Framework comparison

Though a number of deep learning software frameworks exist, it is hard to understand their feature parity. The table Feature parity of DL frameworks outlines each of these frameworks with their feature parity:

Recently, Shaohuai Shi and their co-authors in their paper (https://arxiv.org/pdf/1608.07249.pdf) also presented a comprehensive performance benchmarking of four popular frameworks from as preceding: Caffe, CNTK, TensorFlow and Torch. They first benchmark the performance of these frameworks on three most popular types of neural networks—fully connected neural network (FCN), CNN, and recurrent neural network (RNN). They also benchmark performance of these systems when they use multiple GPUs as well as CPUs.

In their paper, they outline the comparative performance of all the systems. Their experimental results demonstrate that all the frameworks can utilize GPUs very efficiently and show performance gains over CPUs. However, there is still no clear winner among all of them, which suggests there are still improvements to be made across all of these frameworks.