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 \;

    rm "$wheel"
    wheel="$new_wheel"
  fi

  TF_WHEEL="$wheel" bats /usertools/wheel_verification.bats --timing

  echo "Usage: $0 <path_to_tensorflow_wheel> <python_version>"
  exit 1
fi

TENSORFLOW_WHEEL_PATH="$1"
PYTHON_VERSION="$2"

# All commands run relative to this directory
cd "$(dirname "${BASH_SOURCE[0]}")"
cd ../requirements_updater || exit 1

# Create the requirements_wheel_test.in file
echo "tensorflow @ file://localhost/$TENSORFLOW_WHEEL_PATH" > requirements_wheel_test.in

# Create the requirements_lock file

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`

# 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

# TODO(b/304751256): Adjust the numpy pin to a single version, when ready
numpy ~= 1.23.5 ; python_version <= "3.11"
numpy ~= 1.26.0 ; python_version >= "3.12"
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras

## 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

# Pyenv version. We enable this for macOS x86 builds as the default Pyenv on
# those VMs does not support installing Python 3.12 and above which we need
# for running smoke tests in nightly/release wheel builds.
if [[ "${TFCI_MACOS_UPGRADE_PYENV_ENABLE}" == 1 ]]; then
  # The TFCI Mac VM image seems to have uncommitted local changes to the Pyenv
  # repository so we have to discard them and reset the working directory before

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

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"