setup.py

# Copyright (c) 2019 PaddlePaddle Authors. 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.
"""Setup for pip package."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


from setuptools import find_packages
from setuptools import setup
from plsc.version import plsc_version


REQUIRED_PACKAGES = [
    'sklearn', 'easydict', 'Pillow', 'numpy', 'scipy', 'six'
]


setup(
    name="plsc",
    version=plsc_version,
    description=
    ("PaddlePaddle Large Scale Classfication Package."),
    long_description='',
    url='https://github.com/PaddlePaddle/PLSC',
    author='PaddlePaddle Authors',
    author_email='paddle-dev@baidu.com',
    install_requires=REQUIRED_PACKAGES,
    packages=find_packages(),
    # PyPI package information.
    classifiers=[
        'Development Status :: 4 - Beta',
        'Intended Audience :: Developers',
        'Intended Audience :: Education',
        'Intended Audience :: Science/Research',
        'License :: OSI Approved :: Apache Software License',
        'Programming Language :: Python :: 2.7',
        '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',
    ],
    license="Apache 2.0",
    keywords=
    ('plsc paddlepaddle large-scale classification model-parallelism distributed-training'))