The following is a conversation with Rajat Manga.

He's an engineering director at Google,

leading the TensorFlow team.

TensorFlow is an open source library

at the center of much of the work going on in the world

in deep learning, both the cutting edge research

and the large scale application of learning based approaches.

But it's quickly becoming much more

than a software library.

It's now an ecosystem of tools for the deployment

of machine learning in the cloud, on the phone,

in the browser, on both generic and specialized hardware.

TPU, GPU, and so on.

Plus, there's a big emphasis on growing

a passionate community of developers.

Rajat, Jeff Dean, and a large team of engineers at Google

Brain are working to define the future of machine learning

with TensorFlow 2.0, which is now in alpha.

I think the decision to open source TensorFlow

is a definitive moment in the tech industry.

It showed that open innovation can be successful

and inspire many companies to open source their code,

to publish, and in general engage in the open exchange

of ideas.

This conversation is part of the artificial intelligence

podcast.

If you enjoy it, subscribe on YouTube, iTunes,

or simply connect with me on Twitter

at Lex Friedman, spelled FRID.

And now, here's my conversation with Rajat Manga.

You were involved with Google Brain since its start in 2011

with Jeff Dean.

It started with disbelief, the proprietary machine learning

library, and turned into TensorFlow 2014,

the open source library.

So what were the early days of Google Brain like?

What were the goals, the missions?

How do you even proceed forward once there's

so much possibilities before you?

It was interesting back then when I started,

or when you were even just talking about it.

The idea of deep learning was interesting

and intriguing in some ways.

It hadn't yet taken off, but it held some promise.

It had shown some very promising and early results.

I think the idea where Andrew and Jeff had started

was what if we can take this, what people are doing in research,

and scale it to what Google has in terms of the compute power,

and also put that kind of data together, what does it mean?

And so far, the results had been if you scale the computer,

scale the data, it does better, and would that work?

And so that was the first year or two.

Can we prove that outright?

And with disbelief, when we started the first year,

we got some early wins, which is always great.

What were the wins like?

What was the wins where there are some problems to this?

This is going to be good.

I think the two early wins were one was speech

that we collaborated very closely with the speech research

team, who was also getting interested in this.

And the other one was on images where

the cat paper, as we call it, that was covered by a lot of folks.

And the birth of Google Brain was around neural networks.

So it was deep learning from the very beginning.

That was the whole mission.

So in terms of scale, what was the dream

of what this could become?

Were there echoes of this open source TensorFlow community

that might be brought in?

Was there a sense of TPUs?

Was there a sense of machine learning

is now going to be at the core of the entire company?

Is going to grow into that direction?

Yeah, I think so that was interesting.

And if I think back to 2012 or 2011,

and first was can we scale it in the year or so,

we had started scaling it to hundreds and thousands

of machines.

In fact, we had some runs even going to 10,000 machines.

And all of those shows great promise.

In terms of machine learning at Google,

the good thing was Google's been doing machine learning

for a long time.

Deep learning was new.

But as we scale this up, we showed that, yes, that was

possible, and it was going to impact lots of things.

Like, we started seeing real products wanting to use this.

Again, speech was the first.

There were image things that photos came out of

in many other products as well.

So that was exciting.

As we went into with that a couple of years,

externally also academia started to,

there was lots of push on, OK, deep learning's

interesting, we should be doing more, and so on.

And so by 2014, we were looking at, OK, this is a big thing.

It's going to grow.

And not just internally, externally as well.

Yes, maybe Google's ahead of where everybody is,

but there's a lot to do.

So a lot of this start to make sense and come together.

So the decision to open source, I was just chatting with Chris

Flattner about this, the decision to go open source

with TensorFlow, I would say for me personally,

seems to be one of the big seminal moments in all

of software engineering ever.

I think that when a large company like Google

decides to take a large project that many lawyers might argue

has a lot of IP, just decide to go open source with it.

And in so doing, lead the entire world in saying,

you know what, open innovation is a pretty powerful thing.

And it's OK to do.

That was, I mean, that's an incredible moment in time.

So do you remember those discussions happening?

Are there open source should be happening?

What was that like?

I would say, I think, so the initial idea came from Jeff,

who was a big proponent of this.

I think it came off of two big things.

One was research wise, we were a research group.

We were putting all our research out there if you wanted to.

We were building on other's research,

and we wanted to push the state of the art forward.

And part of that was to share the research.

That's how I think deep learning and machine learning

has really grown so fast.

So the next step was, OK, now word software

help for that.

And it seemed like they were existing a few libraries

out there, Tiano being one, Torch being another,

and a few others.

But they were all done by academia,

and so the level was significantly different.

The other one was, from a software perspective,

Google had done lots of software that we used internally.

And we published papers.

Often there was an open source project

that came out of that, that somebody else

picked up that paper and implemented,

and they were very successful.

Back then, it was like, OK, there's

Hadoop, which has come off of tech that we've built.

We know that tech we've built is way better

for a number of different reasons.

We've invested a lot of effort in that.

And turns out, we have Google Cloud,

and we are now not really providing our tech,

but we are saying, OK, we have Bigtable, which

is the original thing.

We are going to now provide HBase APIs on top of that, which

isn't as good, but that's what everybody's used to.

So there's like, can we make something that is better

and really just provide?

Helps the community in lots of ways,

but it also helps push the right, a good standard forward.

So how does Cloud fit into that?

There's a TensorFlow open source library.

And how does the fact that you can

use so many of the resources that Google provides

and the Cloud fit into that strategy?

So TensorFlow itself is open, and you can use it anywhere.

And we want to make sure that continues to be the case.

On Google Cloud, we do make sure that there's

lots of integrations with everything else,

and we want to make sure that it works really, really well there.

You're leading the TensorFlow effort.

Can you tell me the history and the timeline of TensorFlow

project in terms of major design decisions,

like the open source decision, but really, what to include

and not?

There's this incredible ecosystem that I'd

like to talk about, there's all these parts.

But if you just some sample moments that

defined what TensorFlow eventually became through its,

I don't know if you were allowed to say history when it's just,

but in deep learning, everything moves so fast

in just a few years, it's already history.

Yes, yes.

So looking back, we were building TensorFlow.

I guess we open sourced it in 2015, November 2015.

We started on it in summer of 2014, I guess.

And somewhere like three to six late 2014,

by then we had decided that, OK, there's

a high likelihood we'll open source it.

So we started thinking about that and making sure

that we're heading down that path.

At that point, by that point, we'd

seen a few lots of different use cases at Google.

So there were things like, OK, yes,

you want to run in at large scale in the data center.

Yes, we need to support different kind of hardware.

We had GPUs at that point.

We had our first GPU at that point

or was about to come out roughly around that time.

So the design included those.

We had started to push on mobile.

So we were running models on mobile.

At that point, people were customizing code.

So we wanted to make sure TensorFlow could support that

as well so that that became part of that overall

design.

When you say mobile, you mean like pretty complicated

algorithms of running on the phone?

That's correct.

So when you have a model that you

deploy on the phone and run it there, right?

So already at that time, there was ideas of running machine

learning on the phone.

That's correct.

We already had a couple of products

that were doing that by then.

And in those cases, we had basically

customized handcrafted code or some internal libraries

that we're using.

So I was actually at Google during this time in a parallel,

I guess, universe.

But we were using Theano and CAFE.

Was there some degree to which you were bouncing,

like trying to see what CAFE was offering people,

trying to see what Theano was offering

that you want to make sure you're delivering on whatever that

is, perhaps the Python part of thing.

Maybe did that influence any design decisions?

Totally.

So when we built this belief, and some of that

was in parallel with some of these libraries

coming up, I mean, Theano itself is older.

But we were building this belief focused on our internal thing

because our systems were very different.

By the time we got to this, we looked

at a number of libraries that were out there.

Theano, there were folks in the group

who had experience with Torch, with Lua.

There were folks here who had seen CAFE.

I mean, actually, Yang Cheng was here as well.

There's what other libraries?

I think we looked at a number of things.

Might even have looked at Jane and her back then.

I'm trying to remember if it was there.

In fact, yeah, we did discuss ideas around, OK,

should we have a graph or not?

And they were supporting all these together

was definitely, you know, there were key decisions

that we wanted.

We had seen limitations in our prior disbelief things.

A few of them were just in terms of research

was moving so fast.

We wanted the flexibility.

We want the hardware was changing fast.

We expected to change that so that those probably were two

things.

And yeah, I think the flexibility in terms

of being able to express all kinds of crazy things

was definitely a big one then.

So what the graph decisions, though,

with moving towards TensorFlow 2.0, there's more,

by default, there'll be eager execution.

So sort of hiding the graph a little bit

because it's less intuitive in terms of the way

people develop and so on.

What was that discussion like with in terms of using graphs?

It seemed it's kind of the theano way.

Did it seem the obvious choice?

So I think where it came from was our disbelief,

had a graph like thing as well.

It wasn't a general graph.

It was more like a straight line thing.

More like what you might think of Cafe,

I guess, in that sense.

And we always cared about the production stuff.

Even with disbelief, we were deploying a whole bunch of stuff

in production.

So graph did come from that when we thought of, OK,

should we do that in Python and we experimented with some ideas

where it looked a lot simpler to use,

but not having a graph meant, OK, how do you deploy now?

So that was probably what tilted the balance for us.

And eventually, we ended up with the graph.

And I guess the question there is, did you?

I mean, production seems to be the really good thing to focus on.

But did you even anticipate the other side of it

where there could be, what is it?

What are the numbers?

Something crazy, 41 million downloads?

Yep.

I mean, was that even like a possibility in your mind

that it would be as popular as it became?

So I think we did see a need for this a lot

from the research perspective and early days of deep learning

in some ways.

41 million?

No, I don't think I imagine this number then.

It seemed like there's a potential future where lots more people

would be doing this.

And how do we enable that?

I would say this kind of growth, I probably

started seeing somewhat after the open sourcing where it was

like, OK, deep learning is actually

growing way faster for a lot of different reasons.

And we are in just the right place to push on that

and leverage that and deliver on lots of things

that people want.

So what changed once the open source?

Like how this incredible amount of attention

from a global population of developers,

how did the projects start changing?

I don't even actually remember it during those times.

I know looking now, there's really good documentation.

There's an ecosystem of tools.

There's a YouTube channel now.

It's very community driven.

Back then, I guess 0.1 version.

Is that the version?

I think we called it 0.6 or 5, something like that.

Something like that.

What changed leading into 1.0?

It's interesting.

I think we've gone through a few things there.

When we started out, when we first came out,

people loved the documentation we have.

Because it was just a huge step up from everything else.

Because all of those were academic projects, people

don't think about documentation.

I think what that changed was instead of deep learning

being a research thing, some people who were just developers

could now suddenly take this out and do

some interesting things with it.

Who had no clue what machine learning was before then.

And that, I think, really changed

how things started to scale up in some ways and pushed on it.

Over the next few months, as we looked at,

how do we stabilize things?

As we look at not just researchers,

now we want stability.

People want to deploy things.

That's how we started planning for 1.0.

And there are certain needs for that perspective.

And so, again, documentation comes up,

designs, more kinds of things to put that together.

And so that was exciting to get that to a stage where

more and more enterprises wanted to buy in and really

get behind that.

And I think post 1.0 and with the next few releases,

their enterprise adoption also started to take off.

I would say between the initial release and 1.0,

it was, OK, researchers, of course.

Then a lot of hobbies and early interest,

people excited about this who started to get on board.

And then over the 1.x thing, lots of enterprises.

I imagine anything that's below 1.0

gets pressured to be enterprise problem or something

that's stable.

Exactly.

And do you have a sense now that TensorFlow is stable?

It feels like deep learning, in general,

is extremely dynamic field.

So much is changing.

Do you have a, and TensorFlow has been growing incredibly.

Do you have a sense of stability at the helm of this?

I mean, I know you're in the midst of it.

Yeah.

I think in the midst of it, it's often easy to forget what

an enterprise wants and what some of the people on that side

want.

There are still people running models

that are three years old, four years old.

So inception is still used by tons of people.

Even less than 50 is what, a couple of years old now or more.

But there are tons of people who use that, and they're fine.

They don't need the last couple of bits of performance or quality.

They want some stability in things that just work.

And so there is value in providing that with that kind

of stability and making it really simpler,

because that allows a lot more people to access it.

And then there's the research crowd, which wants, OK,

they want to do these crazy things exactly like you're

saying, not just deep learning in the straight up models

that used to be there.

They want RNNs, and even RNNs are maybe old.

They are transformers now, and now it

needs to combine with RL and GANs and so on.

So there's definitely that area, the boundary that's

shifting and pushing the state of the art.

But I think there's more and more of the past

that's much more stable.

And even stuff that was two, three years old

is very, very usable by lots of people.

So that part makes it a lot easier.

So I imagine maybe you can correct me if I'm wrong.

One of the biggest use cases is essentially

taking something like ResNet 50 and doing

some kind of transfer learning on a very particular problem

that you have.

It's basically probably what majority of the world does.

And you want to make that as easy as possible.

So I would say, for the hobbyist perspective,

that's the most common case.

In fact, the apps on phones and stuff

that you'll see, the early ones, that's the most common case.

I would say there are a couple of reasons for that.

One is that everybody talks about that.

It looks great on slides.

That's a great presentation.

Exactly.

What enterprises want is that is part of it,

but that's not the big thing.

Enterprises really have data that they

want to make predictions on.

This is often what they used to do with the people who

were doing ML was just regression models,

linear regression, logistic regression, linear models,

or maybe gradient booster trees and so on.

Some of them still benefit from deep learning,

but they weren't that that's the bread and butter,

like the structured data and so on.

So depending on the audience you look at,

they're a little bit different.

And they just have, I mean, the best of enterprise

probably just has a very large data set

where deep learning can probably shine.

That's correct.

That's right.

And then I think the other pieces

that they wanted, again, to point out

that the developer summit we put together

is that the whole TensorFlow Extended

piece, which is the entire pipeline,

they care about stability across doing their entire thing.

They want simplicity across the entire thing.

I don't need to just train a model.

I need to do that every day again, over and over again.

I wonder to which degree you have a role in, I don't know.

So I teach a course on deep learning.

I have people like lawyers come up to me and say,

when is machine learning going to enter legal,

the legal realm?

The same thing in all kinds of disciplines, immigration,

insurance.

Often when I see what it boils down to is these companies

are often a little bit old school in the way

they organize the data.

So the data is just not ready yet.

It's not digitized.

Do you also find yourself being in the role of an evangelist

for let's organize your data, folks,

and then you'll get the big benefit of TensorFlow?

Do you have those conversations?

Yeah, I get all kinds of questions there from, OK,

what do I need to make this work, right?

Do we really need deep learning?

I mean, there are all these things.

I already used this linear model.

Why would this help?

I don't have enough data, let's say.

Or I want to use machine learning,

but I have no clue where to start.

So it's a great start to all the way to the experts

who wise were very specific things, so it's interesting.

Is there a good answer?

It boils down to oftentimes digitizing data.

So whatever you want automated, whatever data

you want to make prediction based on,

you have to make sure that it's in an organized form.

Like with an intensive flow ecosystem,

there's now you're providing more and more data

sets and more and more pretrained models.

Are you finding yourself also the organizer of data sets?

Yes, I think with TensorFlow data sets

that we just released, that's definitely come up where people

want these data sets.

Can we organize them and can we make that easier?

So that's definitely one important thing.

The other related thing I would say is I often tell people,

you know what, don't think of the most fanciest thing

that the newest model that you see.

Make something very basic work, and then

you can improve it.

There's just lots of things you can do with it.

Yeah, start with the basics.

Sure.

One of the big things that makes TensorFlow even more

accessible was the appearance, whenever

that happened, of Keras, the Keras standard outside of TensorFlow.

I think it was Keras on top of Tiano at first only,

and then Keras became on top of TensorFlow.

Do you know when Keras chose to also add TensorFlow as a back end,

who was it just the community that drove that initially?

Do you know if there was discussions, conversations?

Yeah, so Franco started the Keras project

before he was at Google, and the first thing was Tiano.

I don't remember if that was after TensorFlow

was created or way before.

And then at some point, when TensorFlow

started becoming popular, there were enough similarities

that he decided to create this interface

and put TensorFlow as a back end.

I believe that might still have been before he joined Google.

So we weren't really talking about that.

He decided on his own and thought that was interesting

and relevant to the community.

In fact, I didn't find out about him being at Google

until a few months after he was here.

He was working on some research ideas.

And doing Keras and his nights and weekends project and stuff.

I wish this thing.

So he wasn't part of the TensorFlow.

He didn't join initially.

He joined research, and he was doing some amazing research.

He has some papers on that and research.

He's a great researcher as well.

And at some point, we realized, oh, he's doing this good stuff.

People seem to like the API, and he's right here.

So we talked to him, and he said, OK,

why don't I come over to your team

and work with you for a quarter?

And let's make that integration happen.

And we talked to his manager, and he said, sure,

what, quarter's fine.

And that quarter's been something like two years now.

So he's fully on this.

So Keras got integrated into TensorFlow in a deep way.

And now with TensorFlow 2.0, Keras

is kind of the recommended way for a beginner

to interact with TensorFlow, which

makes that initial sort of transfer learning

or the basic use cases, even for an enterprise,

super simple, right?

That's correct.

That's right.

So what was that decision like?

That seems like it's kind of a bold decision as well.

We did spend a lot of time thinking about that one.

We had a bunch of APIs some bit by us.

There was a parallel layers API that we were building

and when we decided to do Keras in parallel,

so they were like, OK, two things that we are looking at.

And the first thing we was trying to do

is just have them look similar, be as integrated as possible,

share all of that stuff.

There were also three other APIs that others had built over time

because we didn't have a standard one.

But one of the messages that we kept hearing from the community,

OK, which one do we use?

And they kept seeing, OK, here's a model in this one,

and here's a model in this one, which should I pick?

So that's sort of like, OK, we had to address that

straight on with 2.0.

The whole idea was we need to simplify.

We had to pick one.

Based on where we were, we were like, OK, let's see what

are the people like.

And Keras was clearly one that lots of people loved.

There were lots of great things about it.

So we settled on that.

Organically.

That's kind of the best way to do it.

It was great.

But it was surprising, nevertheless,

to sort of bring in and outside.

I mean, there was a feeling like Keras might be almost

like a competitor in a certain kind of a two tensor flow.

And in a sense, it became an empowering element

of tensor flow.

That's right.

Yeah, it's interesting how you can put two things together

which can align right.

And in this case, I think Francois, the team,

and a bunch of us have chatted and I think we all

want to see the same kind of things.

We all care about making it easier for the huge set

of developers out there.

And that makes a difference.

So Python has Guido van Rossum, who

until recently held the position of benevolent

dictator for life.

Right, so there's a huge successful open source

project like tensor flow.

Need one person who makes a final decision.

So you did a pretty successful tensor flow Dev Summit

just now, last couple of days.

There's clearly a lot of different new features

being incorporated in amazing ecosystem, so on.

How are those design decisions made?

Is there a BDFL in tensor flow?

And or is it more distributed and organic?

I think it's somewhat different, I would say.

I've always been involved in the key design directions.

But there are lots of things that

are distributed where their number of people, Martin

Wick being one who has really driven a lot of our open source

stuff, a lot of the APIs.

And there are a number of other people

who have been pushed and been responsible

for different parts of it.

We do have regular design reviews.

Over the last year, we've really spent a lot of time opening up

to the community and adding transparency.

We're setting more processes in place,

so RFCs, special interest groups, really

grow that community and scale that.

I think the kind of scale that ecosystem is in,

I don't think we could scale with having me as the lone

point of decision maker.

I got it.

So yeah, the growth of that ecosystem,

maybe you can talk about it a little bit.

First of all, when I started with Andre Karpathi

when he first did ComNet.js, the fact

that you can train in your own network

and the browser in JavaScript was incredible.

So now TensorFlow.js is really making

that a serious, a legit thing, a way

to operate, whether it's in the back end or the front end.

Then there's the TensorFlow Extended, like you mentioned.

There's TensorFlow Lite for mobile.

And all of it, as far as I can tell,

it's really converging towards being

able to save models in the same kind of way.

You can move around, you can train on the desktop,

and then move it to mobile, and so on.

That's right.

So this is that cohesiveness.

So can you maybe give me whatever

I missed, a bigger overview of the mission of the ecosystem

that's trying to be built, and where is it moving forward?

Yeah.

So in short, the way I like to think of this

is our goals to enable machine learning.

And in a couple of ways, one is we

have lots of exciting things going on in ML today.

We started with deep learning, but we now

support a bunch of other algorithms too.

So one is to, on the research side,

keep pushing on the state of the art.

Can we, how do we enable researchers

to build the next amazing thing?

So BERT came out recently.

It's great that people are able to do new kinds of research.

There are lots of amazing research

that happens across the world.

So that's one direction.

The other is, how do you take that

across all the people outside who want to take that research

and do some great things with it and integrate it

to build real products, to have a real impact on people?

And so if that's the other axes in some ways.

And a high level, one way I think about it

is there are a crazy number of computer devices

across the world.

And we often used to think of ML and training and all of this

as, OK, something you do either in the workstation

or the data center or cloud.

But we see things running on the phones.

We see things running on really tiny chips.

And we had some demos at the developer summit.

And so the way I think about this ecosystem

is, how do we help get machine learning on every device that

has a compute capability?

And that continues to grow.

And so in some ways, this ecosystem

has looked at various aspects of that

and grown over time to cover more of those.

And we continue to push the boundaries.

In some areas, we've built more tooling and things

around that to help you.

I mean, the first tool we started was TensorBoard.

You want to learn just the training piece, the effects

for TensorFlow Extended to really do your entire ML

pipelines if you care about all that production stuff,

but then going to the edge, going to different kinds of things.

And it's not just us now.

We are a place where there are lots of libraries being built

on top.

So there are some for research, maybe things

like TensorFlow Agents or TensorFlow Probability that

started as research things or for researchers

for focusing on certain kinds of algorithms,

but they're also being deployed or reduced by production folks.

And some have come from within Google, just teams

across Google who wanted to do the build these things.

Others have come from just the community

because there are different pieces

that different parts of the community care about.

And I see our goal as enabling even that.

It's not we cannot and won't build every single thing.

That just doesn't make sense.

But if we can enable others to build the things

that they care about, and there's a broader community that

cares about that, and we can help encourage that,

and that's great.

That really helps the entire ecosystem, not just those.

One of the big things about 2.0 that we're pushing on

is, OK, we have these so many different pieces, right?

How do we help make all of them work well together?

There are a few key pieces there that we're pushing on,

one being the core format in there

and how we share the models themselves through SAVE model

and what TensorFlow Hub and so on.

And a few of the pieces that we really put this together.

I was very skeptical that that's, when TensorFlow.js came out,

it didn't seem or deep learning.js.

Yeah, that was the first.

It seems like technically very difficult project.

As a standalone, it's not as difficult.

But as a thing that integrates into the ecosystem,

it seems very difficult.

So I mean, there's a lot of aspects of this

you're making look easy.

But on the technical side, how many challenges

have to be overcome here?

A lot.

And still have to be overcome.

That's the question here, too.

There are lots of steps to it.

I think we've iterated over the last few years,

so there's a lot we've learned.

I, yeah, and often when things come together well,

things look easy.

And that's exactly the point.

It should be easy for the end user.

But there are lots of things that go behind that.

If I think about still challenges ahead,

there are we have a lot more devices coming on board,

for example, from the hardware perspective.

How do we make it really easy for these vendors

to integrate with something like TensorFlow?

So there's a lot of compiler stuff

that others are working on.

There are things we can do in terms of our APIs

and so on that we can do.

As we, TensorFlow started as a very monolithic system.

And to some extent, it still is.

There are less lots of tools around it,

but the core is still pretty large and monolithic.

One of the key challenges for us to scale that out

is how do we break that apart with clear interfaces?

It's, in some ways, it's software engineering one

one, but for a system that's now four years old, I guess,

or more, and that's still rapidly evolving

and that we're not slowing down with,

it's hard to change and modify and really break apart.

It's sort of like, as people say, right,

it's like changing the engine with a car running

or fixed benefits.

That's exactly what we're trying to do.

So there's a challenge here, because the downside

of so many people being excited about TensorFlow

and becoming to rely on it in many other applications

is that you're kind of responsible.

It's the technical debt.

You're responsible for previous versions

to some degree still working.

So when you're trying to innovate,

I mean, it's probably easier to just start from scratch

every few months.

Absolutely.

So do you feel the pain of that?

2.0 does break some back compatibility, but not too much.

It seems like the conversion is pretty straightforward.

Do you think that's still important,

given how quickly deep learning is changing?

Can you just, the things that you've learned,

can you just start over?

Or is there pressure to not?

It's a tricky balance.

So if it was just a researcher writing a paper who

a year later will not look at that code again,

sure, it doesn't matter.

There are a lot of production systems

that rely on TensorFlow, both at Google

and across the world.

And people worry about this.

I mean, these systems run for a long time.

So it is important to keep that compatibility and so on.

And yes, it does come with a huge cost.

We have to think about a lot of things

as we do new things and make new changes.

I think it's a trade off, right?

You can, you might slow certain kinds of things down,

but the overall value you're bringing because of that

is much bigger because it's not just

about breaking the person yesterday.

It's also about telling the person tomorrow that, you know what?

This is how we do things.

We're not going to break you when you come on board

because there are lots of new people who are also

going to come on board.

So one way I like to think about this,

and I always push the team to think about it as well,

when you want to do new things, you

want to start with a clean slate,

design with a clean slate in mind,

and then we'll figure out how to make sure all the other things

work.

And yes, we do make compromises occasionally.

But unless you design with the clean slate

and not worry about that, you'll never get to a good place.

That's brilliant.

So even if you are responsible in the idea stage,

when you're thinking of new, just put all that behind you.

OK, that's really well put.

So I have to ask this because a lot of students, developers,

asked me how I feel about PyTorch versus TensorFlow.

So I've recently completely switched my research group

to TensorFlow.

I wish everybody would just use the same thing.

And TensorFlow is as close to that, I believe, as we have.

But do you enjoy competition?

So TensorFlow is leading in many ways, many dimensions

in terms of the ecosystem, in terms of the number of users,

momentum power, production level, so on.

But a lot of researchers are now also using PyTorch.

Do you enjoy that kind of competition,

or do you just ignore it and focus

on making TensorFlow the best that it can be?

So just like research or anything people are doing,

it's great to get different kinds of ideas.

And when we started with TensorFlow,

like I was saying earlier, it was very important for us

to also have production in mind.

We didn't want just research, right?

And that's why we chose certain things.

Now PyTorch came along and said, you know what?

I only care about research.

This is what I'm trying to do.

What's the best thing I can do for this?

And it started iterating and said, OK,

I don't need to worry about graphs.

Let me just run things.

I don't care if it's not as fast as it can be,

but let me just make this part easy.

And there are things you can learn from that, right?

They, again, had the benefit of seeing what had come before,

but also exploring certain different kinds of spaces.

And they had some good things there,

building on, say, things like Jainer and so on before that.

So competition is definitely interesting.

It made us, you know, this is an area

that we had thought about, like I said, very early on.

Over time, we had revisited this a couple of times.

Should we add this again?

At some point, we said, you know what,

here's it seems like this can be done well.

So let's try it again.

And that's how we started pushing on eager execution.

How do we combine those two together,

which has finally come very well together in 2.0,

but it took us a while to get all the things together

and so on.

So let me, I mean, ask, put another way.

I think eager execution is a really powerful thing,

those added.

Do you think he wouldn't have been,

you know, Muhammad Ali versus Frazier, right?

Do you think it wouldn't have been added as quickly

if PyTorch wasn't there?

It might have taken longer.

No longer.

It was, I mean, we had tried some variants of that before.

So I'm sure it would have happened,

but it might have taken longer.

I'm grateful that TensorFlow is part of the way they did.

That's doing some incredible work last couple of years.

What other things that we didn't talk about?

Are you looking forward in 2.0?

That comes to mind.

So we talked about some of the ecosystem stuff,

making it easily accessible to Keras, eager execution.

Is there other things that we missed?

Yeah, so I would say one is just where 2.0 is,

and, you know, with all the things that we've talked about,

I think as we think beyond that,

there are lots of other things that it enables us to do

and that we're excited about.

So what it's setting us up for,

okay, here are these really clean APIs.

We've cleaned up the surface for what the users want.

What it also allows us to do a whole bunch of stuff

behind the scenes once we are ready with 2.0.

So for example, in TensorFlow with graphs

and all the things you could do,

you could always get a lot of good performance

if you spent the time to tune it, right?

And we've clearly shown that, lots of people do that.

With 2.0, with these APIs where we are,

we can give you a lot of performance

just with whatever you do.

You know, because we see these, it's much cleaner.

We know most people are gonna do things this way.

We can really optimize for that

and get a lot of those things out of the box.

And it really allows us, you know,

both for single machine and distributed and so on,

to really explore other spaces behind the scenes

after 2.0 in the future versions as well.

So right now, the team's really excited about that,

that over time, I think we'll see that.

The other piece that I was talking about

in terms of just restructuring the monolithic thing

into more pieces and making it more modular,

I think that's gonna be really important

for a lot of the other people in the ecosystem,

other organizations and so on that wanted to build things.

Can you elaborate a little bit what you mean

by making TensorFlow more ecosystem or modular?

So the way it's organized today is there's one,

there are lots of repositories

in the TensorFlow organization at GitHub,

the core one where we have TensorFlow,

it has the execution engine,

it has, you know, the key backends for CPUs and GPUs,

it has the work to do distributed stuff.

And all of these just work together

in a single library or binary,

there's no way to split them apart easily.

I mean, there are some interfaces,

but they're not very clean.

In a perfect world, you would have clean interfaces where,

okay, I wanna run it on my fancy cluster

with some custom networking,

just implement this and do that.

I mean, we kind of support that,

but it's hard for people today.

I think as we are starting to see more interesting things

in some of these spaces,

having that clean separation will really start to help.

And again, going to the large size of the ecosystem

and the different groups involved there,

enabling people to evolve and push on things

more independently just allows it to scale better.

And by people, you mean individual developers and?

And organizations.

And organizations.

That's right.

So the hope is that everybody sort of major,

I don't know, Pepsi or something uses,

like major corporations go to TensorFlow to this kind of.

Yeah, if you look at enterprise like Pepsi or these,

I mean, a lot of them are already using TensorFlow.

They are not the ones that do the development

or changes in the core.

Some of them do, but a lot of them don't.

I mean, they touch small pieces.

There are lots of these, some of them being,

let's say hardware vendors who are building

their custom hardware and they want their own pieces.

Or some of them being bigger companies, say IBM.

I mean, they're involved in some of our special interest

groups and they see a lot of users

who want certain things and they want to optimize for that.

So folks like that often.

Autonomous vehicle companies, perhaps.

Exactly, yes.

So yeah, like I mentioned, TensorFlow

has been down on it 41 million times, 50,000 commits,

almost 10,000 pull requests, 1,800 contributors.

So I'm not sure if you can explain it,

but what does it take to build a community like that?

In retrospect, what do you think?

What is the critical thing that allowed for this growth

to happen and how does that growth continue?

Yeah, that's an interesting question.

I wish I had all the answers there, I guess,

so you could replicate it.

I think there are a number of things

that need to come together, right?

One, just like any new thing, there's

a sweet spot of timing, what's needed,

does it grow with what's needed.

So in this case, for example, TensorFlow

is not just grown because it has a good tool,

it's also grown with the growth of deep learning itself.

So those factors come into play.

Other than that, though, I think just

hearing, listening to the community, what they're

doing, what they need, being open to,

like in terms of external contributions,

we've spent a lot of time in making sure

we can accept those contributions well,

we can help the contributors in adding those,

putting the right process in place,

getting the right kind of community,

welcoming them, and so on.

Like over the last year, we've really pushed on transparency.

That's important for an open source project.

People want to know where things are going,

and we're like, OK, here's a process for you.

You can do that, here are our seasons, and so on.

So thinking through, there are lots of community aspects

that come into that you can really work on.

As a small project, it's maybe easy to do,

because there's two developers, and you can do those.

As you grow, putting more of these processes in place,

thinking about the documentation,

thinking about what two developers

care about, what kind of tools would they want to use,

all of these come into play, I think.

So one of the big things, I think,

that feeds the TensorFlow fire is people building something

on TensorFlow, and implement a particular architecture

that does something cool and useful,

and they put that on GitHub.

And so it just feeds this growth.

Do you have a sense that with 2.0 and 1.0,

that there may be a little bit of a partitioning like there

is with Python 2 and 3, that there'll be a code base

in the older versions of TensorFlow

that will not be as compatible easily,

or are you pretty confident that this kind of conversion

is pretty natural and easy to do?

So we're definitely working hard to make that very easy to do.

There's lots of tooling that we talked about at the developer

summit this week, and we'll continue

to invest in that tooling.

It's when you think of these significant version changes,

that's always a risk, and we are really pushing hard

to make that transition very, very smooth.

I think, so at some level, people

want to move when they see the value in the new thing.

They don't want to move just because it's a new thing.

And some people do, but most people want a really good thing.

And I think over the next few months,

as people start to see the value,

we'll definitely see that shift happening.

So I'm pretty excited and confident that we

will see people moving.

As you said earlier, this field is also moving rapidly,

so that'll help because we can do more things.

And all the new things will clearly

happen in 2.x, so people will have lots of good reasons to move.

So what do you think TensorFlow 3.0 looks like?

Is there things happening so crazily

that even at the end of this year,

seems impossible to plan for?

Or is it possible to plan for the next five years?

I think it's tricky.

There are some things that we can expect in terms of, OK,

change, yes, change is going to happen.

Are there some things going to stick around

and some things not going to stick around?

I would say the basics of deep learning,

the convolutional models or the basic kind of things,

they'll probably be around in some form still in five years.

Will Aurel and Gans stay very likely based on where they are?

Will we have new things?

Probably, but those are hard to predict.

And some directionally, some things that we can see

is in things that we're starting to do

with some of our projects right now is just

to point out combining eager execution and graphs,

where we're starting to make it more like just your natural

programming language.

You're not trying to program something else.

Similarly, with Swift for TensorFlow,

we're taking that approach.

Can you do something round up?

So some of those ideas seem like, OK,

that's the right direction in five years

we expect to see more in that area.

Other things we don't know is, will hardware accelerators

be the same?

Will we be able to train with four bits instead of 32 bits?

And I think the TPU side of things is exploring.

I mean, TPU is already on version three.

It seems that the evolution of TPU and TensorFlow

are coevolving in terms of both their learning

from each other and from the community

and from the applications where the biggest benefit is achieved.

That's right.

You've been trying with eager with Keras

to make TensorFlow as accessible and easy to use as possible.

What do you think for beginners is the biggest thing

they struggle with?

Have you encountered that?

Or is basically what Keras is solving

is that eager, like we talked about TensorFlow?

For some of them, like you said, the beginners

want to just be able to take some image model.

They don't care if it's inception or rest net or something else

and do some training or transfer learning

on their kind of model.

Being able to make that easy is important.

So in some ways, if you do that by providing them

simple models with, say, in Hub or so on,

they don't care about what's inside that box,

but they want to be able to use it.

So we're pushing on, I think, different levels.

If you look at just a component that you get, which

has the layers already smushed in,

the beginners probably just want that.

Then the next step is, OK, look at building

layers with Keras.

If you go out to research, then they

are probably writing custom layers themselves

or doing their own loops.

So there's a whole spectrum there.

And then providing the preentrain models

seems to really decrease the time from you trying to start.

So you could basically, in a Colab notebook,

achieve what you need.

So I'm basically answering my own question,

because I think what TensorFlow delivered on recently

is trivial for beginners.

So I was just wondering if there was other pain points

you're trying to ease, but I'm not sure there would.

No, those are probably the big ones.

I mean, I see high schoolers doing a whole bunch of things

now, which is pretty amazing.

It's both amazing and terrifying.

Yes.

In a sense that when they grow up,

some incredible ideas will be coming from them.

So there's certainly a technical aspect to your work,

but you also have a management aspect

to your role with TensorFlow, leading the project,

a large number of developers and people.

So what do you look for in a good team?

What do you think Google has been at the forefront

of exploring what it takes to build a good team?

And TensorFlow is one of the most cutting edge technologies

in the world.

So in this context, what do you think

makes for a good team?

It's definitely something I think a fair bit about.

I think in terms of the team being

able to deliver something well, one of the things that's

important is a cohesion across the team.

So being able to execute together and doing things,

it's not an end.

Like at this scale, an individual engineer

can only do so much.

There's a lot more that they can do together,

even though we have some amazing superstars across Google

and in the team.

But there's often the way I see it

is the product of what the team generates

is way larger than the whole individual put together.

And so how do we have all of them work together,

the culture of the team itself?

Hiring good people is important.

But part of that is it's not just that, OK,

we hire a bunch of smart people and throw them together

and let them do things.

It's also people have to care about what they're building.

People have to be motivated for the right kind of things.

That's often an important factor.

And finally, how do you put that together

with a somewhat unified vision of where we want to go?

So are we all looking in the same direction

or just going all over?

And sometimes it's a mix.

Google's a very bottom up organization in some sense.

Also research even more so.

And that's how we started.

But as we've become this larger product and ecosystem,

I think it's also important to combine that well with a mix

of, OK, here's the direction we want to go in.

There is exploration we'll do around that.

But let's keep staying in that direction, not just

all over the place.

And is there a way you monitor the health of the team?

Sort of like, is there a way you know you did a good job?

The team is good.

I mean, you're saying nice things, but it's sometimes

difficult to determine how aligned.

Because it's not binary, it's not like there's tensions

and complexities and so on.

And the other element of this is the mesh of superstars.

There's so much, even at Google, such a large percentage

of work is done by individual superstars too.

So there's a, and sometimes those superstars

could be against the dynamic of a team and those tensions.

I mean, I'm sure TensorFlow might be a little bit easier

because the mission of the project is so beautiful.

You're at the cutting edge, so it's exciting.

But have you had struggle with that?

Has there been challenges?

There are always people challenges

in different kinds of ways.

That said, I think we've been what's

good about getting people who care and have

the same kind of culture, and that's Google in general

to a large extent.

But also, like you said, given that the project has had

so many exciting things to do, there's

been room for lots of people to do different kinds of things

and grow, which does make the problem a bit easier, I guess.

And it allows people, depending on what they're doing,

if there's room around them, then that's fine.

But yes, we do care about whether a superstar or not

that they need to work well with the team across Google.

That's interesting to hear.

So it's like superstar or not, the productivity broadly

is about the team.

Yeah.

I mean, they might add a lot of value,

but if they're hurting the team, then that's a problem.

So in hiring engineers, it's so interesting, right?

The high rank process, what do you look for?

How do you determine a good developer

or a good member of a team from just a few minutes

or hours together?

Again, no magic answers, I'm sure.

Yeah.

And Google has a hiring process that we've refined

over the last 20 years, I guess, and that you've probably

heard and seen a lot about.

So we do work with the same hiring process in that.

That's really helped.

For me in particular, I would say,

in addition to the core technical skills,

what does matter is their motivation

in what they want to do.

Because if that doesn't align well with where we want to go,

that's not going to lead to long term success

for either them or the team.

And I think that becomes more important the more senior

the person is, but it's important at every level.

Like even the junior most engineer,

if they're not motivated to do well at what they're trying to do,

however smart they are, it's going

to be hard for them to succeed.

Does the Google hiring process touch on that passion?

So like trying to determine.

Because I think as far as I understand,

maybe you can speak to it that the Google hiring process sort

of helps the initial like determines the skill set there,

is your puzzle solving ability, problem solving ability good.

But I'm not sure, but it seems that the determining

whether the person is like fire inside them

that burns to do anything really doesn't really matter.

It's just some cool stuff.

I'm going to do it that I don't know.

Is that something that ultimately ends up

when they have a conversation with you

or once it gets closer to the team?

So one of the things we do have as part of the process

is just a culture fit, like part of the interview process

itself, in addition to just the technical skills.

And each engineer or whoever the interviewer is,

is supposed to rate the person on the culture and the culture

fit with Google and so on.

So that is definitely part of the process.

Now, there are various kinds of projects

and different kinds of things.

So there might be variants in the kind of culture

you want there and so on.

And yes, that does vary.

So for example, TensorFlow has always

been a fast moving project.

And we want people who are comfortable with that.

But at the same time now, for example,

we are at a place where we are also very full fledged product.

And we want to make sure things that work really, really

work right.

You can't cut corners all the time.

So balancing that out and finding the people

who are the right fit for those is important.

And I think those kind of things do vary a bit

across projects and teams and product areas across Google.

And so you'll see some differences there

in the final checklist.

But a lot of the core culture, it

comes along with just the engineering, excellence,

and so on.

What is the hardest part of your job?

I'll take your pick, I guess.

It's fun, I would say.

Hard, yes.

I mean, lots of things at different times.

I think that does vary.

So let me clarify that difficult things are fun

when you solve them, right?

It's fun in that sense.

I think the key to a successful thing across the board,

and in this case, it's a large ecosystem now,

but even a small product, is striking that fine balance

across different aspects of it.

Sometimes it's how fast you go versus how perfect it is.

Sometimes it's how do you involve this huge community?

Who do you involve?

Or do you decide, OK, now is not a good time to involve them

because it's not the right fit?

Sometimes it's saying no to certain kinds of things.

Those are often the hard decisions.

Some of them you make quickly because you don't have the time.

Some of them you get time to think about them,

but they're always hard.

So both choices are pretty good, those decisions.

What about deadlines?

Is this defined TensorFlow to be driven by deadlines

to a degree that a product might?

Or is there still a balance to where it's less deadline?

You had the Dev Summit, they came together incredibly.

Looked like there's a lot of moving pieces and so on.

So did that deadline make people rise to the occasion,

releasing TensorFlow 2.0 Alpha?

I'm sure that was done last minute as well.

I mean, up to the last point.

Again, it's one of those things that you

need to strike the good balance.

There's some value that deadlines bring

that does bring a sense of urgency

to get the right things together.

Instead of getting the perfect thing out,

you need something that's good and works well.

And the team definitely did a great job in putting that

together, so it was very amazed and excited by everything,

how that came together.

That said, across the year, we try not

to put out official deadlines.

We focus on key things that are important,

figure out how much of it's important,

and we are developing in the open, internally and externally,

everything's available to everybody.

So you can pick and look at where things are.

We do releases at a regular cadence,

so fine if something doesn't necessarily end up with this

month, it'll end up in the next release in a month or two.

And that's OK, but we want to keep moving

as fast as we can in these different areas.

Because we can iterate and improve on things, sometimes

it's OK to put things out that aren't fully ready.

If you make sure it's clear that, OK, this is experimental,

but it's out there if you want to try and give feedback.

That's very, very useful.

I think that quick cycle and quick iteration is important.

That's what we often focus on rather than here's

a deadline where you get everything else.

It's 2.0, is there pressure to make that stable?

Or like, for example, WordPress 5.0 just came out,

and there was no pressure to, it was a lot of build updates

that delivered way too late.

And they said, OK, well, we're going

to release a lot of updates really quickly to improve it.

Do you see TensorFlow 2.0 in that same kind of way,

or is there this pressure to once it hits 2.0,

once you get to the release candidate,

and then you get to the final, that's

going to be the stable thing?

So it's going to be stable in just like 1.0X

was where every API that's there is going to remain in work.

It doesn't mean we can't change things under the covers.

It doesn't mean we can't add things.

So there's still a lot more for us to do,

and we continue to have more releases.

So in that sense, there's still, I

don't think we'd be done in like two months

when we release this.

I don't know if you can say, but is there, you know,

there's not external deadlines for TensorFlow 2.0,

but is there internal deadlines, artificial or otherwise,

that you're trying to set for yourself,

or is it whenever it's ready?

So we want it to be a great product, right?

And that's a big, important piece for us.

TensorFlow is already out there.

We have 41 million downloads for 1.x,

so it's not like we have to have this.

Yeah, exactly.

So it's not like a lot of the features

that we've really polishing and putting them together

are there, we don't have to rush that just because.

So in that sense, we want to get it right

and really focus on that.

That said, we have said that we are

looking to get this out in the next few months,

in the next quarter, and as far as possible,

we'll definitely try to make that happen.

Yeah, my favorite line was, spring is a relative concept.

I love it.

Spoken like a true developer.

So something I'm really interested in,

and your previous line of work is, before TensorFlow,

you let a team and Google on search ads.

I think this is a very interesting topic on every level,

on a technical level, because if their best ads connect people

to the things they want and need,

and that they're worse, they're just these things

that annoy the heck out of you to the point of ruining

the entire user experience of whatever you're actually doing.

So they have a bad rep, I guess.

And on the other end, so that this connecting users

to the thing they need to want is a beautiful opportunity

for machine learning to shine, like huge amounts of data

that's personalized, and you've got

to map to the thing they actually won't get annoyed.

So what have you learned from this Google that's

leading the world in this aspect?

What have you learned from that experience?

And what do you think is the future of ads?

Take you back to the end of that.

Yes, it's been a while, but I totally agree with what you said.

I think the search ads, the way it was always looked at,

and I believe it still is, is it's

an extension of what search is trying to do.

The goal is to make the information

and make the world's information accessible.

With ads, it's not just information,

but it may be products or other things

that people care about.

And so it's really important for them

to align with what the users need.

And in search ads, there's a minimum quality level

before that ad would be shown.

If we don't have an ad that hits that quality bar,

it will not be shown, even if we have it.

And OK, maybe we lose some money there.

That's fine.

That is really, really important,

and I think that that is something I really

liked about being there.

Advertising is a key part.

I mean, as a model, it's been around for ages, right?

It's not a new model.

It's been adapted to the web and became a core part of search

and in many other search engines across the world.

I do hope, like I said, there are aspects of ads

that are annoying.

And I go to a website, and if it just

keeps popping an ad in my face, not to let me read,

that's going to be annoying clearly.

So I hope we can strike that balance between showing a good

ad where it's valuable to the user

and provides the monetization to the service.

And this might be search.

This might be a website.

All of these, they do need the monetization for them

to provide that service.

But if it's done in a good balance between showing

just some random stuff that's distracting

versus showing something that's actually valuable.

So do you see it moving forward as to continue

being a model that funds businesses like Google?

That's a significant revenue stream.

Because that's one of the most exciting things,

but also limiting things on the internet

is nobody wants to pay for anything.

And advertisements, again, coupled at their best

are actually really useful and not annoying.

Do you see that continuing and growing and improving?

Or is there GC sort of more Netflix type models

where you have to start to pay for content?

I think it's a mix.

I think it's going to take a long while for everything

to be paid on the internet, if at all.

Probably not.

I mean, I think there's always going

to be things that are sort of monetized with things like ads.

But over the last few years, I would say

we've definitely seen that transition

towards more paid services across the web

and people are willing to pay for them

because they do see the value.

I mean, Netflix is a great example.

I mean, we have YouTube doing things.

People pay for the apps they buy, more people

they find are willing to pay for newspaper content,

for the good news websites across the web.

That wasn't the case even a few years ago, I would say.

And I just see that change in myself as well

and just lots of people around me.

So definitely hopeful that we'll transition to that mix model

where maybe you get to try something out for free,

maybe with ads.

But then there is a more clear revenue model

that sort of helps go beyond that.

So speaking of revenue, how is it

that a person can use the TPU in a Google Colab for free?

So what's the, I guess, the question is,

what's the future of TensorFlow in terms of empowering,

say, a class of 300 students?

And I'm asked by MIT, what is going

to be the future of them being able to do their homework

in TensorFlow?

Where are they going to train these networks, right?

What's that future look like with TPUs, with cloud services,

and so on?

I think a number of things there.

I mean, any TensorFlow open source,

you can run it wherever.

You can run it on your desktop, and your desktops

always keep getting more powerful, so maybe you can do more.

My phone is like, I don't know how many times more powerful

than my first desktop.

You'll probably train it on your phone, though.

Yeah, that's true.

Right, so in that sense, the power

you have in your hand is a lot more.

Clouds are actually very interesting from, say,

students or courses perspective, because they

make it very easy to get started.

I mean, Colab, the great thing about it

is go to a website, and it just works.

No installation needed, nothing to, you know,

you're just there, and things are working.

That's really the power of cloud, as well.

And so I do expect that to grow.

Again, Colab is a free service.

It's great to get started, to play with things,

to explore things.

That said, with free, you can only get so much, maybe.

So just like we were talking about free versus paid,

and there are services you can pay for and get a lot more.

Great.

So if I'm a complete beginner interested in machine

learning and TensorFlow, what should I do?

Probably start with going to a website and playing there.

Just go to TensorFlow.org and start clicking on things.

Yep, check out tutorials and guides.

There's stuff you can just click there and go to Colab

and do things.

No installation needed.

You can get started right there.

OK, awesome.

Roger, thank you so much for talking today.

Thank you, Lex.

Have fun this week.