About this episode

The episode discusses the evolution of the Ray framework and its applications in AI workloads, featuring insights from its founding engineers.

When OpenAI trained GPT-3, 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, then quietly faded as RL hit a wall. Until ChatGPT 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 Liaw, two founding engineers behind Ray and Anyscale, join 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, KubRay for Kubernetes, and where Ray fits alongside Dask, multiprocessing, and asyncio. 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

People in this episode

Host: Michael Kennedy

Guests: Edward Oakes, Richard Liaw

Topics covered

• Parallel computing
• Ray framework
• AI workloads
• Reinforcement learning
• Distributed execution
• Python scaling
• Machine learning

Keywords

• Ray
• parallel Python
• AI
• reinforcement learning
• distributed computing
• Python
• machine learning
• GPU
• Kubernetes
• data pipelines

Mentioned in this episode

Organizations: OpenAI, Ray, Anyscale, UC Berkeley, Apache Spark

Products: GPT-3, ChatGPT, Ray Data, KubRay, VS Code, Dask, multiprocessing, asyncio

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