Hi PyTorch team,
What is the recommended approach for deploying python trained models to a high performance c++ runtime (ideally supporting accelerators) as of October 2019?
There seem to be many approaches right now and I’m confused as to:

What is the best way right now?
What will be the best way in 6-12 months? (I.e. where are we headed?)

Use case: We need the flexibility of Python for training but the performance of TensorRT (ideally) for deployment in a c++ env.

What is the ultimate goal for torchscript/libtorch? Is it just for converting into c++? Or will it also provide direct support for hardware acceleration?
Will torchscript/libtorch replace the need for Caffe2? How does ONNX fit into this?
What is the recommended optimized runtime format? (Onnxruntime, glow, tensorrt, tvm)? Will your recommendation change in 6-12 months?
Do we have any performance benchmarks for each of these formats? Python vs Libtorch? Libtorch vs Glow?

Apologies for the questions. Perhaps someone can write a summary article to cover this topic?
Thanks in advance!