Deep learning is slowly, but steadily, hitting a memory bottleneck. While the
tensor computation in top-of-the-line GPUs increased by 32x over the last five
years, the total available memory only grew by 2.5x. This prevents researchers
from exploring larger architectures, as training large networks requires more
memory for storing intermediate outputs. In this paper, we present MONeT, an
automatic framework that minimizes both the memory footprint and computational
overhead of deep networks. MONeT jointly optimizes the checkpointing schedule
and the implementation of various operators. MONeT is able to outperform all
prior hand-tuned operations as well as automated checkpointing. MONeT reduces
the overall memory requirement by 3x for various PyTorch models, with a 9-16%
overhead in computation. For the same computation cost, MONeT requires 1.2-1.8x
less memory than current state-of-the-art automated checkpointing frameworks.
Our code is available at this https URL

本文介绍了 MONeT，一个自动框架，通过最小化深度神经网络的内存占用和计算负荷，优化检查点计划和各种算子的实现，实现了总体内存需求减少 3 倍的效果，能够优于所有以前的手调操作以及自动检查点。