PyTorch 1.0 is now offering optimizations for production deployment.
NVIDIA TensorRT also offers optimizations for production deployment. From https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html
"It includes parsers for importing existing models from Caffe, ONNX, or TensorFlow, and C++ and Python APIs for building models programmatically.’’
It seems, TensorRT does not support PyTorch models yet.
If we develop in PyTorch, it is of course preferable to do everything (training & production deployment) in PyTorch.
Question: In terms of deployment, which one should be preferred? PyTorch or TensorRT? Is there any optimization that TensorRT is doing better than PyTorch?