MLI: An API for Distributed Machine Learning

Autor:	Sparks, Evan R., Talwalkar, Ameet, Smith, Virginia, Kottalam, Jey, Pan, Xinghao, Gonzalez, Joseph, Franklin, Michael J., Jordan, Michael I., Kraska, Tim
Rok vydání:	2013
Předmět:	Computer Science - Learning Computer Science - Distributed Parallel and Cluster Computing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1310.5426 Zobrazit plný text záznamu View this record from Arxiv