{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/performance-analysis-of-open-source-machine","title":"Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes","arxiv_id":"1708.08670","date":"2017-08-29","proceeding":null,"authors":["Yuriy Kochura","Sergii Stirenko","Oleg Alienin","Michail Novotarskiy","Yuri Gordienko"],"abstract":"The basic features of some of the most versatile and popular open source\nframeworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are\nconsidered and compared. Their comparative analysis was performed and\nconclusions were made as to the advantages and disadvantages of these\nplatforms. The performance tests for the de facto standard MNIST data set were\ncarried out on H2O framework for deep learning algorithms designed for CPU and\nGPU platforms for single-threaded and multithreaded modes of operation Also, we\npresent the results of testing neural networks architectures on H2O platform\nfor various activation functions, stopping metrics, and other parameters of\nmachine learning algorithm. It was demonstrated for the use case of MNIST\ndatabase of handwritten digits in single-threaded mode that blind selection of\nthese parameters can hugely increase (by 2-3 orders) the runtime without the\nsignificant increase of precision. This result can have crucial influence for\noptimization of available and new machine learning methods, especially for\nimage recognition problems.","url_abs":"http://arxiv.org/abs/1708.08670v1","url_pdf":"http://arxiv.org/pdf/1708.08670v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"performance-analysis-of-open-source-machine","repo_url":"https://github.com/h2oai/h2o-3","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}