Chris Lattner

But to me, it's about a bag of tricks.

It's about a system and a framework that you can hang complexity.

It's a system that can then generalize and it can work on problems that are bigger than fit in one human's head, right?

And so what that means, what a good stack and what the modular stack provides is the ability to walk up to it with a new problem and it'll generally work quite well.

And that's something that a lot of machine learning infrastructure and tools and technologies don't have.

Typical state of the art today is you walk up, particularly if you're deploying, if you walk up with a new model, you try to push it through the converter and the converter crashes.

That's crazy.

The state of ML tooling today is not anything that a C programmer would ever accept, right?

And it's always been this kind of flaky set of tooling that's never been integrated well, and it's never worked together because it's not designed together.

It's built by different teams.

It's built by different hardware vendors.

It's built by different systems.

It's built by different internet companies that are trying to solve their problems, right?

And so that means that we get this fragmented, terrible mess of complexity.

Vectorize, as he showed, is built into the library.

So think about this in hierarchical levels of abstraction.

If you zoom all the way into a compute problem, you have one floating point number.

So then you say, okay, I can do things one at a time in an interpreter.

It's pretty slow.

So I can get to doing one at a time in a compiler.