Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
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DOI：
10.1007/978-3-319-70581-1_17
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发表时间：
2017-08
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通讯作者：
Yuriy Kochura;S. Stirenko;Oleg Alienin;Michail Novotarskiy;Yuri G. Gordienko
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作者：
Yuriy Kochura;S. Stirenko;Oleg Alienin;Michail Novotarskiy;Yuri G. Gordienko
本文考虑并比较了一些最通用和最流行的机器学习开源框架（TensorFlow、Deep Learning 4j和H2O）的基本功能。对它们进行了比较分析，并就这些平台的优缺点得出了结论。事实上的标准MNIST数据集的性能测试是在H2O框架上进行的，用于为CPU和GPU平台设计的深度学习算法，用于单线程和多线程操作模式。此外，我们还展示了在H2O平台上测试神经网络架构的结果，用于各种激活函数，停止度量和机器学习算法的其他参数。对于单线程模式下的手写数字MNIST数据库的用例，证明了这些参数的盲选可以极大地增加运行时间（2-3阶），而不会显着增加精度。这一结果可能对现有和新的机器学习方法的优化产生至关重要的影响，特别是对于图像识别问题。
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 Also, we present the results of testing neural networks architectures on H2O platform for various activation functions, stopping metrics, and other parameters of machine learning algorithm. It was demonstrated for the use case of MNIST database of handwritten digits in single-threaded mode that blind selection of these parameters can hugely increase (by 2–3 orders) the runtime without the significant increase of precision. This result can have crucial influence for optimization of available and new machine learning methods, especially for image recognition problems.