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setup.py
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import sys
from subprocess import call, check_call
from setuptools import find_packages, setup
from setuptools.command.develop import develop
from setuptools.command.egg_info import egg_info
from setuptools.command.install import install
# Histolab has a dependency that requires options
histolab_dep_commands = [
sys.executable, # the python interpreter (needed to install in the right pipenv)
"-m",
"pip",
"install",
"large-image-source-openslide",
"--find-links",
"https://girder.github.io/large_image_wheels",
]
class CustomInstallCommand(install):
def run(self):
install.run(self)
command = call(histolab_dep_commands)
assert command == 0
class CustomDevelopCommand(develop):
def run(self):
develop.run(self)
command = call(histolab_dep_commands)
assert command == 0
class CustomEggInfoCommand(egg_info):
def run(self):
egg_info.run(self)
command = check_call(histolab_dep_commands)
assert command == 0
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setup(
name="flamby",
version="0.0.1",
python_requires=">=3.7.0",
license="MIT",
classifiers=[
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Intended Audience :: Science/Research",
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
],
install_requires=[
"argparse",
"numpy",
"pandas",
"pre-commit",
"scikit-learn",
"scipy",
"seaborn",
"setuptools==59.5.0",
"tensorboard",
"torch",
"torchvision",
"torchaudio",
"tqdm",
"umap-learn",
"opacus",
"substrafl==0.32.0",
"wget",
"matplotlib==3.5.2",
"inquirer"
],
description="FLamby: A cross-silo Federated Learning Benchmark.",
long_description=long_description,
author="FL-datasets team",
author_email="unknown",
packages=find_packages(),
include_package_data=True,
cmdclass={
"install": CustomInstallCommand,
"develop": CustomDevelopCommand,
"egg_info": CustomEggInfoCommand,
},
)