"cuda_configure",
)

cuda_configure(name = "local_config_cuda")

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

nccl_redist_init_repository()

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

    set_hermetic_cuda_compute_capabilities(environ_cp)
    set_cuda_local_path(environ_cp, 'CUDA', 'LOCAL_CUDA_PATH')
    set_cuda_local_path(environ_cp, 'CUDNN', 'LOCAL_CUDNN_PATH')
    set_cuda_local_path(environ_cp, 'NCCL', 'LOCAL_NCCL_PATH')

    if 'LD_LIBRARY_PATH' in environ_cp and environ_cp.get(
        'LD_LIBRARY_PATH') != '1':
      write_action_env_to_bazelrc('LD_LIBRARY_PATH',

#     cuda_clang    Build with CUDA Clang support.
#     rocm:         Build with AMD GPU support (rocm)
#     mkl:          Enable full mkl support.
#     nogcp:        Disable GCS support.
#     nonccl:       Disable nccl support.
#
#
# Remote build execution options (only configured to work with TF team projects for now.)
#     rbe_base:  General RBE options shared by all flavors.
#     rbe_linux: General RBE options used on all linux builds.

## Breaking Changes

*   Prebuilt binaries are now (as of TensorFlow 1.10) built against NCCL 2.2 and
    no longer include NCCL in the binary install. TensorFlow usage with multiple
    GPUs and NCCL requires upgrade to
    [NCCL 2.2](https://developer.nvidia.com/nccl). See updated install guides:
    [TensorFlow GPU support](https://www.tensorflow.org/install/gpu) and