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  • JOANA CARRASQUEIRA: Welcome, everybody.

  • It's an absolute pleasure to be here with you today.

  • As Jocelyn mentioned, I'm Joana Carrasqueira.

  • And I'm a program manager for TensorFlow at Google.

  • I'm joined by my colleague Nicole Pang.

  • NICOLE PANG: Yes, I'm Nicole.

  • I'm a product manager for TensorFlow at Google.

  • JOANA CARRASQUEIRA: And we're going

  • to talk about the TensorFlow community

  • and the many exciting ways by which you can get involved

  • in the work that we do.

  • So let me start by saying thank you.

  • Thank you to you, thank you to the community for all

  • the hard work that you've done.

  • Since we've open-sourced TensorFlow in 2015,

  • we've received so many contributions and so much

  • support from the community that really the project, where

  • it is today, is due to you, to all your efforts

  • and all your hard work.

  • So thank you for that.

  • Just on core TensorFlow alone, we've

  • received more than 6,000 commits from over 2000 contributors.

  • This is so impressive.

  • But not just only this.

  • On Stack Overflow, we have received

  • more than 50,000 questions.

  • And we have onboarded more than 120 machine learning experts

  • through our Google Developer Experts program.

  • And we have established 50 user groups all around the world.

  • We've also had 25 guest posts on our TensorFlow

  • blog, which is fantastic.

  • And our community only continues to grow.

  • Here is a snapshot where you can see

  • that the number of commits from four years ago

  • has been rapidly growing.

  • And there's so much support and excitement from the community.

  • We truly couldn't have gotten this far

  • if it wasn't for you, for the contributors, for all

  • the work that you do.

  • So thank you so much for that.

  • NICOLE PANG: And it's not just the contributions you see

  • and the feedback we get from our community on GitHub and Stack

  • Overflow.

  • But of course, as you all know, TensorFlow

  • has a global world-wide community.

  • And we see a lot of love for TensorFlow

  • also on other avenues.

  • You probably have heard a lot about TF 2.0 today, yesterday.

  • And you certainly will hear more about it tomorrow.

  • But TF 2.0 is one instance where our global community responds

  • really positively.

  • And we see so many cases of that.

  • And today, we'll touch on these cases and, of course,

  • how you can get involved in our communities.

  • So briefly, what we'll talk about today.

  • We want to tell you how you can learn TensorFlow,

  • how you can get started in your own journey of using

  • TensorFlow, whether you're more in the beginning stages,

  • or you're really advanced user of TensorFlow

  • in your applications.

  • Then we want to showcase to you our global community, really

  • run through some really amazing use cases,

  • really tell you what we've seen people do with TensorFlow

  • or people use TensorFlow for.

  • And hopefully, that can be very inspirational for all of us

  • in the community.

  • And, of course, why you're here today--

  • you want to know how to get involved at TensorFlow.

  • So we'll walk you through not just the ways that you might

  • first think of, which might be contributing code

  • because TensorFlow is open source, but also

  • a lot of community groups, a lot of special interest groups.

  • And those, again, are all over the world.

  • So both for everyone here in this room and, of course,

  • everyone watching online, there's many, many resources.

  • And we're so excited to share with you.

  • JOANA CARRASQUEIRA: So as you could see,

  • we truly have a vibrant global community

  • that continues to grow because there's

  • so much that you can do, so much that we can all

  • contribute to TensorFlow.

  • And let's have a look at where our community is phased,

  • and what they're doing right now.

  • So the TensorFlow user groups, they

  • are a wonderful way of getting involved with TensorFlow.

  • Either online or face-to-face, you

  • can meet with other like-minded contributors and developers

  • really to answer questions, to solve problems, challenges

  • and building those use cases on really

  • how you can implement TensorFlow across different industries.

  • So just an example--

  • one of our user groups in Korea.

  • That one is the biggest that we have in the world.

  • And we have engaged more than 46,000 members.

  • It is very impressive.

  • And in China alone, it's the country with most user groups.

  • And they have user groups across 15 different cities.

  • It's really impressive how the community is growing so fast

  • all over the world.

  • And one of the key messages that Nicole and I would

  • like you to retain from our presentation today

  • is that if you don't have a user group where you're

  • based or in your region, feel free to start one,

  • share your experiences, connect with

  • other like-minded developers, and start

  • talking about TensorFlow.

  • We are here to support you throughout this process

  • and this journey.

  • So feel free to reach out to us.

  • We're very happy to guide you through the process.

  • And like I mentioned, if you would

  • like to start your user group, here are

  • some of the resources that you can have a look online

  • if you are interested in starting your own group.

  • We also are sharing our alias, so you can really

  • get to know the team and how you can

  • start creating your user group.

  • NICOLE PANG: So in the spirit of honoring our global community,

  • we want to briefly touch on what the TensorFlow

  • team has been doing worldwide.

  • So like Joana just said, we have so many user groups.

  • And you really can see that they are global.

  • And as you heard this morning in the keynote,

  • the TensorFlow team was really excited

  • and really lucky to be able to go to many cities,

  • and meet many of these users, and meet many of the companies,

  • and meet many of the startups that

  • are using TensorFlow in so many different cities in the world.

  • And, of course, we're so excited that you're here today,

  • on one of our stops on the TensorFlow

  • roadshow in Santa Clara today.

  • And we're really, really excited to, again,

  • be able to see the use cases.

  • And we'd love to share briefly some

  • of these use cases with you.

  • So first off, when we look at Asia and Asia Pacific,

  • there's a really big, vibrant community there.

  • And as Joana just said, a lot of people in Korea,

  • a lot of people in India, a lot of people in China,

  • they're all using TensorFlow with two amazing applications.

  • So in China, for instance, TensorFlow is actually not

  • just active on our applications, but also the community

  • is really active on our official TensorFlow WeChat channel.

  • And this WeChat channel showcases

  • a lot of use cases of TF Lite on mobile.

  • Like you can see this one example of a video platform

  • called IT with image segmentation on mobile devices.

  • So again, they're doing really awesome work.

  • And not just doing awesome work but also

  • sharing with all of the community on the WeChat blog.

  • And we're really, really glad that we're partnering with them

  • and really glad to see these use cases come up.

  • JOANA CARRASQUEIRA: Yes, and Nicole and I

  • were really fortunate that we were able to join the roadshows

  • and really connect with the local communities worldwide.

  • So for example, at the roadshow in Latin America,

  • we connected with ALeRCE, which is a startup in Chile.

  • And they are trying to detect supernovas and galaxies

  • through the [INAUDIBLE] of child processes and machine learning.

  • And this was really cool.

  • And they used conventional neural networks

  • to classify astronomical objects contained

  • in a stream of about 200,000 images per day.

  • The work that they're doing is so impressive.

  • And it's absolutely worth sharing

  • with the rest of the community.

  • Another example-- in Europe, we connected

  • with EyeEm, which is a library of photos that uses TensorFlow

  • for object classification.

  • And their algorithm scores photos

  • based on their static quality but also on the relevance

  • to your brand's visual identity.

  • And then every photo is automatically tagged

  • with keywords just to make sure that the entire library is

  • searchable.

  • It's really impressive.

  • And then they use TensorFlow Lite on mobile

  • to make it easier and also more accessible for their users

  • to use EyeEm.

  • And then lastly, in Africa, we met with many exciting startups

  • trying to find solutions for problems

  • at a global scale that were relevant to the region.

  • But we would like to highlight the great work of Tambua

  • Health, who leverages the power of machine

  • learning and spectral analysis to really turn

  • any smartphone into a powerful non-invasive screening

  • tool for pneumonia, asthma, and other pulmonary diseases.

  • So they use convolution neural network

  • for modeling spectrograms that were

  • generated from audio analysis through their smartphones.

  • And then to save models, they're frozen and converted

  • into TensorFlow Lite.

  • And the converted model is then deployed to a mobile device

  • to perform interference.

  • So these were some of the cases that we connected

  • with during the roadshows.

  • And it was brilliant to see all these very innovative ways

  • that the community is using and building around TensorFlow.

  • So these were just a few pictures of our roadshows,

  • where we truly engaged with the community.

  • And it's palpable.

  • It's very tangible, the excitement

  • that we see not only from contributors but also

  • from users of TensorFlow.

  • It's fantastic to see how many of these startups

  • and other companies are truly impacting and changing

  • the world.

  • And this is all you using TensorFlow.

  • So thank you for that.

  • NICOLE PANG: So like we said in the beginning,

  • we wanted to do a brief overview, just

  • a very small sample of some of the awesome use

  • cases of TensorFlow, but then really

  • dig into what is available for you.

  • So one of the first pillars that we'll talk about is education.

  • Now, why is education important for us at TensorFlow,

  • and also, we hope, it's important for you

  • in the community?

  • Well, TensorFlow is, of course, as you are very hardly

  • knowing, it's open source.

  • But also another aspect of that open source nature

  • is that we want to make sure learning

  • resources are available to everyone in the world.

  • And we really value not making just the products

  • better for learners.

  • So for instance, TF 2.0--

  • it's easy debugging.

  • And the usability of Keras is designed for that better

  • experience for learners.

  • So not just the product, but also the educational resources.

  • So I'd love to go into some of them in a bit more detail.

  • This morning you heard about our launch of the new Learn ML hub

  • on tensorflow.org.

  • This is a great tool.

  • Because we heard people's feedback

  • that they would like more curated resources

  • on tensorflow.org.

  • They would like more path of learning from whatever level

  • of machine learning and deep learning

  • knowledge you have into more advanced applications

  • of TensorFlow.

  • So we heard you, and we now responded

  • with this new resource of Learn ML.

  • So it's not just a compilation of curated resources,

  • but it's also guided path--

  • whether you're a beginner in TensorFlow

  • or whether you're more advanced-- which resources,

  • and what tutorials, what guides might be helpful.

  • And also if you're interested in TF.js, TensorFlow

  • on the browser, we have a very detailed,

  • very nicely organized learning resource there.

  • And we hope that you'll progress through it

  • in whatever stage you are.

  • If you're more advanced with TensorFlow,

  • you might still be interested in our MOOCs,

  • our massive multi-part online courses.

  • As you probably already know, TensorFlow

  • has great relationship, great partnerships

  • with both deeplearning.ai at Coursera and also Udacity.

  • And these courses are available, again,

  • to everyone, so to everyone in this room,

  • to everyone watching online.

  • And we really hope that you'll take the stuff

  • that we have in these courses, which

  • is both from TensorFlow instructors

  • and also renowned academic instructors too.

  • And we really want to give everyone ample opportunity

  • to learn TensorFlow.

  • And as you heard this morning, there

  • is a new specialization on Coursera for TensorFlow data

  • and deployment, and really taking modeling,

  • not just understanding how to build a model,

  • but also deploying it in applications.

  • And again, as you move up these steps of knowing TensorFlow,

  • we really hope you'll check out our new and updated tutorials

  • and guides.

  • This is thanks to the amazing work

  • on our TensorFlow developer relations team.

  • They're constantly writing new documentation,

  • new guides, new tutorials.

  • And with the launch of TF 2.0, all of these new guides

  • are available for you to check out TF 2.0

  • and really understand how to use Keras,

  • and really understand all the use cases.

  • There's some really amazing detailed documentation here,

  • so we really hope you'll take advantage of these resources

  • that we provide.

  • And finally, let's jump into how to get

  • involved with contributing.

  • So now you know TensorFlow, you're advanced in TensorFlow,

  • you've deployed it to applications.

  • You want to be contributing to the open source community.

  • Well, one of the first ways that everyone thinks about

  • is contributing code.

  • And we're happy to describe to you a way

  • that we use on the TensorFlow team

  • to consult widely with both design docs, API designs.

  • And also some of them are driven by community members

  • as the request for comments, or RFCs.

  • So this is actually the main way we communicate changes

  • to our API and receive design feedback.

  • So we'd love to invite everyone here to take a look

  • and also join.

  • This is one example of an RFC.

  • This is an approved RFC, TensorForest Estimator.

  • And I would like to take this opportunity to, of course,

  • thank everyone who has authored or reviewed an RFC.

  • And we actually have 45 RFCs accepted today, which

  • is really an incredible number.

  • And they have ranged from TFX, to TF Lite, TF.js.

  • And each RFC expands the usage of TensorFlow.

  • It really helps the community.

  • And it also is a great boon to the TensorFlow team.

  • So we'd love to have you also propose designs.

  • You can check out more about RFCs.

  • And, of course, talk to any of us about this also.

  • JOANA CARRASQUEIRA: And also for bigger projects

  • in which we have to work as a team,

  • we've created the special interest groups, the SIGs,

  • which is a program that organizes

  • the contributors into more focused streams of work.

  • Everything started with the SIG Build.

  • And nowadays, we have 11 SIGs, which

  • is really impressive how the SIGs have also grown so

  • much over the past few years.

  • So all the contributors, you, are

  • very welcome to join the SIGs.

  • And really join the SIG that resonates more with the parts

  • that you either enjoy or care the most about TensorFlow.

  • Just an overview of our contributor ecosystem--

  • as you can see highlighted in the darker orange,

  • we have the SIG Add-ons, the SIG Build, IO, Networking,

  • JVM, and Micro, and Rust, which are community-led open source

  • SIGs.

  • And the others, which include Keras, Swift, MLIR,

  • and TensorBoard, they are Google-led with an open design

  • philosophy.

  • So if you see a SIG that resonates with the work

  • that you do, or if you care about the topic

  • and would love to learn more, the SIGs

  • have monthly or weekly calls.

  • And you're very welcome to join as well.

  • I would like to give you an overview of our open source

  • community-led SIGs.

  • And just briefly going through some of the key aspects

  • of the SIGs--

  • the SIG Add-ons.

  • It maintains important additions to TensorFlow

  • and adopted some of the parts of TF Contrib.

  • And this SIG is led by Sean Morgan and Tzu-Wei Sung.

  • The SIG Build-- we have one of the leads actually here with us

  • today--

  • actually focuses on building and packaging TensorFlow

  • for different distribution environments

  • and is led by Jason Zaman and Austin Anderson.

  • The SIG IO focuses on supporting extra file systems and file

  • formats for TensorFlow.

  • And his initiative is led by Yong Tang and then

  • Anton Dmitriev.

  • And as we all know, high-performance computing

  • resources, they require lightning-fast

  • interconnectivity.

  • And the SIG Networking focuses exactly on that,

  • on building more network support for TensorFlow.

  • And this is an initiative led by Bairen Yi and Jeroen Bedorf.

  • And finally, the SIG Keras.

  • We've had the SIG Keras to continue improve

  • the Keras API for TensorFlow.

  • So those are some of the SIGs that you can join.

  • But we also have, like I mentioned before,

  • the other SIGs that are also Google-led

  • but with an open philosophy.

  • You're very welcome to have a look at the SIG playbook

  • at the tensorflow.org, where you'll

  • find more information on how you can

  • join the SIGs and the ongoing projects

  • that they have right now.

  • If you see that none of the SIGs that currently exist

  • are a fit for you or for your work, if we see there's enough

  • evidence and enough support from the community,

  • you can also start and establish your own SIG.

  • And if you head to GitHub, on our community resources, that's

  • where you'll see how the SIGs operate,

  • what are the resources and tools that

  • are available for you to help you throughout this process.

  • But also we have more information not only

  • about the SIGs but also our RFC process

  • and our code of conduct.

  • So I strongly encourage you to have a look

  • after TensorFlow World.

  • And today, I'm also extremely excited to announce

  • that we've achieved another milestone with TensorFlow

  • and our community.

  • We have hosted the first Contributor Summit just

  • on Monday and Tuesday for almost 100 participants.

  • And it was a great way to really connect with the SIG leads

  • and with the broader community, and to really

  • understand how together we can move forward

  • with the open source project, what

  • are the strategic developments that we can implement

  • in TensorFlow, what are the documentation needs, project

  • management, community management.

  • It was a great conversation that we had over two days.

  • So I strongly encourage you, if you didn't have the chance

  • to participate this time, to have

  • a look at the online resources that

  • will be available afterwards.

  • It was a great opportunity to connect with you all.

  • NICOLE PANG: Awesome.

  • So some of the SIGs, like Joana mentioned,

  • are led by what we call Machine Learning Google Developer

  • Experts.

  • And so we'd love to show you a little bit about what

  • that means.

  • So our ML GDEs are a global network of ML experts

  • that Google works closely with.

  • And we provide latest information to them,

  • they give us feedback.

  • It's an awesome relationship.

  • So we're really excited we have 126 ML GDEs to date worldwide.

  • And this year alone, these ML GDEs have given over 400 talks

  • worldwide, hosted over 250 workshops

  • worldwide, and also written over 200 articles.

  • And this is incredible because we actually

  • know that these talks, workshops, and articles have

  • reached a worldwide audience of 435,000 developers.

  • So as you can imagine, TensorFlow team,

  • we want to reach as many people as we can.

  • But with ML GDEs, we really just amplify that reach of impact

  • that we can have in the world of teaching it TensorFlow

  • and really helping people all around the world

  • understand about TensorFlow.

  • So we're really excited.

  • We would love to tell you--

  • if you want to become a GDE, this

  • is also a link to become a GDE.

  • We also have a lot of links for connecting with other GDEs.

  • And today we would also love to welcome one of our GDEs

  • up to the stage to give a brief chat with us.

  • So please welcome Jason Zaman.

  • JASON ZAMAN: Hi, everyone.

  • So I'm one of the community leads for SIG Build.

  • We have a few members of Build around here.

  • Thank you.

  • And the Build being the first SIG--

  • it was from two years ago?

  • Quite a while.

  • So I've really seen the community

  • grow a lot in that time.

  • It's really nice seeing now we have so many SIGs doing

  • all kinds of things.

  • And I started Build because I saw problems

  • when I was trying to use it.

  • And I wanted to make it better.

  • And really the group has grown and done a lot of great things.

  • I want to encourage everyone to get involved.

  • You can join the SIG that already exists.

  • You can find a thing you want to do, work on it,

  • and find more people that are also interested,

  • maybe start a new SIG.

  • A lot of people around to help.

  • These people are wonderful.

  • And I'm also one of the ML GDEs.

  • So it's a great program.

  • It's really nice to hear from other ML GDEs.

  • They work on all kinds of cutting

  • edge stuff, all kinds of different fields,

  • stuff that I don't even know or hear about other than them.

  • So really good, yeah.

  • Thank you.

  • [APPLAUSE]

  • NICOLE PANG: Thank you so much, Jason.

  • And we're really lucky to have another ML GDE in the audience.

  • And please welcome Margaret Maynard-Reid.

  • [APPLAUSE]

  • MARGARET MAYNARD-REID: Hello, everyone.

  • I'm a Machine Learning GDE.

  • I'm also the lead organizer of Google Developer Group Seattle

  • and another group called the Seattle Data, Analytics,

  • and Machine Learning.

  • I became a Machine Learning GDE in 2018.

  • And here's why I love being part of this amazing community.

  • I get to collaborate with other Machine

  • Learning GDEs and Googlers on various projects.

  • For example, I get to write some tutorials

  • that you will find on tensorflow.org, in some

  • of the blog posts that were published on TensorFlow Medium

  • publication.

  • And earlier this year, I helped to organize the Global

  • TensorFlow Docs Sprint with Page, Sergey, and other Machine

  • Learning GDEs and GDG organizers.

  • It was an incredible experience to work

  • on such a high-impact project, which was even mentioned

  • in the keynote this morning.

  • So I speak about TensorFlow and on-device machine learning

  • at various conferences.

  • And I really enjoyed the opportunity

  • to be able to preview Google products and provide feedback.

  • So many of the Machine Learning GDEs

  • are well-known educators, speakers, or O'Reilly book

  • authors.

  • It's really great to be able to learn from my fellow GDEs

  • and Googlers.

  • And once a year, we'll gather together

  • for our global GDE summit the GDEs from around the world.

  • And we've just had the summit a few days ago before TensorFlow

  • World.

  • So to become a GDE, Machine Learning GDE in particular,

  • you need to be able to demonstrate both your community

  • contribution as well as knowledge in machine learning.

  • We will love to see more of you join our growing Machine

  • Learning GDE community.

  • Thank you.

  • [APPLAUSE]

  • JOANA CARRASQUEIRA: Thank you so much, Margaret.

  • This is fantastic.

  • I am sure I can speak for both of us.

  • But I'm always so impressed by the fantastic and amazing work

  • that our GDEs do.

  • It's really nice to see how engaged the community is.

  • However, there's many other ways by which you

  • can contribute to TensorFlow.

  • It doesn't have to be only through code.

  • So if you are a coder, but you would like to learn or develop

  • a new skill set, there's many other ways

  • that you can get involved with TensorFlow.

  • So when it comes down to non-code contributions,

  • there are three main pillars that we normally encourage

  • our contributors to join.

  • Primarily, on user support, which

  • includes creating documentation, translation, training courses

  • that really will help other contributors getting

  • involved and onboarded within the project.

  • In terms of community management,

  • really through organizing events, meet-ups,

  • and all the initiatives that get the community together,

  • and energized and excited about machine learning in TensorFlow.

  • And then on the project management side,

  • creating the tools and resources that will help

  • advance our projects, but also keep

  • the health and the sustainability

  • of the initiatives that we do.

  • Sometimes we work really on cross-functional teams

  • on really building the use cases on how TensorFlow can be

  • implemented in different ways.

  • And then finally, I would like to highlight

  • that we have a code of conduct in our TensorFlow community.

  • So we apply this code of conduct to all the events

  • and the initiatives that we do.

  • And we would like to remind you that this

  • is a safe space, where you can truly

  • be yourself as a contributor.

  • And we welcome that diversity of ideas, opinions,

  • and suggestions.

  • So if you see that something is just not right,

  • please feel free that you know that you

  • can escalate those problems to also the community stewards.

  • We're here for you.

  • We are here to make sure that you feel engaged,

  • that you feel heard, and that you feel that you belong

  • to a community of excited machine learning experts,

  • contributors, and users.

  • NICOLE PANG: So we want to wrap up our conversation

  • by revisiting the links and the different resources

  • that we've given you in this talk.

  • So again, after [INAUDIBLE],, you're wondering,

  • how do we keep up with the latest news and the latest

  • deep dives from TensorFlow?

  • Well, these are the ways you can keep up with us.

  • So, of course, Twitter is very great for a lot of the latest

  • announcements and updates from the TensorFlow team.

  • Our blog is actually an amazing resource--

  • a lot of deep dives, a lot of understanding,

  • specific use cases.

  • You might be wondering how to use TensorFlow

  • in a certain application.

  • And the blog may actually have a guest post or post

  • from the TensorFlow team that can address that.

  • So we really suggest that you check out the blog.

  • And YouTube-- I think many of you probably

  • already have seen the TensorFlow YouTube channel.

  • But in case you haven't, it's actually a really awesome

  • resource to learn TensorFlow.

  • So we have a lot of videos that highlight

  • our new announcements, how to use TensorFlow,

  • how to use specific things like TF Keras,

  • we have videos about that.

  • And one of our most popular videos

  • is actually done by someone on our developer relations

  • team, Laurence.

  • And it's the "ML Zero to Hero" video.

  • And it's a great resource.

  • So again, if you haven't seen these social resources,

  • we really highly suggest you follow.

  • And that's how you'll get updates

  • from TensorFlow outside of TF World.

  • And finally, this is some of the links that we showed earlier.

  • We really want to emphasize again,

  • TensorFlow, the community, would not

  • be possible without everyone in the room,

  • without everyone in a community globally.

  • So we really encourage you, if you aren't in a SIG

  • or in a user group, if you're interested,

  • you can check out everything on our tensorflow.org/community

  • links.

  • You can check out the educational resources

  • I mentioned also at the beginning.

  • And we are so excited that so many of you

  • are among us in the group today.

  • So we'd really love to welcome you to also share

  • with your fellow conference attendees

  • what it's like being in a SIG, what it's like leading a SIG

  • or being ML GDE too.

  • JOANA CARRASQUEIRA: With that, we

  • have our call to action to you, which is, join the user groups,

  • join the SIGs, be part of the community.

  • Contribute code to TensorFlow, documentation, translations,

  • educational resources, events.

  • There's so many different and exciting ways

  • to contribute to TensorFlow.

  • So thank you for being with us today.

  • It's been really a pleasure speaking

  • to you about the many ways that you can get

  • involved with the community.

  • And I hope that we can continue these conversations.

  • What do you think, Nicole?

  • NICOLE PANG: Yeah, that sounds perfect.

  • Let us know if you have any questions, of course.

  • And we're so happy that you want to be a part of the TensorFlow

  • community.

  • Thank you.

  • JOANA CARRASQUEIRA: Thank you.

  • [APPLAUSE]

JOANA CARRASQUEIRA: Welcome, everybody.

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