Introduce hermetic CUDA in Google ML projects.

[copybara-service]

Introduce hermetic CUDA in Google ML projects.

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

   Default location of CUDA redistributions

   Default location of NCCL redistributions

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")
   

[cliffwoolley]

As far as I can tell, all my feedback has been addressed now, thanks.
LGTM