OpTorch: Optimized deep learning architectures for resource limited environments

TL;DR

OpTorch is a deep learning library optimized for resource-limited environments, reducing memory usage by about 50% while maintaining accuracy on standard datasets through innovative training techniques.

Contribution

The paper introduces OpTorch, a novel deep learning library that enhances training efficiency and reduces memory consumption without sacrificing accuracy.

Findings

Achieved similar accuracy to existing libraries on CIFAR-10 and CIFAR-100.

Reduced memory usage to approximately 50%.

Parallel encoding-decoding with sequential checkpoints improves efficiency.

Abstract

Deep learning algorithms have made many breakthroughs and have various applications in real life. Computational resources become a bottleneck as the data and complexity of the deep learning pipeline increases. In this paper, we propose optimized deep learning pipelines in multiple aspects of training including time and memory. OpTorch is a machine learning library designed to overcome weaknesses in existing implementations of neural network training. OpTorch provides features to train complex neural networks with limited computational resources. OpTorch achieved the same accuracy as existing libraries on Cifar-10 and Cifar-100 datasets while reducing memory usage to approximately 50%. We also explore the effect of weights on total memory usage in deep learning pipelines. In our experiments, parallel encoding-decoding along with sequential checkpoints results in much improved memory and time usage while keeping the accuracy similar to existing pipelines. OpTorch python package is available at available at https://github.com/cbrl-nuces/optorch