## Wheel Test

This directory is dedicated to tests that require a built TensorFlow wheel
file for testing, such as:

* Ensuring the entire API is importable
* Testing downstream projects against the wheel

Ensure you have Bazel installed and accessible from your command line.

These tests use hermetic Python. They also require a built TensorFlow wheel file
and a requirements_lock file. The requirements_lock file is generated by the

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

set -euxo pipefail

# Run this from inside the tensorflow github directory.
# Usage: setup_venv_test.sh venv_and_symlink_name "glob pattern for one wheel file"
# Example: setup_venv_test.sh bazel_pip "/tf/pkg/*.whl"
# 
# This will create a venv with that wheel file installed in it, and a symlink
# in ./venv_and_symlink_name/tensorflow to ./tensorflow. We use this for the
# "pip" tests.

python -m venv /$1
mkdir -p $1

      - 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'

      with:
        python-version: "3.9"
    - name: Install Python dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pylint==2.13.9 numpy wheel
    - name: Run PyLint on changed files
      run: |

# 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

werkzeug==3.0.1 \
    --hash=sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc \
    --hash=sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10
    # via tb-nightly
wheel==0.41.3 \
    --hash=sha256:488609bc63a29322326e05560731bf7bfea8e48ad646e1f5e40d366607de0942 \
    --hash=sha256:4d4987ce51a49370ea65c0bfd2234e8ce80a12780820d9dc462597a6e60d0841
    # via
    #   -r requirements.in