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setup.py
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setup.py
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# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
from distutils import log
from fnmatch import fnmatch
from typing import Dict, List, Tuple
from setuptools import find_packages, setup
from setuptools.command.install import install
from utils.artifacts import (
check_wand_binaries_exist,
download_wand_binaries,
get_release_and_version,
)
# Load version and release info from deepsparse package
package_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "src", "deepsparse"
)
(
is_release,
is_enterprise,
version,
version_major,
version_minor,
version_bug,
) = get_release_and_version(package_path)
version_base = f"{version_major}.{version_minor}.0"
_PACKAGE_NAME = (
"deepsparse-ent"
if is_enterprise
else "deepsparse"
if is_release
else "deepsparse-nightly"
)
if is_enterprise:
license = "Apache"
else:
license = "Neural Magic DeepSparse Community License, Apache"
if is_enterprise:
# do not include the LICENSE-NEURALMAGIC file
# in the deepsparse-ent installation folder
license_nm_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "LICENSE-NEURALMAGIC"
)
os.remove(license_nm_path)
# File regexes for binaries to include in package_data
binary_regexes = ["*/*.so", "*/*.so.*", "*.bin", "*/*.bin", "*/*.dylib"]
# regexes for things to include as license files in the .dist-info
# see https://github.com/pypa/setuptools/blob/v65.6.0/docs/references/keywords.rst
# for more info
license_files = [
"NOTICE",
"LICENSE*",
"licenses/*",
"src/deepsparse/licenses/*",
]
def _parse_requirements_file(file_path):
with open(file_path, "r") as requirements_file:
lines = requirements_file.read().splitlines()
return [line for line in lines if len(line) > 0 and line[0] != "#"]
_deps = [
"numpy>=1.16.3",
"onnx>=1.5.0,<1.15.0",
"pydantic>=1.8.2,<2.0.0",
"requests>=2.0.0",
"tqdm>=4.0.0",
"protobuf>=3.12.2",
"click>=7.1.2,!=8.0.0", # latest version < 8.0 + blocked version with reported bug
]
_nm_deps = [f"{'sparsezoo' if is_release else 'sparsezoo-nightly'}~={version_base}"]
_dev_deps = [
"beautifulsoup4>=4.9.3",
"black==22.12.0",
"flake8>=3.8.3",
"isort>=5.7.0",
"m2r2~=0.2.7",
"mistune==0.8.4",
"myst-parser~=0.14.0",
"flaky~=3.7.0",
"ndjson>=0.3.1",
"rinohtype>=0.4.2",
"sphinx>=3.4.0",
"sphinx-copybutton>=0.3.0",
"sphinx-markdown-tables>=0.0.15",
"wheel>=0.36.2",
"pytest>=6.0.0",
"sphinx-multiversion==0.2.4",
"sphinx-rtd-theme",
"onnxruntime>=1.7.0",
"flask>=1.0.0",
"flask-cors>=3.0.0",
"Pillow>=8.3.2",
]
_server_deps = [
"uvicorn>=0.15.0",
"fastapi>=0.70.0,<0.87.0",
"requests>=2.26.0",
"python-multipart>=0.0.5",
"prometheus-client>=0.14.1",
"psutil>=5.9.4",
"anyio<4.0.0",
]
_onnxruntime_deps = [
"onnxruntime>=1.7.0",
]
_torch_deps = ["torch>=1.7.0,<=2.0"]
_image_classification_deps = [
"torchvision>=0.3.0,<0.14",
"opencv-python<=4.6.0.66",
]
_yolo_integration_deps = [
"torchvision>=0.3.0,<=0.15.1",
"opencv-python<=4.6.0.66",
]
_openpifpaf_integration_deps = [
"openpifpaf==0.13.11",
"opencv-python<=4.6.0.66",
"pycocotools >=2.0.6",
"scipy==1.10.1",
]
_yolov8_integration_deps = _yolo_integration_deps + ["ultralytics==8.0.124"]
_transformers_integration_deps = [
"transformers<4.35",
"datasets<2.13",
"scikit-learn",
"seqeval",
]
_sentence_transformers_integration_deps = ["optimum-deepsparse"] + _torch_deps
# haystack dependencies are installed from a requirements file to avoid
# conflicting versions with NM's deepsparse/transformers
_haystack_requirements_file_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"src",
"deepsparse",
"transformers",
"haystack",
"haystack_reqs.txt",
)
_haystack_integration_deps = _parse_requirements_file(_haystack_requirements_file_path)
_clip_deps = [
"open_clip_torch==2.20.0",
"scipy<1.9.2,>=1.8",
"transformers<4.35",
]
def _check_supported_system():
if sys.platform.startswith("linux"):
# linux is supported, allow install to go through
return
if sys.platform.startswith("win32") or sys.platform.startswith("cygwin"):
# windows is not supported, raise error on install
raise OSError(
"Native Windows is currently unsupported for DeepSparse. "
"Please run on a Linux system or within a Linux container on Windows. "
"More info can be found in our docs here: "
"https://docs.neuralmagic.com/deepsparse/source/hardware.html"
)
if sys.platform.startswith("darwin"):
# beta support for mac, allow install to go through
return
# unknown system, raise error on install
raise OSError(
f"Unknown OS given of {sys.platform}; "
"it is unsupported for DeepSparse. "
"Please run on a Linux system. "
"More info can be found in our docs here: "
"https://docs.neuralmagic.com/deepsparse/source/hardware.html"
)
def _check_supported_python_version():
supported_major = 3
supported_minor = [8, 9, 10, 11]
if (
sys.version_info[0] != supported_major
or sys.version_info[1] not in supported_minor
):
raise EnvironmentError(
f"Python {supported_major}.{supported_minor} "
f"is only supported for DeepSparse; found {sys.version}. "
"Please run on a system with the proper Python version installed. "
"More info can be found in our docs here: "
"https://docs.neuralmagic.com/deepsparse/source/hardware.html"
)
# Ensure system and python environment is compatible
_check_supported_system()
_check_supported_python_version()
# Download WAND binaries if needed
if not check_wand_binaries_exist(package_path):
download_wand_binaries(
package_path, f"{version_major}.{version_minor}.{version_bug}", is_release
)
class OverrideInstall(install):
"""
Install class to run checks for correcting binary file permissions after install.
"""
def run(self):
super().run()
self._fix_file_modes()
def _fix_file_modes(self):
mode = 0o755
for filepath in self.get_outputs():
if any(fnmatch(filepath, regex) for regex in binary_regexes):
log.info("changing mode of %s to %s" % (filepath, oct(mode)))
os.chmod(filepath, mode)
def _setup_package_dir() -> Dict:
return {"": "src"}
def _setup_packages() -> List:
return find_packages(
"src", include=["deepsparse", "deepsparse.*"], exclude=["*.__pycache__.*"]
)
def _setup_package_data() -> Dict:
return {"deepsparse": binary_regexes}
def _setup_install_requires() -> List:
return _nm_deps + _deps
def _setup_extras() -> Dict:
return {
"dev": _dev_deps,
"server": _server_deps,
"onnxruntime": _onnxruntime_deps,
"image_classification": _image_classification_deps,
"yolo": _yolo_integration_deps,
"yolov5": _yolo_integration_deps,
"haystack": _haystack_integration_deps,
"openpifpaf": _openpifpaf_integration_deps,
"yolov8": _yolov8_integration_deps,
"transformers": _transformers_integration_deps,
"llm": _transformers_integration_deps,
"sentence_transformers": _sentence_transformers_integration_deps,
"torch": _torch_deps,
"clip": _clip_deps,
}
def _setup_entry_points() -> Dict:
data_api_entrypoint = "deepsparse.transformers.pipelines_cli:cli"
eval_downstream = "deepsparse.transformers.eval_downstream:main"
ic_eval = "deepsparse.image_classification.validation_script:main"
return {
"console_scripts": [
f"deepsparse.transformers.run_inference={data_api_entrypoint}",
f"deepsparse.transformers.eval_downstream={eval_downstream}",
"deepsparse.infer=deepsparse.transformers.inference.infer:main",
"deepsparse.debug_analysis=deepsparse.debug_analysis:main",
"deepsparse.analyze=deepsparse.analyze:main",
"deepsparse.check_hardware=deepsparse.cpu:print_hardware_capability",
"deepsparse.benchmark=deepsparse.benchmark.benchmark_model:main",
"deepsparse.benchmark_pipeline=deepsparse.benchmark.benchmark_pipeline:main", # noqa E501
"deepsparse.benchmark_sweep=deepsparse.benchmark.benchmark_sweep:main",
"deepsparse.server=deepsparse.server.cli:main",
"deepsparse.openai=deepsparse.server.cli:openai",
"deepsparse.object_detection.annotate=deepsparse.yolo.annotate:main",
"deepsparse.yolov8.annotate=deepsparse.yolov8.annotate:main",
"deepsparse.yolov8.eval=deepsparse.yolov8.validation:main",
"deepsparse.pose_estimation.annotate=deepsparse.open_pif_paf.annotate:main",
"deepsparse.pose_estimation.eval=deepsparse.open_pif_paf.validation:main",
"deepsparse.image_classification.annotate=deepsparse.image_classification.annotate:main", # noqa E501
"deepsparse.instance_segmentation.annotate=deepsparse.yolact.annotate:main",
f"deepsparse.image_classification.eval={ic_eval}",
"deepsparse.license=deepsparse.license:main",
"deepsparse.validate_license=deepsparse.license:validate_license_cli",
]
}
def _setup_long_description() -> Tuple[str, str]:
return open("README.md", "r", encoding="utf-8").read(), "text/markdown"
setup(
name=_PACKAGE_NAME,
version=version,
author="Neuralmagic, Inc.",
author_email="[email protected]",
description=(
"An inference runtime offering GPU-class performance on CPUs "
"and APIs to integrate ML into your application"
),
long_description=_setup_long_description()[0],
long_description_content_type=_setup_long_description()[1],
keywords=(
"inference, machine learning, x86, x86_64, avx2, avx512, neural network, "
"sparse, inference engine, cpu, runtime, deepsparse, computer vision, "
"object detection, sparsity"
),
license=license,
license_files=license_files,
url="https://github.com/neuralmagic/deepsparse",
package_dir=_setup_package_dir(),
include_package_data=True,
package_data=_setup_package_data(),
packages=_setup_packages(),
install_requires=_setup_install_requires(),
extras_require=_setup_extras(),
entry_points=_setup_entry_points(),
python_requires=">=3.8, <3.12",
classifiers=[
"Development Status :: 5 - Production/Stable",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Intended Audience :: Information Technology",
"Intended Audience :: Science/Research",
"License :: Other/Proprietary License",
"License :: OSI Approved :: Apache Software License",
"Operating System :: POSIX :: Linux",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Mathematics",
"Topic :: Software Development",
"Topic :: Software Development :: Libraries :: Python Modules",
],
cmdclass={"install": OverrideInstall},
)