I hope so too. But hasn't Julia's TF/Torch equivalent, Flux, had performance problems? That was the rumor I heard anyway, I haven't had the chance to use it myself.
This is slated to be fixed in the short term with an abstract tracing framework which eliminates memory allocations. Given Julia's type information and ability to manipulate IR from third party programs, this is more general and powerful than pytorch's tracing system. Works on a larger set of code (the whole language), doesn't require actually running the code (abstract tracing), and allows for other program transforms/ analysis like compilation to XLA, shape inference, compile time errors, source to source prob programming : https://github.com/MikeInnes/Poirot.jl, and other things.
That's in the short term and should bring flux up to SOTA for speed (it already is on CPU). In the medium term, a general framework for optimizer passes will allow for more general compile time memory management.
As with most things in Julia, the code developers don’t just want to hack changes that work, but make changes that are flexible, extensible, and can solve many problems at once. So, Flux isn’t ready for prime time yet, but it is definitely worth keeping your eye on it.