forked from tensorflow/tfx
/
setup.py
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/
setup.py
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# Copyright 2019 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Package Setup script for TFX."""
from __future__ import print_function
import glob
import os
import subprocess
import sys
from distutils.spawn import find_executable
from setuptools import find_packages
from setuptools import setup
# Find the Protocol Compiler.
if 'PROTOC' in os.environ and os.path.exists(os.environ['PROTOC']):
protoc = os.environ['PROTOC']
elif os.path.exists('../src/protoc'):
protoc = '../src/protoc'
elif os.path.exists('../src/protoc.exe'):
protoc = '../src/protoc.exe'
elif os.path.exists('../vsprojects/Debug/protoc.exe'):
protoc = '../vsprojects/Debug/protoc.exe'
elif os.path.exists('../vsprojects/Release/protoc.exe'):
protoc = '../vsprojects/Release/protoc.exe'
else:
protoc = find_executable('protoc')
def generate_proto(source):
"""Invokes the Protocol Compiler to generate a _pb2.py."""
output = source.replace('.proto', '_pb2.py')
if (not os.path.exists(output) or
(os.path.exists(source) and
os.path.getmtime(source) > os.path.getmtime(output))):
print('Generating %s...' % output)
if not os.path.exists(source):
sys.stderr.write('Cannot find required file: %s\n' % source)
sys.exit(-1)
if protoc is None:
sys.stderr.write(
'protoc is not installed nor found in ../src. Please compile it '
'or install the binary package.\n')
sys.exit(-1)
protoc_command = [protoc, '-I.', '--python_out=.', source]
if subprocess.call(protoc_command) != 0:
sys.exit(-1)
_PROTO_FILE_PATTERNS = [
'tfx/proto/*.proto',
'tfx/orchestration/kubeflow/proto/*.proto',
]
for file_pattern in _PROTO_FILE_PATTERNS:
for proto_file in glob.glob(file_pattern):
generate_proto(proto_file)
# Get various package dependencies list.
with open('tfx/dependencies.py') as fp:
globals_dict = {}
exec(fp.read(), globals_dict) # pylint: disable=exec-used
_make_required_install_packages = globals_dict['make_required_install_packages']
_make_required_test_packages = globals_dict['make_required_test_packages']
_make_extra_packages_docker_image = globals_dict[
'make_extra_packages_docker_image']
# Get version from version module.
with open('tfx/version.py') as fp:
globals_dict = {}
exec(fp.read(), globals_dict) # pylint: disable=exec-used
__version__ = globals_dict['__version__']
# TODO(b/121329572): Remove the following comment after we can guarantee the
# required versions of packages through kokoro release workflow.
# Note: In order for the README to be rendered correctly, make sure to have the
# following minimum required versions of the respective packages when building
# and uploading the zip/wheel package to PyPI:
# setuptools >= 38.6.0, wheel >= 0.31.0, twine >= 1.11.0
# Get the long description from the README file.
with open('README.md') as fp:
_LONG_DESCRIPTION = fp.read()
setup(
name='tfx',
version=__version__,
author='Google LLC',
author_email='tensorflow-extended-dev@googlegroups.com',
license='Apache 2.0',
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'Intended Audience :: Education',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: Apache Software License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Topic :: Scientific/Engineering',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Topic :: Scientific/Engineering :: Mathematics',
'Topic :: Software Development',
'Topic :: Software Development :: Libraries',
'Topic :: Software Development :: Libraries :: Python Modules',
],
namespace_packages=[],
install_requires=_make_required_install_packages(),
extras_require={
'docker-image': _make_extra_packages_docker_image(),
},
setup_requires=['pytest-runner'],
tests_require=_make_required_test_packages(),
python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,<4',
packages=find_packages(),
include_package_data=True,
description='TensorFlow Extended (TFX) is a TensorFlow-based general-purpose machine learning platform implemented at Google',
long_description=_LONG_DESCRIPTION,
long_description_content_type='text/markdown',
keywords='tensorflow tfx',
url='https://www.tensorflow.org/tfx',
download_url='https://github.com/tensorflow/tfx/tags',
requires=[],
# Below console_scripts, each line identifies one console script. The first
# part before the equals sign (=) which is 'tfx', is the name of the script
# that should be generated, the second part is the import path followed by a
# colon (:) with the Click command group. After installation, the user can
# invoke the CLI using "tfx <command_group> <sub_command> <flags>"
entry_points="""
[console_scripts]
tfx=tfx.tools.cli.cli_main:cli_group
""")