
exporting to C++-only runtimes for use in larger projects
optimizing mobile systems on iPhone, Android, Qualcomm and other systems
using more efficient data layouts and performing kernel fusion to do faster inference (saving 10% of speed or memory at scale is a big win)
quantized inference (such as 8-bit inference)

Startups, large companies and anyone who wants to build a product around PyTorch have asked for production support. At Facebook (the largest stakeholder for PyTorch) we have Caffe2, which has been the production-ready platform, running in our datacenters and shipping to more than 1 billion phones spanning eight generations of iPhones and six generations of Android CPU architectures. It has server-optimized inference on Intel / ARM, TensorRT support, and all the necessary bits for production. Considering all this value locked-in to a platform that the PyTorch team works quite closely with, we decided to marry PyTorch and Caffe2 which gives the production-level readiness for PyTorch.