Various Frameworks and Libraries of Machine Learning and Deep Learning: A Survey

With the rapid development of deep learning in various fields, the big companies and research teams have developed independent and unique tools. This paper collects 18 common deep learning frameworks and libraries (Caffe, Caffe2, Tensorflow, Theano include Keras Lasagnes and Blocks, MXNet, CNTK, Torch, PyTorch, Pylearn2, Scikit-learn, Matlab include MatconvNet Matlab deep learning and Deep learning tool box, Chainer, Deeplearning4j) and introduces a large number of benchmarking data. In addition, we give the overall score of the current eight mainstream deep learning frameworks from six aspects (model design ability, interface property, deployment ability, performance, framework design and prospects for development). Based on our overview, the deep learning researchers can choose the appropriate development tools according to the evaluation criteria. By summarizing the 18 deep learning frameworks and libraries, we have found that most of the deep learning tools are moving closer to the mobile terminal, and the role of ASICs is gradually emerging. It is believed that the future deep learning applications will be inseparable from the ASIC support.