# 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.

   
   **1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**
   
   
   Make sure you are using compatible TF and CUDA versions.
   Please refer following TF version and CUDA version compatibility table.
   
   | TF  | CUDA |
   
   | :-------------: | :-------------: |
   
   | 2.5.0  | 11.2 |
   
   | 2.4.0  | 11.0 |
   
   | 2.1.0 - 2.3.0  | 10.1 |
   

fi
# VERY basic check to ensure the [and-cuda] package variant is installable.
# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
# relevant. All of the GPU test machines have CUDA installed via other means,
# so I am not sure how to verify that the dependencies themselves are valid for
# the moment.
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then

build:mkl_aarch64_threadpool -c opt

# CUDA: This config refers to building CUDA op kernels with nvcc.
build:cuda --repo_env TF_NEED_CUDA=1
build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain
build:cuda --@local_config_cuda//:enable_cuda

# CUDA: This config refers to building CUDA op kernels with clang.
build:cuda_clang --config=cuda
# Enable TensorRT optimizations https://developer.nvidia.com/tensorrt

    // --config=cuda.  I have not looked closely at why that is happening, but
    // most likely some of the partial declustering passes behave differently
    // with --config=cuda because of different HostMemory.  So for now only test
    // the non-CUDA config, under the assumption that regressions with
    // --config=cuda would also be detected as regressions without
    // --config=cuda.

        "keras_imagenet_main_graph_mode.pbtxt",
        "opens2s_gnmt_mixed_precision.golden_summary",
        "opens2s_gnmt_mixed_precision.pbtxt.gz",
    ],
    tags = ["config-cuda-only"],
    deps = [
        ":auto_clustering_test_helper",
        "//tensorflow/core:test",
        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "device_compiler_test_helper",

  """set TF_CUDA_CLANG action_env.

  Args:
    environ_cp: copy of the os.environ.
  """
  question = 'Do you want to use clang as CUDA compiler?'
  yes_reply = 'Clang will be used as CUDA compiler.'
  no_reply = 'nvcc will be used as CUDA compiler.'
  set_action_env_var(
      environ_cp,
      'TF_CUDA_CLANG',
      None,
      True,
      question=question,
      yes_reply=yes_reply,

    done < $BATS_TEST_TMPDIR/missing_deps
    exit 1
  fi
}

# The Python package is not allowed to depend on any CUDA packages.
@test "Pip package doesn't depend on CUDA" {
  bazel cquery \
    --experimental_cc_shared_library \
    --@local_config_cuda//:enable_cuda \
    "somepath(//tensorflow/tools/pip_package:wheel, " \

# Install devtoolset devel dependencies
COPY setup.sources.sh /setup.sources.sh
COPY setup.packages.sh /setup.packages.sh
COPY devel.packages.txt /devel.packages.txt
COPY cuda.packages.txt /cuda.packages.txt
RUN /setup.sources.sh && /setup.packages.sh /devel.packages.txt

# Install various tools.
# - bats: bash unit testing framework
#         NOTE: v1.6.0 seems to have a bug that made "git" in setup_file break

    done < $BATS_TEST_TMPDIR/missing_deps
    exit 1
  fi
}

# The Python package is not allowed to depend on any CUDA packages.
@test "Pip package doesn't depend on CUDA" {
  bazel cquery \
    --experimental_cc_shared_library \
    --@local_config_cuda//:enable_cuda \
    "somepath(//tensorflow/tools/pip_package:build_pip_package, " \