1) Hermetic CUDA rules allow building wheels with GPU support on a machine without GPUs, as well as running Bazel GPU tests on a machine with only GPUs and NVIDIA driver installed. When `--config=cuda` is provided in Bazel options, Bazel will download CUDA, CUDNN and NCCL redistributions in the cache, and use them during build and test phases.

    [Default location of CUNN redistributions](https://developer.download.nvidia.com/compute/cudnn/redist/)

    [Default location of CUDA redistributions](https://developer.download.nvidia.com/compute/cuda/redist/)

    [Default location of NCCL redistributions](https://pypi.org/project/nvidia-nccl-cu12/#history)

2) To include hermetic CUDA rules in your project, add the following in the WORKSPACE of the downstream project dependent on XLA.

   Note: use `@local_tsl` instead of `@tsl` in Tensorflow project.

   ```
   load(
      "@tsl//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl",
      "cuda_json_init_repository",
   )

   cuda_json_init_repository()

   load(
      "@cuda_redist_json//:distributions.bzl",
      "CUDA_REDISTRIBUTIONS",
      "CUDNN_REDISTRIBUTIONS",
   )
   load(
      "@tsl//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl",
      "cuda_redist_init_repositories",
      "cudnn_redist_init_repository",
   )

   cuda_redist_init_repositories(
      cuda_redistributions = CUDA_REDISTRIBUTIONS,
   )

   cudnn_redist_init_repository(
      cudnn_redistributions = CUDNN_REDISTRIBUTIONS,
   )

   load(
      "@tsl//third_party/gpus/cuda/hermetic:cuda_configure.bzl",
      "cuda_configure",
   )

   cuda_configure(name = "local_config_cuda")

   load(
      "@tsl//third_party/nccl/hermetic:nccl_redist_init_repository.bzl",
      "nccl_redist_init_repository",
   )

   nccl_redist_init_repository()

   load(
      "@tsl//third_party/nccl/hermetic:nccl_configure.bzl",
      "nccl_configure",
   )

   nccl_configure(name = "local_config_nccl")
   ```

PiperOrigin-RevId: 662981325