Placeholder Image

Subtitles section Play video

  • ♪ (upbeat music) ♪

  • Good morning everyone, I'm Alina,

  • program manager for TensorFlow.

  • (applause)

  • Thank you.

  • Welcome to the 2019 TensorFlow Developer Summit.

  • This is our third annual and largest developer summit to date,

  • and I'm so happy to have all of you here

  • both right here in the room and on the Livestream.

  • Welcome.

  • So I'm just curious by a show of hands,

  • who traveled a little bit further, maybe, to get here?

  • Europe?

  • Asia?

  • Africa?

  • As far as Australia?

  • Woo, awesome.

  • Welcome to all of you.

  • We have a lot of great talks ahead, some exciting announcements

  • and cool demos, so let's get going.

  • We are living in a formative moment of history right now,

  • where machine learning is experiencing an unprecedented revolution.

  • The way we fundamentally think about and interact with computer systems

  • has inherently changed

  • due to the breakthroughs in the field of AI.

  • and this is due to three major factors.

  • First, we have lots more compute specially designed ML accelerator

  • like these TPUs,

  • let you train models faster than ever before.

  • Secondly, we have breakthroughs in the field of machine learning.

  • Their novel algorithms created every month

  • like a BERT and an innovative approach to natural language processing

  • which lets anyone around the world

  • train their own state-of-the-art question-answering system.

  • And finally, we have lots and lots of data.

  • We're seeing new waves of data sets come from all kinds of disciplines.

  • For example, the new open images extended data set.

  • This is a collection of over 478,000 images

  • that volunteers have added

  • with the pursuit of inclusivity and diversity.

  • So, all three of these are basically changing

  • how we solve challenging, real-world problems,

  • and it's really cool to see that TensorFlow is the platform

  • that's powering this machine learning revolution.

  • It's allowing developers, businesses and researchers around the world

  • to benefit from intelligent applications.

  • And we've been really amazed

  • by what the community has built with TensorFlow.

  • Developers have been using TensorFlow to solve problems

  • in in their local communities.

  • So I don't know if any of you were in the Bay Area

  • during the tragic Paradise fire,

  • but one of the consequences was

  • that air quality was really bad.

  • It was in the high to mid to 200s on the Air Quality Index.

  • And as difficult as this was for us in Delhi, India during winter.

  • The air quality can get up to about the 400s on Air Quality Index,

  • and this is considered very dangerous.

  • So pollution sensors can help gauge air quality

  • but they're very expensive to deploy at scale.

  • So a group of students in Delhi built image classifiers in TensorFlow

  • and use those to build an app called Air Cognizer,

  • and what it does is basically just by using the images on a smartphone

  • it gives an accurate estimation of the air quality.

  • Businesses are also fundamentally

  • improving their products and services built with TensorFlow,

  • for example, Twitter strives to keep its global users informed

  • with relevant and healthy content.

  • But this can be hard,

  • when the users follow hundreds or even thousands of people,

  • so to solve this, Twitter launched ranked timeline, an ML power feed

  • which has the most relevant tweets at the top of the time timeline,

  • ensuring users never missed their best and most relevant content.

  • And by using TensorFlow's ecosystem of tools

  • like TensorFlow Hub, TensorBoard

  • and TensorFlow Model Analysis,

  • Twitter was able to reduce both training and model iteration time

  • as well as increase the timeline quality and engagement for users.

  • Specific industries are also being very much transformed by ML.

  • GE Healthcare, for example

  • is using TensorFlow to improve MRI imaging.

  • These TensorFlow models, they're real-time on MRI scanners

  • and can actually detect the orientation of the patient inside the scanner.

  • And this is really great

  • because not only does this help the diagnosis,

  • but also lowers the errors and exam time.

  • But also, what's really cool is it basically expands this technology

  • to many many more people around the world.

  • TensorFlow also powers bleeding-edge research.

  • A team of scientists, researchers and engineers

  • at nurse Oak Ridge National Laboratory at VIDYA

  • recently won the Gordon Bell Prize

  • for applying deeplearning

  • to study the effects of extreme weather patterns

  • using high-performance computing.

  • They built and scaled a neural network using TensorFlow,

  • of course, to a run on Summit, the world's fastest supercomputer.

  • They achieved a peak and sustained throughput

  • of 1.13 exaFLOPS and FPC-16

  • which is equivalent to more than a quantalian computations per second.

  • I think I need to pause for a second because that is ridiculously fast.

  • Right?

  • In addition to these awesome examples,

  • there are thousands and thousands of people all over the world

  • doing amazing work using TensorFlow,

  • and the power and impact of TensorFlow would not be what it is

  • without all of you, thank you.

  • It's with your help and interest

  • that TensorFlow has become the most widely adopted ML framework in the world.

  • And right here, I'd like to show the latest map of GitHub stars

  • who self-identified their location.

  • I'm sure many of the dots on this map are right here

  • in the room and on the Livestream,

  • so I just want to say thank you one more time.

  • And this growth has been absolutely amazing.

  • TensorFlow has been downloaded over 41 million times,

  • and has over 1800 contributors worldwide.

  • Last November, we celebrated TensorFlow's third birthday

  • by taking a look back at the different components

  • that we've added throughout the years.

  • But today, we'd like to talk

  • about how TensorFlow has matured as a platform

  • to become an entire end-to-end ecosystem.

  • And TensorFlow 2.0 is the start of a new era,

  • and we're committed and focused on making it

  • the best ML platform for all our users.

  • To talk more about TensorFlow 2.0

  • I'd like to introduce Rajat Monga,

  • Engineering Director of TensorFlow on stage.

  • Thank you.

  • (applause)

  • Thank You, Alina.

  • Hello, everyone, I'm Rajat.

  • I am an engineer at TensorFlow

  • and have been involved with this since the very beginning.

  • It's been great to see what we've been up to

  • over the last few years.

  • All the amazing growth

  • and all the amazing things that you've done with it.

  • It's also been great to hear from you.

  • You told us what you like about TensorFlow

  • and equally importantly, what you would like to see improved

  • in TensorFlow.

  • Your feedback has been loud and clear.

  • You asked for simpler, more intuitive APIs in developer experiences.

  • You pointed out areas of redundancy and complexity,

  • and you asked for better documentation and examples,

  • and this is exactly what we've been focusing on with TensorFlow 2.0.

  • To make it easy, we focused on Keras

  • for a single set of API's

  • and combine it with Eager Execution for the simplicity of Python.

  • With flexibility to try the craziest ideas

  • and ability to go beyond an exaFLOP

  • TensorFlow is more powerful than ever.

  • With the same robustness and performance

  • you expect in production, battle-tested in Google.

  • Let's start with the overall architecture for TensorFlow.

  • You may be familiar with this high-level architecture.

  • There have been lots of components and features

  • we've added throughout the years

  • to help support workloads to go from training to deployment.

  • With TensorFlow 2.0, we're really making sure

  • that these components work better together.

  • Here's how these powerful API components fit together

  • for the entire training workflow.

  • With tf.data for data ingestion and transformation,

  • keras and premade estimators from model building,

  • training with eager execution and graphs,

  • and finally packaging for deployment with SavedModel.

  • Let's take a look at some examples.

  • The first thing you need is data.

  • Often, you may want to validate results or test your new ideas

  • in common public data set.

  • TensorFlow