References & Citations
Computer Science > Machine Learning
Title: BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling
(Submitted on 16 Oct 2023 (v1), last revised 25 Apr 2024 (this version, v2))
Abstract: 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.
Submission history
From: Raphael Ruschel Dos Santos [view email][v1] Mon, 16 Oct 2023 23:14:56 GMT (1645kb,D)
[v2] Thu, 25 Apr 2024 18:06:46 GMT (1593kb,D)
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