Most importantly, these modes will be built into the core of PyTorch so that mixing and matching them with your existing code can happen seamlessly.
Note: The name JIT for these components is a bit of a misnomer and comes from historical reasons. The tracing/function execution in PyTorch started out as an optimizing JIT compiler that generated fused CUDA kernels but then grew to encompass optimization, @script, and export. When it is ready for release we will likely rename this functionality to the hybrid frontend, but we wanted to present it here as it is named in the code so that you can follow along as we develop it.
Other changes and improvements
Production support is the big feature for 1.0, but we will continue optimizing and fixing other parts of PyTorch as course of the standard release process.