[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118505-en":3,"doc-seo-118505-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118505,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Performance and Accuracy Evaluation of an Image Classifier using Multiple Machine Learning Models and Multiple GPUs","A performance and accuracy comparison evaluates six machine learning models using image classification on flower images with five NVIDIA GPUs. The study adapts a 2019 Bonner-based flower classifier and runs model loading and training, classifier execution, and results generation across two GPU-access environments: Google Colaboratory and University of Tennessee ISAAC Open OnDemand. It analyzes accuracy for training and flower prediction, resource utilization, performance characteristics, and the access/run challenges unique to each environment, concluding with recommendations for future work.","University of Tennessee, Knoxville  \nTRACE: Tennessee Research and Creative Exchange  \n\n| Electrical Engineering and Computer Science Publications and Other Works | Min H. Kao Department of Electrical Engineering and Computer Science |\n| --- | --- |\n| 7-2024\u003Cbr>Performance and Accuracy Evaluation of an Image Classifier using Multiple Machine Learning Models and Multiple GPUs\u003Cbr>Victor Hazlewood\u003Cbr>University of Tennessee, Knoxville, [victor@utk.edu](victor@utk.edu)\u003Cbr>Logan Scott\u003Cbr>University of Tennessee, Knoxville, [lscott32@vols.utk.edu](lscott32@vols.utk.edu)\u003Cbr>Follow this and additional works at: [https://trace.tennessee.edu/utk-comppubs](https://trace.tennessee.edu/utk-comppubs)\u003Cbr> Part of the Computer Sciences Commons |  |\n\nRecommended Citation  \nVictor Hazlewood and Logan Scott. 2024. Performance and Accuracy Evaluation of an Image Classifier using Multiple Machine Learning Models and Multiple GPUs. In Practice and Experience in Advanced Research Computing (PEARC ’24), July 21–25, 2024, Providence, RI, USA. ACM, New York, NY, USA, 8 pages. [https://doi.org/10.1145/3626203.3670528](https://doi.org/10.1145/3626203.3670528)  \nThis Article is brought to you for free and open access by the Min H. Kao Department of Electrical Engineering and Computer Science at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Electrical Engineering and Computer Science Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [trace@utk.edu](trace@utk.edu).  \nPerformance and Accuracy Evaluation of an Image Classifier using Multiple Machine Learning Models and Multiple GPUs  \nVictor Hazlewood∗ [victor@utk.edu](victor@utk.edu)  \nUniversity of Tennessee, Knoxville Knoxville, TN, USA  \nLogan Scott  \n[lscott32@vols.utk.edu](lscott32@vols.utk.edu)[ ](lscott32@vols.utk.edu)University of Tennessee, Knoxville Knoxville, TN, USA  \nABSTRACT  \nA performance and accuracy comparison is presented using six different machine learning models with five different NVIDIA GPUs to perform image classification offlower images. The image classifier is an adaptation based on the 2019 machine learning work by Bonner for flower image prediction. The image classifier was run employing six machine learning models using five types of GPUs accessed via Google Colaboratory and the University of Tennessee ISAAC Open OnDemand environment. In the Google Colaboratory environment, 16 GB NVIDIA T4 and P100 GPUs were used. A 32 GB NVIDIA V100S, 48 GB NVIDIA A40, and 80 GB NVIDIA H100 were used in the ISAAC Open OnDemand environment. Both environments were used to perform model load and training, perform image classifier runs, and generate results. Each environment that provided GPU access had its own challenges for access and running the models which will be discussed. The machine learning models each have their own advantages and disadvantages described in detail in their related publications with the focus of this work on results and analysis on accuracy of training, accuracy of flower image prediction, resource utilization, and performance. Our paper describes the image classifier, the machine learning approach using multiple models, GPUs used, analysis of performance, accuracy of results, conclusions, and suggestions for future work.  \nCCS CONCEPTS  \n• Computing methodologies → Machine learning.  \nKEYWORDS  \nBenchmark, GPU, machine learning, image classification  \nACM Reference Format:  \nVictor Hazlewood and Logan Scott. 2024. Performance and Accuracy Evaluation of an Image Classifier using Multiple Machine Learning Models and Multiple GPUs. In Practice and Experience in Advanced Research Computing (PEARC’24), July 21–25, 2024, Providence, RI, USA. ACM, New York, NY, USA, 8 pages. [https://doi.org/10.1145/3626203.3670528](https://doi.org/10.1145/3626203.3670528)  \n∗ Primary Author  \nPermission to make digital or hard copies of all or part of this","cbCairedcpoGxnR5","https://ap.wps.com/l/cbCairedcpoGxnR5","pdf",675466,1,9,"English","en",105,"# Introduction\n## Experimental setup and evaluation environments\n## GPU resources and model execution\n## Performance and accuracy analysis\n## Conclusions and future work","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"To compare performance and accuracy of multiple machine learning models for image classification using five NVIDIA GPUs across two computing environments.\"},{\"question\":\"Which GPU environments were used to run the experiments?\",\"answer\":\"The experiments were run in Google Colaboratory and the University of Tennessee ISAAC Open OnDemand environment.\"},{\"question\":\"What models and hardware are compared?\",\"answer\":\"Six machine learning models were evaluated using NVIDIA T4, P100, V100S, A40, and H100 GPUs.\"}]","Performance and Accuracy Evaluation of an Image Classifier using Multiple Machine Learning Models and Multiple GPUs | 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