Add support for testing with minimum supported Nvidia Drivers to release validations

[atalman]

To avoid issues like these: pytorch/pytorch#130684
I would like to add support for testing with minimum supported Nvidia Drivers to release validations

1. Add nvidia-driver parameter to linux_job.yml:
   https://github.com/pytorch/test-infra/blob/main/.github/workflows/linux_job.yml

2. Make sure we pass this parameter to:
   https://github.com/pytorch/test-infra/blob/main/.github/actions/setup-nvidia/action.yml

3. Add option to validate release binaries to https://github.com/pytorch/builder/actions/workflows/validate-binaries.yml

[malfet]

An easier way to accomplish that would probably be having a different AMI with the driver we want..

[atalman]

@malfet not sure. Building AMI and managing it quite a big headache. This should be straight forward .

setup-nvidia action already supports driver-version as a parameter:
https://github.com/pytorch/test-infra/blob/main/.github/actions/setup-nvidia/action.yml#L6

Hence all we have to do is to pass it.

[ptrblck]

Adding more driver tests sounds generally like a valid idea.
However, I don't think pytorch/pytorch#130684 is the best motivation for it, as it's unclear to me if we even support PyTorch + CUDA 11.8 + Triton, see: pytorch/pytorch#106144 (comment)

From the linked issue:

Yes, triton always uses cuda-12

It's somewhat hard to test something like that in CI, as runners are provisioned with the latest kernel driver in order be usable with both CUDA-12 and CUDA-11.8. Also, older driver is less stable, so we run into a multiple hangs/segfaults that were mitigated by installing newer driver.

I would not want to add the risk of using older drivers (unless these are additional tests) to test a potentially invalid or unsupported PyTorch + Triton combination or is Triton now fully supported in our CUDA 11.8 builds?