#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:
#   https://github.com/NVIDIA/nvidia-container-toolkit
#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you
#   will need to run `docker exec tf pkill bazel` to quit bazel.
#

# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn8=8.9.6.50-1+cuda12.2
libcudnn8-dev=8.9.6.50-1+cuda12.2

# 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.

# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly
fi

# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly

TFCI_MACOS_INSTALL_BAZELISK_ENABLE=
TFCI_MACOS_INSTALL_BAZELISK_URL=
TFCI_MACOS_PYENV_INSTALL_ENABLE=
TFCI_MACOS_TWINE_INSTALL_ENABLE=
TFCI_MACOS_UPGRADE_PYENV_ENABLE=
TFCI_NIGHTLY_UPDATE_VERSION_ENABLE=
TFCI_NVIDIA_SMI_ENABLE=
TFCI_OUTPUT_DIR=
TFCI_PYCPP_SWAP_TO_BUILD_ENABLE=
TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS=
TFCI_PYTHON_VERSION=
TFCI_WHL_AUDIT_ENABLE=
TFCI_WHL_AUDIT_PLAT=
TFCI_WHL_BAZEL_TEST_ENABLE=

    assignees = apple_silicon_assignees;
  if (lowercased_title.includes('tf-trt') && domain.includes('nvidia.com')) {
    assignees.push(
        'DEKHTIARJonathan', 'meena-at-work', 'nluehr', 'pjannaty', 'poulsbo');
  } else if (
      lowercased_title.includes('nvidia') && domain.includes('nvidia.com')) {
    if (lowercased_title.includes('jax')) {
      assignees.push('hawkinsp', 'yashk2810', 'skye');
    }

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64
# Note: this is not set to "--cpu", because that changes the package name
# to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow"
# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python
TFCI_DOCKER_PULL_ENABLE=1

[ -z "$PS1" ] && return

# Set up attractive prompt
export PS1="\[\e[31m\]tf-docker\[\e[m\] \[\e[33m\]\w\[\e[m\] > "
export TERM=xterm-256color
alias grep="grep --color=auto"
alias ls="ls --color=auto"
# Fix nvidia-docker

              break;
            case "apple.com":
              console.log(await script.filter({github, context, domain}));
              break;
            case "nvidia.com":
              console.log(await script.filter({github, context, domain}));
              break;
            case "linaro.org":
              console.log(await script.filter({github, context, domain}));

COPY builder.patchelf/build_patchelf.sh /build_patchelf.sh
COPY apt.conf /etc/apt/
RUN /build_patchelf.sh

################################################################################
FROM nvidia/cuda:12.3.1-devel-ubuntu20.04 as devel
################################################################################
COPY --from=builder /dt10 /dt10