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Fixes #83973 (This is a substitute PR for https://github.com/pytorch/pytorch/pull/85024)
First of all, thanks for your invaluable contributions to PyTorch everyone!
Given how extensively `torch.cuda.is_available` is used in the PyTorch ecosystem, IMHO it's worthwhile to provide downstream libraries/frameworks/users the ability to alter the default behavior of `torch.cuda.is_available` in the context of their PyTorch usage.
I'm confident there are many current and future such use cases which could benefit from leveraging a weakened, NVML-based `torch.cuda.is_available` assessment at a downstream framework's explicit direction (thanks @malfet
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| amp_examples.rst | ||
| autograd.rst | ||
| broadcasting.rst | ||
| cpu_threading_runtimes.svg | ||
| cpu_threading_torchscript_inference.rst | ||
| cpu_threading_torchscript_inference.svg | ||
| cuda.rst | ||
| ddp.rst | ||
| extending.rst | ||
| faq.rst | ||
| gradcheck.rst | ||
| hip.rst | ||
| large_scale_deployments.rst | ||
| modules.rst | ||
| mps.rst | ||
| multiprocessing.rst | ||
| numerical_accuracy.rst | ||
| randomness.rst | ||
| serialization.rst | ||
| windows.rst | ||
