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setup.py
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setup.py
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import os
import sys
import re
import datetime
from setuptools import find_packages, setup
is_nightly = os.environ.get( 'FASTESTIMATOR_IS_NIGHTLY', None )
if is_nightly is not None:
sys.stderr.write( "Using '%s=%s' environment variable!\n" % (
'FASTESTIMATOR_IS_NIGHTLY', is_nightly ) )
def get_version():
path = os.path.dirname(__file__)
version_re = re.compile(r'''__version__ = ['"](.+)['"]''')
with open(os.path.join(path, 'fastestimator', '__init__.py')) as f:
init = f.read()
now = datetime.datetime.now()
version = version_re.search(init).group(1)
if is_nightly:
return "{}.dev{:04}{:02}{:02}{:02}{:02}".format(version, now.year, now.month, now.day, now.hour, now.minute)
else:
return version
def get_name():
if is_nightly:
return "fastestimator-nightly"
else:
return "fastestimator"
setup(
name=get_name(),
version=get_version(),
description="Deep learning Application framework",
packages=find_packages(),
package_dir={'': '.'},
long_description="FastEstimator is a high-level deep learning API. With the help of FastEstimator, you can easily \
build a high-performance deep learning model and run it anywhere.",
author="FastEstimator Dev",
url='https://github.com/fastestimator/fastestimator',
license="Apache License 2.0",
keywords="fastestimator tensorflow",
classifiers=[
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3", ],
# Declare minimal set for installation
install_requires=[
'numpy',
'pyfiglet',
'pandas',
'pillow',
'sklearn',
'wget',
'matplotlib',
'seaborn>= 0.9.0',
'scipy',
'pytest',
'pytest-cov',
'tensorflow-probability',
'umap-learn',
'tqdm',
'opencv-python',
'papermill',
'tf-explain',
'slackclient',
'nest_asyncio',
'pycocotools-fix'
],
# Declare extra set for installation
extras_require={},
scripts=['bin/fastestimator'])