Distributed Deep Learning-based Model for Large Image Data Classification
Pages 283 - 291
Abstract
Artificial intelligence has shown great potential in a variety of applications, from natural language models to audio visual recognition, classification, and manipulation. AI Researchers have to work with massive amount of collected data for use in machine learning, raising some challenges in effectively managing and utilizing the collected data in the training phase to develop and iterate on more accurate, and more generalized models. In this paper we conducted a review on parallel and distributed machine learning methods and challenges. We also propose a distributed and scalable deep learning model architecture which can span across multiple processing nodes. We tested the model on the MIT Indoor dataset, to evaluate the performance and scalability of the model using multiple hardware nodes, and showed the scaling characteristics of the different model using different model sizes. We find that distributed training is 80% faster using 2 GPUs than 1 GPU. We also find that the model keeps the benefits of distributed training such as speed and accuracy regardless of its size or training batch size.
References
[1]
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, 2016. Tensorflow: a system for large-scale machine learning. In Osdi, Vol. 16. Savannah, GA, USA, 265–283.
[2]
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015).
[3]
Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman. 2014. Project Adam: Building an Efficient and Scalable Deep Learning Training System. In 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14). USENIX Association, Broomfield, CO, 571–582. https://www.usenix.org/conference/osdi14/technical-sessions/presentation/chilimbi
[4]
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc' aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc Le, and Andrew Ng. 2012. Large Scale Distributed Deep Networks. In Advances in Neural Information Processing Systems, F. Pereira, C.J. Burges, L. Bottou, and K.Q. Weinberger (Eds.). Vol. 25. Curran Associates, Inc.https://proceedings.neurips.cc/paper_files/paper/2012/file/6aca97005c68f1206823815f66102863-Paper.pdf
[5]
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. 2018. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. arxiv:1706.02677 [cs.CV]
[6]
Renlong Hang, Qingshan Liu, Danfeng Hong, and Pedram Ghamisi. 2019. Cascaded recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57, 8 (2019), 5384–5394.
[7]
Xianyan Jia, Shutao Song, Wei He, Yangzihao Wang, Haidong Rong, Feihu Zhou, Liqiang Xie, Zhenyu Guo, Yuanzhou Yang, Liwei Yu, Tiegang Chen, Guangxiao Hu, Shaohuai Shi, and Xiaowen Chu. 2018. Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes. arxiv:1807.11205 [cs.LG]
[8]
Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, Rick Boyle, Pierre-luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell, Mike Daley, Matt Dau, Jeffrey Dean, Ben Gelb, Tara Vazir Ghaemmaghami, Rajendra Gottipati, William Gulland, Robert Hagmann, C. Richard Ho, Doug Hogberg, John Hu, Robert Hundt, Dan Hurt, Julian Ibarz, Aaron Jaffey, Alek Jaworski, Alexander Kaplan, Harshit Khaitan, Daniel Killebrew, Andy Koch, Naveen Kumar, Steve Lacy, James Laudon, James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross, Matt Ross, Amir Salek, Emad Samadiani, Chris Severn, Gregory Sizikov, Matthew Snelham, Jed Souter, Dan Steinberg, Andy Swing, Mercedes Tan, Gregory Thorson, Bo Tian, Horia Toma, Erick Tuttle, Vijay Vasudevan, Richard Walter, Walter Wang, Eric Wilcox, and Doe Hyun Yoon. 2017. In-Datacenter Performance Analysis of a Tensor Processing Unit. In Proceedings of the 44th Annual International Symposium on Computer Architecture (Toronto, ON, Canada) (ISCA ’17). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3079856.3080246
[9]
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. 2017. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. arxiv:1609.04836 [cs.LG]
[10]
Amith R Mamidala, Georgios Kollias, Chris Ward, and Fausto Artico. 2018. MXNET-MPI: Embedding MPI parallelism in Parameter Server Task Model for scaling Deep Learning. arxiv:1801.03855 [cs.DC]
[11]
Stefano Markidis, Steven Wei Der Chien, Erwin Laure, Ivy Bo Peng, and Jeffrey S. Vetter. 2018. NVIDIA Tensor Core Programmability, Performance & Precision. In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 522–531. https://doi.org/10.1109/IPDPSW.2018.00091
[12]
Antonio Plaza, Jon Atli Benediktsson, Joseph W. Boardman, Jason Brazile, Lorenzo Bruzzone, Gustavo Camps-Valls, Jocelyn Chanussot, Mathieu Fauvel, Paolo Gamba, Anthony Gualtieri, Mattia Marconcini, James C. Tilton, and Giovanna Trianni. 2009. Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment 113 (2009), S110–S122. https://doi.org/10.1016/j.rse.2007.07.028 Imaging Spectroscopy Special Issue.
[13]
Ariadna Quattoni and Antonio Torralba. 2009. Recognizing indoor scenes. In 2009 IEEE conference on computer vision and pattern recognition. IEEE, 413–420.
[14]
Rajat Raina, Anand Madhavan, and Andrew Y. Ng. 2009. Large-Scale Deep Unsupervised Learning Using Graphics Processors. In Proceedings of the 26th Annual International Conference on Machine Learning (Montreal, Quebec, Canada) (ICML ’09). Association for Computing Machinery, New York, NY, USA, 873–880. https://doi.org/10.1145/1553374.1553486
[15]
Zhenheng Tang, Shaohuai Shi, Xiaowen Chu, Wei Wang, and Bo Li. 2020. Communication-Efficient Distributed Deep Learning: A Comprehensive Survey. ArXiv abs/2003.06307 (2020).
[16]
Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg, Tim Verbelen, and Jan S. Rellermeyer. 2020. A Survey on Distributed Machine Learning. ACM Comput. Surv. 53, 2, Article 30 (mar 2020), 33 pages. https://doi.org/10.1145/3377454
[17]
Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, and Xiaowen Chu. 2020. Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training. arxiv:1909.06842 [cs.DC]
[18]
Paul Zikopoulos, Chris Eaton, and IBM. 2011. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data (1st ed.). McGraw-Hill Osborne Media.
Index Terms
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Published In
December 2023
808 pages
ISBN:9798400709036
DOI:10.1145/3644713
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
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ICFNDS '23
ICFNDS '23: The International Conference on Future Networks and Distributed Systems
December 21 - 22, 2023
Dubai, United Arab Emirates
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