Python Ray, developed by UC Berkeley’s RISELab, is a dynamic framework revolutionizing distributed computing. It simplifies parallel and distributed Python applications, streamlining complex tasks for ML engineers, data scientists, and developers. This article explores Ray’s layers, core concepts, installation, and its versatility in various areas of data processing and model training.
Python Ray is a framework created by UC Berkeley’s RISELab that is changing the way we think about distributed computing. This powerful tool makes parallel and distributed computing in Python much easier and more streamlined. Whether you are an ML engineer, data scientist, or developer, Ray is versatile enough to handle everything from data processing to reinforcement learning. In this article, we explore the various layers and foundational concepts of Ray, as well as how to install and use it effectively.
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