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setup.py
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#!/usr/bin/env python
from setuptools import setup
xpysom_version = '1.0.7.2'
# read the contents of your README file
from os import path
this_directory = path.abspath(path.dirname(__file__))
with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
description = 'Minimalistic implementation of batch Self Organizing Maps (SOM) for parallel execution on CPU, GPU or Dask.'
keywords = ['machine learning', 'neural networks', 'clustering', 'dimentionality reduction']
setup(name='XPySom-dask',
version='1.0.7',
description=description,
long_description=long_description,
long_description_content_type="text/markdown",
author='Julio Faracco',
author_email="[email protected]",
package_data={'': ['README.md']},
include_package_data=True,
license="Creative Commons Attribution 3.0 Unported",
packages=['xpysom_dask'],
install_requires=['numpy'],
extras_require={
'cuda100': ['cupy-cuda100'],
'cuda101': ['cupy-cuda101'],
'cuda102': ['cupy-cuda102'],
'cuda110': ['cupy-cuda110'],
'cuda111': ['cupy-cuda111'],
'cuda112': ['cupy-cuda112'],
'cuda113': ['cupy-cuda113'],
'cuda117': ['cupy-cuda117'],
'cuda118': ['cupy-cuda118'],
},
url='https://github.com/jcfaracco/xpysom-dask',
download_url=f'https://github.com/jcfaracco/xpysom-dask/archive/v{xpysom_version}.tar.gz',
keywords=keywords,
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
],
zip_safe=False,
)