Benchmarking Neural Network Training Algorithms
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866916798065016832 |
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| author | Dahl, George E. Schneider, Frank Nado, Zachary Agarwal, Naman Sastry, Chandramouli Shama Hennig, Philipp Medapati, Sourabh Eschenhagen, Runa Kasimbeg, Priya Suo, Daniel Bae, Juhan Gilmer, Justin Peirson, Abel L. Khan, Bilal Anil, Rohan Rabbat, Mike Krishnan, Shankar Snider, Daniel Amid, Ehsan Chen, Kongtao Maddison, Chris J. Vasudev, Rakshith Badura, Michal Garg, Ankush Mattson, Peter |
| author_facet | Dahl, George E. Schneider, Frank Nado, Zachary Agarwal, Naman Sastry, Chandramouli Shama Hennig, Philipp Medapati, Sourabh Eschenhagen, Runa Kasimbeg, Priya Suo, Daniel Bae, Juhan Gilmer, Justin Peirson, Abel L. Khan, Bilal Anil, Rohan Rabbat, Mike Krishnan, Shankar Snider, Daniel Amid, Ehsan Chen, Kongtao Maddison, Chris J. Vasudev, Rakshith Badura, Michal Garg, Ankush Mattson, Peter |
| contents | Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workloads (e.g., better update rules, tuning protocols, learning rate schedules, or data selection schemes) could save time, save computational resources, and lead to better, more accurate, models. Unfortunately, as a community, we are currently unable to reliably identify training algorithm improvements, or even determine the state-of-the-art training algorithm. In this work, using concrete experiments, we argue that real progress in speeding up training requires new benchmarks that resolve three basic challenges faced by empirical comparisons of training algorithms: (1) how to decide when training is complete and precisely measure training time, (2) how to handle the sensitivity of measurements to exact workload details, and (3) how to fairly compare algorithms that require hyperparameter tuning. In order to address these challenges, we introduce a new, competitive, time-to-result benchmark using multiple workloads running on fixed hardware, the AlgoPerf: Training Algorithms benchmark. Our benchmark includes a set of workload variants that make it possible to detect benchmark submissions that are more robust to workload changes than current widely-used methods. Finally, we evaluate baseline submissions constructed using various optimizers that represent current practice, as well as other optimizers that have recently received attention in the literature. These baseline results collectively demonstrate the feasibility of our benchmark, show that non-trivial gaps between methods exist, and set a provisional state-of-the-art for future benchmark submissions to try and surpass. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_07179 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Benchmarking Neural Network Training Algorithms Dahl, George E. Schneider, Frank Nado, Zachary Agarwal, Naman Sastry, Chandramouli Shama Hennig, Philipp Medapati, Sourabh Eschenhagen, Runa Kasimbeg, Priya Suo, Daniel Bae, Juhan Gilmer, Justin Peirson, Abel L. Khan, Bilal Anil, Rohan Rabbat, Mike Krishnan, Shankar Snider, Daniel Amid, Ehsan Chen, Kongtao Maddison, Chris J. Vasudev, Rakshith Badura, Michal Garg, Ankush Mattson, Peter Machine Learning Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workloads (e.g., better update rules, tuning protocols, learning rate schedules, or data selection schemes) could save time, save computational resources, and lead to better, more accurate, models. Unfortunately, as a community, we are currently unable to reliably identify training algorithm improvements, or even determine the state-of-the-art training algorithm. In this work, using concrete experiments, we argue that real progress in speeding up training requires new benchmarks that resolve three basic challenges faced by empirical comparisons of training algorithms: (1) how to decide when training is complete and precisely measure training time, (2) how to handle the sensitivity of measurements to exact workload details, and (3) how to fairly compare algorithms that require hyperparameter tuning. In order to address these challenges, we introduce a new, competitive, time-to-result benchmark using multiple workloads running on fixed hardware, the AlgoPerf: Training Algorithms benchmark. Our benchmark includes a set of workload variants that make it possible to detect benchmark submissions that are more robust to workload changes than current widely-used methods. Finally, we evaluate baseline submissions constructed using various optimizers that represent current practice, as well as other optimizers that have recently received attention in the literature. These baseline results collectively demonstrate the feasibility of our benchmark, show that non-trivial gaps between methods exist, and set a provisional state-of-the-art for future benchmark submissions to try and surpass. |
| title | Benchmarking Neural Network Training Algorithms |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2306.07179 |