BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910423625760768 |
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| author | Ruschel, Raphael Iftekhar, A. S. M. Manjunath, B. S. You, Suya |
| author_facet | Ruschel, Raphael Iftekhar, A. S. M. Manjunath, B. S. You, Suya |
| contents | The increasing complexity of modern deep neural network models and the expanding sizes of datasets necessitate the development of optimized and scalable training methods. In this white paper, we addressed the challenge of efficiently training neural network models using sequences of varying sizes. To address this challenge, we propose a novel training scheme that enables efficient distributed data-parallel training on sequences of different sizes with minimal overhead. By using this scheme we were able to reduce the padding amount by more than 100$x$ while not deleting a single frame, resulting in an overall increased performance on both training time and Recall in our experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_10879 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling Ruschel, Raphael Iftekhar, A. S. M. Manjunath, B. S. You, Suya Machine Learning Distributed, Parallel, and Cluster Computing The increasing complexity of modern deep neural network models and the expanding sizes of datasets necessitate the development of optimized and scalable training methods. In this white paper, we addressed the challenge of efficiently training neural network models using sequences of varying sizes. To address this challenge, we propose a novel training scheme that enables efficient distributed data-parallel training on sequences of different sizes with minimal overhead. By using this scheme we were able to reduce the padding amount by more than 100$x$ while not deleting a single frame, resulting in an overall increased performance on both training time and Recall in our experiments. |
| title | BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2310.10879 |