>> Hi, everybody.

We have a big show for you today.

So, if you have -- now would be a

great time to turn -- the exit behind you.

[ Applause ]

>> Hi, everybody.

Welcome.

Welcome to the 2018 TensorFlow Lite summit.

We have a good day with lots of cool talks.

As you know -- we are embarking on the

--

and the controllers in Europe are

useing

to project the trajectory of flight

through the air of Belgium, Luxembourg, Germany and the

Netherlands. This has more than 1.

8 million flights and it is one of the

most dense air spaces in the world.

And teary farming.

We know that a cow's health is vital

to the survival of the dairy industry.

And -- connected our company in the Netherlands, they wondered

if they can

use machine learning to track the health

of cows and be able to provide insights to farmers and

veterinarians on actions

to be taken to ensure we have happy,

healthy cows that are high yielding.

In California, and also from the Netherlands.

And

--

music, the machine learning algorithm,

the neural networks --

>> And changed by machine learning.

The popular Google home, or the pixel or search or YouTube or

even maps. Do you know what is fascinateing in all of these

examples?

TensorFlow is at the forefront of them. Makeing it all

possible.

A machine learning platform that can solve challengeing problems

for all of us.

Join us on this incredible journey to make TensorFlow

powerful, scaleable and the best machine learning platform for

everybody.

I now -- with TensorFlow to tell us more about this. Thank you.

>> So, let's take a look at what we have been doing over the last

few years. It's been really amazing.

There's lots of new -- we have seen the popularity of

TensorFlow Lite grow. Especially over the last year,

we focused on makeing TensorFlow easy to

use, and the degrees -- and new

programming paradigms like -- execution really make that

easyier. Earlier this year, we hit the

milestone of 11 million downloads. We are really

exciteed to see how much users are uses this and how much

impact it's had in the world.

Here's a map showing self-identifyied

locations of folks on Git hub that

started TV -- TensorFlow. It goes up and down. In fact,

TensorFlow is useed in every time zone in the world.

An important part of any open source product is the

contributeors themselves. The people who make this project

successful. I'm exciteed to see over a thousand contributeors

from outside Google who

are makeing contributions not just by improveing code, but

also by helping the rest of the committee by answering

questions,

responding to queries and so on.

Our commitment to this community is by

share

-- sharing our direction in the

roadmap, have the design direction, and

focus on the key needs like TensorBoard. We will be talking

about this later this afternoon in detail.

Today we are launching a new TensorFlow Lite blog. We'll be

shareing work by the team in the community on this blog, and we

would like to invite you to participate in this as well.

We're also launching a new YouTube channel for TensorFlow

that brings together all the great content for TensorFlow.

Again, all of these are for the community to really help build

and communicate. All day today we will be shareing a number of

posts on the blog and videos on the channel.

The talks you are hear hearing here will be made available

there as well, along with lots of conversations and interviews

with the speakers.

To make views and shareing easyier, today we are launching

TensorFlow hub.

This library of components is easyily integrateed into your

models. Now, again, goes back to really

makeing things easy for you.

Library.

With the focus on deep learning and neural networks. It's a

rich collection of machine learning environments.

It includes items like regressions and

decision trees commonly used for many structured data

classification problems. There's a broad collection of

state of

the art tools for stats and

Baysian analysis.

You can check out the blog post for details.

As I mentioned earlier, one of the big key focus points for us

is to make TensorFlow it easy to use. And we have been pushing

on simpler APIs, and making them more intuitive. The lowest

level -- our focus is to consolidate a lot of the APIs we

have and make it easier to build these models and train them.

At the noise level the TensorFlow APIs are really

flexible and let users build anything they want to.

But these same APIs are easier to use.

TensorFlow contains a full

implementation of Keras. You can offer lots of layers to

train them as well.

Keras works with both executions as well. For distributed

execution, we provide

estimators so you can take models and distribute them

across machines.

You could also get estimators from the Keras models.

And finally, we provide premade estimators. A library of ready

to go implementations of common machine learning environments.

So, let's take a look at how this works. So, first, you

would often define were model. This is a

nice and easy way to define your model. Shows a convolution

model here with just a few lines here.

Now, once you've defined that, often

you want to do some input processing.

We have a great idea of the data introduced in 1.

4 that makes it easy to process inputs and lets us do lots of

optimizations behind the scenes. And you will see a lot more

detail on this later today as well. Once you have those, the

model and the info data, now, you can put them

together by equating the data-set, computing gradients

and updating parameters themselves.

You need a few lines to put these together.

And you can use your debugger to debug that and involve problems

as well.

And, of course, you can do it even fewer lines by using the

pre-defined lines we have in Keras.

In this case, it executes the model as a graph with all the

optimizations that come with it. This is great for a single

machine or a single device.

Now, often, given the high, heavy computation needs for

deep learning or machine learning, we

want to use more than one actuator. We have estimators.

The same datasets that you had, you

can build an estimator and really use

that to train across the cluster or multiple devices on a single

machine. That's great. Why not use a cloud cluster?

Why not use a single block box if you can do it faster?

This is used for training ML models at scale. And the focus

is to take everything you have been doing and build a TPU

estimator to allow you to scale the same model.

And finally, once you have trained

that model, use that one line at the

bottom for the deployment itself. Your deployment is

important, you often do that in data centers. But more and more

we are seeing the need to deploy this on the phones, on other

devices as well.

And so, for that, we have TensorFlow lithe.

And we have a custom format that's designed for devices and

lightweight and really fast to get started with. And then once

you have that format, you can include that in your

application, integrate

TensorFlow Lite with a few lines, and you have an

application to do predictions and include ML. Whatever task

you want to perform.

So, TensorFlow runs not just on many platforms, but in many

languages as well.

Today I'm excited to add Swift to the mix. And it brings a

fresh approach to machine learning.

Don't miss the talk by Chris Lattner this afternoon that

covers the exciting

details of how we are doing this. JavaScript is a language

that's synonymous with the web development community.

I'm excited to announce TensorFlow.JS, bringing it to

the web developers. Let's take a brief look at this.

The same TensorFlow applications in JavaScript, you

can call them just as plain JavaScript code.

And a full-fledged layer of API on top. And full support for

TensorFlow and

Keras models so you can pick the best deployment for you.

And under the covers, these APIs are actuated.

And we have NodeJS support coming

soon, which will give you the power to

actuate on CPUs and GPUs.

And I would like to welcome Megan

Kacholia to talk about how TensorFlow does performance.

[ Applause ] >> Thank you. All right.

Thanks, Rajat so, performance across all platforms is critical

to TensorFlow Lite's success. I want to take a quick step back

and talk about some of the things we think about when

measuring and assessing TensorFlow's performance. One

of the things we want to do is focus on real world data and

time to accuracy. We want to have reproducible benchmarks and

make sure they're realistic of the workloads and types of

things that users like you are doing on a daily basis. Another

thing, like Rajat talked about, is we want to make sure we have

clean APIs.

And we don't want to have a fast version and a pretty version.

The fast version is the pretty version.

All the APIs that we talked about that we're talking about

through various talks, these are the things you can use to get

the best performance out of TensorFlow. You don't have to

worry about what is fast or pretty, use the pretty one, it

is fast.

TF Data from Derek after the keynote. As well as

distribution strategy from Igor. And these are great examples of

things

we have been pushing on to ensure good performance and good

APIs. We want good performance, whether it's a large data center

like here, or maybe you're using something like on the image

here.

A GPU or CPU box under your desk.

Making use of a cloud platform or a mobile or embedded device.

We want TensorFlow to perform well across all of them. Now

the numbers, because what is a performance talk if I don't show

you slides and numbers. First, look at things on the mobile

side.

This is highlighting TensorFlow Lite performance. There's a

talk giving a lot more detail how it works and the things we

were thinking of when making it later

today

by Sarah.

And weft the speed yum with Qu -- and it's critical to have

strong performance regardless of the platform, and we're really

excited to see these gains in mobile. In looking past mobile,

just beyond, there are a number of companies in the

hardware space which continues to expand. The contributions

that come out of the collaborations that we have with

these companies, the contributions they give back to

TensorFlow and back to the community at large, are critical

to making sure that TensorFlow performs well on these specific

platforms for the users that each group really cares about.

One of the first ones I want to highlight is Intel.

So, the Intel MKL-DNN library, open

sourced and highly optimized for TensorFlow.

We have a 3X inference speedup on Intel platforms, as well as

great scaling efficiency on training. And this is one of

those things that highlights how important it is to have strong

collaborations with different folks in the community. And

we're excited to see things like this to go back to all the

users.

And I want to call out a new of the collaborations with NVIDIA