**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 |
   

nvidia-cublas-cu12>=12.5.3.2,<13.0
nvidia-cuda-cupti-cu12>=12.5.82,<13.0
nvidia-cuda-nvcc-cu12>=12.5.82,<13.0
nvidia-cuda-nvrtc-cu12>=12.5.82,<13.0
nvidia-cuda-runtime-cu12>=12.5.82,<13.0
# The upper bound is set for the CUDNN API compatibility.
# See
# https://docs.nvidia.com/deeplearning/cudnn/backend/latest/developer/forward-compatibility.html#cudnn-api-compatibility
nvidia-cudnn-cu12>=9.3.0.75,<10.0
nvidia-cufft-cu12>=11.2.3.61,<12.0

# The RBE machine itself has older kernel mode driver, and it requires
# nvidia driver to be installed.
nvidia-driver-580-open
# TODO(b/445248346): The Docker image shouldn't have cuda-compat installed.
# However, hermetic CUDA forward-compatibility mode is still missing some
# libraries.

load(
    "@rules_ml_toolchain//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(
    "@rules_ml_toolchain//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl",

    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

    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 \
    --@local_config_cuda//cuda:include_cuda_libs=false \

Em muitos casos, sua aplicação pode precisar de configurações externas, por exemplo chaves secretas, credenciais de banco de dados, credenciais para serviços de e-mail, etc.

A maioria dessas configurações é variável (pode mudar), como URLs de banco de dados. E muitas podem ser sensíveis, como segredos.

Por esse motivo, é comum fornecê-las em variáveis de ambiente lidas pela aplicação.

/// tip | Dica

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/ /

    "$python" -m pip install numpy==1.26.4
  else
    "$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