Update CUDA_UPGRADE_GUIDE.MD

Author: atalman

CUDA_UPGRADE_GUIDE.MD

@@ -83,8 +83,8 @@ Testing the new version in CI is crucial for finding regressions and should be d
     1. Please check the Driver Version table in [the release notes](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html) to see if a driver update is necessary.
 2. For Linux, we need to update code to use the magma we built! This can be done in the same PR when you actually add Linux CI, but here's an independent example for 11.5: [PR 68665](https://github.com/pytorch/pytorch/pull/68665)
 3. The configuration files will be subject to change, but usually you just have to replace an older CUDA version with the new version you're adding. **Code reference for 11.5**: [PR 68745](https://github.com/pytorch/pytorch/pull/68745) for Linux and [PR 69377](https://github.com/pytorch/pytorch/pull/69377) for Windows, and **code reference for 11.3** where we just replaced verbatim yaml and updated magma for conda for Linux: [PR 57223 for Windows](https://github.com/pytorch/pytorch/pull/57223) and [PR 57222 for Linux](https://github.com/pytorch/pytorch/pull/57222)
-4. For Windows you may need to rebuild the test AMI, please refer to this [PR](https://github.com/pytorch/test-infra/pull/154) . After this is done, run the release of Windows AMI using this [proecedure](https://github.com/pytorch/test-infra/tree/main/aws/ami/windows). As time of this writing this is manual steps performed on dev machine. Please note that packer, aws cli needs to be installed and configured!
-5. After step 4 is complete and new Windows AMI have been deployed to AWS. We need to deploy the new AMI to our canary environment (https://github.com/pytorch/pytorch-canary) through https://github.com/fairinternal/pytorch-gha-infra . After this is completed Submit the code for windows workflows to https://github.com/pytorch/pytorch-canary and make sure all test are passing.
+4. For Windows you will need to rebuild the test AMI, please refer to this [PR](https://github.com/pytorch/test-infra/pull/285) . After this is done, run the release of Windows AMI using this [proecedure](https://github.com/pytorch/test-infra/tree/main/aws/ami/windows). As time of this writing this is manual steps performed on dev machine. Please note that packer, aws cli needs to be installed and configured!
+5. After step 4 is complete and new Windows AMI have been deployed to AWS. We need to deploy the new AMI to our canary environment (https://github.com/pytorch/pytorch-canary) through https://github.com/fairinternal/pytorch-gha-infra example : [PR](https://github.com/fairinternal/pytorch-gha-infra/pull/31) . After this is completed Submit the code for windows workflows to https://github.com/pytorch/pytorch-canary and make sure all test are passing.
 6. After that we can deploy the Windows AMI out to prod using the same pytorch-gha-infra repository.
 7. It is likely that there will be tests that no longer pass with the new CUDA version or GPU driver. Disable them for the time being, notify people who can help, and make issues to track them (like [so](https://github.com/pytorch/pytorch/issues/57482)).