We have a containerized PyTorch that runs neural networks.
When we run this on Linux PC it is quite fast.
But when we switch to Windows PC that runs HyperV, that runs WSL, that runs Docker, that runs Ubuntu, that runs PyTorch, it is quite slower. Maybe 4 to 10 time slower.
There are several potential sources for this. Do you know if there are known overhead when running PyTorch with this type of configuration?
Of course this seems something quite stupid to do but there are complex constraints due to the rest of the project.