**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 |
   

    write_repo_env_to_bazelrc('cuda', env_var, local_path)


def set_other_cuda_vars(environ_cp):
  """Set other CUDA related variables."""
  # If CUDA is enabled, always use GPU during build and test.
  if environ_cp.get('TF_CUDA_CLANG') == '1':
    write_to_bazelrc('build --config=cuda_clang')
  else:
    write_to_bazelrc('build --config=cuda')

#     release_cpu_linux:               Toolchain and CUDA options for Linux CPU builds.
#     release_gpu_linux:               Toolchain and CUDA options for Linux GPU builds.
#     release_cpu_macos:               Toolchain and CUDA options for MacOS CPU builds.
#     release_cpu_windows:             Toolchain and CUDA options for Windows CPU builds.
# LINT.IfChange

        flag.

        ```bash
        export flags="--config=linux --config=cuda -k"
        ```

    *   For TensorFlow versions prior v.2.18.0: Add CUDA paths to
        LD_LIBRARY_PATH and add the `cuda` option flag.

        ```bash
        export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"

export DEBIAN_FRONTEND=noninteractive

# Fetch the NVIDIA key.
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub;

# Set up sources for NVIDIA CUDNN.
cat >/etc/apt/sources.list.d/nvidia.list <<SOURCES
# NVIDIA
deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /

RUN ln -sf /usr/bin/python3.12 /usr/bin/python
RUN ln -sf /usr/lib/python3.12 /usr/lib/tf_python

# Link the compat driver to the location if available.
RUN if [ -e "/usr/local/cuda/compat/libcuda.so.1" ]; then ln -s /usr/local/cuda/compat/libcuda.so.1 /usr/lib/x86_64-linux-gnu/libcuda.so.1; fi

# Install various tools.
# - bats: bash unit testing framework
# - bazelisk: always use the correct bazel version

    "$python" -m pip install numpy==1.26.4
  else
    "$python" -m pip install numpy==1.26.0
  fi
fi
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then
  echo "Checking to make sure tensorflow[and-cuda] is installable..."
  "$python" -m pip install "$(echo *.whl)[and-cuda]" $TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS
else
  "$python" -m pip install *.whl $TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS
fi

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

# Config setting that is satisfied when TensorFlow is being built with CUDA
# support through e.g. `--config=cuda` (or `--config=cuda_clang` in OSS).
alias(
    name = "is_cuda_enabled",
    actual = if_oss(
        "@local_config_cuda//:is_cuda_enabled",
        "@local_config_cuda//cuda:using_config_cuda",
    ),
)

# Config setting that is satisfied when CUDA device code should be compiled

-   type: input id: Compiler attributes: label: GCC/Compiler version
    description: if compiling from source placeholder: validations: required:
    false

-   type: input id: Cuda attributes: label: CUDA/cuDNN version description:
    placeholder: validations: required: false

-   type: input id: Gpu attributes: label: GPU model and memory description: if