# 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

# 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.

# These flags share the user's gcloud credentials with the container, so that bazel
# inside the container can authenticate. Note: TF's CI does not have any credential
# stored here.

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"

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

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.:

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=

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cpu
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow_cpu"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=tensorflow/build:2.16-python${TFCI_PYTHON_VERSION}
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--build-arg PYTHON_VERSION=python$TFCI_PYTHON_VERSION --target=devel tensorflow/tools/tf_sig_build_dockerfiles"
TFCI_INDEX_HTML_ENABLE=1

TFCI_ARTIFACT_FINAL_GCS_SA_PATH=
TFCI_ARTIFACT_FINAL_GCS_URI=
TFCI_ARTIFACT_FINAL_PYPI_ARGS=
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=
TFCI_ARTIFACT_LATEST_GCS_URI=
TFCI_ARTIFACT_STAGING_GCS_ENABLE=
TFCI_ARTIFACT_STAGING_GCS_URI=

See the [TensorFlow install guide](https://www.tensorflow.org/install) for the
[pip package](https://www.tensorflow.org/install/pip), to
[enable GPU support](https://www.tensorflow.org/install/gpu), use a
[Docker container](https://www.tensorflow.org/install/docker), and
[build from source](https://www.tensorflow.org/install/source).

To install the current release, which includes support for

#   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.
#