to welcome our next speakers, Paige Bailey and Brennan Saeta,

who are going to talk to us about Swift for TensorFlow,

a next generation machine learning platform

that leverages innovation.

So please help me in welcoming Paige and Brennan.

[APPLAUSE]

PAIGE BAILEY: Good morning, everyone.

So thank you for coming to join us during the lunchtime.

We very much appreciate you-- you

taking the time to come to TensorFlow world

and also to learn a little bit more about Swift.

I'm Paige.

I'm the product manager for Swift for TF and--

BRENNAN SAETA: I'm Brennan.

I'm tech lead manager for Swift for TensorFlow.

PAIGE BAILEY: Excellent.

And so, first, we want to get a feel for the room.

How many of you have used Swift before?

Excellent.

BRENNAN SAETA: Awesome.

PAIGE BAILEY: And how many of you

attended Paris and Tim and their session, their workshop on--

earlier this week?

Few hands, cool.

So this is a great place to get started if you've never

used Swift before.

And it's also a wonderful place to learn

how Swift is getting more and more functionality

added specifically for machine learning and for deep learning.

So it's an exciting time.

Do you want to get started, Brennan?

BRENNAN SAETA: Yeah, let's do it.

PAIGE BAILEY: Perfect.

BRENNAN SAETA: So today we want to bring you a little bit

different kind of a presentation since a lot of you

actually know Swift, at least to some degree.

We wanted to really dig one level deeper and really

explore Swift for TensorFlow from its foundations.

So before we get into the meat of the talk,

I wanted to take a moment to acknowledge

a few very important folks.

So number one is--

this work would not be possible without the rest

of the Swift for TensorFlow team that really

does a huge amount of work to make this platform awesome,

so huge thank you to them.

But also not just the core team itself,

But there are a number of other teams

that we collaborate with at Google--

from the JAX team to the rest of TensorFlow and beyond.

And so we really are collaborating together

and building on each other's work to make something awesome.

So thank you to everyone, both at Google

and then, of course, our awesome open source contributors

beyond.

We have a lot of contributors that

are really making a huge difference to the project.

And, finally, for those of you who are familiar with Fast.ai,

some of the slides may be familiar.

So Jeremy Howard and Chris Lattner

co-presented a little bit Swift for TensorFlow

earlier this year.

And I encourage you all to check that out.

So, to begin with, I like to start with a question--

why Swift for TensorFlow?

PAIGE BAILEY: And I could give my answer.

BRENNAN SAETA: Let's hear it.

PAIGE BAILEY: Awesome.

So I've been using Python for a long time.

I really love the flexibility and often the user experience

that you get using the language.

It feels very understandable.

You read the code on the page and it reads almost

like a plain sentence and it also

has a vibrant ecosystem of data science tooling.

But something that I was always really frustrated

about whenever I was trying to deploy

Python models into production is that often you

have to do a lot of rewrites.

Part of the-- part of Python's benefits

don't necessarily translate so well to deployment scenarios.

So Swift is, to me, at least, a happy way

to get a lot of the great benefits of Python,

but also the benefits of a typed language.

BRENNAN SAETA: That's right.

And so what we are going for with Swift for TensorFlow

is an infinitely hackable platform

that gets you all the way from research, into production,

and, most importantly, that everything is customizable.

Because although there's a lot of flexibility

that you need to do research, you

need just the same amount of flexibility and capability,

often sometimes slightly different,

to really make a high quality production application.

PAIGE BAILEY: Yeah.

BRENNAN SAETA: And that's what we're

really excited about with Swift for TensorFlow,

and especially with the Swift platform,

how we can do interesting things to optimize our productivity.

So let's take a moment to ask the question what is Swift?

PAIGE BAILEY: And Swift is a programming language

that was created by Chris Lattner pretty recently.

BRENNAN SAETA: Yeah.

PAIGE BAILEY: One, he was spending some time

over at Apple.

Since then, Chris has come to join us and worked at Google.

You'll be hearing a lot about one of Chris's other projects

later this week.

MLIR, I believe, is in the keynote tomorrow.

But Swift is a programming language

that's focused on that extensibility component

that Brennan just mentioned as well as readability, usability,

and production readiness.

BRENNAN SAETA: That's right.

So this is Swift for TensorFlow from foundation.

So let's really dig in and let's really go from the foundations.

So I think an important question to understand

what is Swift at a deeper level is to understand

what is a compiler.

And so as you program computers, there's

two fundamental things that are immovable

and that like to think about the world

in their own particular ways that

are sometimes incompatible.

You've got humans, on one hand, that

want to think about the world in a particular way

and you've got hardware which also

thinks about the world in its own particular way.

And a programming language is basically an intermediate point

in between these two immovable, fundamental truths

of technology.

And so programming languages are designed for humans

to express ideas, but they're also designed

to be executed on hardware.

And they're different points in that space that you can pick

and Swift is just one of such points.

But we think it's a really interesting one

and we'll talk about why in a couple of minutes.

PAIGE BAILEY: Right, so one of the benefits of having

a typed language is that a lot of the responsibility that

might be pushed on you if you're using a language like Python

is suddenly abstracted away.

So I don't have to care so much about baking performance

optimizations into my code immediately,

the compiler can just do it for me.

Another great thing is that if you look at the screen

right now, you can see an example

of a few commands in Swift.

It looks very, very similar to Python.

There's some lets sprinkled in, there

are some-- a few indications of types, but other than that,

pretty straightforward to understand.

If you're only experienced using typed languages as something

like C++, this probably looks a great deal friendlier.

BRENNAN SAETA: That's right.

And Swift is a very modern and powerful programming language.

So even though Swift is statically typed,

you don't actually see very many types in advanced Swift code.

Here we're defining a dictionary numbers

from strings to lists of integers,

the compiler can just figure this out for you.

One of the principles of Swift is that you

have a very helpful compiler.

And you can define higher order operations,

reduces a common pattern from functional

programming across a sequence of elements.

And you can then call this x.sum,

mapped over all the values in the dictionary.

And voila, you've now used a number of higher order

functions together without having

to write types in a very seamless and nice manner.

So what we're trying to get across

is that Swift is almost as high level as Python.