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setup.py
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69 lines (65 loc) · 2.39 KB
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"""Setup script for poet-torch package."""
from setuptools import setup, find_packages
import os
# Read README
readme_path = os.path.join(os.path.dirname(__file__), "README.md")
if os.path.exists(readme_path):
with open(readme_path, "r", encoding="utf-8") as f:
long_description = f.read()
else:
long_description = "POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation"
# Read requirements
requirements_path = os.path.join(os.path.dirname(__file__), "requirements.txt")
if os.path.exists(requirements_path):
with open(requirements_path, "r", encoding="utf-8") as f:
install_requires = [line.strip() for line in f if line.strip() and not line.startswith("#")]
else:
install_requires = [
"torch>=2.7.0",
"numpy",
"triton>=3.4.0"
]
setup(
name="poet-torch",
version="0.0.1",
description="POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/Sphere-AI-Lab/poet",
project_urls={
"Bug Tracker": "https://github.com/Sphere-AI-Lab/poet/issues",
"Documentation": "https://github.com/Sphere-AI-Lab/poet/blob/main/README.md",
"Source": "https://github.com/Sphere-AI-Lab/poet",
},
packages=find_packages(include=["poet_torch", "poet_torch.*"]),
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: BSD 3-Clause License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development :: Libraries :: Python Modules",
],
python_requires=">=3.9",
install_requires=install_requires,
extras_require={
},
keywords=[
"deep learning",
"machine learning",
"neural networks",
"optimization",
"llm training",
"parameter efficient",
"orthogonal transformation",
"pytorch",
],
license="BSD 3-Clause License",
zip_safe=False,
)