When Open AI trained GPT3, they didn't roll their own orchestration layer.

They used Ray, an open source Python framework born out of the same Berkeley Research Lab lineage

that gave us Apache Spark. And here's the twist. Ray was originally built for reinforcement

learning research and then quietly faded as RL hit a wall. Until ChatTPT showed up, suddenly reinforcement learning was back,

as the post-training step that turns a raw language model into something genuinely useful.

Edward Oakes and Richard Law, two founding engineers behind Ray and AnyScale,

joined me on Talk Python to tell that story.

We'll trace Ray from its RISE lab origins at UC Berkeley to powering some of the largest

training runs in the world.

We'll talk about what Ray actually is, a distributed execution engine for AI workloads,

and how a few lines of Python become work running across hundreds of GPUs.

We'll cover Ray data

for multimodal pipelines, the dashboard,

the VS code remote debugger,

Cube Ray for Kubernetes, and where Ray fits

alongside Dask, multi-processing, and async.

If you've ever stared at a single machine

Python script and thought, there has to be a better way to

scale this. This one's for you. It's Talk Python and thought, there has to be a better way to scale this.

This one's for you.

It's Talk Python and Me.

Episode 547 recorded April 27, 2006.

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