The Exchange makes it easier for machine learning developers to convert models between PyTorch and Caffe2 to reduce the lag time between research and productization.
Facebook has long maintained the distinction between its FAIR and AML machine learning groups. Facebook AI Research (FAIR) handles bleeding edge research while Applied Machine Learning (AML) brings intelligence to products.
Choice of deep learning framework underlies this key ideological distinction. FAIR is accustomed to working with PyTorch — a deep learning framework optimized for achieving state of the art results in research, regardless of resource constraints.
Unfortunately in the real world, most of us are limited by the computational capabilities of our smartphones and computers. When AML wants to build something for deployment and scale, it opts for Caffe2. Caffe2 is also a deep learning framework but it’s optimized for resource efficiency, particularly with respect to Caffe2Go that’s optimized for running machine learning models on underpowered mobile devices.
