I would like to use the C++ api for inference. Though that’s fine on heavy duty machines, it’s very hard to do on low power, low resource, even embedded devices. I had trouble compiling libtorch on a raspberry pi 3. It kept on running out of memory and eventually crashed. It would be great if we could simplify a CPU-only build that produced a shared library and a monolithic static library (that contains all its dependencies). Ideally, it would be great if we could get it to a stage where we could build libtorch for Android, raspberry pi zero and even more embedded devices. Currently I train with the pytorch api, convert to onnx and use another lightweight framework for inference. There’s no real problem with that. But if you’re looking for ideas, I would pitch “pytorch-lite” or “pytorch-embedded”. In that light, maybe an OpenCL or Vulkan backend would be a good idea. By the way Tencent and Alibaba are using Vulkan now.