Getting involved in the TensorFlow Community (TF World ’19)

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 This is a great tool. Because we heard
people’s feedback that they would like
more curated resources on 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 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,
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
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, 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 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]

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