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setup.py
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setup.py
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#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import sys
from setuptools import find_packages, setup
# Minimum required python version
REQUIRED_MAJOR = 3
REQUIRED_MINOR = 9
# Requirements for testing, formatting, and tutorials
TEST_REQUIRES = ["pytest", "pytest-cov"]
FMT_REQUIRES = ["flake8", "ufmt", "flake8-docstrings"]
TUTORIALS_REQUIRES = [
"ax-platform",
"cma",
"jupyter",
"kaleido",
"matplotlib",
"memory_profiler",
"papermill",
"pykeops",
"torchvision",
]
# Check for python version
if sys.version_info < (REQUIRED_MAJOR, REQUIRED_MINOR):
error = (
"Your version of python ({major}.{minor}) is too old. You need "
"python >= {required_major}.{required_minor}."
).format(
major=sys.version_info.major,
minor=sys.version_info.minor,
required_minor=REQUIRED_MINOR,
required_major=REQUIRED_MAJOR,
)
sys.exit(error)
# Assign root dir location for later use
root_dir = os.path.dirname(__file__)
def read_deps_from_file(filname):
"""Read in requirements file and return items as list of strings"""
with open(os.path.join(root_dir, filname), "r") as fh:
return [line.strip() for line in fh.readlines() if not line.startswith("#")]
# Read in the requirements from the requirements.txt file
install_requires = read_deps_from_file("requirements.txt")
# Allow non-pinned (usually dev) versions of gpytorch and linear_operator
if os.environ.get("ALLOW_LATEST_GPYTORCH_LINOP"):
# Allows more recent previously installed versions. If there is no
# previously installed version, installs the latest release.
install_requires = [
(
dep.replace("==", ">=")
if "gpytorch" in dep or "linear_operator" in dep
else dep
)
for dep in install_requires
]
# Read in pinned versions of the formatting tools
FMT_REQUIRES += read_deps_from_file("requirements-fmt.txt")
# Dev is test + formatting + docs generation
DEV_REQUIRES = TEST_REQUIRES + FMT_REQUIRES + ["sphinx"]
# read in README.md as the long description
with open(os.path.join(root_dir, "README.md"), "r") as fh:
long_description = fh.read()
setup(
name="botorch",
description="Bayesian Optimization in PyTorch",
author="Meta Platforms, Inc.",
license="MIT",
url="https://botorch.org",
project_urls={
"Documentation": "https://botorch.org",
"Source": "https://github.com/pytorch/botorch",
"conda": "https://anaconda.org/pytorch/botorch",
},
keywords=["Bayesian optimization", "PyTorch"],
classifiers=[
"Development Status :: 4 - Beta",
"Programming Language :: Python :: 3 :: Only",
"License :: OSI Approved :: MIT License",
"Topic :: Scientific/Engineering",
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
],
long_description=long_description,
long_description_content_type="text/markdown",
python_requires=">=3.9",
packages=find_packages(exclude=["test", "test.*"]),
install_requires=install_requires,
extras_require={
"dev": DEV_REQUIRES,
"test": TEST_REQUIRES,
"tutorials": TUTORIALS_REQUIRES,
},
)