setup.py

import re

from setuptools import find_namespace_packages, setup


# Ensure we match the version set in src/optimum/version.py
try:
    filepath = "optimum/version.py"
    with open(filepath) as version_file:
        (__version__,) = re.findall('__version__ = "(.*)"', version_file.read())
except Exception as error:
    assert False, "Error: Could not open '%s' due %s\n" % (filepath, error)


REQUIRED_PKGS = [
    "coloredlogs",
    "sympy",
    "transformers[sentencepiece]>=4.26.0",
    "torch>=1.9",
    "torchvision",
    "packaging",
    "numpy",
    "huggingface_hub>=0.8.0",
    "datasets",
]

TESTS_REQUIRE = [
    "pytest",
    "requests",
    "parameterized",
    "pytest-xdist",
    "Pillow",
    "sacremoses",
    "diffusers",
    "torchaudio",
]

QUALITY_REQUIRE = ["black~=23.1", "ruff>=0.0.241,<=0.0.259"]

BENCHMARK_REQUIRE = ["optuna", "tqdm", "scikit-learn", "seqeval", "torchvision", "evaluate>=0.2.0"]

EXTRAS_REQUIRE = {
    "onnxruntime": [
        "onnx<1.14.0",
        "onnxruntime>=1.9.0",
        "datasets>=1.2.1",
        "evaluate",
        "protobuf>=3.20.1",
    ],
    "onnxruntime-gpu": [
        "onnx<1.14.0",
        "onnxruntime-gpu>=1.9.0",
        "datasets>=1.2.1",
        "evaluate",
        "protobuf>=3.20.1",
    ],
    "exporters": ["onnx<1.14.0", "onnxruntime", "timm"],
    "exporters-gpu": ["onnx<1.14.0", "onnxruntime-gpu", "timm"],
    "exporters-tf": ["tensorflow>=2.4,<2.11", "tf2onnx", "onnx", "onnxruntime", "timm", "h5py", "numpy<1.24.0"],
    "intel": "optimum-intel",
    "openvino": "optimum-intel[openvino]",
    "nncf": "optimum-intel[nncf]",
    "neural-compressor": "optimum-intel[neural-compressor]",
    "graphcore": "optimum-graphcore",
    "habana": "optimum-habana",
    "neuron": "optimum-neuron[neuron]",
    "neuronx": "optimum-neuron[neuronx]",
    "dev": TESTS_REQUIRE + QUALITY_REQUIRE,
    "tests": TESTS_REQUIRE,
    "quality": QUALITY_REQUIRE,
    "benchmark": BENCHMARK_REQUIRE,
}

setup(
    name="optimum",
    version=__version__,
    description="Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to "
    "integrate third-party libraries from Hardware Partners and interface with their specific "
    "functionality.",
    long_description=open("README.md", "r", encoding="utf-8").read(),
    long_description_content_type="text/markdown",
    classifiers=[
        "Development Status :: 5 - Production/Stable",
        "License :: OSI Approved :: Apache Software License",
        "Intended Audience :: Developers",
        "Intended Audience :: Education",
        "Intended Audience :: Science/Research",
        "Operating System :: OS Independent",
        "Programming Language :: Python :: 3.7",
        "Programming Language :: Python :: 3.8",
        "Programming Language :: Python :: 3.9",
        "Topic :: Scientific/Engineering :: Artificial Intelligence",
    ],
    keywords="transformers, quantization, pruning, optimization, training, inference, onnx, onnx runtime, intel, "
    "habana, graphcore, neural compressor, ipu, hpu",
    url="https://github.com/huggingface/optimum",
    author="HuggingFace Inc. Special Ops Team",
    author_email="hardware@huggingface.co",
    license="Apache",
    packages=find_namespace_packages(include=["optimum*"]),
    install_requires=REQUIRED_PKGS,
    extras_require=EXTRAS_REQUIRE,
    python_requires=">=3.7.0",
    include_package_data=True,
    zip_safe=False,
    entry_points={"console_scripts": ["optimum-cli=optimum.commands.optimum_cli:main"]},
)