This is a very interesting claim. I find it credible because it stands to reason that projects like DeepSpeed[1] and TorchScript[2] wouldn't need to exist if in

This is a very interesting claim. I find it credible because it stands to reason that projects like DeepSpeed[1] and TorchScript[2] wouldn't need to exist if inference performance of research PyTorch models was satisfactory for production, but often case it isn't. It appears as though Flashlight is built on ArrayFire. I haven't seen how gradients are managed in arrayfire-ml, but perhaps it is the case that the autograd implementation in PyTorch was a bottleneck and this is a ground up approach. Editing as I didn't address your second point. I can neither confirm or deny the motivations for creating Torch being related to FB's desire to depend on an Alphabet-managed codebase. I know there are lots of reasons why programmers prefer the UX of the PyTorch Python API (I do as well), and there are probably other reasons I can't recall off the top of my head. I am only saying that PyTorch is contributing to the ML ecosystem already by the sole virtue that it isn't a Google product. [1] = https://github.com/microsoft/DeepSpeed https://github.com/microsoft/DeepSpeed [2] = https://pytorch.org/docs/stable/jit.html https://pytorch.org/docs/stable/jit.html