pytorch

ce56ee11 - Extend torch.cuda.is_available() to attempt an NVML-based CUDA availability assessment when explicitly requested by the user (#85951)

Extend torch.cuda.is_available() to attempt an NVML-based CUDA availability assessment when explicitly requested by the user (#85951) 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 https://github.com/pytorch/pytorch/commit/81da50a972fc402a6dd880fe392af0f0051cb6de !). Though one could always patch out the `torch.cuda.is_available` function with another implementation in a downstream library, I think this environmental variable based configuration option is more convenient and the cost to including the option is quite low. As discussed in https://github.com/pytorch/pytorch/pull/85024#issuecomment-1261542045, this PR gates new non-default NVML-based CUDA behavior with an environmental variable (PYTORCH_NVML_BASED_CUDA_CHK) that allows a user/framework to invoke non-default, NVML-based `is_available()` assessments if desired. Thanks again for your work everyone! @ngimel @malfet @awaelchli Pull Request resolved: https://github.com/pytorch/pytorch/pull/85951 Approved by: https://github.com/ngimel