
#546: Self hosting apps for Python people
Apr 27, 2026 - 63:12
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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...
#547: Parallel Python at Anyscale with Ray is an episode from Talk Python To Me - Python conversations for passionate developers by Michael Kennedy. When OpenAI trained GPT-3, they didn't roll their own orchestration layer. They used Ray, a...
This episode belongs to Talk Python To Me - Python conversations for passionate developers.
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Published May 6, 2026, 59:16 long, audio available.
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 Episode sponsors Sentry Error Monitoring, Code talkpython26 AgentField AI Talk Python Courses Links from the show Guests Richard Liaw : github.com Edward Oakes : github.com Ray : Example code (we used for walk-through) : docs.ray.io Getting Started with Ray : docs.ray.io Ray Libraries : docs.ray.io kuberay : github.com Watch this episode on YouTube : youtube.com Episode deep-dive : talkpython.fm/547 Episode transcripts : talkpython.fm Theme Song: Developer Rap π₯ Served in a Flask πΈ : talkpython.fm/flasksong ---== Don't be a stranger ==--- YouTube : youtube.com/@talkpython Bluesky : @talkpython.fm Mastodon : @talkpython@fosstodon.org X.com : @talkpython Michael on Bluesky : @mkennedy.codes Michael on Mastodon : @mkennedy@fosstodon.org Michael on X.com : @mkennedy
You can listen to #547: Parallel Python at Anyscale with Ray online on Radio and Podcast. Open the player on this page to stream the available audio.
#547: Parallel Python at Anyscale with Ray is an episode from Talk Python To Me - Python conversations for passionate developers by Michael Kennedy.
This episode is 59:16 long.
This episode was published on May 6, 2026.
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#547: Parallel Python at Anyscale with Ray is from Talk Python To Me - Python conversations for passionate developers by Michael Kennedy.
Published May 6, 2026 and 59:16 long