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)

      - name: Checkout repository for nightly (skipped for releases)
        if: ${{ github.event_name == 'schedule' }}
        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
        with:
          ref: 'nightly'
      - name: Checkout repository
        if: ${{ github.event_name == 'push' }}

val DslContext.uuidPrefix: String
    get() = settingsRoot.id.toString().substringAfter("Gradle")

data class VersionedSettingsBranch(
    val branchName: String,
) {
    /**
     * 0~23.
     * To avoid nightly promotion jobs running at the same time,
     * we run each branch on different hours.
     * master - 0:00
     * release - 1:00
     * release6x - 2:00
     * release7x - 3:00
     * ...
     * releaseNx - (N-4):00

  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  python_bin=python3
  # TODO(belitskiy): Add a `python3` alias/symlink to Windows Docker image.
  if [[ $(uname -s) = MSYS_NT* ]]; then
    python_bin="python"
  fi
  tfrun "$python_bin" tensorflow/tools/ci_build/update_version.py --nightly
  # replace tensorflow to tf_nightly in the wheel name

# If building installer wheels, set the required environment variables that are
# read by setup.py.
if [[ "$TFCI_INSTALLER_WHL_ENABLE" == 1 ]]; then
  export collaborator_build=True
  # If building nightly installer wheels, set the project name to
  # nightly equivalent.
  if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
    export TFCI_INSTALLER_WHL_PROJECT_NAME="$TFCI_INSTALLER_WHL_NIGHTLY_PROJECT_NAME"
  fi

        prepTask = branch.prepNightlyTaskName(),
        triggerName = "QuickFeedback",
    ) {
    init {
        id("Promotion_SnapshotFromQuickFeedbackStepUpload")
        name = "Nightly Snapshot (from QuickFeedback) - Upload"
        description = "Builds and uploads the latest successful changes on '${branch.branchName}' from Quick Feedback as a new distribution"

        steps {
            buildStep(

        |           |--------- QuickFeedback
        |           |--------- ...
        |           |--------- ReadyForRelease
        |
        |---------- Promotion
        |           |--------- Publish Nightly Snapshot
        |           |--------- Publish Branch Snapshot
        |           |--------- ...
        |
        |---------- Util
                    |--------- ...

    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

RUN mkdir -p /tf/venv
RUN chown -R buildslave:buildslave /tf

# All lines past this point are reset when $CACHEBUSTER is set. We need this
# for Python specifically because we install some nightly packages which are
# likely to change daily.
ARG CACHEBUSTER=0
RUN echo $CACHEBUSTER

# Setup build and environment
COPY devel.bashrc /root/.bashrc
COPY ld.so.conf /dt10/etc/

# Make sure clang is on the path

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

if "dev" in __version__:   # pylint: disable=undefined-variable
  _logging.warning("""

  TensorFlow's `tf-nightly` package will soon be updated to TensorFlow 2.0.

  Please upgrade your code to TensorFlow 2.0:
    * https://www.tensorflow.org/guide/migrate

  Or install the latest stable TensorFlow 1.X release: