OpTorch: Optimized deep learning architectures for resource limited environments
This work addresses resource limitations for deep learning practitioners, offering incremental improvements in efficiency.
The paper tackles the problem of high computational resource demands in deep neural network training by proposing OpTorch, an optimized library that reduces memory usage by approximately 50% on Cifar-10 and Cifar-100 datasets while maintaining accuracy.
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