https://github.com/antoinecarme/pyaf
Revision 4ab4a659801ba2d48ca519f605d6d97754879922 authored by Antoine CARME on 23 February 2023, 19:33:48 UTC, committed by Antoine CARME on 23 February 2023, 19:33:48 UTC
1 parent cbab41b
Tip revision: 4ab4a659801ba2d48ca519f605d6d97754879922 authored by Antoine CARME on 23 February 2023, 19:33:48 UTC
Don't import torch if not used in the model. Can fail when some platforms don't have enough memory.
Don't import torch if not used in the model. Can fail when some platforms don't have enough memory.
Tip revision: 4ab4a65
setup.py
from setuptools import setup
from setuptools import find_packages
with open("README.md", "r") as fh:
pyaf_long_description = fh.read()
setup(name='pyaf',
version='4.0',
description='Python Automatic Forecasting',
long_description=pyaf_long_description,
long_description_content_type="text/markdown",
author='Antoine CARME',
author_email='antoine.carme@outlook.com',
url='https://github.com/antoinecarme/pyaf',
license='BSD 3-clause',
packages=find_packages(include=['pyaf', 'pyaf.*']),
python_requires='>=3',
classifiers=['Development Status :: 5 - Production/Stable',
'Programming Language :: Python :: 3'],
keywords='arx automatic-forecasting autoregressive benchmark cycle decomposition exogenous forecasting heroku hierarchical-forecasting horizon jupyter pandas python scikit-learn seasonal time-series transformation trend web-service',
install_requires=[
'scipy',
'pandas',
'scikit-learn',
'matplotlib',
'pydot',
'dill',
'sqlalchemy'
])
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