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You didn't look hard enough.

ML on CUDA is very much part of Julia already, and Meta seems into that lib as well. =)

Is it good? PyCUDA was so bad (as of 2018) that after 3 major bugfixes from me and no end in sight it still almost sank a major project until I gave up on it and went with native CUDA.

Anecdotally, the Julia approach certainly seems... nicer via e.g. KernelAbstractions etc. I don't know if the performance/flexibility is quite there vs. native CUDA, but going from zero to GPU kernel programming in Julia is as close to painless as I've ever seen, especially given how modular everything is (for example, you can use OffsetArrays.jl directly in GPU kernel code).

"Is it good?"

Compared to the CUDA dumpster fire at a cat food factory, it is often trivial and nearly transparent syntax for the users familiar with ML.

Really depends on the use-case =)

The conventional options are fairly well documented:

https://sciml.ai/

https://fluxml.ai/Flux.jl/stable/gpu/

https://github.com/SciML/DiffEqFlux.jl

https://github.com/alan-turing-institute/MLJ.jl

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