the community, | |
25:12.080 --> 25:13.200 | |
OK, which one do we use? | |
25:13.200 --> 25:15.560 | |
And they kept seeing, OK, here's a model in this one, | |
25:15.560 --> 25:18.840 | |
and here's a model in this one, which should I pick? | |
25:18.840 --> 25:22.680 | |
So that's sort of like, OK, we had to address that | |
25:22.680 --> 25:24.000 | |
straight on with 2.0. | |
25:24.000 --> 25:26.320 | |
The whole idea was we need to simplify. | |
25:26.320 --> 25:28.600 | |
We had to pick one. | |
25:28.600 --> 25:34.600 | |
Based on where we were, we were like, OK, let's see what | |
25:34.600 --> 25:35.640 | |
are the people like. | |
25:35.640 --> 25:39.280 | |
And Keras was clearly one that lots of people loved. | |
25:39.280 --> 25:41.600 | |
There were lots of great things about it. | |
25:41.600 --> 25:43.880 | |
So we settled on that. | |
25:43.880 --> 25:44.680 | |
Organically. | |
25:44.680 --> 25:46.560 | |
That's kind of the best way to do it. | |
25:46.560 --> 25:47.160 | |
It was great. | |
25:47.160 --> 25:48.720 | |
But it was surprising, nevertheless, | |
25:48.720 --> 25:51.120 | |
to sort of bring in and outside. | |
25:51.120 --> 25:54.440 | |
I mean, there was a feeling like Keras might be almost | |
25:54.440 --> 25:58.000 | |
like a competitor in a certain kind of a two tensor flow. | |
25:58.000 --> 26:01.320 | |
And in a sense, it became an empowering element | |
26:01.320 --> 26:02.200 | |
of tensor flow. | |
26:02.200 --> 26:03.280 | |
That's right. | |
26:03.280 --> 26:07.200 | |
Yeah, it's interesting how you can put two things together | |
26:07.200 --> 26:08.280 | |
which can align right. | |
26:08.280 --> 26:11.760 | |
And in this case, I think Francois, the team, | |
26:11.760 --> 26:15.480 | |
and a bunch of us have chatted and I think we all | |
26:15.480 --> 26:17.320 | |
want to see the same kind of things. | |
26:17.320 --> 26:20.360 | |
We all care about making it easier for the huge set | |
26:20.360 --> 26:21.440 | |
of developers out there. | |
26:21.440 --> 26:23.440 | |
And that makes a difference. | |
26:23.440 --> 26:27.280 | |
So Python has Guido van Rossum, who | |
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until recently held the position of benevolent | |
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dictator for life. | |
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Right, so there's a huge successful open source | |
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project like tensor flow. | |
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Need one person who makes a final decision. | |
26:40.680 --> 26:45.480 | |
So you did a pretty successful tensor flow Dev Summit | |
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just now, last couple of days. | |
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There's clearly a lot of different new features | |
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being incorporated in amazing ecosystem, so on. | |
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How are those design decisions made? | |
26:57.320 --> 27:00.960 | |
Is there a BDFL in tensor flow? | |
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And or is it more distributed and organic? | |
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I think it's somewhat different, I would say. | |
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I've always been involved in the key design directions. | |
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But there are lots of things that | |
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are distributed where their number of people, Martin | |
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Wick being one who has really driven a lot of our open source | |
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stuff, a lot of the APIs. | |
27:27.360 --> 27:29.200 | |
And there are a number of other people | |
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who have been pushed and been responsible | |
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for different parts of it. | |
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We do have regular design reviews. | |
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Over the last year, we've really spent a lot of time opening up | |
27:40.680 --> 27:44.160 | |
to the community and adding transparency. | |
27:44.160 --> 27:45.880 | |
We're setting more processes in place, | |
27:45.880 --> 27:49.600 | |
so RFCs, special interest groups, really | |
27:49.600 --> 27:53.560 | |
grow that community and scale that. | |
27:53.560 --> 27:57.680 | |
I think the kind of scale that ecosystem is in, | |
27:57.680 --> 28:00.240 | |
I don't think we could scale with having me as the lone | |
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point of decision maker. | |
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I got it. | |
28:03.440 --> 28:05.880 | |
So yeah, the growth of that ecosystem, | |
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maybe you can talk about it a little bit. | |
28:08.040 --> 28:10.720 | |
First of all, when I started with Andre Karpathi | |
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when he first did ComNet.js, the fact | |
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that you can train in your own network | |
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and the browser in JavaScript was incredible. | |
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So now TensorFlow.js is really making | |
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that a serious, a legit thing, a way | |
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to operate, whether it's in the back end or the front end. | |
28:29.560 --> 28:32.720 | |
Then there's the TensorFlow Extended, like you mentioned. | |
28:32.720 --> 28:35.360 | |
There's TensorFlow Lite for mobile. | |
28:35.360 --> 28:37.480 | |
And all of it, as far as I can tell, | |
28:37.480 --> 28:39.640 | |
it's really converging towards being | |
28:39.640 --> 28:43.440 | |
able to save models in the same kind of way. | |
28:43.440 --> 28:46.680 | |
You can move around, you can train on the desktop, | |
28:46.680 --> 28:48.800 | |
and then move it to mobile, and so on. | |
28:48.800 --> 28:49.280 | |
That's right. | |
28:49.280 --> 28:52.320 | |
So this is that cohesiveness. | |
28:52.320 --> 28:55.240 | |
So can you maybe give me whatever | |
28:55.240 --> 28:58.840 | |
I missed, a bigger overview of the mission of the ecosystem | |
28:58.840 --> 29:02.120 | |
that's trying to be built, and where is it moving forward? | |
29:02.120 --> 29:02.800 | |
Yeah. | |
29:02.800 --> 29:05.720 | |
So in short, the way I like to think of this | |
29:05.720 --> 29:09.760 | |
is our goals to enable machine learning. | |
29:09.760 --> 29:13.320 | |
And in a couple of ways, one is we | |
29:13.320 --> 29:16.560 | |
have lots of exciting things going on in ML today. | |
29:16.560 --> 29:18.160 | |
We started with deep learning, but we now | |
29:18.160 --> 29:21.400 | |
support a bunch of other algorithms too. | |
29:21.400 --> 29:23.760 | |
So one is to, on the research side, | |
29:23.760 --> 29:25.360 | |
keep pushing on the state of the art. | |
29:25.360 --> 29:27.240 | |
Can we, how do we enable researchers | |
29:27.240 --> 29:28.960 | |
to build the next amazing thing? | |
29:28.960 --> 29:31.800 | |
So BERT came out recently. | |
29:31.800 --> 29:34.000 | |
It's great that people are able to do new kinds of research. | |
29:34.000 --> 29:35.400 | |
There are lots of amazing research | |
29:35.400 --> 29:37.600 | |
that happens across the world. | |
29:37.600 --> 29:38.880 | |
So that's one direction. | |
29:38.880 --> 29:41.400 | |
The other is, how do you take that | |
29:41.400 --> 29:45.200 | |
across all the people outside who want to take that research | |
29:45.200 --> 29:47.400 | |
and do some great things with it and integrate it | |
29:47.400 --> 29:51.800 | |
to build real products, to have a real impact on people? | |
29:51.800 --> 29:56.720 | |
And so if that's the other axes in some ways. | |
29:56.720 --> 29:58.520 | |
And a high level, one way I think about it | |
29:58.520 --> 30:02.480 | |
is there are a crazy number of computer devices | |
30:02.480 --> 30:04.240 | |
across the world. | |
30:04.240 --> 30:08.440 | |
And we often used to think of ML and training and all of this | |
30:08.440 --> 30:10.800 | |
as, OK, something you do either in the workstation | |
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or the data center or cloud. | |
30:13.600 --> 30:15.720 | |
But we see things running on the phones. | |
30:15.720 --> 30:17.640 | |
We see things running on really tiny chips. | |
30:17.640 --> 30:20.760 | |
And we had some demos at the developer summit. | |
30:20.760 --> 30:25.160 | |
And so the way I think about this ecosystem | |
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is, how do we help get machine learning on every device that | |
30:30.280 --> 30:32.520 | |
has a compute capability? | |
30:32.520 --> 30:33.760 | |
And that continues to grow. | |
30:33.760 --> 30:37.240 | |
And so in some ways, this ecosystem | |
30:37.240 --> 30:40.280 | |
has looked at various aspects of that | |
30:40.280 --> 30:42.440 | |
and grown over time to cover more of those. | |
30:42.440 --> 30:44.640 | |
And we continue to push the boundaries. | |
30:44.640 --> 30:48.640 | |
In some areas, we've built more tooling and things | |
30:48.640 --> 30:50.040 | |
around that to help you. | |
30:50.040 --> 30:52.800 | |
I mean, the first tool we started was TensorBoard. | |
30:52.800 --> 30:56.920 | |
You want to learn just the training piece, the effects | |
30:56.920 --> 30:59.840 | |
for TensorFlow Extended to really do your entire ML | |
30:59.840 --> 31:04.760 | |
pipelines if you care about all that production stuff, | |
31:04.760 --> 31:09.520 | |
but then going to the edge, going to different kinds of things. | |
31:09.520 --> 31:11.800 | |
And it's not just us now. | |
31:11.800 --> 31:15.120 | |
We are a place where there are lots of libraries being built | |
31:15.120 --> 31:15.840 | |
on top. | |
31:15.840 --> 31:18.440 | |
So there are some for research, maybe things | |
31:18.440 --> 31:21.240 | |
like TensorFlow Agents or TensorFlow Probability that | |
31:21.240 --> 31:23.480 | |
started as research things or for researchers | |
31:23.480 --> 31:26.160 | |
for focusing on certain kinds of algorithms, | |
31:26.160 --> 31:30.280 | |
but they're also being deployed or reduced by production folks. | |
31:30.280 --> 31:34.000 | |
And some have come from within Google, just teams | |
31:34.000 --> 31:37.040 | |
across Google who wanted to do the build these things. | |
31:37.040 --> 31:39.680 | |
Others have come from just the community | |
31:39.680 --> 31:41.840 | |
because there are different pieces | |
31:41.840 --> 31:44.640 | |
that different parts of the community care about. | |
31:44.640 --> 31:49.520 | |
And I see our goal as enabling even that. | |
31:49.520 --> 31:53.240 | |
It's not we cannot and won't build every single thing. | |
31:53.240 --> 31:54.840 | |
That just doesn't make sense. | |
31:54.840 --> 31:57.320 | |
But if we can enable others to build the things | |
31:57.320 --> 32:00.640 | |
that they care about, and there's a broader community that | |
32:00.640 --> 32:02.880 | |
cares about that, and we can help encourage that, | |
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and that's great. | |
32:05.240 --> 32:08.600 | |
That really helps the entire ecosystem, not just those. | |
32:08.600 --> 32:11.280 | |
One of the big things about 2.0 that we're pushing on | |
32:11.280 --> 32:14.680 | |
is, OK, we have these so many different pieces, right? | |
32:14.680 --> 32:18.440 | |
How do we help make all of them work well together? | |
32:18.440 --> 32:21.960 | |
There are a few key pieces there that we're pushing on, | |
32:21.960 --> 32:23.840 | |
one being the core format in there | |
32:23.840 --> 32:27.480 | |
and how we share the models themselves through SAVE model | |
32:27.480 --> 32:30.440 | |
and what TensorFlow Hub and so on. | |
32:30.440 --> 32:34.000 | |
And a few of the pieces that we really put this together. | |
32:34.000 --> 32:37.240 | |
I was very skeptical that that's, when TensorFlow.js came out, | |
32:37.240 --> 32:40.120 | |
it didn't seem or deep learning.js. | |
32:40.120 --> 32:41.680 | |
Yeah, that was the first. | |
32:41.680 --> 32:45.040 | |
It seems like technically very difficult project. | |
32:45.040 --> 32:47.040 | |
As a standalone, it's not as difficult. | |
32:47.040 --> 32:49.920 | |
But as a thing that integrates into the ecosystem, | |
32:49.920 --> 32:51.240 | |
it seems very difficult. | |
32:51.240 --> 32:53.200 | |
So I mean, there's a lot of aspects of this | |
32:53.200 --> 32:54.200 | |
you're making look easy. | |
32:54.200 --> 32:58.160 | |
But on the technical side, how many challenges | |
32:58.160 --> 33:00.560 | |
have to be overcome here? | |
33:00.560 --> 33:01.520 | |
A lot. | |
33:01.520 --> 33:03.080 | |
And still have to be overcome. | |
33:03.080 --> 33:04.840 | |
That's the question here, too. | |
33:04.840 --> 33:06.160 | |
There are lots of steps to it. | |
33:06.160 --> 33:08.160 | |
I think we've iterated over the last few years, | |
33:08.160 --> 33:10.720 | |
so there's a lot we've learned. | |
33:10.720 --> 33:14.200 | |
I, yeah, and often when things come together well, | |
33:14.200 --> 33:15.080 | |
things look easy. | |
33:15.080 --> 33:16.400 | |
And that's exactly the point. | |
33:16.400 --> 33:18.280 | |
It should be easy for the end user. | |
33:18.280 --> 33:21.320 | |
But there are lots of things that go behind that. | |
33:21.320 --> 33:25.320 | |
If I think about still challenges ahead, | |
33:25.320 --> 33:32.880 | |
there are we have a lot more devices coming on board, | |
33:32.880 --> 33:35.280 | |
for example, from the hardware perspective. | |
33:35.280 --> 33:37.640 | |
How do we make it really easy for these vendors | |
33:37.640 --> 33:42.040 | |
to integrate with something like TensorFlow? | |
33:42.040 --> 33:43.640 | |
So there's a lot of compiler stuff | |
33:43.640 --> 33:45.320 | |
that others are working on. | |
33:45.320 --> 33:48.320 | |
There are things we can do in terms of our APIs | |
33:48.320 --> 33:50.520 | |
and so on that we can do. | |
33:50.520 --> 33:55.840 | |
As we, TensorFlow started as a very monolithic system. | |
33:55.840 --> 33:57.680 | |
And to some extent, it still is. | |
33:57.680 --> 33:59.400 | |
There are less lots of tools around it, | |
33:59.400 --> 34:02.960 | |
but the core is still pretty large and monolithic. | |
34:02.960 --> 34:05.760 | |
One of the key challenges for us to scale that out | |
34:05.760 --> 34:10.440 | |
is how do we break that apart with clear interfaces? | |
34:10.440 --> 34:13.720 | |
It's, in some ways, it's software engineering one | |
34:13.720 --> 34:18.520 | |
one, but for a system that's now four years old, I guess, | |
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or more, and that's still rapidly evolving | |
34:21.600 --> 34:24.000 | |
and that we're not slowing down with, | |
34:24.000 --> 34:28.240 | |
it's hard to change and modify and really break apart. | |
34:28.240 --> 34:29.880 | |
It's sort of like, as people say, right, | |
34:29.880 --> 34:32.560 | |
it's like changing the engine with a car running | |
34:32.560 --> 34:33.560 | |
or fixed benefits. | |
34:33.560 --> 34:35.200 | |
That's exactly what we're trying to do. | |
34:35.200 --> 34:39.960 | |
So there's a challenge here, because the downside | |
34:39.960 --> 34:43.840 | |
of so many people being excited about TensorFlow | |
34:43.840 --> 34:48.600 | |
and becoming to rely on it in many other applications | |
34:48.600 --> 34:52.200 | |
is that you're kind of responsible. | |
34:52.200 --> 34:53.520 | |
It's the technical debt. | |
34:53.520 --> 34:55.640 | |
You're responsible for previous versions | |
34:55.640 --> 34:57.720 | |
to some degree still working. | |
34:57.720 --> 34:59.920 | |
So when you're trying to innovate, | |
34:59.920 --> 35:03.760 | |
I mean, it's probably easier to just start from scratch | |
35:03.760 --> 35:05.800 | |
every few months. | |
35:05.800 --> 35:07.160 | |
Absolutely. | |
35:07.160 --> 35:10.880 | |
So do you feel the pain of that? | |
35:10.880 --> 35:15.360 | |
2.0 does break some back compatibility, but not too much. | |
35:15.360 --> 35:18.120 | |
It seems like the conversion is pretty straightforward. | |
35:18.120 --> 35:20.240 | |
Do you think that's still important, | |
35:20.240 --> 35:22.880 | |
given how quickly deep learning is changing? | |
35:22.880 --> 35:26.360 | |
Can you just, the things that you've learned, | |
35:26.360 --> 35:27.440 | |
can you just start over? | |
35:27.440 --> 35:30.120 | |
Or is there pressure to not? | |
35:30.120 --> 35:31.640 | |
It's a tricky balance. | |
35:31.640 --> 35:36.840 | |
So if it was just a researcher writing a paper who | |
35:36.840 --> 35:39.400 | |
a year later will not look at that code again, | |
35:39.400 --> 35:41.560 | |
sure, it doesn't matter. | |
35:41.560 --> 35:43.440 | |
There are a lot of production systems | |
35:43.440 --> 35:45.480 | |
that rely on TensorFlow, both at Google | |
35:45.480 --> 35:47.240 | |
and across the world. | |
35:47.240 --> 35:49.760 | |
And people worry about this. | |
35:49.760 --> 35:53.400 | |
I mean, these systems run for a long time. | |
35:53.400 --> 35:57.240 | |
So it is important to keep that compatibility and so on. | |
35:57.240 --> 36:00.960 | |
And yes, it does come with a huge cost. | |
36:00.960 --> 36:02.920 | |
We have to think about a lot of things | |
36:02.920 --> 36:06.960 | |
as we do new things and make new changes. | |
36:06.960 --> 36:09.120 | |
I think it's a trade off, right? | |
36:09.120 --> 36:12.960 | |
You can, you might slow certain kinds of things down, | |
36:12.960 --> 36:15.440 | |
but the overall value you're bringing because of that | |
36:15.440 --> 36:18.440 | |
is much bigger because it's not just | |
36:18.440 --> 36:20.520 | |
about breaking the person yesterday. | |
36:20.520 --> 36:24.840 | |
It's also about telling the person tomorrow that, you know what? | |
36:24.840 --> 36:26.320 | |
This is how we do things. | |
36:26.320 --> 36:28.520 | |
We're not going to break you when you come on board | |
36:28.520 --> 36:30.320 | |
because there are lots of new people who are also | |
36:30.320 --> 36:32.880 | |
going to come on board. | |
36:32.880 --> 36:34.680 | |
So one way I like to think about this, | |
36:34.680 --> 36:37.960 | |
and I always push the team to think about it as well, | |
36:37.960 --> 36:39.640 | |
when you want to do new things, you | |
36:39.640 --> 36:42.000 | |
want to start with a clean slate, | |
36:42.000 --> 36:44.880 | |
design with a clean slate in mind, | |
36:44.880 --> 36:48.160 | |
and then we'll figure out how to make sure all the other things | |
36:48.160 --> 36:48.640 | |
work. | |
36:48.640 --> 36:52.160 | |
And yes, we do make compromises occasionally. | |
36:52.160 --> 36:55.200 | |
But unless you design with the clean slate | |
36:55.200 --> 36:58.400 | |
and not worry about that, you'll never get to a good place. | |
36:58.400 --> 36:59.120 | |
That's brilliant. | |
36:59.120 --> 37:04.080 | |
So even if you are responsible in the idea stage, | |
37:04.080 --> 37:07.680 | |
when you're thinking of new, just put all that behind you. | |
37:07.680 --> 37:09.600 | |
OK, that's really well put. | |
37:09.600 --> 37:12.480 | |
So I have to ask this because a lot of students, developers, | |
37:12.480 --> 37:16.280 | |
asked me how I feel about PyTorch versus TensorFlow. | |
37:16.280 --> 37:19.720 | |
So I've recently completely switched my research group | |
37:19.720 --> 37:20.920 | |
to TensorFlow. | |
37:20.920 --> 37:23.280 | |
I wish everybody would just use the same thing. | |
37:23.280 --> 37:26.960 | |
And TensorFlow is as close to that, I believe, as we have. | |
37:26.960 --> 37:32.000 | |
But do you enjoy competition? | |
37:32.000 --> 37:35.800 | |
So TensorFlow is leading in many ways, many dimensions | |
37:35.800 --> 37:39.000 | |
in terms of the ecosystem, in terms of the number of users, | |
37:39.000 --> 37:41.200 | |
momentum power, production level, so on. | |
37:41.200 --> 37:46.000 | |
But a lot of researchers are now also using PyTorch. | |
37:46.000 --> 37:47.520 | |
Do you enjoy that kind of competition, | |
37:47.520 --> 37:49.440 | |
or do you just ignore it and focus | |
37:49.440 --> 37:52.320 | |
on making TensorFlow the best that it can be? | |
37:52.320 --> 37:55.480 | |
So just like research or anything people are doing, | |
37:55.480 --> 37:58.120 | |
it's great to get different kinds of ideas. | |
37:58.120 --> 38:01.440 | |
And when we started with TensorFlow, | |
38:01.440 --> 38:05.480 | |
like I was saying earlier, it was very important for us | |
38:05.480 --> 38:07.440 | |
to also have production in mind. | |
38:07.440 --> 38:08.960 | |
We didn't want just research, right? | |
38:08.960 --> 38:11.280 | |
And that's why we chose certain things. | |
38:11.280 --> 38:13.480 | |
Now PyTorch came along and said, you know what? | |
38:13.480 --> 38:14.880 | |
I only care about research. | |
38:14.880 --> 38:16.320 | |
This is what I'm trying to do. | |
38:16.320 --> 38:18.400 | |
What's the best thing I can do for this? | |
38:18.400 --> 38:21.120 | |
And it started iterating and said, OK, | |
38:21.120 --> 38:22.520 | |
I don't need to worry about graphs. | |
38:22.520 --> 38:25.200 | |
Let me just run things. | |
38:25.200 --> 38:27.440 | |
I don't care if it's not as fast as it can be, | |
38:27.440 --> 38:30.480 | |
but let me just make this part easy. | |
38:30.480 --> 38:32.560 | |
And there are things you can learn from that, right? | |
38:32.560 --> 38:36.720 | |
They, again, had the benefit of seeing what had come before, | |
38:36.720 --> 38:40.520 | |
but also exploring certain different kinds of spaces. | |
38:40.520 --> 38:43.560 | |
And they had some good things there, | |
38:43.560 --> 38:46.680 | |
building on, say, things like Jainer and so on before that. | |
38:46.680 --> 38:49.320 | |
So competition is definitely interesting. | |
38:49.320 --> 38:51.040 | |
It made us, you know, this is an area | |
38:51.040 --> 38:53.720 | |
that we had thought about, like I said, very early on. | |
38:53.720 --> 38:56.600 | |
Over time, we had revisited this a couple of times. | |
38:56.600 --> 38:59.000 | |
Should we add this again? | |
38:59.000 --> 39:00.480 | |
At some point, we said, you know what, | |
39:00.480 --> 39:02.920 | |
here's it seems like this can be done well. | |
39:02.920 --> 39:04.280 | |
So let's try it again. | |
39:04.280 --> 39:07.680 | |
And that's how we started pushing on eager execution. | |
39:07.680 --> 39:09.880 | |
How do we combine those two together, | |
39:09.880 --> 39:13.080 | |
which has finally come very well together in 2.0, | |
39:13.080 --> 39:15.720 | |
but it took us a while to get all the things together | |
39:15.720 --> 39:16.320 | |
and so on. | |
39:16.320 --> 39:19.320 | |
So let me, I mean, ask, put another way. | |
39:19.320 --> 39:21.800 | |
I think eager execution is a really powerful thing, | |
39:21.800 --> 39:22.680 | |
those added. | |
39:22.680 --> 39:24.320 | |
Do you think he wouldn't have been, | |
39:25.840 --> 39:28.400 | |
you know, Muhammad Ali versus Frazier, right? | |
39:28.400 --> 39:31.200 | |
Do you think it wouldn't have been added as quickly | |
39:31.200 --> 39:33.760 | |
if PyTorch wasn't there? | |
39:33.760 --> 39:35.440 | |
It might have taken longer. | |
39:35.440 --> 39:36.280 | |
No longer. | |
39:36.280 --> 39:38.960 | |
It was, I mean, we had tried some variants of that before. | |
39:38.960 --> 39:40.920 | |
So I'm sure it would have happened, | |
39:40.920 --> 39:42.240 | |
but it might have taken longer. | |
39:42.240 --> 39:44.800 | |
I'm grateful that TensorFlow is part of the way they did. | |
39:44.800 --> 39:47.760 | |
That's doing some incredible work last couple of years. | |
39:47.760 --> 39:49.640 | |
What other things that we didn't talk about? | |
39:49.640 --> 39:51.520 | |
Are you looking forward in 2.0? | |
39:51.520 --> 39:54.040 | |
That comes to mind. | |
39:54.040 --> 39:56.520 | |
So we talked about some of the ecosystem stuff, | |
39:56.520 --> 40:01.440 | |
making it easily accessible to Keras, eager execution. | |
40:01.440 --> 40:02.880 | |
Is there other things that we missed? | |
40:02.880 --> 40:07.480 | |
Yeah, so I would say one is just where 2.0 is, | |
40:07.480 --> 40:10.760 | |
and, you know, with all the things that we've talked about, | |
40:10.760 --> 40:13.760 | |
I think as we think beyond that, | |
40:13.760 --> 40:16.640 | |
there are lots of other things that it enables us to do | |
40:16.640 --> 40:18.760 | |
and that we're excited about. | |
40:18.760 --> 40:20.720 | |
So what it's setting us up for, | |
40:20.720 --> 40:22.520 | |
okay, here are these really clean APIs. | |
40:22.520 --> 40:25.640 | |
We've cleaned up the surface for what the users want. | |
40:25.640 --> 40:28.320 | |
What it also allows us to do a whole bunch of stuff | |
40:28.320 --> 40:31.600 | |
behind the scenes once we are ready with 2.0. | |
40:31.600 --> 40:36.600 | |
So for example, in TensorFlow with graphs | |
40:36.760 --> 40:37.720 | |
and all the things you could do, | |
40:37.720 --> 40:40.600 | |
you could always get a lot of good performance | |
40:40.600 --> 40:43.280 | |
if you spent the time to tune it, right? | |
40:43.280 --> 40:47.720 | |
And we've clearly shown that, lots of people do that. | |
40:47.720 --> 40:52.720 | |
With 2.0, with these APIs where we are, | |
40:53.040 --> 40:55.120 | |
we can give you a lot of performance | |
40:55.120 --> 40:57.040 | |
just with whatever you do. | |
40:57.040 --> 41:01.400 | |
You know, because we see these, it's much cleaner. | |
41:01.400 --> 41:03.720 | |
We know most people are gonna do things this way. | |
41:03.720 --> 41:05.520 | |
We can really optimize for that | |
41:05.520 --> 41:09.040 | |
and get a lot of those things out of the box. | |
41:09.040 --> 41:10.400 | |
And it really allows us, you know, | |
41:10.400 --> 41:13.880 | |
both for single machine and distributed and so on, | |
41:13.880 --> 41:17.200 | |
to really explore other spaces behind the scenes | |
41:17.200 --> 41:19.680 | |
after 2.0 in the future versions as well. | |
41:19.680 --> 41:23.000 | |
So right now, the team's really excited about that, | |
41:23.000 --> 41:25.800 | |
that over time, I think we'll see that. | |
41:25.800 --> 41:27.720 | |
The other piece that I was talking about | |
41:27.720 --> 41:31.600 | |
in terms of just restructuring the monolithic thing | |
41:31.600 --> 41:34.320 | |
into more pieces and making it more modular, | |
41:34.320 --> 41:36.800 | |
I think that's gonna be really important | |
41:36.800 --> 41:41.800 | |
for a lot of the other people in the ecosystem, | |
41:41.800 --> 41:44.760 | |
other organizations and so on that wanted to build things. | |
41:44.760 --> 41:46.360 | |
Can you elaborate a little bit what you mean | |
41:46.360 --> 41:50.680 | |
by making TensorFlow more ecosystem or modular? | |
41:50.680 --> 41:55.000 | |
So the way it's organized today is there's one, | |
41:55.000 --> 41:56.280 | |
there are lots of repositories | |
41:56.280 --> 41:58.320 | |
in the TensorFlow organization at GitHub, | |
41:58.320 --> 42:01.080 | |
the core one where we have TensorFlow, | |
42:01.080 --> 42:04.080 | |
it has the execution engine, | |
42:04.080 --> 42:08.280 | |
it has, you know, the key backends for CPUs and GPUs, | |
42:08.280 --> 42:12.560 | |
it has the work to do distributed stuff. | |
42:12.560 --> 42:14.360 | |
And all of these just work together | |
42:14.360 --> 42:17.240 | |
in a single library or binary, | |
42:17.240 --> 42:18.800 | |
there's no way to split them apart easily. | |
42:18.800 --> 42:19.960 | |
I mean, there are some interfaces, | |
42:19.960 --> 42:21.600 | |
but they're not very clean. | |
42:21.600 --> 42:24.800 | |
In a perfect world, you would have clean interfaces where, | |
42:24.800 --> 42:27.720 | |
okay, I wanna run it on my fancy cluster | |
42:27.720 --> 42:29.360 | |
with some custom networking, | |
42:29.360 --> 42:30.960 | |
just implement this and do that. | |
42:30.960 --> 42:32.640 | |
I mean, we kind of support that, | |
42:32.640 --> 42:34.560 | |
but it's hard for people today. | |
42:35.480 --> 42:38.160 | |
I think as we are starting to see more interesting things | |
42:38.160 --> 42:39.400 | |
in some of these spaces, | |
42:39.400 --> 42:42.280 | |
having that clean separation will really start to help. | |
42:42.280 --> 42:47.280 | |
And again, going to the large size of the ecosystem | |
42:47.360 --> 42:50.120 | |
and the different groups involved there, | |
42:50.120 --> 42:53.440 | |
enabling people to evolve and push on things | |
42:53.440 --> 42:56.040 | |
more independently just allows it to scale better. | |
42:56.040 --> 42:59.080 | |
And by people, you mean individual developers and? | |
42:59.080 --> 42:59.920 | |
And organizations. | |
42:59.920 --> 43:00.920 | |
And organizations. | |
43:00.920 --> 43:01.760 | |
That's right. | |
43:01.760 --> 43:04.200 | |
So the hope is that everybody sort of major, | |
43:04.200 --> 43:06.880 | |
I don't know, Pepsi or something uses, | |
43:06.880 --> 43:11.040 | |
like major corporations go to TensorFlow to this kind of. | |
43:11.040 --> 43:13.640 | |
Yeah, if you look at enterprise like Pepsi or these, | |
43:13.640 --> 43:15.520 | |
I mean, a lot of them are already using TensorFlow. | |
43:15.520 --> 43:18.960 | |
They are not the ones that do the development | |
43:18.960 --> 43:20.360 | |
or changes in the core. | |
43:20.360 --> 43:21.920 | |
Some of them do, but a lot of them don't. | |
43:21.920 --> 43:23.720 | |
I mean, they touch small pieces. | |
43:23.720 --> 43:26.400 | |
There are lots of these, some of them being, | |
43:26.400 --> 43:28.200 | |
let's say hardware vendors who are building | |
43:28.200 --> 43:30.840 | |
their custom hardware and they want their own pieces. | |
43:30.840 --> 43:34.160 | |
Or some of them being bigger companies, say IBM. | |
43:34.160 --> 43:37.320 | |
I mean, they're involved in some of our special interest | |
43:37.320 --> 43:39.960 | |
groups and they see a lot of users | |
43:39.960 --> 43:42.640 | |
who want certain things and they want to optimize for that. | |
43:42.640 --> 43:44.480 | |
So folks like that often. | |
43:44.480 --> 43:46.400 | |
Autonomous vehicle companies, perhaps. | |
43:46.400 --> 43:48.200 | |
Exactly, yes. | |
43:48.200 --> 43:50.520 | |
So yeah, like I mentioned, TensorFlow | |
43:50.520 --> 43:54.120 | |
has been down on it 41 million times, 50,000 commits, | |
43:54.120 --> 43:58.360 | |
almost 10,000 pull requests, 1,800 contributors. | |
43:58.360 --> 44:02.160 | |
So I'm not sure if you can explain it, | |
44:02.160 --> 44:06.840 | |
but what does it take to build a community like that? | |
44:06.840 --> 44:09.200 | |
In retrospect, what do you think? | |
44:09.200 --> 44:12.080 | |
What is the critical thing that allowed for this growth | |
44:12.080 --> 44:14.600 | |
to happen and how does that growth continue? | |
44:14.600 --> 44:17.920 | |
Yeah, that's an interesting question. | |
44:17.920 --> 44:20.240 | |
I wish I had all the answers there, I guess, | |
44:20.240 --> 44:22.520 | |
so you could replicate it. | |
44:22.520 --> 44:25.520 | |
I think there are a number of things | |
44:25.520 --> 44:27.880 | |
that need to come together, right? | |
44:27.880 --> 44:33.720 | |
One, just like any new thing, there's | |
44:33.720 --> 44:37.960 | |
a sweet spot of timing, what's needed, | |
44:37.960 --> 44:39.520 | |
does it grow with what's needed. | |
44:39.520 --> 44:41.960 | |
So in this case, for example, TensorFlow | |
44:41.960 --> 44:43.640 | |
is not just grown because it has a good tool, | |
44:43.640 --> 44:46.640 | |
it's also grown with the growth of deep learning itself. | |
44:46.640 --> 44:49.000 | |
So those factors come into play. | |
44:49.000 --> 44:53.120 | |
Other than that, though, I think just | |
44:53.120 --> 44:55.560 | |
hearing, listening to the community, what they're | |
44:55.560 --> 44:58.400 | |
doing, what they need, being open to, | |
44:58.400 --> 45:01.080 | |
like in terms of external contributions, | |
45:01.080 --> 45:04.520 | |
we've spent a lot of time in making sure | |
45:04.520 --> 45:06.840 | |
we can accept those contributions well, | |
45:06.840 --> 45:09.400 | |
we can help the contributors in adding those, | |
45:09.400 --> 45:11.240 | |
putting the right process in place, | |
45:11.240 --> 45:13.320 | |
getting the right kind of community, | |
45:13.320 --> 45:16.120 | |
welcoming them, and so on. | |
45:16.120 --> 45:19.000 | |
Like over the last year, we've really pushed on transparency. | |
45:19.000 --> 45:22.200 | |
That's important for an open source project. | |
45:22.200 --> 45:23.760 | |
People want to know where things are going, | |
45:23.760 --> 45:26.400 | |
and we're like, OK, here's a process for you. | |
45:26.400 --> 45:29.320 | |
You can do that, here are our seasons, and so on. | |
45:29.320 --> 45:32.880 | |
So thinking through, there are lots of community aspects | |
45:32.880 --> 45:36.400 | |
that come into that you can really work on. | |
45:36.400 --> 45:38.720 | |
As a small project, it's maybe easy to do, | |
45:38.720 --> 45:42.240 | |
because there's two developers, and you can do those. | |
45:42.240 --> 45:46.960 | |
As you grow, putting more of these processes in place, | |
45:46.960 --> 45:49.080 | |
thinking about the documentation, | |
45:49.080 --> 45:51.400 | |
thinking about what two developers | |
45:51.400 --> 45:55.080 | |
care about, what kind of tools would they want to use, | |
45:55.080 --> 45:56.840 | |
all of these come into play, I think. | |
45:56.840 --> 45:58.400 | |
So one of the big things, I think, | |
45:58.400 --> 46:02.560 | |
that feeds the TensorFlow fire is people building something | |
46:02.560 --> 46:07.680 | |
on TensorFlow, and implement a particular architecture | |
46:07.680 --> 46:09.480 | |
that does something cool and useful, | |
46:09.480 --> 46:11.080 | |
and they put that on GitHub. | |
46:11.080 --> 46:15.640 | |
And so it just feeds this growth. | |
46:15.640 --> 46:19.560 | |
Do you have a sense that with 2.0 and 1.0, | |
46:19.560 --> 46:21.880 | |
that there may be a little bit of a partitioning like there | |
46:21.880 --> 46:26.040 | |
is with Python 2 and 3, that there'll be a code base | |
46:26.040 --> 46:28.320 | |
in the older versions of TensorFlow | |
46:28.320 --> 46:31.120 | |
that will not be as compatible easily, | |
46:31.120 --> 46:35.600 | |
or are you pretty confident that this kind of conversion | |
46:35.600 --> 46:37.960 | |
is pretty natural and easy to do? | |
46:37.960 --> 46:41.480 | |
So we're definitely working hard to make that very easy to do. | |
46:41.480 --> 46:44.040 | |
There's lots of tooling that we talked about at the developer | |
46:44.040 --> 46:46.480 | |
summit this week, and we'll continue | |
46:46.480 --> 46:48.280 | |
to invest in that tooling. | |
46:48.280 --> 46:52.560 | |
It's when you think of these significant version changes, | |
46:52.560 --> 46:55.720 | |
that's always a risk, and we are really pushing hard | |
46:55.720 --> 46:59.160 | |
to make that transition very, very smooth. | |
46:59.160 --> 47:03.000 | |
I think, so at some level, people | |
47:03.000 --> 47:05.520 | |
want to move when they see the value in the new thing. | |
47:05.520 --> 47:07.640 | |
They don't want to move just because it's a new thing. | |
47:07.640 --> 47:11.400 | |
And some people do, but most people want a really good thing. | |
47:11.400 --> 47:13.760 | |
And I think over the next few months, | |
47:13.760 --> 47:15.400 | |
as people start to see the value, | |
47:15.400 --> 47:17.640 | |
we'll definitely see that shift happening. | |
47:17.640 --> 47:20.080 | |
So I'm pretty excited and confident that we | |
47:20.080 --> 47:22.440 | |
will see people moving. | |
47:22.440 --> 47:24.680 | |
As you said earlier, this field is also moving rapidly, | |
47:24.680 --> 47:26.720 | |
so that'll help because we can do more things. | |
47:26.720 --> 47:28.520 | |
And all the new things will clearly | |
47:28.520 --> 47:32.280 | |
happen in 2.x, so people will have lots of good reasons to move. | |
47:32.280 --> 47:36.160 | |
So what do you think TensorFlow 3.0 looks like? | |
47:36.160 --> 47:40.320 | |
Is there things happening so crazily | |
47:40.320 --> 47:42.520 | |
that even at the end of this year, | |
47:42.520 --> 47:45.320 | |
seems impossible to plan for? | |
47:45.320 --> 47:49.440 | |
Or is it possible to plan for the next five years? | |
47:49.440 --> 47:50.800 | |
I think it's tricky. | |
47:50.800 --> 47:55.760 | |
There are some things that we can expect in terms of, OK, | |
47:55.760 --> 47:59.720 | |
change, yes, change is going to happen. | |
47:59.720 --> 48:01.680 | |
Are there some things going to stick around | |
48:01.680 --> 48:03.720 | |
and some things not going to stick around? | |
48:03.720 --> 48:08.160 | |
I would say the basics of deep learning, | |
48:08.160 --> 48:12.680 | |
the convolutional models or the basic kind of things, | |
48:12.680 --> 48:16.280 | |
they'll probably be around in some form still in five years. | |
48:16.280 --> 48:21.160 | |
Will Aurel and Gans stay very likely based on where they are? | |
48:21.160 --> 48:22.840 | |
Will we have new things? | |
48:22.840 --> 48:24.680 | |
Probably, but those are hard to predict. | |
48:24.680 --> 48:29.080 | |
And some directionally, some things that we can see | |
48:29.080 --> 48:32.800 | |
is in things that we're starting to do | |
48:32.800 --> 48:36.560 | |
with some of our projects right now is just | |
48:36.560 --> 48:39.120 | |
to point out combining eager execution and graphs, | |
48:39.120 --> 48:42.240 | |
where we're starting to make it more like just your natural | |
48:42.240 --> 48:43.160 | |
programming language. | |
48:43.160 --> 48:45.640 | |
You're not trying to program something else. | |
48:45.640 --> 48:47.240 | |
Similarly, with Swift for TensorFlow, | |
48:47.240 --> 48:48.280 | |
we're taking that approach. | |
48:48.280 --> 48:50.040 | |
Can you do something round up? | |
48:50.040 --> 48:52.080 | |
So some of those ideas seem like, OK, | |
48:52.080 --> 48:55.000 | |
that's the right direction in five years | |
48:55.000 --> 48:58.360 | |
we expect to see more in that area. | |
48:58.360 --> 49:01.760 | |
Other things we don't know is, will hardware accelerators | |
49:01.760 --> 49:03.200 | |
be the same? | |
49:03.200 --> 49:09.000 | |
Will we be able to train with four bits instead of 32 bits? | |
49:09.000 --> 49:11.440 | |
And I think the TPU side of things is exploring. | |
49:11.440 --> 49:13.960 | |
I mean, TPU is already on version three. | |
49:13.960 --> 49:17.520 | |
It seems that the evolution of TPU and TensorFlow | |
49:17.520 --> 49:24.080 | |
are coevolving in terms of both their learning | |
49:24.080 --> 49:25.720 | |
from each other and from the community | |
49:25.720 --> 49:29.720 | |
and from the applications where the biggest benefit is achieved. | |
49:29.720 --> 49:30.560 | |
That's right. | |
49:30.560 --> 49:33.320 | |
You've been trying with eager with Keras | |
49:33.320 --> 49:36.480 | |
to make TensorFlow as accessible and easy to use as possible. | |
49:36.480 --> 49:39.040 | |
What do you think for beginners is the biggest thing | |
49:39.040 --> 49:40.000 | |
they struggle with? | |
49:40.000 --> 49:42.080 | |
Have you encountered that? | |
49:42.080 --> 49:44.280 | |
Or is basically what Keras is solving | |
49:44.280 --> 49:48.680 | |
is that eager, like we talked about TensorFlow? | |
49:48.680 --> 49:51.480 | |
For some of them, like you said, the beginners | |
49:51.480 --> 49:54.840 | |
want to just be able to take some image model. | |
49:54.840 --> 49:58.040 | |
They don't care if it's inception or rest net or something else | |
49:58.040 --> 50:00.760 | |
and do some training or transfer learning | |
50:00.760 --> 50:02.440 | |
on their kind of model. | |
50:02.440 --> 50:04.400 | |
Being able to make that easy is important. | |
50:04.400 --> 50:08.560 | |
So in some ways, if you do that by providing them | |
50:08.560 --> 50:11.360 | |
simple models with, say, in Hub or so on, | |
50:11.360 --> 50:13.680 | |
they don't care about what's inside that box, | |
50:13.680 --> 50:15.120 | |
but they want to be able to use it. | |
50:15.120 --> 50:17.600 | |
So we're pushing on, I think, different levels. | |
50:17.600 --> 50:20.120 | |
If you look at just a component that you get, which | |
50:20.120 --> 50:22.800 | |
has the layers already smushed in, | |
50:22.800 --> 50:25.200 | |
the beginners probably just want that. | |
50:25.200 --> 50:27.360 | |
Then the next step is, OK, look at building | |
50:27.360 --> 50:29.000 | |
layers with Keras. | |
50:29.000 --> 50:30.600 | |
If you go out to research, then they | |
50:30.600 --> 50:33.120 | |
are probably writing custom layers themselves | |
50:33.120 --> 50:34.360 | |
or doing their own loops. | |
50:34.360 --> 50:36.320 | |
So there's a whole spectrum there. | |
50:36.320 --> 50:38.600 | |
And then providing the preentrain models | |
50:38.600 --> 50:44.760 | |
seems to really decrease the time from you trying to start. | |
50:44.760 --> 50:46.800 | |
So you could basically, in a Colab notebook, | |
50:46.800 --> 50:49.080 | |
achieve what you need. | |
50:49.080 --> 50:51.280 | |
So I'm basically answering my own question, | |
50:51.280 --> 50:54.240 | |
because I think what TensorFlow delivered on recently | |
50:54.240 --> 50:57.000 | |
is trivial for beginners. | |
50:57.000 --> 51:00.760 | |
So I was just wondering if there was other pain points | |
51:00.760 --> 51:02.480 | |
you're trying to ease, but I'm not sure there would. | |
51:02.480 --> 51:04.240 | |
No, those are probably the big ones. | |
51:04.240 --> 51:07.080 | |
I mean, I see high schoolers doing a whole bunch of things | |
51:07.080 --> 51:08.840 | |
now, which is pretty amazing. | |
51:08.840 --> 51:11.360 | |
It's both amazing and terrifying. | |
51:11.360 --> 51:12.640 | |
Yes. | |
51:12.640 --> 51:16.920 | |
In a sense that when they grow up, | |
51:16.920 --> 51:19.280 | |
some incredible ideas will be coming from them. | |
51:19.280 --> 51:21.800 | |
So there's certainly a technical aspect to your work, | |
51:21.800 --> 51:24.600 | |
but you also have a management aspect | |
51:24.600 --> 51:28.000 | |
to your role with TensorFlow, leading the project, | |
51:28.000 --> 51:31.080 | |
a large number of developers and people. | |
51:31.080 --> 51:34.680 | |
So what do you look for in a good team? | |
51:34.680 --> 51:37.400 | |
What do you think Google has been at the forefront | |
51:37.400 --> 51:40.440 | |
of exploring what it takes to build a good team? | |
51:40.440 --> 51:45.520 | |
And TensorFlow is one of the most cutting edge technologies | |
51:45.520 --> 51:46.120 | |
in the world. | |
51:46.120 --> 51:48.080 | |
So in this context, what do you think | |
51:48.080 --> 51:50.480 | |
makes for a good team? | |
51:50.480 --> 51:53.200 | |
It's definitely something I think a fair bit about. | |
51:53.200 --> 51:59.560 | |
I think in terms of the team being | |
51:59.560 --> 52:02.120 | |
able to deliver something well, one of the things that's | |
52:02.120 --> 52:05.800 | |
important is a cohesion across the team. | |
52:05.800 --> 52:10.400 | |
So being able to execute together and doing things, | |
52:10.400 --> 52:11.440 | |
it's not an end. | |
52:11.440 --> 52:14.120 | |
Like at this scale, an individual engineer | |
52:14.120 --> 52:15.400 | |
can only do so much. | |
52:15.400 --> 52:18.200 | |
There's a lot more that they can do together, | |
52:18.200 --> 52:21.640 | |
even though we have some amazing superstars across Google | |
52:21.640 --> 52:22.600 | |
and in the team. | |
52:22.600 --> 52:26.200 | |
But there's often the way I see it | |
52:26.200 --> 52:28.360 | |
is the product of what the team generates | |
52:28.360 --> 52:34.440 | |
is way larger than the whole individual put together. | |
52:34.440 --> 52:37.320 | |
And so how do we have all of them work together, | |
52:37.320 --> 52:40.000 | |
the culture of the team itself? | |
52:40.000 --> 52:43.000 | |
Hiring good people is important. | |
52:43.000 --> 52:45.600 | |
But part of that is it's not just that, OK, | |
52:45.600 --> 52:48.120 | |
we hire a bunch of smart people and throw them together | |
52:48.120 --> 52:49.720 | |
and let them do things. | |
52:49.720 --> 52:52.920 | |
It's also people have to care about what they're building. | |
52:52.920 --> 52:57.320 | |
People have to be motivated for the right kind of things. | |
52:57.320 --> 53:01.400 | |
That's often an important factor. | |
53:01.400 --> 53:04.600 | |
And finally, how do you put that together | |
53:04.600 --> 53:08.840 | |
with a somewhat unified vision of where we want to go? | |
53:08.840 --> 53:11.200 | |
So are we all looking in the same direction | |
53:11.200 --> 53:13.520 | |
or just going all over? | |
53:13.520 --> 53:16.040 | |
And sometimes it's a mix. | |
53:16.040 --> 53:21.400 | |
Google's a very bottom up organization in some sense. | |
53:21.400 --> 53:24.680 | |
Also research even more so. | |
53:24.680 --> 53:26.320 | |
And that's how we started. | |
53:26.320 --> 53:30.840 | |
But as we've become this larger product and ecosystem, | |
53:30.840 --> 53:35.040 | |
I think it's also important to combine that well with a mix | |
53:35.040 --> 53:37.920 | |
of, OK, here's the direction we want to go in. | |
53:37.920 --> 53:39.880 | |
There is exploration we'll do around that. | |
53:39.880 --> 53:43.320 | |
But let's keep staying in that direction, not just | |
53:43.320 --> 53:44.360 | |
all over the place. | |
53:44.360 --> 53:46.880 | |
And is there a way you monitor the health of the team? | |
53:46.880 --> 53:51.920 | |
Sort of like, is there a way you know you did a good job? | |
53:51.920 --> 53:53.000 | |
The team is good. | |
53:53.000 --> 53:56.960 | |
I mean, you're saying nice things, but it's sometimes | |
53:56.960 --> 54:01.120 | |
difficult to determine how aligned. | |
54:01.120 --> 54:04.480 | |
Because it's not binary, it's not like there's tensions | |
54:04.480 --> 54:06.680 | |
and complexities and so on. | |
54:06.680 --> 54:09.400 | |
And the other element of this is the mesh of superstars. | |
54:09.400 --> 54:12.880 | |
There's so much, even at Google, such a large percentage | |
54:12.880 --> 54:16.000 | |
of work is done by individual superstars too. | |
54:16.000 --> 54:19.920 | |
So there's a, and sometimes those superstars | |
54:19.920 --> 54:25.120 | |
could be against the dynamic of a team and those tensions. | |
54:25.120 --> 54:27.320 | |
I mean, I'm sure TensorFlow might be a little bit easier | |
54:27.320 --> 54:31.720 | |
because the mission of the project is so beautiful. | |
54:31.720 --> 54:34.760 | |
You're at the cutting edge, so it's exciting. | |
54:34.760 --> 54:36.640 | |
But have you had struggle with that? | |
54:36.640 --> 54:38.360 | |
Has there been challenges? | |
54:38.360 --> 54:39.800 | |
There are always people challenges | |
54:39.800 --> 54:41.240 | |
in different kinds of ways. | |
54:41.240 --> 54:44.520 | |
That said, I think we've been what's | |
54:44.520 --> 54:49.320 | |
good about getting people who care and have | |
54:49.320 --> 54:51.440 | |
the same kind of culture, and that's Google in general | |
54:51.440 --> 54:53.480 | |
to a large extent. | |
54:53.480 --> 54:56.760 | |
But also, like you said, given that the project has had | |
54:56.760 --> 54:59.160 | |
so many exciting things to do, there's | |
54:59.160 --> 55:02.080 | |
been room for lots of people to do different kinds of things | |
55:02.080 --> 55:06.440 | |
and grow, which does make the problem a bit easier, I guess. | |
55:06.440 --> 55:09.920 | |
And it allows people, depending on what they're doing, | |
55:09.920 --> 55:13.120 | |
if there's room around them, then that's fine. | |
55:13.120 --> 55:19.160 | |
But yes, we do care about whether a superstar or not | |
55:19.160 --> 55:22.560 | |
that they need to work well with the team across Google. | |
55:22.560 --> 55:23.760 | |
That's interesting to hear. | |
55:23.760 --> 55:27.960 | |
So it's like superstar or not, the productivity broadly | |
55:27.960 --> 55:30.520 | |
is about the team. | |
55:30.520 --> 55:31.520 | |
Yeah. | |
55:31.520 --> 55:32.960 | |
I mean, they might add a lot of value, | |
55:32.960 --> 55:35.720 | |
but if they're hurting the team, then that's a problem. | |
55:35.720 --> 55:38.720 | |
So in hiring engineers, it's so interesting, right? | |
55:38.720 --> 55:41.840 | |
The high rank process, what do you look for? | |
55:41.840 --> 55:44.240 | |
How do you determine a good developer | |
55:44.240 --> 55:47.280 | |
or a good member of a team from just a few minutes | |
55:47.280 --> 55:50.320 | |
or hours together? | |
55:50.320 --> 55:51.920 | |
Again, no magic answers, I'm sure. | |
55:51.920 --> 55:52.760 | |
Yeah. | |
55:52.760 --> 55:56.240 | |
And Google has a hiring process that we've refined | |
55:56.240 --> 56:00.880 | |
over the last 20 years, I guess, and that you've probably | |
56:00.880 --> 56:02.200 | |
heard and seen a lot about. | |
56:02.200 --> 56:05.280 | |
So we do work with the same hiring process in that. | |
56:05.280 --> 56:08.280 | |
That's really helped. | |
56:08.280 --> 56:10.880 | |
For me in particular, I would say, | |
56:10.880 --> 56:14.200 | |
in addition to the core technical skills, | |
56:14.200 --> 56:17.560 | |
what does matter is their motivation | |
56:17.560 --> 56:19.560 | |
in what they want to do. | |
56:19.560 --> 56:22.960 | |
Because if that doesn't align well with where we want to go, | |
56:22.960 --> 56:25.320 | |
that's not going to lead to long term success | |
56:25.320 --> 56:27.640 | |
for either them or the team. | |
56:27.640 --> 56:30.640 | |
And I think that becomes more important the more senior | |
56:30.640 --> 56:33.520 | |
the person is, but it's important at every level. | |
56:33.520 --> 56:34.920 | |
Like even the junior most engineer, | |
56:34.920 --> 56:37.680 | |
if they're not motivated to do well at what they're trying to do, | |
56:37.680 --> 56:39.080 | |
however smart they are, it's going | |
56:39.080 --> 56:40.320 | |
to be hard for them to succeed. | |
56:40.320 --> 56:44.520 | |
Does the Google hiring process touch on that passion? | |
56:44.520 --> 56:46.440 | |
So like trying to determine. | |
56:46.440 --> 56:48.440 | |
Because I think as far as I understand, | |
56:48.440 --> 56:52.000 | |
maybe you can speak to it that the Google hiring process sort | |
56:52.000 --> 56:56.360 | |
of helps the initial like determines the skill set there, | |
56:56.360 --> 56:59.840 | |
is your puzzle solving ability, problem solving ability good. | |
56:59.840 --> 57:05.000 | |
But I'm not sure, but it seems that the determining | |
57:05.000 --> 57:07.560 | |
whether the person is like fire inside them | |
57:07.560 --> 57:09.840 | |
that burns to do anything really doesn't really matter. | |
57:09.840 --> 57:11.520 | |
It's just some cool stuff. | |
57:11.520 --> 57:15.320 | |
I'm going to do it that I don't know. | |
57:15.320 --> 57:17.000 | |
Is that something that ultimately ends up | |
57:17.000 --> 57:18.840 | |
when they have a conversation with you | |
57:18.840 --> 57:22.600 | |
or once it gets closer to the team? | |
57:22.600 --> 57:25.400 | |
So one of the things we do have as part of the process | |
57:25.400 --> 57:28.600 | |
is just a culture fit, like part of the interview process | |
57:28.600 --> 57:31.040 | |
itself, in addition to just the technical skills. | |
57:31.040 --> 57:34.240 | |
And each engineer or whoever the interviewer is, | |
57:34.240 --> 57:38.800 | |
is supposed to rate the person on the culture and the culture | |
57:38.800 --> 57:39.960 | |
fit with Google and so on. | |
57:39.960 --> 57:42.160 | |
So that is definitely part of the process. | |
57:42.160 --> 57:45.800 | |
Now, there are various kinds of projects | |
57:45.800 --> 57:46.960 | |
and different kinds of things. | |
57:46.960 --> 57:50.040 | |
So there might be variants in the kind of culture | |
57:50.040 --> 57:51.320 | |
you want there and so on. | |
57:51.320 --> 57:52.720 | |
And yes, that does vary. | |
57:52.720 --> 57:54.920 | |
So for example, TensorFlow has always | |
57:54.920 --> 57:56.920 | |
been a fast moving project. | |
57:56.920 --> 58:00.920 | |
And we want people who are comfortable with that. | |
58:00.920 --> 58:02.640 | |
But at the same time now, for example, | |
58:02.640 --> 58:05.200 | |
we are at a place where we are also very full fledged product. | |
58:05.200 --> 58:08.440 | |
And we want to make sure things that work really, really | |
58:08.440 --> 58:09.320 | |
work right. | |
58:09.320 --> 58:11.680 | |
You can't cut corners all the time. | |
58:11.680 --> 58:14.320 | |
So balancing that out and finding the people | |
58:14.320 --> 58:17.560 | |
who are the right fit for those is important. | |
58:17.560 --> 58:19.720 | |
And I think those kind of things do vary a bit | |
58:19.720 --> 58:23.200 | |
across projects and teams and product areas across Google. | |
58:23.200 --> 58:25.240 | |
And so you'll see some differences there | |
58:25.240 --> 58:27.640 | |
in the final checklist. | |
58:27.640 --> 58:29.600 | |
But a lot of the core culture, it | |
58:29.600 --> 58:32.200 | |
comes along with just the engineering, excellence, | |
58:32.200 --> 58:34.720 | |
and so on. | |
58:34.720 --> 58:39.680 | |
What is the hardest part of your job? | |
58:39.680 --> 58:41.920 | |
I'll take your pick, I guess. | |
58:41.920 --> 58:44.440 | |
It's fun, I would say. | |
58:44.440 --> 58:45.520 | |
Hard, yes. | |
58:45.520 --> 58:47.240 | |
I mean, lots of things at different times. | |
58:47.240 --> 58:49.160 | |
I think that does vary. | |
58:49.160 --> 58:52.640 | |
So let me clarify that difficult things are fun | |
58:52.640 --> 58:55.720 | |
when you solve them, right? | |
58:55.720 --> 58:57.480 | |
It's fun in that sense. | |
58:57.480 --> 59:02.600 | |
I think the key to a successful thing across the board, | |
59:02.600 --> 59:05.320 | |
and in this case, it's a large ecosystem now, | |
59:05.320 --> 59:09.800 | |
but even a small product, is striking that fine balance | |
59:09.800 --> 59:12.000 | |
across different aspects of it. | |
59:12.000 --> 59:17.000 | |
Sometimes it's how fast you go versus how perfect it is. | |
59:17.000 --> 59:21.400 | |
Sometimes it's how do you involve this huge community? | |
59:21.400 --> 59:22.360 | |
Who do you involve? | |
59:22.360 --> 59:25.440 | |
Or do you decide, OK, now is not a good time to involve them | |
59:25.440 --> 59:30.160 | |
because it's not the right fit? | |
59:30.160 --> 59:33.640 | |
Sometimes it's saying no to certain kinds of things. | |
59:33.640 --> 59:36.880 | |
Those are often the hard decisions. | |
59:36.880 --> 59:41.000 | |
Some of them you make quickly because you don't have the time. | |
59:41.000 --> 59:43.200 | |
Some of them you get time to think about them, | |
59:43.200 --> 59:44.480 | |
but they're always hard. | |
59:44.480 --> 59:49.200 | |
So both choices are pretty good, those decisions. | |
59:49.200 --> 59:50.360 | |
What about deadlines? | |
59:50.360 --> 59:58.200 | |
Is this defined TensorFlow to be driven by deadlines | |
59:58.200 --> 1:00:00.360 | |
to a degree that a product might? | |
1:00:00.360 --> 1:00:04.920 | |
Or is there still a balance to where it's less deadline? | |
1:00:04.920 --> 1:00:08.920 | |
You had the Dev Summit, they came together incredibly. | |
1:00:08.920 --> 1:00:11.440 | |
Looked like there's a lot of moving pieces and so on. | |
1:00:11.440 --> 1:00:15.080 | |
So did that deadline make people rise to the occasion, | |
1:00:15.080 --> 1:00:18.360 | |
releasing TensorFlow 2.0 Alpha? | |
1:00:18.360 --> 1:00:20.360 | |
I'm sure that was done last minute as well. | |
1:00:20.360 --> 1:00:25.600 | |
I mean, up to the last point. | |
1:00:25.600 --> 1:00:28.600 | |
Again, it's one of those things that you | |
1:00:28.600 --> 1:00:29.960 | |
need to strike the good balance. | |
1:00:29.960 --> 1:00:32.040 | |
There's some value that deadlines bring | |
1:00:32.040 --> 1:00:33.920 | |
that does bring a sense of urgency | |
1:00:33.920 --> 1:00:35.720 | |
to get the right things together. | |
1:00:35.720 --> 1:00:38.280 | |
Instead of getting the perfect thing out, | |
1:00:38.280 --> 1:00:41.280 | |
you need something that's good and works well. | |
1:00:41.280 --> 1:00:43.720 | |
And the team definitely did a great job in putting that | |
1:00:43.720 --> 1:00:46.560 | |
together, so it was very amazed and excited by everything, | |
1:00:46.560 --> 1:00:48.680 | |
how that came together. | |
1:00:48.680 --> 1:00:50.640 | |
That said, across the year, we try not | |
1:00:50.640 --> 1:00:52.520 | |
to put out official deadlines. | |
1:00:52.520 --> 1:00:56.960 | |
We focus on key things that are important, | |
1:00:56.960 --> 1:01:00.600 | |
figure out how much of it's important, | |
1:01:00.600 --> 1:01:05.760 | |
and we are developing in the open, internally and externally, | |
1:01:05.760 --> 1:01:07.920 | |
everything's available to everybody. | |
1:01:07.920 --> 1:01:11.120 | |
So you can pick and look at where things are. | |
1:01:11.120 --> 1:01:13.160 | |
We do releases at a regular cadence, | |
1:01:13.160 --> 1:01:16.320 | |
so fine if something doesn't necessarily end up with this | |
1:01:16.320 --> 1:01:19.600 | |
month, it'll end up in the next release in a month or two. | |
1:01:19.600 --> 1:01:22.840 | |
And that's OK, but we want to keep moving | |
1:01:22.840 --> 1:01:26.520 | |
as fast as we can in these different areas. | |
1:01:26.520 --> 1:01:30.080 | |
Because we can iterate and improve on things, sometimes | |
1:01:30.080 --> 1:01:32.920 | |
it's OK to put things out that aren't fully ready. | |
1:01:32.920 --> 1:01:35.640 | |
If you make sure it's clear that, OK, this is experimental, | |
1:01:35.640 --> 1:01:37.960 | |
but it's out there if you want to try and give feedback. | |
1:01:37.960 --> 1:01:39.400 | |
That's very, very useful. | |
1:01:39.400 --> 1:01:43.560 | |
I think that quick cycle and quick iteration is important. | |
1:01:43.560 --> 1:01:47.200 | |
That's what we often focus on rather than here's | |
1:01:47.200 --> 1:01:49.200 | |
a deadline where you get everything else. | |
1:01:49.200 --> 1:01:52.880 | |
It's 2.0, is there pressure to make that stable? | |
1:01:52.880 --> 1:01:57.760 | |
Or like, for example, WordPress 5.0 just came out, | |
1:01:57.760 --> 1:02:01.760 | |
and there was no pressure to, it was a lot of build updates | |
1:02:01.760 --> 1:02:04.960 | |
that delivered way too late. | |
1:02:04.960 --> 1:02:06.440 | |
And they said, OK, well, we're going | |
1:02:06.440 --> 1:02:09.680 | |
to release a lot of updates really quickly to improve it. | |
1:02:09.680 --> 1:02:12.240 | |
Do you see TensorFlow 2.0 in that same kind of way, | |
1:02:12.240 --> 1:02:15.240 | |
or is there this pressure to once it hits 2.0, | |
1:02:15.240 --> 1:02:16.760 | |
once you get to the release candidate, | |
1:02:16.760 --> 1:02:19.440 | |
and then you get to the final, that's | |
1:02:19.440 --> 1:02:22.480 | |
going to be the stable thing? | |
1:02:22.480 --> 1:02:26.680 | |
So it's going to be stable in just like 1.0X | |
1:02:26.680 --> 1:02:32.080 | |
was where every API that's there is going to remain in work. | |
1:02:32.080 --> 1:02:34.800 | |
It doesn't mean we can't change things under the covers. | |
1:02:34.800 --> 1:02:36.720 | |
It doesn't mean we can't add things. | |
1:02:36.720 --> 1:02:39.200 | |
So there's still a lot more for us to do, | |
1:02:39.200 --> 1:02:41.080 | |
and we continue to have more releases. | |
1:02:41.080 --> 1:02:42.920 | |
So in that sense, there's still, I | |
1:02:42.920 --> 1:02:44.680 | |
don't think we'd be done in like two months | |
1:02:44.680 --> 1:02:46.160 | |
when we release this. | |
1:02:46.160 --> 1:02:49.880 | |
I don't know if you can say, but is there, you know, | |
1:02:49.880 --> 1:02:53.680 | |
there's not external deadlines for TensorFlow 2.0, | |
1:02:53.680 --> 1:02:58.520 | |
but is there internal deadlines, artificial or otherwise, | |
1:02:58.520 --> 1:03:00.840 | |
that you're trying to set for yourself, | |
1:03:00.840 --> 1:03:03.080 | |
or is it whenever it's ready? | |
1:03:03.080 --> 1:03:05.680 | |
So we want it to be a great product, right? | |
1:03:05.680 --> 1:03:09.880 | |
And that's a big, important piece for us. | |
1:03:09.880 --> 1:03:11.160 | |
TensorFlow is already out there. | |
1:03:11.160 --> 1:03:13.720 | |
We have 41 million downloads for 1.x, | |
1:03:13.720 --> 1:03:15.880 | |
so it's not like we have to have this. | |
1:03:15.880 --> 1:03:17.280 | |
Yeah, exactly. | |
1:03:17.280 --> 1:03:19.320 | |
So it's not like a lot of the features | |
1:03:19.320 --> 1:03:22.080 | |
that we've really polishing and putting them together | |
1:03:22.080 --> 1:03:26.240 | |
are there, we don't have to rush that just because. | |
1:03:26.240 --> 1:03:28.040 | |
So in that sense, we want to get it right | |
1:03:28.040 --> 1:03:29.920 | |
and really focus on that. | |
1:03:29.920 --> 1:03:31.520 | |
That said, we have said that we are | |
1:03:31.520 --> 1:03:33.520 | |
looking to get this out in the next few months, | |
1:03:33.520 --> 1:03:37.120 | |
in the next quarter, and as far as possible, | |
1:03:37.120 --> 1:03:40.000 | |
we'll definitely try to make that happen. | |
1:03:40.000 --> 1:03:44.360 | |
Yeah, my favorite line was, spring is a relative concept. | |
1:03:44.360 --> 1:03:45.960 | |
I love it. | |
1:03:45.960 --> 1:03:47.680 | |
Spoken like a true developer. | |
1:03:47.680 --> 1:03:50.200 | |
So something I'm really interested in, | |
1:03:50.200 --> 1:03:53.840 | |
and your previous line of work is, before TensorFlow, | |
1:03:53.840 --> 1:03:57.720 | |
you let a team and Google on search ads. | |
1:03:57.720 --> 1:04:02.840 | |
I think this is a very interesting topic on every level, | |
1:04:02.840 --> 1:04:07.200 | |
on a technical level, because if their best ads connect people | |
1:04:07.200 --> 1:04:10.080 | |
to the things they want and need, | |
1:04:10.080 --> 1:04:12.280 | |
and that they're worse, they're just these things | |
1:04:12.280 --> 1:04:15.840 | |
that annoy the heck out of you to the point of ruining | |
1:04:15.840 --> 1:04:20.240 | |
the entire user experience of whatever you're actually doing. | |
1:04:20.240 --> 1:04:23.600 | |
So they have a bad rep, I guess. | |
1:04:23.600 --> 1:04:28.080 | |
And on the other end, so that this connecting users | |
1:04:28.080 --> 1:04:32.120 | |
to the thing they need to want is a beautiful opportunity | |
1:04:32.120 --> 1:04:35.360 | |
for machine learning to shine, like huge amounts of data | |
1:04:35.360 --> 1:04:36.720 | |
that's personalized, and you've got | |
1:04:36.720 --> 1:04:40.400 | |
to map to the thing they actually won't get annoyed. | |
1:04:40.400 --> 1:04:43.760 | |
So what have you learned from this Google that's | |
1:04:43.760 --> 1:04:45.160 | |
leading the world in this aspect? | |
1:04:45.160 --> 1:04:47.560 | |
What have you learned from that experience? | |
1:04:47.560 --> 1:04:51.520 | |
And what do you think is the future of ads? | |
1:04:51.520 --> 1:04:54.040 | |
Take you back to the end of that. | |
1:04:54.040 --> 1:04:59.720 | |
Yes, it's been a while, but I totally agree with what you said. | |
1:04:59.720 --> 1:05:03.200 | |
I think the search ads, the way it was always looked at, | |
1:05:03.200 --> 1:05:05.520 | |
and I believe it still is, is it's | |
1:05:05.520 --> 1:05:08.240 | |
an extension of what search is trying to do. | |
1:05:08.240 --> 1:05:10.560 | |
The goal is to make the information | |
1:05:10.560 --> 1:05:14.680 | |
and make the world's information accessible. | |
1:05:14.680 --> 1:05:17.120 | |
With ads, it's not just information, | |
1:05:17.120 --> 1:05:19.120 | |
but it may be products or other things | |
1:05:19.120 --> 1:05:20.800 | |
that people care about. | |
1:05:20.800 --> 1:05:23.360 | |
And so it's really important for them | |
1:05:23.360 --> 1:05:26.480 | |
to align with what the users need. | |
1:05:26.480 --> 1:05:30.920 | |
And in search ads, there's a minimum quality level | |
1:05:30.920 --> 1:05:32.320 | |
before that ad would be shown. | |
1:05:32.320 --> 1:05:34.200 | |
If we don't have an ad that hits that quality bar, | |
1:05:34.200 --> 1:05:35.960 | |
it will not be shown, even if we have it. | |
1:05:35.960 --> 1:05:38.080 | |
And OK, maybe we lose some money there. | |
1:05:38.080 --> 1:05:39.560 | |
That's fine. | |
1:05:39.560 --> 1:05:41.200 | |
That is really, really important, | |
1:05:41.200 --> 1:05:43.000 | |
and I think that that is something I really | |
1:05:43.000 --> 1:05:45.040 | |
liked about being there. | |
1:05:45.040 --> 1:05:48.120 | |
Advertising is a key part. | |
1:05:48.120 --> 1:05:51.680 | |
I mean, as a model, it's been around for ages, right? | |
1:05:51.680 --> 1:05:52.920 | |
It's not a new model. | |
1:05:52.920 --> 1:05:57.440 | |
It's been adapted to the web and became a core part of search | |
1:05:57.440 --> 1:06:02.120 | |
and in many other search engines across the world. | |
1:06:02.120 --> 1:06:05.920 | |
I do hope, like I said, there are aspects of ads | |
1:06:05.920 --> 1:06:06.680 | |
that are annoying. | |
1:06:06.680 --> 1:06:09.600 | |
And I go to a website, and if it just | |
1:06:09.600 --> 1:06:12.160 | |
keeps popping an ad in my face, not to let me read, | |
1:06:12.160 --> 1:06:13.800 | |
that's going to be annoying clearly. | |
1:06:13.800 --> 1:06:22.080 | |
So I hope we can strike that balance between showing a good | |
1:06:22.080 --> 1:06:25.040 | |
ad where it's valuable to the user | |
1:06:25.040 --> 1:06:30.960 | |
and provides the monetization to the service. | |
1:06:30.960 --> 1:06:32.000 | |
And this might be search. | |
1:06:32.000 --> 1:06:33.680 | |
This might be a website. | |
1:06:33.680 --> 1:06:37.320 | |
All of these, they do need the monetization for them | |
1:06:37.320 --> 1:06:39.640 | |
to provide that service. | |
1:06:39.640 --> 1:06:45.720 | |
But if it's done in a good balance between showing | |
1:06:45.720 --> 1:06:48.040 | |
just some random stuff that's distracting | |
1:06:48.040 --> 1:06:50.920 | |
versus showing something that's actually valuable. | |
1:06:50.920 --> 1:06:55.360 | |
So do you see it moving forward as to continue | |
1:06:55.360 --> 1:07:00.960 | |
being a model that funds businesses like Google? | |
1:07:00.960 --> 1:07:05.160 | |
That's a significant revenue stream. | |
1:07:05.160 --> 1:07:08.080 | |
Because that's one of the most exciting things, | |
1:07:08.080 --> 1:07:09.680 | |
but also limiting things on the internet | |
1:07:09.680 --> 1:07:12.200 | |
is nobody wants to pay for anything. | |
1:07:12.200 --> 1:07:15.360 | |
And advertisements, again, coupled at their best | |
1:07:15.360 --> 1:07:17.360 | |
are actually really useful and not annoying. | |
1:07:17.360 --> 1:07:22.320 | |
Do you see that continuing and growing and improving? | |
1:07:22.320 --> 1:07:26.680 | |
Or is there GC sort of more Netflix type models | |
1:07:26.680 --> 1:07:28.960 | |
where you have to start to pay for content? | |
1:07:28.960 --> 1:07:31.000 | |
I think it's a mix. | |
1:07:31.000 --> 1:07:32.840 | |
I think it's going to take a long while for everything | |
1:07:32.840 --> 1:07:35.320 | |
to be paid on the internet, if at all. | |
1:07:35.320 --> 1:07:36.160 | |
Probably not. | |
1:07:36.160 --> 1:07:37.400 | |
I mean, I think there's always going | |
1:07:37.400 --> 1:07:40.760 | |
to be things that are sort of monetized with things like ads. | |
1:07:40.760 --> 1:07:42.800 | |
But over the last few years, I would say | |
1:07:42.800 --> 1:07:44.760 | |
we've definitely seen that transition | |
1:07:44.760 --> 1:07:48.560 | |
towards more paid services across the web | |
1:07:48.560 --> 1:07:50.360 | |
and people are willing to pay for them | |
1:07:50.360 --> 1:07:51.760 | |
because they do see the value. | |
1:07:51.760 --> 1:07:53.600 | |
I mean, Netflix is a great example. | |
1:07:53.600 --> 1:07:56.520 | |
I mean, we have YouTube doing things. | |
1:07:56.520 --> 1:07:59.720 | |
People pay for the apps they buy, more people | |
1:07:59.720 --> 1:08:03.120 | |
they find are willing to pay for newspaper content, | |
1:08:03.120 --> 1:08:07.240 | |
for the good news websites across the web. | |
1:08:07.240 --> 1:08:11.040 | |
That wasn't the case even a few years ago, I would say. | |
1:08:11.040 --> 1:08:13.280 | |
And I just see that change in myself as well | |
1:08:13.280 --> 1:08:14.840 | |
and just lots of people around me. | |
1:08:14.840 --> 1:08:19.240 | |
So definitely hopeful that we'll transition to that mix model | |
1:08:19.240 --> 1:08:23.400 | |
where maybe you get to try something out for free, | |
1:08:23.400 --> 1:08:24.120 | |
maybe with ads. | |
1:08:24.120 --> 1:08:27.080 | |
But then there is a more clear revenue model | |
1:08:27.080 --> 1:08:30.600 | |
that sort of helps go beyond that. | |
1:08:30.600 --> 1:08:34.760 | |
So speaking of revenue, how is it | |
1:08:34.760 --> 1:08:39.400 | |
that a person can use the TPU in a Google Colab for free? | |
1:08:39.400 --> 1:08:43.920 | |
So what's the, I guess, the question is, | |
1:08:43.920 --> 1:08:48.880 | |
what's the future of TensorFlow in terms of empowering, | |
1:08:48.880 --> 1:08:51.880 | |
say, a class of 300 students? | |
1:08:51.880 --> 1:08:55.920 | |
And I'm asked by MIT, what is going | |
1:08:55.920 --> 1:08:58.640 | |
to be the future of them being able to do their homework | |
1:08:58.640 --> 1:09:00.200 | |
in TensorFlow? | |
1:09:00.200 --> 1:09:02.800 | |
Where are they going to train these networks, right? | |
1:09:02.800 --> 1:09:07.720 | |
What's that future look like with TPUs, with cloud services, | |
1:09:07.720 --> 1:09:08.920 | |
and so on? | |
1:09:08.920 --> 1:09:10.240 | |
I think a number of things there. | |
1:09:10.240 --> 1:09:12.600 | |
I mean, any TensorFlow open source, | |
1:09:12.600 --> 1:09:13.640 | |
you can run it wherever. | |
1:09:13.640 --> 1:09:15.880 | |
You can run it on your desktop, and your desktops | |
1:09:15.880 --> 1:09:19.480 | |
always keep getting more powerful, so maybe you can do more. | |
1:09:19.480 --> 1:09:22.040 | |
My phone is like, I don't know how many times more powerful | |
1:09:22.040 --> 1:09:23.520 | |
than my first desktop. | |
1:09:23.520 --> 1:09:25.200 | |
You'll probably train it on your phone, though. | |
1:09:25.200 --> 1:09:26.200 | |
Yeah, that's true. | |
1:09:26.200 --> 1:09:28.080 | |
Right, so in that sense, the power | |
1:09:28.080 --> 1:09:31.440 | |
you have in your hand is a lot more. | |
1:09:31.440 --> 1:09:34.400 | |
Clouds are actually very interesting from, say, | |
1:09:34.400 --> 1:09:37.840 | |
students or courses perspective, because they | |
1:09:37.840 --> 1:09:40.040 | |
make it very easy to get started. | |
1:09:40.040 --> 1:09:42.040 | |
I mean, Colab, the great thing about it | |
1:09:42.040 --> 1:09:45.120 | |
is go to a website, and it just works. | |
1:09:45.120 --> 1:09:47.560 | |
No installation needed, nothing to, you know, | |
1:09:47.560 --> 1:09:49.960 | |
you're just there, and things are working. | |
1:09:49.960 --> 1:09:52.280 | |
That's really the power of cloud, as well. | |
1:09:52.280 --> 1:09:55.320 | |
And so I do expect that to grow. | |
1:09:55.320 --> 1:09:57.920 | |
Again, Colab is a free service. | |
1:09:57.920 --> 1:10:00.840 | |
It's great to get started, to play with things, | |
1:10:00.840 --> 1:10:03.080 | |
to explore things. | |
1:10:03.080 --> 1:10:08.200 | |
That said, with free, you can only get so much, maybe. | |
1:10:08.200 --> 1:10:11.080 | |
So just like we were talking about free versus paid, | |
1:10:11.080 --> 1:10:15.280 | |
and there are services you can pay for and get a lot more. | |
1:10:15.280 --> 1:10:16.000 | |
Great. | |
1:10:16.000 --> 1:10:18.480 | |
So if I'm a complete beginner interested in machine | |
1:10:18.480 --> 1:10:21.560 | |
learning and TensorFlow, what should I do? | |
1:10:21.560 --> 1:10:24.240 | |
Probably start with going to a website and playing there. | |
1:10:24.240 --> 1:10:26.560 | |
Just go to TensorFlow.org and start clicking on things. | |
1:10:26.560 --> 1:10:28.440 | |
Yep, check out tutorials and guides. | |
1:10:28.440 --> 1:10:30.680 | |
There's stuff you can just click there and go to Colab | |
1:10:30.680 --> 1:10:31.320 | |
and do things. | |
1:10:31.320 --> 1:10:32.360 | |
No installation needed. | |
1:10:32.360 --> 1:10:34.040 | |
You can get started right there. | |
1:10:34.040 --> 1:10:34.840 | |
OK, awesome. | |
1:10:34.840 --> 1:10:36.720 | |
Roger, thank you so much for talking today. | |
1:10:36.720 --> 1:10:37.440 | |
Thank you, Lex. | |
1:10:37.440 --> 1:10:46.680 | |
Have fun this week. | |