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setup.py
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setup.py
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#!/usr/bin/env python
import glob
import os
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from pkg_resources import parse_requirements
from setuptools import find_packages, setup
_PATH_ROOT = os.path.dirname(__file__)
_PATH_SOURCE = os.path.join(_PATH_ROOT, "src")
_PATH_REQUIRES = os.path.join(_PATH_ROOT, "requirements")
def _load_py_module(fname, pkg="lightning_graphcore"):
spec = spec_from_file_location(os.path.join(pkg, fname), os.path.join(_PATH_SOURCE, pkg, fname))
py = module_from_spec(spec)
spec.loader.exec_module(py)
return py
def _load_requirements(path_dir: str = _PATH_ROOT, file_name: str = "requirements.txt") -> list:
reqs = parse_requirements(open(os.path.join(path_dir, file_name)).readlines())
return list(map(str, reqs))
about = _load_py_module("__about__.py")
with open(os.path.join(_PATH_ROOT, "README.md"), encoding="utf-8") as fopen:
readme = fopen.read()
def _prepare_extras(requirements_dir: str = _PATH_REQUIRES) -> dict:
# https://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-extras
# Define package extras. These are only installed if you specify them.
# From remote, use like `pip install pytorch-lightning[dev, docs]`
# From local copy of repo, use like `pip install ".[dev, docs]"`
req_files = [Path(p) for p in glob.glob(os.path.join(requirements_dir, "*.txt"))]
extras = {
p.stem: _load_requirements(file_name=p.name, path_dir=str(p.parent))
for p in req_files
if not p.name.startswith("_")
}
# todo: eventually add some custom aggregations such as `develop`
extras = {name: sorted(set(reqs)) for name, reqs in extras.items()}
print("The extras are: ", extras)
return extras
# https://packaging.python.org/discussions/install-requires-vs-requirements /
# keep the meta-data here for simplicity in reading this file... it's not obvious
# what happens and to non-engineers they won't know to look in init ...
# the goal of the project is simplicity for researchers, don't want to add too much
# engineer specific practices
setup(
name="lightning-graphcore",
version=about.__version__,
description=about.__docs__,
author=about.__author__,
author_email=about.__author_email__,
url=about.__homepage__,
download_url="https://github.com/Lightning-AI/lightning-graphcore",
license=about.__license__,
packages=find_packages(where="src"),
package_dir={"": "src"},
long_description=readme,
long_description_content_type="text/markdown",
include_package_data=True,
zip_safe=False,
keywords=["deep learning", "pytorch", "AI"],
python_requires=">=3.8",
setup_requires=["wheel"],
install_requires=_load_requirements(),
extras_require=_prepare_extras(),
project_urls={
"Bug Tracker": "https://github.com/Lightning-AI/lightning-graphcore/issues",
"Documentation": "https://lightning-graphcore.rtfd.io/en/latest/",
"Source Code": "https://github.com/Lightning-AI/lightning-graphcore",
},
classifiers=[
"Environment :: Console",
"Natural Language :: English",
# How mature is this project? Common values are
# 3 - Alpha, 4 - Beta, 5 - Production/Stable
"Development Status :: 3 - Alpha",
# Indicate who your project is intended for
"Intended Audience :: Developers",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Information Analysis",
# Pick your license as you wish
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
# Specify the Python versions you support here. In particular, ensure
# that you indicate whether you support Python 2, Python 3 or both.
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
],
)