source ci/official/envs/linux_x86
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_gpu_linux"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ARGS="--gpus all"
TFCI_LIB_SUFFIX="-gpu-linux-x86_64"

#   TF_BISECT_BAD: First bad commit (e.g. commit from the first failing job)
#   TF_BISECT_SCRIPT: The build script path relative to the TF root dir, e.g.
#     ci/official/wheel.sh
#   TFCI: The env config path, relative to the TF root dir, e.g.
#     ci/official/envs/an_env_config
#
# Note that you can combine bisect.sh with any.sh to bisect a single test:
#
#   export TFCI=...

# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which
# drops all the bazel dependencies for each py_test; this makes all the tests
# use the wheel's TensorFlow installation instead of the one made available

  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly
  # replace tensorflow to tf_nightly in the wheel name
  export TFCI_BUILD_PIP_PACKAGE_ARGS="$(echo $TFCI_BUILD_PIP_PACKAGE_ARGS | sed 's/tensorflow/tf_nightly/')"
fi

tfrun bazel build $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/pip_package:wheel $TFCI_BUILD_PIP_PACKAGE_ARGS
tfrun find ./bazel-bin/tensorflow/tools/pip_package -iname "*.whl" -exec cp {} $TFCI_OUTPUT_DIR \;

      - name: Checkout repository for releases (skipped for nightly)
        if: ${{ github.event_name == 'push' }}
        uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
      - name: Build and test pip wheel
        shell: bash
        run: |
          is_nightly=0 && tf_project_name='tensorflow_cpu_aws' && ${{ github.event_name == 'schedule' }} && is_nightly=1 && tf_project_name='tf_nightly_cpu_aws'

# Suite of verification tests for the SINGLE TensorFlow wheel in /tf/pkg
# or whatever path is set as $TF_WHEEL.

setup_file() {
    cd /tf/pkg
    if [[ -z "$TF_WHEEL" ]]; then
        export TF_WHEEL=$(find /tf/pkg -iname "*.whl")
    fi
}

teardown_file() {
    rm -rf /tf/venv
}

@test "Wheel is manylinux2014 (manylinux_2_17) compliant" {
    python3 -m auditwheel show "$TF_WHEEL" > audit.txt

system, Kokoro, schedules our CI jobs by combining a build script with a file
from the `envs` directory that is filled with configuration options:

-   Nightly jobs (Run nightly on the `nightly` branch)
    -   Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh`
-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

# 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
TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/linux_arm64"

# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which
# drops all the bazel dependencies for each py_test; this makes all the tests
# use the wheel's TensorFlow installation instead of the one made available

# This script is aware of TFCI_ variables, so it doesn't need any arguments.
# Puts new wheel through auditwheel to rename and verify it, deletes the old
# one, checks the filesize, and then ensures the new wheel is installable.
set -euxo pipefail

cd "$TFCI_OUTPUT_DIR"

# Move extra wheel files somewhere out of the way. This script
# expects just one wheel file to exist.
if [[ "$(ls *.whl | wc -l | tr -d ' ')" != "1" ]]; then