  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.

Computer Vision and Pattern Recognition