Resbench: an Analysis of Deep Learning Frameworks for Image Classification
Abstract
Deep learning frameworks are of particular note, as they have been shown to drastically alter the performance of Deep Learning while training, as well as the final result of the training, despite the theoretically identical math being done while training. This study expands upon the works of “DLBench: a comprehensive experimental evaluation of deep learning frameworks” by Radwa Elshawi, Abdul Wahab, Ahmed Barnawi & Sherif Sakr through testing only ResNet50v1.5 with the frameworks TensorFlow, PyTorch, and MxNet, without changing the architecture. This gives us less variation between framework tests, as the DLBench study used different architectures as well as different databases when testing, leaving the reason for changes in performance up to multiple indistinguishable factors. Our study performed tests using 2 Tesla V100 GPUs from Nvidia, and was evaluated with each framework on 2 datasets: CIFAR100 and ImageNet.
Resbench: an Analysis of Deep Learning Frameworks for Image Classification
HSS 209
Deep learning frameworks are of particular note, as they have been shown to drastically alter the performance of Deep Learning while training, as well as the final result of the training, despite the theoretically identical math being done while training. This study expands upon the works of “DLBench: a comprehensive experimental evaluation of deep learning frameworks” by Radwa Elshawi, Abdul Wahab, Ahmed Barnawi & Sherif Sakr through testing only ResNet50v1.5 with the frameworks TensorFlow, PyTorch, and MxNet, without changing the architecture. This gives us less variation between framework tests, as the DLBench study used different architectures as well as different databases when testing, leaving the reason for changes in performance up to multiple indistinguishable factors. Our study performed tests using 2 Tesla V100 GPUs from Nvidia, and was evaluated with each framework on 2 datasets: CIFAR100 and ImageNet.
