Deep learning methods have resulted in significant performance improvements in several application domains and as such several software frameworks have been developed to facilitate their implementation. This paper presents a comparative study of four deep learning frameworks, namely Caffe, Neon, Theano, and Torch, on three aspects: extensibility, hardware utilization, and speed. The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings. The speed performance metrics used here include the gradient computation time, which is important during the training phase of deep networks, and the forward time, which is important from the deployment perspective of trained networks. For convolutional networks, we also report how each of these frameworks support various convolutional algorithms and their corresponding performance. From our experiments, we observe that Theano and Torch are the most easily extensible frameworks. We observe that Torch is best suited for any deep architecture on CPU, followed by Theano. It also achieves the best performance on the GPU for large convolutional and fully connected networks, followed closely by Neon. Theano achieves the best performance on GPU for training and deployment of LSTM networks. Finally Caffe is the easiest for evaluating the performance of standard deep architectures.

本文比较了五种深度学习框架（Caffe，Neon，TensorFlow，Theano和Torch）在三个方面的性能（可扩展性，硬件利用率和速度指标），并评估了它们在训练和部署各种深度学习结构时的性能。 研究结果表明：Theano和Torch是最易于扩展的框架，中央处理器上最适用的框架是Torch，然后是Theano，此外Caffe可以轻松地评估标准深度结构的性能，TensorFlow是非常灵活的框架，但目前其性能与其他研究的框架相比不具竞争力。

深度学习软件框架的比较研究