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

TL;DR

This paper compares popular open source machine learning frameworks—TensorFlow, Deep Learning4j, and H2O—evaluating their features and performance on MNIST in single- and multi-threaded CPU and GPU modes.

Contribution

It provides a comparative analysis of these frameworks' features and performance, highlighting their advantages and disadvantages in different computational modes.

Findings

H2O performs well on CPU and GPU for MNIST.

Frameworks differ significantly in multi-threaded performance.

TensorFlow and Deep Learning4j have distinct strengths and weaknesses.

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.