Brook: An Easy and Efficient Framework for Distributed Machine Learning
نویسندگان
چکیده
We present Brook, a new framework for distributed machine learning problems. Like some previous frameworks, Brook adopts the parameter server paradigm that simplifies the task of distributed programming. Unlike these frameworks, we build a novel system component called parameter agent that masks the communication details between workers and servers by mapping remote servers to local in-memory file. In this way, Brook provides a simple and platform-independent interface called RWW, where users can migrate existing single-machine programs, written in any programming language, to the distributed environment with minimal modification. In addition, to achieve system efficiency and scalability, Brook is designed to minimize network traffic, maximize CPU and memory utilizations, and support flexible fault-tolerance strategies. Our evaluation results show that Brook has the highly competitive performance and scalability, while providing enhanced ease of use compared to existing frameworks.
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