Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes

Abstract

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.

4 pages, 6 figures, 4 tables; XIIth International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT 2017), Lviv, Ukraine

Cited by in corpus (7)

• Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases
• Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks
• Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions
• Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions
• Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
• Generating and Estimating Nonverbal Alphabets for Situated and Multimodal Communications
• Music Transcription by Deep Learning with Data and "Artificial Semantic" Augmentation