Although it aims to provide an easy and straightforward way to experiment with DL and leverage community contributions of new models and algorithms, Caffe2 is used at production level at Facebook while development is done in PyTorch. Caffe2 differs from Caffe in several improvement directions, namely by adding mobile deployment and new hardware support (in addition to CPU and CUDA). It is headed towards industrial-strength applications with a heavy focus on mobile. The basic unit of computation in Caffe2 is operator, which is a more flexible version of Caffe’s layer. There are more than 400 different operators available in Caffe2 and more are expected to be implemented by the community. Caffe2 provides command line python scripts capable of translating existing Caffe models into the Caffe2. However, the conversion process needs to perform a manual verification of the accuracy and loss rates. It is possible to convert Torch models to Caffe2 models via Caffe.