use HAS_GPU to determine of cuda is available

[jperez999]

This PR changes how we determine if cuda is available on the system. We move from numba to using HAS_GPU which uses nvml device count. If there are no devices, then cuda is not available. Otherwise cuda is available.

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Documentation preview

https://nvidia-merlin.github.io/core/review/pr-364

[pentschev]

Although this is tripping that block, I would suggest always using PyNVML to query GPU information, specifically what I mention in https://github.com/NVIDIA-Merlin/core/issues/363#issuecomment-1888595036 can be dangerous with Dask if for some reason the cuda = None is removed in the future.

[jperez999]

This is not ready, the failures during writing have to do with when you are writing a file with a client available. Will continue investigating.

[jperez999]

Investigated seems that the logic for int_slice_size was not full proof. Because of the floor divide you can find yourself in a scenario where you have less records in the df than the int_slice_size and that can result in a zero. Then when you go to mod on zero the thread raises an exception. I do wonder how we hit this now and not before.

[jperez999]

/ok to test

[rjzamora]

I do wonder how we hit this now and not before.

I agree that this is strange - I wonder if I was wrong about pynvml_mem_size be "the same".

[jperez999]

/ok to test

[jperez999]

/ok to test