In this paper, researchers highlight dataloaders as key to improving ML training, comparing libraries for functionality, usability, and performance.
Authors: Iason Ofeidis, Department of Electrical Engineering, and Yale Institute for Network Science, Yale University, New Haven {Equal contribution}; Diego Kiedanski, Department of Electrical Engineering, and Yale Institute for Network Science, Yale University, New Haven {Equal contribution}; Leandros TassiulasLevon Ghukasyan, Activeloop, Mountain View, CA, USA, Department of Electrical Engineering, and Yale Institute for Network Science, Yale University, New Haven.
International Symposium on Workload Characterization , pp. 1–10. access, 6:64270–64277, 2018 Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A. A. Albumentations: fast and flexible image augmentations. Information, 11: 125, 2020. Coleman, C., Kang, D., Narayanan, D., Nardi, L., Zhao, T., Zhang, J., Bailis, P., Olukotun, K., Re, C., and Zaharia, ´ M. Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark. ACM SIGOPS Operating Systems Review, 53:14–25, 2019. Gao, W., Tang, F., Zhan, J.
Association. Tao, J.-H., Du, Z.-D., Guo, Q., Lan, H.-Y., Zhang, L., Zhou, S.-Y., Xu, L.-J., Liu, C., Liu, H.-F., Tang, S., et al. Benchip: Benchmarking intelligence processors. Journal of Computer Science and Technology, 33:1–23, 2018. Team, A. D. Hub: A dataset format for ai. a simple api for creating, storing, collaborating on ai datasets of any size & streaming them to ml frameworks at scale. GitHub. Note: https://github.com/activeloopai/Hub, 2022a. Team, S. D.
In this paper, we explored the current landscape of Pytorch libraries that allow machine learning practitioners to load their datasets into their models. These libraries offer a wide array of features from increased speed, creating views of only a subset of the data, and loading data from remote storage. We believe that remote loading holds the most promise for all these features since it enables the de-coupling of data storage and model training.
Baidu-Research. DeepBench, 2020. URL https:// github.com/baidu-research/DeepBench. Ben-Nun, T., Besta, M., Huber, S., Ziogas, A. N., Peter, D., and Hoefler, T. A modular benchmarking infrastructure for high-performance and reproducible deep learning. In 2019 International Symposium on Workload Characterization , pp. 35–48.
https://www.usenix.org/conference/ fast20/presentation/kumar. Leclerc, G., Ilyas, A., Engstrom, L., Park, S. M., Salman, H., and Madry, A. ffcv. https://github.com/ libffcv/ffcv/, 2022. commit xxxxxxx. Li, S., Zhao, Y., Varma, R., Salpekar, O., Noordhuis, P., Li, T., Paszke, A., Smith, J., Vaughan, B., Damania, P., et al. Pytorch distributed: Experiences on accelerating data parallel training. arXiv preprint arXiv:2006.15704, 2020. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P.
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