EDD WILDER-JAMES: Hey, everybody.

How you doing?

Good.

Good.

Excellent.

That was an amazing set of demos, wasn't it?

So I work with TensorFlow, helping

build community and collaboration

around the open source project.

And usually, I put the thank you slide at the end,

for listening to me.

But actually, I want to thank you for contributing and being

part of the TensorFlow project.

Whether you're here in this room or on the livestream--

there's been some amazing talk on YouTube from people

in China, and India, and Japan, and all over the world joining

us virtually today--

thank you for your contributions.

The project is where it is today because of you.

Of course, in core TensorFlow alone, we've

had this many commits.

Now this figure is out of date-- over 50,000

from 1,800 contributors, and much more than just code

commits.

There's been 39,000 Stack Overflow

questions about TensorFlow.

We have 66 machine learning Google Developer Experts,

many of whom are here with us today.

So welcome, and thank you guys.

[APPLAUSE]

It's really great to have you with us.

And thank you for everything you do helping

teach people about TensorFlow.

And we've had 14 guest posts to the TensorFlow blog,

and that keeps going up.

There are so many ways that people are contributing.

Whether you're organizing a meetup, whether you're

teaching other people, whether you're speaking at conferences,

thank you.

You're really helping build out the TensorFlow ecosystem.

So in this talk, what I want to do

is discuss how we're growing the ecosystem

and report back on some of the changes

that we've made over the last year.

So I'm going to cover how we're making it easier

to get involved in TensorFlow.

How, also, we're trying to consult better

with the users in the community and be

more transparent about our development.

I'm going to cover how we're empowering everybody

to get involved, and to do more, and increasing

the number of contact points where you can

get involved in the project.

Finally, I'm going to go into a bit more depth

about the conference that was announced this morning,

the TensorFlow World.

So let's talk about how we're making contribution easier

to TensorFlow.

One of the most important things to help

people contribute to the project is increasing its modularity.

You heard Martin talk, this morning,

about the low-level APIs.

And with the move to TensorFlow 2.0,

we're trying to make it less of a monolith, both

in terms of code and in terms of people organization.

When you come and you want to contribute to an open source

project, it helps to be able to find where to contribute

and who to work with.

By splitting things out, we're creating more surface area

where it's easy to start building and creating

new projects.

And our special interest groups play a big part in this,

and I'll talk a bit more about them later.

But it's not just code.

There's so many more places to contribute this year, compared

to where we were last year.

So I'm going to talk briefly about our documentation

groups, the groups getting involved

in testing, people who are blogging, and on YouTube,

and more.

I was super excited to see, last week,

that we have published a TensorFlow tutorial now

in Korean.

And that's not a translation that we've done on our team,

but that has come from the community.

So thank you so much to Hasin Park for the Korean work.

Similarly, we're able, also, to publish it in Russian.

Thank you to Andrew Steppen.

This is just so exciting, to see that TensorFlow

is being taken to more areas around the world,

thanks to you.

I'm also really excited about the TensorFlow 2.0 testing

group.

Led by Paige Bailey, this is a bunch

of contributors and Google Developer

Experts who are working to give TensorFlow 2.0 a thorough test.

And you see, on the screen, an example of a friction log.

And so what's happening here is that folks

are going through ML workflows with TensorFlow 2.0,

documenting what they find delightful and awesome,

and also things that could be a little bit better.

If you'd like to join in this work,

this group meets weekly and often has guests talks

from maintainers, and SIG leaders, and so on,

and is really helping bring TensorFlow 2.0

from the cutting edge into something that is thoroughly

tested and ready for use.

Already mentioned, we have over 14 posts

from guests on the TensorFlow blog.

This is from a great post about realtime person segmentation

in the browser with TensorFlow.js.

It comes from a grad student and researcher and ITP.

So whether it's testing, whether it's documentation,

whether it's blogs and conference talks, thank you.

Now I want to talk a little bit about TensorFlow RFCs.

As you probably know, RFC means Request For Comments.

This time last year, we weren't that organized

about how we evolved TensorFlow's design,

in terms of communicating it.

And I stood on this stage and told you

about how we were going to launch the RFC process.

Well, now we've accepted 21 RFCs over the period

of the last year.

This is our key way to communicate design, where

before code gets landed in the project,

we post an RFC about the design and consult widely.

This isn't just about code that's

coming in from the TensorFlow core team outwards.

They can be created and commented on by anyone.

We've had several RFCs that come from the broader community.

And I expect to see so many more of those in the future.

We have several, for instance, from. the SIG groups already.

One of the things I'm most proud about

is how the RFC process is underpinning

the 2.0 transition.

This was mentioned earlier, but all the major changes

in TensorFlow 2.0 have been proposed and consulted

with in RFCs.

This isn't just a great way of consulting and getting

information feedback.

Going forward, you now have a big repository

of technical documentation about why design choices were made

a certain way in TensorFlow.

And it's a great educational resource, as well,

for people who are coming on and want to get involved

in contributing to the project.

So I really want to give a big thanks

to anyone who has authored or reviewed an RFC.

You've played a vital role in making TensorFlow better.

Now let's talk a bit about the social structure of TensorFlow.

Last year I talked about how coming to a large project

can be a little bit daunting.

You don't know where people are, where

the people that have your interests in common are.

And so we created the Special Interest Groups, or SIGs,

as a way of organizing our work.

There are so many uses of TensorFlow,

so many environments, so many architectures.

And many of them are outside of the scope

that the core team can resource.

And what we wanted to do was enable TensorFlow

to grow and be more sustainable by creating

a way for like-minded people to collaborate

around well-defined projects.

So this is why SIGs exist.

They're groups of people who are working together

for a defined project focus.

We started last year with SIG Build,

and now we have six of them up and running.

I'm going to give you a quick state of the SIGs.

Many-- in fact, most-- of all the SIG leaders

are here with us today as well, so I'll

give a shout-out to them.

And hopefully, you'll also be able to talk to them

in the lunch and tomorrow.

So SIG Addons first--

thank you to Shaun Morgan and Amanda Fandango

for leading this group.

Martin mentioned, at the beginning of the day,

that tf.contrib is no longer a part of TensorFlow

going into TensorFlow 2.0.

And SIG Addons is a place where a lot of that code is going.

So these are parts of TensorFlow that don't fall into the core,

but do conform to these well-defined APIs--

so more losses, ops, layers, and so on.

Now there's already an RFC published

about where you can find things that you

used to find in contrib that have gone into addons.

And addons are also going to publish another RFC real soon

to say, well, how can you get involved,

if you have your favorite app or whatever,

that you want to step up and be a maintainer,

and maintain it for everybody, how

you can join in the project.

So I'd encourage you to take a look at that.

SIG Build-- SIG Build really is where TensorFlow

meets the outside world.

And it's not always the most glamorous piece of work,

but building TensorFlow, and packaging it,

and distributing it is tough.

And so thank you so much to Jason Ziman and Austin

Anderson, who lead that SIG.

SIG Build has achieved a lot in the last few months.

One thing, it's the home for third-party

contributed builds for architectures

that we don't ship out as part of core--

so IBM Power, Intel MKL optimized builds.

And SIG Build works on improving the TensorFlow build

and helps us be a better neighbor in the Python

ecosystem as well.

As you can imagine, machine learning

generates a lot of extreme situations

that need changes in ways we evolve in packaging

and distributing software.

SIG IO is a fantastic group that helps connect TensorFlow

to other systems.

Out in the real world, your data exists somewhere.

You're using other systems in other formats.

So this group is led by Yong Tang and Anton Dimitriev.

IO really ships support for extra file systems, extra file

formats.

So if you're using any of these things in the Apache ecosystem

or any of these file formats, you

can use the SIG Addons module to use that data in TensorFlow.

This group is prolific.

They've already dropped four releases.

Last week, they just created their 0.4 release.

And they also ship R integration with their module too.

SIG Networking is where a lot of the alternative networking

schemes that are available in contrib are going to.

This is led by Byron Yi and Jeron Bedoff.

So if you're using GDR, VERBS, MPI,

this is where you can find that.

SIG Rust, led by Adam Crume, is developing idiomatic language

bindings for the Rust programming language.

If you're interested in this, or any of the other SIGs,

please talk to the leaders.

They really do want more help.

And now they're up and running, they're in a great place

to bring people on.

If you're looking for a way to get

involved in contributing to TensorFlow,

this is an ideal one.

Finally, let me touch on SIG TensorBoard.

We've rebooted SIG TensorBoard this year

to really work closely with the community.

And so this is a great time to be involved, as the TensorBoard