Comparative analysis of open source frameworks for machine learning with use case in single-threaded and multi-threaded modes

Comparative analysis of open source frameworks for machine learning with use case in single-threaded and multi-threaded modes
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开源机器学习框架与单线程和多线程模式用例的比较分析

DOI：
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发表时间：
2017
期刊：
International Conference on Computer Science and Information Technologies
影响因子：
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通讯作者：
Yuri G. Gordienko
Yuri G. Gordienko
中科院分区：
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文献类型：
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作者：
Yuriy Kochura;S. Stirenko;A. Rojbi;Oleg Alienin;Michail Novotarskiy;Yuri G. Gordienko

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本文考虑并比较了一些最通用、最流行的机器学习开源框架(TensorFlow、Deep Learning4j和H2O)的基本特征。对这些平台进行了比较分析，得出了各自的优势和劣势。在针对单线程和多线程操作模式的CPU和GPU平台设计的深度学习算法的H2O框架上，对事实上的标准MNIST数据集进行了性能测试。
The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation.