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  • [MUSIC PLAYING]

  • MEGAN KACHOLIA: Hi, everyone.

  • Welcome to the 2020 TensorFlow Developer Summit Livestream.

  • I'm Megan Kacholia, VP of Engineering for TensorFlow.

  • Thanks for tuning into our fourth annual developer

  • Summit and our first ever virtual event.

  • With the recent developments of the coronavirus,

  • we're wishing all of you good health, safety, and well-being.

  • While we can't meet in person, we're

  • hoping the Dev Summit is more accessible than ever

  • to all of you.

  • We have a lot of great talks along

  • with exciting announcements, so let's get started.

  • When we first opensourced TensorFlow,

  • our goal was to give everyone a platform

  • to build AI to solve real world problems.

  • I'd like to share an example of one of those people.

  • Irwin is a radiologist in the Philippines

  • and no stranger to bone fracture images like the ones

  • that you see here.

  • He's a self-proclaimed AI enthusiast

  • and wanted to learn how AI could be applied to radiology

  • but was discouraged because he didn't have a computer science

  • background.

  • But then he discovered TensorFlow.js,

  • which allowed him to build this machine learning application

  • that could classify bone fracture images.

  • Now, he hopes to inspire his fellow radiologists

  • to actively participate in building AI to,

  • ultimately, help their patients.

  • And Irwin is not alone.

  • TensorFlow has been downloaded millions of times

  • with new stories like Irwin's popping up every day.

  • And it's a testament to your hard work and contributions

  • to making TensorFlow what it is today.

  • So on behalf of the team, I want to say a big thank you

  • to everyone in our community.

  • Taking a look back, 2019 was an incredible year for TensorFlow.

  • We certainly accomplished a lot together.

  • We kicked off the year with our Dev Summit,

  • launched several new libraries and online educational courses,

  • hosted our first Google Summer of Code,

  • went to 11 different cities for the TensorFlow roadshow,

  • and hosted the first TensorFlow World last fall.

  • 2019 was also a very special year for TensorFlow

  • because we launched version 2.0.

  • It was an important milestone for the platform

  • because we looked at TensorFlow end to end

  • and asked ourselves, how can we make it easy to use?

  • Some of the changes were simplifying the API,

  • settling on Keras and eager execution,

  • and enabling production to more devices.

  • The community really took the changes to heart,

  • and we've been amazed by what the community has built.

  • Here are some great examples from winners of our 2.0 Dev

  • Post Challenge like Disaster Watch, a crisis mapping

  • platform that aggregates data and predicts

  • physical constraints caused by a natural disaster,

  • or DeepPavlov, an NLP library for dialog systems.

  • And like always, you told us what

  • you liked about the latest version but,

  • more importantly, what you wanted to see improved.

  • Your feedback has been loud and clear.

  • You told us that building models is easier

  • but that performance can be improved.

  • You also are excited about the changes.

  • But migrating your 1.x system to 2.0 is hard.

  • We heard you.

  • And that's why we're excited to share the latest

  • version, TensorFlow 2.2.

  • We're building off of the momentum from 2.0 last year.

  • You've told us speed and performance is important.

  • That's why we've established a new baseline

  • so we can measure performance in a more structured way.

  • For people who have had trouble migrating to 2,

  • we're making the rest of the ecosystem

  • compatible so your favorite libraries

  • and models work with 2.x.

  • Finally, we're committed to that 2.x core library.

  • So we won't be making any major changes.

  • But the latest version is only part

  • of what we'd like to talk about today.

  • Today, we want to spend the time talking about the TensorFlow

  • ecosystem.

  • You've told us that a big reason why you love TensorFlow

  • is the ecosystem.

  • It's made up of libraries and extensions

  • to help you accomplish your end and end ML goals.

  • Whether it's to do cutting edge research

  • or apply ML in the real world, there is a tool for everyone.

  • If you're a researcher, the ecosystem

  • gives you control and flexibility

  • for experimentation.

  • For applied ML engineers or data scientists,

  • you get tools that help your models have real world impact.

  • Finally, there are libraries in the ecosystem that

  • can help create better AI experiences for your users,

  • no matter where they are.

  • All of this is underscored by what all of you, the community,

  • bring to the ecosystem and our common goal of building AI

  • responsibly.

  • We'll touch upon all of these areas today.

  • Let's start first with talking about the TensorFlow

  • ecosystem for research.

  • TensorFlow is being used to push the state of the art of machine

  • learning in many different sub fields.

  • For example, natural language processing

  • is an area where we've seen TensorFlow really

  • help push the limits in model architecture.

  • The T5 model on the left uses the latest

  • in transfer learning to convert every language problem

  • into a text-to-text format.

  • The model has over 11 billion parameters

  • and was trained off of the colossal clean crawled corpus

  • data set.

  • Meanwhile, Meena, the conversational model

  • on the right, has over 2.6 billion parameters

  • and is flexible enough to respond sensibly

  • to conversational context.

  • Both of these models were built using TensorFlow.

  • And these are just a couple examples

  • of what TensorFlow is being used for in research.

  • There are hundreds of papers and posters

  • that were presented at NeurIPS last year that used TensorFlow.

  • We're really impressed with the research

  • produced with TensorFlow every day

  • at Google and outside of it.

  • And we're humbled that you trust TensorFlow

  • with your experiments, so thank you.

  • But we're always looking for ways

  • to make your experience better.

  • I want to highlight a few features

  • in the ecosystem that will help you in your experiments.

  • First, we've gotten a lot of positive feedback

  • from researchers on TensorBoard.dev,

  • a tool we launched last year that lets you upload and share

  • your experiment results by URL.

  • The URL allows for quickly visualizing hyper parameter

  • sweeps.

  • At NeurIPS, we were happy to see papers starting

  • to cite TensorBoard.dev URLs so that other researchers could

  • share experiment results.

  • Second, we're excited to introduce a new performance

  • profiler toolset in TensorBoard that

  • provides consistent monitoring of model performance.

  • We're hoping researchers will love the toolset because it

  • gives you a clear view of how your model is performing,

  • including in-depth debugging guidance.

  • You'll get to hear more about TensorBoard.dev

  • and the new profiler from Gal and [? Schumann's ?] talks

  • later today.

  • Researchers have also told us that the changes in 2.x

  • make it easy for them to implement new ideas, changes

  • like eager execution in the core.

  • It supports numpy arrays directly,

  • just like all the packages in the py data

  • ecosystem you know and love.

  • The tf.data pipelines we rolled out are all reusable.

  • Make sure you don't miss Rohan's tf.data talk today

  • for the latest updates.

  • And TensorFlow data sets are ready right out of the box.

  • Many of the data sets you'll find

  • were added by our Google Summer of Code students.

  • So I want to thank all of them for contributing.

  • This is a great example of how the TF ecosystem is

  • powered by the community.

  • Finally, I want to round out the TensorFlow ecosystem

  • for research by highlighting some

  • of the add ons and extensions that researchers love.

  • Libraries like TF probability and TF agents

  • work with the latest version.

  • And experimental libraries like JAX from Google Research

  • are composable all with TensorFlow

  • like using TensorFlow data pipelines to input data

  • into JAX.

  • But TensorFlow has never just been

  • about pushing the state of the art in deep learning.

  • A model is only as good as the impact

  • it has in the real world.

  • This is one of TensorFlow's core strengths.

  • It has helped AI scale to billions of users.

  • We've seen incredible ML applications

  • being built with TensorFlow.

  • We're really humbled by all the companies, big and small,

  • who trust TensorFlow with their ML workloads.

  • Going from an idea to having your AI create real world

  • impact can be hard.

  • But our users rely on TensorFlow to help them accomplish this.

  • That's because the TensorFlow ecosystem

  • is built to fit your needs.

  • It makes having to go from training

  • to deployment less of a hassle because you have the libraries

  • and resources all in one platform.

  • There's no switching costs involved.

  • I want to highlight a few new things that will help

  • you get to production faster.

  • First, you've told us that you love

  • working with Keras in TensorFlow because it's easy to build

  • and train custom models.

  • So we're committed to keeping tf.keras,

  • the default high level API.

  • But if you're not looking to build models from scratch,

  • TensorFlow Hub hosts all the ready to use pre-trained models

  • in the ecosystem.

  • There are more than 1,000 models available in TF Hub

  • with documentation, code snippets, demos,

  • and interactive collabs, all ready to be used.

  • When you're ready to put your model into production,

  • you can build production ready ML pipelines

  • in TensorFlow Extended to make sure your ML engineering just

  • works, from data validation to ML metadata tracking.

  • And today, I'm very excited to announce

  • that using TensorFlow in production

  • is getting even easier with an exciting launch, Google Cloud

  • AL Platform pipelines.

  • We've partnered with Google Cloud

  • to make it easy to build end-to-end production

  • pipelines using Kubeflow and TensorFlow Extended, hosted

  • by Google Cloud.

  • Cloud AI platform pipelines are available today

  • in your Google Cloud console.

  • And if you're running TensorFlow on Google Cloud,

  • TensorFlow Enterprise, which we announced last year

  • at TF World, gives you the long term support and the enterprise

  • scale that you need.

  • Finally, you can train and deploy

  • your models and pipelines on custom hardware specifically

  • designed for AL workloads, cloud TPUs.

  • In the latest version, TensorFlow

  • is now optimized for cloud TPUs using Keras.

  • This means the same API you started

  • with now helps you scale to petraflops of TPU compute.

  • All of these libraries are within the TensorFlow

  • ecosystem, are 2.2 compatible, and help

  • you scale so your ML application can reach your users.

  • But for AL to have that kind of impact,

  • it needs to be where your users are,

  • which means getting your models on device.

  • Now, we all know this requires working

  • in some constraints like low latency,

  • working with poor network connectivity,

  • all while trying to preserve privacy.

  • You can do all of this by using tools within the TensorFlow

  • ecosystem, like TensorFlow Lite, which can help make your models

  • run as fast as possible, whether it's on CPUs, GPUs,

  • DSPs, or other accelerators.

  • Here's an example of how we've optimized

  • performance for MobileNet V1 from May last year to today.

  • It's a big reduction in latency and something

  • you get right out of the box with TF Lite.

  • We're also adding Android Studio integration

  • so you can deployment models easily.

  • Just simply drag and drop in Android Studio

  • and automatically generate the Java classes for the TF Lite

  • model with just a few clicks.

  • When network connectivity is a problem

  • and you need these power intensive models to work

  • while still offline, you can convert

  • them to run better on device using TensorFlow Lite.

  • In the latest version, we rebuilt the TF Lite converter

  • from the ground up to provide support

  • for more models, more intuitive error messages

  • when conversions fail, and support for control flow

  • operations.

  • The browser has become an exciting place

  • for interactive ML.

  • And TensorFlow.js is allowing JavaScript and web developers

  • to build some incredible applications.

  • There's some exciting new models that are now supported

  • like FaceMesh and MobileBERT.

  • HuggingFace introduced a new NPM package

  • for tf.js, which allows you to do question answering directly

  • in Node.js.

  • Finally, the new WebAssembly backend

  • is available for improved CPU performance.

  • The next few years will see an explosion

  • of platforms and devices for machine learning.

  • And the industry needs a way to keep up.

  • MLIR is a solution to this rapidly changing landscape.

  • It's compiler infrastructure for TF and other frameworks.

  • And it's backed by 95% of the world's hardware accelerator

  • manufacturers and is helping to move the industry forward.

  • We see how important infrastructure like MLIR

  • is to the future of ML, which is why

  • we're investing in the future of TensorFlow's

  • own infrastructure.

  • The new TensorFlow runtime is something

  • you won't be exposed to as a developer or researcher.

  • But it will be working under the covers

  • to give you the best performance possible across a wide variety

  • of domains specific hardware.

  • We're planning on integrating the new runtime

  • later this year.

  • But you'll hear more from Mingsheng later today.

  • So to recap everything you've seen so far,

  • whether you're pushing the state of the art in research,

  • applying ML to real world problems,

  • or looking to deploy AI wherever your users are,

  • there is a tool for you in the TensorFlow ecosystem.

  • Now, I'd like to invite Manasi on stage

  • to talk about how the ecosystem is

  • helping empower responsible AI.

  • Thank you.

  • MANASI JOSHI: Thank you.

  • Thanks, Megan.

  • Hi, everyone.

  • My name is Manasi Joshi.

  • And I'm an engineering director on TensorFlow team.

  • As Megan mentioned and you saw, TensorFlow ecosystem

  • is composed of a number of useful libraries and tools

  • that are useful for a diverse set of use cases,

  • whether they're coming from ML researchers or practitioners

  • alike.

  • However, the field of ML and AI is raising the question

  • whether we are building systems in the most

  • inclusive and secure way.

  • I'm here to tell you how TensorFlow ecosystem empowers

  • its users to build systems responsibly

  • and, moreover, what type of tools and resources

  • are available to our users to accomplish those goals.

  • Before we deep dive into the details of what TensorFlow

  • has to offer its users, let's take a step back and define

  • what we mean by responsible AL.

  • As we know, that machine learning

  • has tremendous power for solving lots of challenging real world

  • problems.

  • However, we have to do this responsibly.

  • Now, to us, in TensorFlow, the way we define responsible

  • AI is based on a five pillar strategy.

  • Number one, general recommended practices for AI--

  • this is all about reliability, all the way from making sure

  • that your model is not over fitting to your training data--

  • it is more generalized than that--

  • making sure you are aware of limitations of your training

  • data when it comes to different feature

  • distribution [INAUDIBLE],, for example, ensuring

  • that the model outputs are robust

  • when the training data gets perturbed,

  • ensuring you're not using only a single metric across all

  • your models to determine its quality

  • because different metrics matter to different context,

  • how your model is used for promotion, demotion, filtering,

  • ranking, so on and so forth.

  • The second principle, fairness--

  • fairness is a fairly evolving thing in AI.

  • For us, we define it as not to create or reinforce

  • unwanted bias.

  • Fairness can be extremely subjective, can

  • be context sensitive.

  • And it is a socio-technical challenge.

  • Third, interpretability-- interpretability

  • is all about understanding the mechanics

  • behind a model's prediction, ensuring that you understand

  • what features really matter to the final output, which

  • features were important, which features were not.

  • Fourth, privacy-- for us, this is all about

  • taking into account sensitivity of your training data

  • and features.

  • And fifth is security.

  • In the context of ML, security really

  • means that you understand [INAUDIBLE] liabilities

  • in your system and the threat models that are associated.

  • Now, for a typical user of TensorFlow,

  • this is how the overall developer workflow looks like.

  • You start by defining a specific goal and an objective

  • for why you want to build a system.

  • Then you go about gathering relevant data

  • for training your model.

  • As we understand, data is gold for machine

  • learning module training.

  • And so you have to prepare the data well.

  • You have to transform it.

  • You have to cleanse it sometimes.

  • And then, once your data is ready,

  • you go about training your model.

  • Once the model is built-- it's converged--

  • you then go about deploying the model in production systems

  • that want to make use of ML.

  • Deployment phase is not a one time task.

  • You have to continuously keep iterating in an ML workflow

  • and improving the quality of the model.

  • Now, along this developer workflow, there are many,

  • many different moments at which you, as a modeler,

  • needs to be asking all of these questions,

  • questions like, who is the audience for my machine

  • learning model?

  • Who are the stakeholders?

  • And what are the individual objectives

  • for the stakeholders?

  • Going onto the data side of it, is my data really representing

  • real world biases or distribution skews?

  • And do I understand those limitations?

  • Am I allowed to use certain features

  • in a privacy preserving way?

  • Or are they are just simply not available due to constraints?

  • Then onto the training side of it,

  • do I understand implications of the data

  • on model outputs or not?

  • Am I doing deployments very blindly or?

  • Am I being a little bit mindful about deploying

  • only reliable and inclusive models?

  • And finally, when we talk about iterative workflow,

  • do I understand complex feedback loops that

  • could be present in my modeling workflow?

  • Now, along all of these questions,

  • I'm happy to tell you that TensorFlow ecosystem has

  • a few set of tools which could be

  • helpful to answer some of them.

  • I'm not going to go through everything

  • here but to just give you a few examples,

  • starting with Fairness Indicators.

  • It's a very effective way by which

  • you can evaluate your model's performance

  • across many different subgroups in a confidence

  • interval powered way such that you

  • can evaluate simple but effective fairness

  • metrics for your models.

  • We have What-If tool that gives you

  • the notion of interpreting the model's output based

  • on the features and changing those features to see

  • the changes in the model's output.

  • It has very compelling textual as well as visual information

  • associated with your data.

  • And then finally, TensorFlow Federated

  • is a TensorFlow 2.x compatible library

  • that helps you train your models with data

  • that's available on device.

  • Cat and Miguel have a talk later today

  • that dives deep into fairness and privacy pillars

  • of the responsible AL strategy.

  • Be sure not to miss it.

  • We are excited to work on this important part of TensorFlow

  • ecosystem with all of you, the TensorFlow community.

  • And now, to talk more about the community,

  • I would like to turn it over to Kemal.

  • Thank you.

  • KEMAL MOUJAHID: Thank you, Manasi.

  • Hi, everyone.

  • My name is Kemal, and I'm the product director

  • for TensorFlow.

  • So you've heard a lot of from Megan and Manasi

  • about our latest innovations.

  • Now, I'm going to talk about the most important part of what

  • we're building.

  • And that's the community.

  • And I want to begin by thanking all of you.

  • Your feedback, your contributions, what you build--

  • this is what makes all of this possible.

  • We have an incredible global community.

  • We love hearing from you.

  • And we really appreciate everyone

  • that came out to a roadshow of-- or TensorFlow World last year.

  • And going into 2020, I want to take some time

  • to highlight more opportunities to get involved

  • in the community and new resources

  • to help you all succeed.

  • Let's start with ways you can get involved locally.

  • One great way to connect is to join a TensorFlow user group.

  • These grassroots communities start organically.

  • And we now have 73 of them globally.

  • We launched our first two in Latin America

  • after the roadshow in San Paolo.

  • And now, we've expanded our presence in Europe.

  • The Korea group is the biggest with 46,000 members.

  • And China has user groups in 16 cities.

  • I'm sure this map can have a lot more dots.

  • So if you want to start a user group, please reach out.

  • And we'll help you get started.

  • Another way to get involved are the special interest groups,

  • or SIGs.

  • To help you build areas of TensorFlow

  • that you care the most about, we will have 12 SIGs

  • with SIG graphics being our latest addition starting

  • at the end of the month.

  • Most SIGs are led by members of the open source

  • community such as our fantastic Google Developer Experts.

  • We love our GDEs.

  • We now have 148 of them.

  • And I want to take a moment to recognize all of them.

  • They give tech talks, organize workshops and doc sprints.

  • And I want to give a special shout out

  • to [? Hasan, ?] pictured above, who organizes

  • a doc sprint in Seoul.

  • They reviewed several PRs and wrote hundreds

  • of comments in five hours.

  • Again, GDEs are amazing.

  • So please let us know if you're interested in becoming one.

  • OK, so TensorFlow user groups, SIGs, and GDEs

  • are great ways to get involved.

  • But we all love a little competition.

  • And we all love Kaggle.

  • As Kaggle now supports 2.x, we've

  • had over 1,000 teams enrolled in our last competition.

  • I want to give a special shout out to our 2.0 prize

  • winner, Deep Thought.

  • And speaking of competition, we saw great projects

  • in our last DevPost challenge, including Psychopathology

  • Assistant, an intelligent assistant that

  • tracks patient's responses during face-to-face and remote

  • sessions, and Everyone Dance Faster, an everybody dance now

  • video generation library using [? HTTPU ?] and TensorFlow 2.0.

  • Thank you to everyone who participated.

  • And today, we have a new challenge.

  • Manasi spoke earlier about how TensorFlow

  • can help empower all users to build AI systems responsibly.

  • So we want to challenge you to create something great

  • with TensorFlow 2.2 and something that

  • has the AI principles at heart.

  • We can't wait to see what you build.

  • So another area that we're investing in a lot

  • is education, starting with supporting

  • our younger community members.

  • For the first time, we participated in Google coding.

  • And it was a success.

  • We were very impressed by the students.

  • And we want to thank all the awesome mentors who

  • made this possible.

  • We hope someday to see the students

  • at our Summer of Code program.

  • I love Summer of Code.

  • It's an awesome opportunity for students

  • to work with TensorFlow engineers.

  • We saw amazing projects.

  • In fact, one of the students worked on data visualization

  • for Swift, which is still being used today by our team.

  • So I'm happy announce we're doing it again this summer.

  • And we're excited to see what new projects students will

  • work on.

  • Programs like this are key to the growth of the developer

  • community.

  • So please visit the Summer of Code website

  • to learn more and apply.

  • We also want to help produce great educational content,

  • starting with our machine learning crash course,

  • a great resource for beginners.

  • So today, we launched an updated version of the course.

  • Our [INAUDIBLE] team completely revamped

  • the programming exercises using Keras 2.0

  • and made them much simpler in the process.

  • Go check it out on this link.

  • And we want to provide resources at every stage of learning.

  • At a university level, we want to empower educators

  • and support them as they design, develop, and teach

  • machine learning courses.

  • Last year, we supported Georgia Tech, University of Hong Kong,

  • Pace University, and many others.

  • And this year, we have a commitment

  • to fund schools from underrepresented communities

  • in AI, historically black and Latinx colleges

  • and universities.

  • So if you're a faculty and you want to teach ML,

  • please reach out.

  • And we also want to help people self-study.

  • That's why we partner with deeplearning.ai

  • to give people access to great educational material.

  • To date, over 200,000 people have enrolled in our courses.

  • The Data and Deployment course is a great specialization

  • course that covers TensorFlow GS, TensorFlow Lite, TensorFlow

  • Dataset, and more advanced scenarios.

  • This is a great option for people

  • who are really looking to build their ML coding skills.

  • And you could audit it for free.

  • And there's more.

  • Imperial College London just released a

  • getting started with TensorFlow course on Coursera.

  • This course was created in part by the TensorFlow funding

  • I mentioned earlier.

  • And we're super happy to see this.

  • So you're taking all these courses.

  • You're becoming better at ML.

  • But how do you show your expertise to the world?

  • This is why I'm excited to announce

  • the launch of the TensorFlow certificate program,

  • an assessment created by the TensorFlow team,

  • covering topics such as text classification using

  • NLP to build spam filters, computer vision using

  • CNN to do image recognition, sequences and prediction.

  • By passing this foundational certification,

  • you'll be able to share your expertise with the world

  • and display your certificate badge on LinkedIn, GitHub,

  • or the TensorFlow certificate network.

  • And to widen access to people of diverse backgrounds

  • and experiences, we're excited to offer

  • a limited number of stipends for covering the certification

  • costs.

  • You can find out more at TensorFlow.org/certificate.

  • So a lot of things to do, and I want

  • to thank you, again, for making the TensorFlow community so

  • awesome.

  • As you've seen, the TensorFlow ecosystem

  • is having incredible impact in the world today.

  • And what it really comes down to is

  • how AI is helping make people's lives better.

  • That's really what inspires us, as a team,

  • to build all these amazing tools.

  • So I'd like to end by sharing one final story.

  • [VIDEO PLAYBACK]

  • [END PLAYBACK]

  • KEMAL MOUJAHID: That's just amazing

  • and incredibly inspiring.

  • When I see something like this, it

  • makes me very proud to be building TensorFlow.

  • So go build amazing things, and we'll be there to help.

  • And with that, I'll pass it on to Paige to kick off our day.

  • Thank you.

  • [MUSIC PLAYING]

[MUSIC PLAYING]

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