setup.py

# ------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------

from setuptools import setup, Extension
from distutils import log as logger
from distutils.command.build_ext import build_ext as _build_ext
from glob import glob, iglob
from os import path, getcwd, environ, remove
from shutil import copyfile
import platform
import subprocess
import sys
import textwrap
import datetime

from pathlib import Path
nightly_build = False
package_name = 'onnxruntime'
wheel_name_suffix = None


def parse_arg_remove_boolean(argv, arg_name):
    arg_value = False
    if arg_name in sys.argv:
        arg_value = True
        argv.remove(arg_name)

    return arg_value


def parse_arg_remove_string(argv, arg_name_equal):
    arg_value = None
    for arg in sys.argv[1:]:
        if arg.startswith(arg_name_equal):
            arg_value = arg[len(arg_name_equal):]
            sys.argv.remove(arg)
            break

    return arg_value


# Any combination of the following arguments can be applied

if parse_arg_remove_boolean(sys.argv, '--nightly_build'):
    package_name = 'ort-nightly'
    nightly_build = True

wheel_name_suffix = parse_arg_remove_string(sys.argv, '--wheel_name_suffix=')

cuda_version = None
rocm_version = None
is_rocm = False
# The following arguments are mutually exclusive
if wheel_name_suffix == 'gpu':
    # TODO: how to support multiple CUDA versions?
    cuda_version = parse_arg_remove_string(sys.argv, '--cuda_version=')
elif parse_arg_remove_boolean(sys.argv, '--use_rocm'):
    is_rocm = True
    package_name = 'onnxruntime-rocm' if not nightly_build else 'ort-rocm-nightly'
    rocm_version = parse_arg_remove_string(sys.argv, '--rocm_version=')
elif parse_arg_remove_boolean(sys.argv, '--use_openvino'):
    package_name = 'onnxruntime-openvino'
elif parse_arg_remove_boolean(sys.argv, '--use_dnnl'):
    package_name = 'onnxruntime-dnnl'
elif parse_arg_remove_boolean(sys.argv, '--use_nuphar'):
    package_name = 'onnxruntime-nuphar'
elif parse_arg_remove_boolean(sys.argv, '--use_stvm'):
    package_name = 'onnxruntime-stvm'
elif parse_arg_remove_boolean(sys.argv, '--use_vitisai'):
    package_name = 'onnxruntime-vitisai'
elif parse_arg_remove_boolean(sys.argv, '--use_acl'):
    package_name = 'onnxruntime-acl'
elif parse_arg_remove_boolean(sys.argv, '--use_armnn'):
    package_name = 'onnxruntime-armnn'

# PEP 513 defined manylinux1_x86_64 and manylinux1_i686
# PEP 571 defined manylinux2010_x86_64 and manylinux2010_i686
# PEP 599 defines the following platform tags:
# manylinux2014_x86_64
# manylinux2014_i686
# manylinux2014_aarch64
# manylinux2014_armv7l
# manylinux2014_ppc64
# manylinux2014_ppc64le
# manylinux2014_s390x
manylinux_tags = [
    'manylinux1_x86_64',
    'manylinux1_i686',
    'manylinux2010_x86_64',
    'manylinux2010_i686',
    'manylinux2014_x86_64',
    'manylinux2014_i686',
    'manylinux2014_aarch64',
    'manylinux2014_armv7l',
    'manylinux2014_ppc64',
    'manylinux2014_ppc64le',
    'manylinux2014_s390x',
]
is_manylinux = environ.get('AUDITWHEEL_PLAT', None) in manylinux_tags


class build_ext(_build_ext):
    def build_extension(self, ext):
        dest_file = self.get_ext_fullpath(ext.name)
        logger.info('copying %s -> %s', ext.sources[0], dest_file)
        copyfile(ext.sources[0], dest_file)


try:
    from wheel.bdist_wheel import bdist_wheel as _bdist_wheel

    class bdist_wheel(_bdist_wheel):
        def finalize_options(self):
            _bdist_wheel.finalize_options(self)
            if not is_manylinux:
                self.root_is_pure = False

        def _rewrite_ld_preload(self, to_preload):
            with open('onnxruntime/capi/_ld_preload.py', 'a') as f:
                if len(to_preload) > 0:
                    f.write('from ctypes import CDLL, RTLD_GLOBAL\n')
                    for library in to_preload:
                        f.write('_{} = CDLL("{}", mode=RTLD_GLOBAL)\n'.format(library.split('.')[0], library))

        def _rewrite_ld_preload_cuda(self, to_preload):
            with open('onnxruntime/capi/_ld_preload.py', 'a') as f:
                if len(to_preload) > 0:
                    f.write('from ctypes import CDLL, RTLD_GLOBAL\n')
                    f.write('try:\n')
                    for library in to_preload:
                        f.write('    _{} = CDLL("{}", mode=RTLD_GLOBAL)\n'.format(library.split('.')[0], library))
                    f.write('except OSError:\n')
                    f.write('    import os\n')
                    f.write('    os.environ["ORT_CUDA_UNAVAILABLE"] = "1"\n')

        def _rewrite_ld_preload_tensorrt(self, to_preload):
            with open('onnxruntime/capi/_ld_preload.py', 'a') as f:
                if len(to_preload) > 0:
                    f.write('from ctypes import CDLL, RTLD_GLOBAL\n')
                    f.write('try:\n')
                    for library in to_preload:
                        f.write('    _{} = CDLL("{}", mode=RTLD_GLOBAL)\n'.format(library.split('.')[0], library))
                    f.write('except OSError:\n')
                    f.write('    import os\n')
                    f.write('    os.environ["ORT_TENSORRT_UNAVAILABLE"] = "1"\n')

        def _rewrite_ld_preload_tvm(self):
            with open('onnxruntime/capi/_ld_preload.py', 'a') as f:
                f.write(textwrap.dedent(
                    """
                    import warnings

                    try:
                        # This import is necessary in order to delegate the loading of libtvm.so to TVM.
                        import tvm
                    except ImportError as e:
                        warnings.warn(
                            f"WARNING: Failed to import TVM, libtvm.so was not loaded. More details: {e}"
                        )
                    try:
                        # Working between the C++ and Python parts in TVM EP is done using the PackedFunc and
                        # Registry classes. In order to use a Python function in C++ code, it must be registered in
                        # the global table of functions. Registration is carried out through the JIT interface,
                        # so it is necessary to call special functions for registration.
                        # To do this, we need to make the following import.
                        import onnxruntime.providers.stvm
                    except ImportError as e:
                        warnings.warn(
                            f"WARNING: Failed to register python functions to work with TVM EP. More details: {e}"
                        )
                    """
                ))

        def run(self):
            if is_manylinux:
                source = 'onnxruntime/capi/onnxruntime_pybind11_state.so'
                dest = 'onnxruntime/capi/onnxruntime_pybind11_state_manylinux1.so'
                logger.info('copying %s -> %s', source, dest)
                copyfile(source, dest)
                result = subprocess.run(['patchelf', '--print-needed', dest],
                                        check=True, stdout=subprocess.PIPE, universal_newlines=True)
                dependencies = ['librccl.so', 'libamdhip64.so', 'librocblas.so', 'libMIOpen.so',
                                'libhsa-runtime64.so', 'libhsakmt.so']
                to_preload = []
                to_preload_cuda = []
                to_preload_tensorrt = []
                cuda_dependencies = []
                args = ['patchelf', '--debug']
                for line in result.stdout.split('\n'):
                    for dependency in dependencies:
                        if dependency in line:
                            to_preload.append(line)
                            args.extend(['--remove-needed', line])
                args.append(dest)
                if len(args) > 3:
                    subprocess.run(args, check=True, stdout=subprocess.PIPE)

                dest = 'onnxruntime/capi/libonnxruntime_providers_' + ('rocm.so' if is_rocm else 'cuda.so')
                if path.isfile(dest):
                    result = subprocess.run(['patchelf', '--print-needed', dest],
                                            check=True, stdout=subprocess.PIPE, universal_newlines=True)
                    cuda_dependencies = ['libcublas.so', 'libcublasLt.so', 'libcudnn.so', 'libcudart.so',
                                         'libcurand.so', 'libcufft.so', 'libnvToolsExt.so', 'libcupti.so']
                    rocm_dependencies = ['librccl.so', 'libamdhip64.so', 'librocblas.so', 'libMIOpen.so',
                                         'libhsa-runtime64.so', 'libhsakmt.so']
                    args = ['patchelf', '--debug']
                    for line in result.stdout.split('\n'):
                        for dependency in (cuda_dependencies + rocm_dependencies):
                            if dependency in line:
                                if dependency not in to_preload:
                                    to_preload_cuda.append(line)
                                args.extend(['--remove-needed', line])
                    args.append(dest)
                    if len(args) > 3:
                        subprocess.run(args, check=True, stdout=subprocess.PIPE)

                dest = 'onnxruntime/capi/libonnxruntime_providers_' + ('migraphx.so' if is_rocm else 'tensorrt.so')
                if path.isfile(dest):
                    result = subprocess.run(['patchelf', '--print-needed', dest],
                                            check=True, stdout=subprocess.PIPE, universal_newlines=True)
                    tensorrt_dependencies = ['libnvinfer.so', 'libnvinfer_plugin.so', 'libnvonnxparser.so']
                    args = ['patchelf', '--debug']
                    for line in result.stdout.split('\n'):
                        for dependency in (cuda_dependencies + tensorrt_dependencies):
                            if dependency in line:
                                if dependency not in (to_preload + to_preload_cuda):
                                    to_preload_tensorrt.append(line)
                                args.extend(['--remove-needed', line])
                    args.append(dest)
                    if len(args) > 3:
                        subprocess.run(args, check=True, stdout=subprocess.PIPE)

                self._rewrite_ld_preload(to_preload)
                self._rewrite_ld_preload_cuda(to_preload_cuda)
                self._rewrite_ld_preload_tensorrt(to_preload_tensorrt)
            if package_name == 'onnxruntime-stvm':
                self._rewrite_ld_preload_tvm()
            _bdist_wheel.run(self)
            if is_manylinux and not disable_auditwheel_repair:
                file = glob(path.join(self.dist_dir, '*linux*.whl'))[0]
                logger.info('repairing %s for manylinux1', file)
                try:
                    subprocess.run(['auditwheel', 'repair', '-w', self.dist_dir, file],
                                   check=True, stdout=subprocess.PIPE)
                finally:
                    logger.info('removing %s', file)
                    remove(file)

except ImportError as error:
    print("Error importing dependencies:")
    print(error)
    bdist_wheel = None

providers_cuda_or_rocm = 'libonnxruntime_providers_' + ('rocm.so' if is_rocm else 'cuda.so')
providers_tensorrt_or_migraphx = 'libonnxruntime_providers_' + ('migraphx.so' if is_rocm else 'tensorrt.so')
# Additional binaries
if platform.system() == 'Linux':
    libs = ['onnxruntime_pybind11_state.so', 'libdnnl.so.2', 'libmklml_intel.so', 'libmklml_gnu.so', 'libiomp5.so',
            'mimalloc.so']
    dl_libs = ['libonnxruntime_providers_shared.so']
    dl_libs.append(providers_cuda_or_rocm)
    dl_libs.append(providers_tensorrt_or_migraphx)
    # DNNL, TensorRT & OpenVINO EPs are built as shared libs
    libs.extend(['libonnxruntime_providers_shared.so'])
    libs.extend(['libonnxruntime_providers_dnnl.so'])
    libs.extend(['libonnxruntime_providers_openvino.so'])
    libs.append(providers_cuda_or_rocm)
    libs.append(providers_tensorrt_or_migraphx)
    # Nuphar Libs
    libs.extend(['libtvm.so.0.5.1'])
    if nightly_build:
        libs.extend(['libonnxruntime_pywrapper.so'])
elif platform.system() == "Darwin":
    libs = ['onnxruntime_pybind11_state.so', 'libdnnl.2.dylib', 'mimalloc.so']  # TODO add libmklml and libiomp5 later.
    # DNNL & TensorRT EPs are built as shared libs
    libs.extend(['libonnxruntime_providers_shared.dylib'])
    libs.extend(['libonnxruntime_providers_dnnl.dylib'])
    libs.extend(['libonnxruntime_providers_tensorrt.dylib'])
    libs.extend(['libonnxruntime_providers_cuda.dylib'])
    if nightly_build:
        libs.extend(['libonnxruntime_pywrapper.dylib'])
else:
    libs = ['onnxruntime_pybind11_state.pyd', 'dnnl.dll', 'mklml.dll', 'libiomp5md.dll']
    # DNNL, TensorRT & OpenVINO EPs are built as shared libs
    libs.extend(['onnxruntime_providers_shared.dll'])
    libs.extend(['onnxruntime_providers_dnnl.dll'])
    libs.extend(['onnxruntime_providers_tensorrt.dll'])
    libs.extend(['onnxruntime_providers_openvino.dll'])
    libs.extend(['onnxruntime_providers_cuda.dll'])
    # DirectML Libs
    libs.extend(['DirectML.dll'])
    # Nuphar Libs
    libs.extend(['tvm.dll'])
    if nightly_build:
        libs.extend(['onnxruntime_pywrapper.dll'])

if is_manylinux:
    data = ['capi/libonnxruntime_pywrapper.so'] if nightly_build else []
    data += [path.join('capi', x) for x in dl_libs if path.isfile(path.join('onnxruntime', 'capi', x))]
    ext_modules = [
        Extension(
            'onnxruntime.capi.onnxruntime_pybind11_state',
            ['onnxruntime/capi/onnxruntime_pybind11_state_manylinux1.so'],
        ),
    ]
else:
    data = [path.join('capi', x) for x in libs if path.isfile(path.join('onnxruntime', 'capi', x))]
    ext_modules = []

# Additional examples
examples_names = ["mul_1.onnx", "logreg_iris.onnx", "sigmoid.onnx"]
examples = [path.join('datasets', x) for x in examples_names]

# Extra files such as EULA and ThirdPartyNotices
extra = ["LICENSE", "ThirdPartyNotices.txt", "Privacy.md"]

# Description
README = path.join(getcwd(), "docs/python/README.rst")
if not path.exists(README):
    this = path.dirname(__file__)
    README = path.join(this, "docs/python/README.rst")
if not path.exists(README):
    raise FileNotFoundError("Unable to find 'README.rst'")
with open(README) as f:
    long_description = f.read()

# Include files in onnxruntime/external if --enable_external_custom_op_schemas build.sh command
# line option is specified.
# If the options is not specified this following condition fails as onnxruntime/external folder is not created in the
# build flow under the build binary directory.
if (path.isdir(path.join("onnxruntime", "external"))):
    # Gather all files under onnxruntime/external directory.
    extra.extend(list(str(Path(*Path(x).parts[1:])) for x in list(iglob(
        path.join(path.join("onnxruntime", "external"), '**/*.*'), recursive=True))))

packages = [
    'onnxruntime',
    'onnxruntime.backend',
    'onnxruntime.capi',
    'onnxruntime.capi.training',
    'onnxruntime.datasets',
    'onnxruntime.tools',
    'onnxruntime.tools.ort_format_model',
    'onnxruntime.tools.ort_format_model.ort_flatbuffers_py',
    'onnxruntime.tools.ort_format_model.ort_flatbuffers_py.fbs',
    'onnxruntime.quantization',
    'onnxruntime.quantization.operators',
    'onnxruntime.quantization.CalTableFlatBuffers',
    'onnxruntime.transformers',
    'onnxruntime.transformers.longformer',
]

requirements_file = "requirements.txt"

local_version = None
enable_training = parse_arg_remove_boolean(sys.argv, '--enable_training')
disable_auditwheel_repair = parse_arg_remove_boolean(sys.argv, '--disable_auditwheel_repair')
default_training_package_device = parse_arg_remove_boolean(sys.argv, '--default_training_package_device')

package_data = {}
data_files = []

classifiers = [
    'Development Status :: 5 - Production/Stable',
    'Intended Audience :: Developers',
    'License :: OSI Approved :: MIT License',
    'Operating System :: POSIX :: Linux',
    'Topic :: Scientific/Engineering',
    'Topic :: Scientific/Engineering :: Mathematics',
    'Topic :: Scientific/Engineering :: Artificial Intelligence',
    'Topic :: Software Development',
    'Topic :: Software Development :: Libraries',
    'Topic :: Software Development :: Libraries :: Python Modules',
    'Programming Language :: Python',
    'Programming Language :: Python :: 3 :: Only',
    'Programming Language :: Python :: 3.6',
    'Programming Language :: Python :: 3.7',
    'Programming Language :: Python :: 3.8',
    'Programming Language :: Python :: 3.9']

if not enable_training:
    classifiers.extend([
        'Operating System :: Microsoft :: Windows',
        'Operating System :: MacOS'])

if enable_training:
    packages.extend(['onnxruntime.training',
                     'onnxruntime.training.amp',
                     'onnxruntime.training.optim',
                     'onnxruntime.training.ortmodule',
                     'onnxruntime.training.ortmodule.experimental',
                     'onnxruntime.training.ortmodule.experimental.json_config',
                     'onnxruntime.training.ortmodule.experimental.hierarchical_ortmodule',
                     'onnxruntime.training.ortmodule.torch_cpp_extensions',
                     'onnxruntime.training.ortmodule.torch_cpp_extensions.cpu.aten_op_executor',
                     'onnxruntime.training.ortmodule.torch_cpp_extensions.cpu.torch_interop_utils',
                     'onnxruntime.training.ortmodule.torch_cpp_extensions.cuda.torch_gpu_allocator',
                     'onnxruntime.training.ortmodule.torch_cpp_extensions.cuda.fused_ops'])
    package_data['onnxruntime.training.ortmodule.torch_cpp_extensions.cpu.aten_op_executor'] = ['*.cc']
    package_data['onnxruntime.training.ortmodule.torch_cpp_extensions.cpu.torch_interop_utils'] = ['*.cc']
    package_data['onnxruntime.training.ortmodule.torch_cpp_extensions.cuda.torch_gpu_allocator'] = ['*.cc']
    package_data['onnxruntime.training.ortmodule.torch_cpp_extensions.cuda.fused_ops'] = \
        ['*.cpp', '*.cu', '*.cuh', '*.h']
    requirements_file = "requirements-training.txt"
    # with training, we want to follow this naming convention:
    # stable:
    # onnxruntime-training-1.7.0+cu111-cp36-cp36m-linux_x86_64.whl
    # nightly:
    # onnxruntime-training-1.7.0.dev20210408+cu111-cp36-cp36m-linux_x86_64.whl
    # this is needed immediately by pytorch/ort so that the user is able to
    # install an onnxruntime training package with matching torch cuda version.
    package_name = 'onnxruntime-training'

    # we want put default training packages to pypi. pypi does not accept package with a local version.
    if not default_training_package_device or nightly_build:
        if cuda_version:
            # removing '.' to make Cuda version number in the same form as Pytorch.
            local_version = '+cu' + cuda_version.replace('.', '')
        elif rocm_version:
            # removing '.' to make Rocm version number in the same form as Pytorch.
            local_version = '+rocm' + rocm_version.replace('.', '')
        else:
            # cpu version for documentation
            local_version = '+cpu'

if package_name == 'onnxruntime-nuphar':
    packages += ["onnxruntime.nuphar"]
    extra += [path.join('nuphar', 'NUPHAR_CACHE_VERSION')]

if package_name == 'onnxruntime-stvm':
    packages += ['onnxruntime.providers.stvm']

package_data["onnxruntime"] = data + examples + extra

version_number = ''
with open('VERSION_NUMBER') as f:
    version_number = f.readline().strip()
if nightly_build:
    # https://docs.microsoft.com/en-us/azure/devops/pipelines/build/variables
    build_suffix = environ.get('BUILD_BUILDNUMBER')
    if build_suffix is None:
        # The following line is only for local testing
        build_suffix = str(datetime.datetime.now().date().strftime("%Y%m%d"))
    else:
        build_suffix = build_suffix.replace('.', '')

    if len(build_suffix) > 8 and len(build_suffix) < 12:
        # we want to format the build_suffix to avoid (the 12th run on 20210630 vs the first run on 20210701):
        # 2021063012 > 202107011
        # in above 2021063012 is treated as the latest which is incorrect.
        # we want to convert the format to:
        # 20210630012 < 20210701001
        # where the first 8 digits are date. the last 3 digits are run count.
        # as long as there are less than 1000 runs per day, we will not have the problem.
        # to test this code locally, run:
        # NIGHTLY_BUILD=1 BUILD_BUILDNUMBER=202107011 python tools/ci_build/build.py --config RelWithDebInfo \
        #   --enable_training --use_cuda --cuda_home /usr/local/cuda --cudnn_home /usr/lib/x86_64-linux-gnu/ \
        #   --nccl_home /usr/lib/x86_64-linux-gnu/ --build_dir build/Linux --build --build_wheel --skip_tests \
        #   --cuda_version 11.1
        def check_date_format(date_str):
            try:
                datetime.datetime.strptime(date_str, '%Y%m%d')
                return True
            except: # noqa
                return False

        def reformat_run_count(count_str):
            try:
                count = int(count_str)
                if count >= 0 and count < 1000:
                    return "{:03}".format(count)
                elif count >= 1000:
                    raise RuntimeError(f'Too many builds for the same day: {count}')
                return ""
            except: # noqa
                return ""

        build_suffix_is_date_format = check_date_format(build_suffix[:8])
        build_suffix_run_count = reformat_run_count(build_suffix[8:])
        if build_suffix_is_date_format and build_suffix_run_count:
            build_suffix = build_suffix[:8] + build_suffix_run_count
    elif len(build_suffix) >= 12:
        raise RuntimeError(f'Incorrect build suffix: "{build_suffix}"')

    if enable_training:
        from packaging import version
        from packaging.version import Version
        # with training package, we need to bump up version minor number so that
        # nightly releases take precedence over the latest release when --pre is used during pip install.
        # eventually this shall be the behavior of all onnxruntime releases.
        # alternatively we may bump up version number right after every release.
        ort_version = version.parse(version_number)
        if isinstance(ort_version, Version):
            # TODO: this is the last time we have to do this!!!
            # We shall bump up release number right after release cut.
            if ort_version.major == 1 and ort_version.minor == 8 and ort_version.micro == 0:
                version_number = '{major}.{minor}.{macro}'.format(
                    major=ort_version.major,
                    minor=ort_version.minor + 1,
                    macro=ort_version.micro)

    version_number = version_number + ".dev" + build_suffix

if local_version:
    version_number = version_number + local_version

if wheel_name_suffix:
    if not (enable_training and wheel_name_suffix == 'gpu'):
        # for training packages, local version is used to indicate device types
        package_name = "{}-{}".format(package_name, wheel_name_suffix)

cmd_classes = {}
if bdist_wheel is not None:
    cmd_classes['bdist_wheel'] = bdist_wheel
cmd_classes['build_ext'] = build_ext

requirements_path = path.join(getcwd(), requirements_file)
if not path.exists(requirements_path):
    this = path.dirname(__file__)
    requirements_path = path.join(this, requirements_file)
if not path.exists(requirements_path):
    raise FileNotFoundError("Unable to find " + requirements_file)
with open(requirements_path) as f:
    install_requires = f.read().splitlines()


if enable_training:
    def save_build_and_package_info(package_name, version_number, cuda_version, rocm_version):
        sys.path.append(path.join(path.dirname(__file__), 'onnxruntime', 'python'))
        from onnxruntime_collect_build_info import find_cudart_versions

        version_path = path.join('onnxruntime', 'capi', 'build_and_package_info.py')
        with open(version_path, 'w') as f:
            f.write("package_name = '{}'\n".format(package_name))
            f.write("__version__ = '{}'\n".format(version_number))

            if cuda_version:
                f.write("cuda_version = '{}'\n".format(cuda_version))

                # cudart_versions are integers
                cudart_versions = find_cudart_versions(build_env=True)
                if cudart_versions and len(cudart_versions) == 1:
                    f.write("cudart_version = {}\n".format(cudart_versions[0]))
                else:
                    print(
                        "Error getting cudart version. ",
                        "did not find any cudart library"
                        if not cudart_versions or len(cudart_versions) == 0
                        else "found multiple cudart libraries")
            elif rocm_version:
                f.write("rocm_version = '{}'\n".format(rocm_version))

    save_build_and_package_info(package_name, version_number, cuda_version, rocm_version)

# Setup
setup(
    name=package_name,
    version=version_number,
    description='ONNX Runtime is a runtime accelerator for Machine Learning models',
    long_description=long_description,
    author='Microsoft Corporation',
    author_email='onnxruntime@microsoft.com',
    cmdclass=cmd_classes,
    license="MIT License",
    packages=packages,
    ext_modules=ext_modules,
    package_data=package_data,
    url="https://onnxruntime.ai",
    download_url='https://github.com/microsoft/onnxruntime/tags',
    data_files=data_files,
    install_requires=install_requires,
    keywords='onnx machine learning',
    entry_points={
        'console_scripts': [
            'onnxruntime_test = onnxruntime.tools.onnxruntime_test:main',
        ]
    },
    classifiers=classifiers,
    )