I've hit problems a couple of times with compute intensive data loading tasks in Pytorch. One involved custom data augmentations on spatial graphs. I couldn't

I've hit problems a couple of times with compute intensive data loading tasks in Pytorch. One involved custom data augmentations on spatial graphs. I couldn't find any python libraries that offered performant implementations of the functions needed and ended up writing custom C++ extensions to call from python. The other involved running video encoders and decoders inside the dataloader. The problem was more Pytorch specific than python per se, but the way Pytorch handles multiprocessing and CUDA streams made efficient parallelism very difficult. It would have been much easier in C++. I've also needed to ditch python for ML inference in production when extremely low latency was required.