Training a Large Video Model on a Single Machine in a Day

• URL: http://arxiv.org/abs/2309.16669v1
• Date: Thu, 28 Sep 2023 17:59:50 GMT
• Title: Training a Large Video Model on a Single Machine in a Day
• Abstract summary: We show how to train a state-of-the-art video model on a single machine with eight consumer-grade GPUs in a day. We identify three bottlenecks, IO, CPU, and GPU, and optimize each. For comparable architectures, our pipeline achieves higher accuracies with $frac18$ of the computation compared to prior work.
• Score: 5.247398948623659
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Videos are big, complex to pre-process, and slow to train on. State-of-the-art large-scale video models are trained on clusters of 32 or more GPUs for several days. As a consequence, academia largely ceded the training of large video models to industry. In this paper, we show how to still train a state-of-the-art video model on a single machine with eight consumer-grade GPUs in a day. We identify three bottlenecks, IO, CPU, and GPU computation, and optimize each. The result is a highly efficient video training pipeline. For comparable architectures, our pipeline achieves higher accuracies with $\frac{1}{8}$ of the computation compared to prior work. Code is available at https://github.com/zhaoyue-zephyrus/AVION.

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