# 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
TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/linux_arm64"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-arm64"
TFCI_OUTPUT_DIR=build_output

        shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository
        shell: bash

# ==============================================================================

setup_file() {
    bazel version  # Start the bazel server

    # Fixes "fatal: detected dubious ownership in repository" for Docker.
    git config --system --add safe.directory '*'
    git config --system protocol.file.allow always

    # Note that you could generate a list of all the affected targets with e.g.:

#   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.
#

        shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash

# 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.

    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash
    # Install Docker first, then this will build and run cpu tests
    tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
    ```

    See

# almost all of the same cache layers
export DOCKER_BUILDKIT=1
for target in jax tf; do
  IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG"
  docker pull "$IMAGE" || true
  # Due to some flakiness of resources pulled in the build, allow the docker
  # command to reattempt build a few times in the case of failure (b/302558736)
  set +e
  for i in $(seq 1 5)
  do
    docker build \

TFCI_ARTIFACT_STAGING_GCS_URI=
TFCI_BAZEL_BAZELRC_ARGS=
TFCI_BAZEL_COMMON_ARGS=
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=
TFCI_BUILD_PIP_PACKAGE_ARGS=
TFCI_DOCKER_ARGS=
TFCI_DOCKER_ENABLE=
TFCI_DOCKER_IMAGE=
TFCI_DOCKER_PULL_ENABLE=
TFCI_DOCKER_REBUILD_ARGS=
TFCI_DOCKER_REBUILD_ENABLE=
TFCI_DOCKER_REBUILD_UPLOAD_ENABLE=
TFCI_GIT_DIR=
TFCI_INDEX_HTML_ENABLE=
TFCI_LIB_SUFFIX=
TFCI_MACOS_BAZEL_TEST_DIR_ENABLE=
TFCI_MACOS_BAZEL_TEST_DIR_PATH=

# complex. Write a well-documented script under utilities/ to encapsulate the
# functionality instead.
tfrun() { "$@"; }

# Run all "tfrun" commands under Docker. See setup_docker.sh for details
if [[ "$TFCI_DOCKER_ENABLE" == 1 ]]; then
  source ./ci/official/utilities/setup_docker.sh
fi

# Generate an overview page describing the build
if [[ "$TFCI_INDEX_HTML_ENABLE" == 1 ]]; then