[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117164-en":3,"doc-seo-117164-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117164,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Energy Efficiency of Python Machine Learning Frameworks","Machine learning applications increasingly require high computational power, yet energy consumption is often ignored when selecting and deploying frameworks. This study compares four widely used Python machine learning frameworks—TensorFlow, Keras, PyTorch, and Scikit-learn—across energy efficiency, memory usage, execution time, and accuracy. Experiments rely on multiple established machine learning benchmark problems spanning different application domains. Results reveal framework-dependent trade-offs between energy use and resource demands, and provide guidance for developers when energy efficiency is a key constraint.","Energy Efficiency of Python Machine Learning Frameworks  \nSalwa Ajel 1(B), Francisco Ribeiro2 , Ridha Ejbali3 , and Jo˜ao Saraiva2  \n1 Faculty of Sciences of Gabes, University of Gabes, Gabes, Tunisia  \n[ajelsaloua@gmail.com](ajelsaloua@gmail.com)  \n2 HASLab/INESC TEC, Universidade do Minho, Braga, Portugal  \n[francisco.j.ribeiro@inesctec.pt](francisco.j.ribeiro@inesctec.pt) , [saraiva@di.uminho.pt](saraiva@di.uminho.pt)  \n3 Research Team in Intelligent Machines, Engineering School of Gabes,  \nUniversity of Gabes, Gabes, Tunisia  \nridha [ejbali@ieee.org](ejbali@ieee.org)  \nAbstract. Although machine learning (ML) is a field that has been the subject of research for decades, a large number of applications with high computational power have recently emerged. Usually, we only focus on solving machine learning problems without considering how much energy has been consumed by the different frameworks used for such applications. This study aims to provide a comparison among four widely used frameworks such as Tensorflow, Keras, Pytorch, and Scikit-learn in terms of many aspects, including energy efficiency, memory usage, execution time, and accuracy. We monitor the performance of such frameworks using different well-known machine learning benchmark problems. Our results show interesting findings, such as slower and faster frameworks consuming less or more energy, higher or lower memory usage, etc. We show how to use our results to provide machine learning developers with information to decide which framework to use for their applications when energy efficiency is a concern.  \nKeywords: Machine Learning · Keras · Energy-Efficient ·  \nDeep Learning · Tensorflow · Memory usage · Pytorch · Execution time  \n1 Introduction  \nComputer architecture researchers have been investigating energy efficiency for decades, especially to develop the most advanced, energy-efficient processors. Machine learning researchers, on the other hand, have mostly concentrated on producing highly accurate models without considering energy consumption as a crucial aspect. This is the case for deep learning, where the objective has been to produce deeper and more accurate models without any constraints in terms of computation. These models have grown in computation and memory requirements. These algorithms require high levels of computing power during training as they must be trained on large amounts of data, while during deployment they  \n􀀁c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  \nA. Abraham et al. (Eds.): ISDA 2022, LNNS 715, pp. 586–595, 2023 .  \n[https://doi.org/10.1007/978-3-031-35507-3](https://doi.org/10.1007/978-3-031-35507-3_57)[_](https://doi.org/10.1007/978-3-031-35507-3_57)[57](https://doi.org/10.1007/978-3-031-35507-3_57)  \nEnergy Efficiency 587  \nmay be used multiple times. Therefore, we believe that efforts towards estimating energy efficiency and developing tools for researchers to advance their research in energy consumption are necessary for a more scalable and sustainable future.  \nWe believe that the reason why the machine learning community has not shown more interest in energy efficient is because of their lack of familiarity with the current methods to estimating energy and the lack of power models in existing machine learning frameworks, for example, in Tensorflow [11] Caffe2 [10], PyTorch [14], and others to support energy evaluations. Developers have constantly improved these frameworks by adding more features and speed improvements to attract more users and foster research. Recently, the efficacy of several deep learning frameworks has been evaluated in [6] . However, the comparison is only focused on the speed of the convolutional frameworks. Hence, this paper expands a comparative study of four machine learning frameworks, namely: Tensorflow, Keras, Pytorch, and Scikit-learn in terms of energy efficient, memory usage, and runtime metrics. To ensure that our study is as comprehensive as possible, we consider multiple benchmark d","cbCaidpqK4mQbpsA","https://ap.wps.com/l/cbCaidpqK4mQbpsA","pdf",619318,1,10,"English","en",105,"# Introduction\n## Background\n# Machine Learning Concepts\n## Support Vector Machine\n## Deep Learning","[{\"question\":\"What four Python machine learning frameworks are compared in the study?\",\"answer\":\"The study compares TensorFlow, Keras, PyTorch, and Scikit-learn.\"},{\"question\":\"Which evaluation aspects are used to compare the frameworks?\",\"answer\":\"Frameworks are evaluated using energy efficiency, memory usage, execution time, and accuracy.\"},{\"question\":\"How do the results help developers choose a framework?\",\"answer\":\"The results highlight trade-offs in energy consumption and resource usage, enabling developers to select an appropriate framework when energy efficiency matters.\"}]","Energy Efficiency of Python Machine Learning Frameworks | PDF",1785674176,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"energy-efficiency-of-python-machine-learning-frameworks","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/energy-efficiency-of-python-machine-learning-frameworks/117164/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What four Python machine learning frameworks are compared in the study?","Question",{"text":76,"@type":77},"The study compares TensorFlow, Keras, PyTorch, and Scikit-learn.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which evaluation aspects are used to compare the frameworks?",{"text":81,"@type":77},"Frameworks are evaluated using energy efficiency, memory usage, execution time, and accuracy.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the results help developers choose a framework?",{"text":85,"@type":77},"The results highlight trade-offs in energy consumption and resource usage, enabling developers to select an appropriate framework when energy efficiency matters.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]