"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."
"Ray, a distributed execution engine for AI workloads, enables a few lines of Python to become work running across hundreds of GPUs. Edward Oakes describes himself as an infrastructure and distributed computing person motivated by building abstractions that let everyday Python developers tap into large-scale computing without needing a PhD in distributed systems."
Ray is a distributed execution engine for AI workloads that originated from UC Berkeley's RISE Lab, the same research lineage that produced Apache Spark. Initially built for reinforcement learning research, Ray faded as the field stalled, but experienced a resurgence with ChatGPT's emergence, which relies on reinforcement learning as a critical post-training step. Edward Oakes and Richard Liaw, founding engineers at Ray and Anyscale, developed the framework to enable Python developers to scale single-machine scripts across distributed systems without requiring deep expertise in distributed computing. Ray provides tools including Ray Data for multimodal pipelines, dashboards, VS Code remote debugging, and KubRay for Kubernetes integration, positioning itself alongside alternatives like Dask, multiprocessing, and asyncio.
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