Features for large-scale deployments
* Fleet-wide operator profiling


API usage logging


Attaching metadata to saved TorchScript models


Build environment considerations


Common extension points


This note talks about several extension points and tricks that might
be useful when running PyTorch within a larger system or operating
multiple systems using PyTorch in a larger organization.
It doesn't cover topics of deploying models to production. Check
"torch.jit" or one of the corresponding tutorials.
The note assumes that you either build PyTorch from source in your
organization or have an ability to statically link additional code to
be loaded when PyTorch is used. Therefore, many of the hooks are
exposed as C++ APIs that can be triggered once in a centralized place,
e.g. in static initialization code.
Fleet-wide operator profiling
PyTorch comes with "torch.autograd.profiler" capable of measuring time