# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.1.1.17-1
libcudnn9-cuda-12=9.1.1.17-1

# This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
# For now it ensures that we install at least version 12.3.107 of PTXAS,
# since 12.3.103 has a bug.

       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
       * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).
     * If you have above configuration and using _**Ubuntu/Linux**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.

    for more details.

## Deprecations

*   TensorFlow 1.2 may be the last time we build with cuDNN 5.1. Starting with
    TensorFlow 1.3, we will try to build all our prebuilt binaries with cuDNN
    6.0. While we will try to keep our source code compatible with cuDNN 5.1, it
    will be best effort.

## Breaking Changes to the API

        'cuda', 'HERMETIC_CUDA_VERSION', hermetic_cuda_version
    )


def set_hermetic_cudnn_version(environ_cp):
  """Set HERMETIC_CUDNN_VERSION."""
  ask_cudnn_version = (
      'Please specify the hermetic cuDNN version you want to use '
      'or leave empty to use the default version. '
  )
  hermetic_cudnn_version = get_from_env_or_user_or_default(
      environ_cp, 'HERMETIC_CUDNN_VERSION', ask_cudnn_version, None
  )

build:cuda --repo_env TF_NEED_CUDA=1
build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain
build:cuda --@local_config_cuda//:enable_cuda
# Default CUDA and CUDNN versions.
build:cuda --repo_env=HERMETIC_CUDA_VERSION="12.5.1"
build:cuda --repo_env=HERMETIC_CUDNN_VERSION="9.3.0"
# This flag is needed to include CUDA libraries.
build:cuda --@local_config_cuda//cuda:include_cuda_libs=true