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setup.py
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setup.py
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# To build and upload a new version, follow the steps below.
# Notes:
# - this is a "Universal Wheels" package that is pure Python and supports Python3
# - Twine is a secure PyPi upload package
# - Make sure you have bumped the version! at ts/version.py
# $ pip install twine
# $ pip install wheel
# $ python setup.py bdist_wheel --universal
# *** TEST YOUR PACKAGE WITH TEST PI ******
# twine upload --repository-url https://test.pypi.org/legacy/ dist/*
# If this is successful then push it to actual pypi
# $ twine upload dist/*
"""
Setup.py for the model server package
"""
import errno
import os
import platform
import subprocess
import sys
from datetime import date
from shutil import copy2, rmtree
import setuptools.command.build_py
from setuptools import Command, find_packages, setup
import ts
pkgs = find_packages(exclude=["ts_scripts", "test"])
build_frontend_command = {
"Windows": ".\\frontend\\gradlew.bat -p frontend clean assemble",
"Darwin": "frontend/gradlew -p frontend clean assemble",
"Linux": "frontend/gradlew -p frontend clean assemble",
}
build_plugins_command = {
"Windows": ".\\plugins\\gradlew.bat -p plugins clean bS",
"Darwin": "plugins/gradlew -p plugins clean bS",
"Linux": "plugins/gradlew -p plugins clean bS",
}
def pypi_description():
"""
Imports the long description for the project page
"""
with open("PyPiDescription.rst") as df:
return df.read()
def get_nightly_version():
today = date.today()
return today.strftime("%Y.%m.%d")
def detect_model_server_version():
sys.path.append(os.path.abspath("ts"))
if "--release" in sys.argv:
sys.argv.remove("--release")
return ts.__version__.strip()
return ts.__version__.strip() + "b" + str(date.today()).replace("-", "")
class BuildFrontEnd(setuptools.command.build_py.build_py):
"""
Class defined to run custom commands.
"""
description = "Build Model Server Frontend"
source_server_file = os.path.abspath("frontend/server/build/libs/server-1.0.jar")
dest_file_name = os.path.abspath("ts/frontend/model-server.jar")
# noinspection PyMethodMayBeStatic
def run(self):
"""
Actual method called to run the build command
:return:
"""
front_end_bin_dir = os.path.abspath(".") + "/ts/frontend"
try:
os.mkdir(front_end_bin_dir)
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(front_end_bin_dir):
pass
else:
raise
if os.path.exists(self.source_server_file):
os.remove(self.source_server_file)
try:
subprocess.check_call(build_frontend_command[platform.system()], shell=True)
except OSError:
assert 0, "build failed"
copy2(self.source_server_file, self.dest_file_name)
class BuildPy(setuptools.command.build_py.build_py):
"""
Class to invoke the custom command defined above.
"""
def run(self):
sys.stderr.flush()
self.run_command("build_frontend")
setuptools.command.build_py.build_py.run(self)
class BuildPlugins(Command):
description = "Build Model Server Plugins"
user_options = [("plugins=", "p", "Plugins installed")]
source_plugin_dir = os.path.abspath("plugins/build/plugins")
def initialize_options(self):
self.plugins = None
def finalize_options(self):
if self.plugins is None:
print("No plugin option provided. Defaulting to 'default'")
self.plugins = "default"
# noinspection PyMethodMayBeStatic
def run(self):
if os.path.isdir(self.source_plugin_dir):
rmtree(self.source_plugin_dir)
try:
if self.plugins == "endpoints":
subprocess.check_call(
build_plugins_command[platform.system()], shell=True
)
else:
raise OSError("No such rule exists")
except OSError:
assert 0, "build failed"
self.run_command("build_py")
if __name__ == "__main__":
# Get nightly version if nightly in name
name = "torchserve"
# Clever code to figure out if setup.py was trigger by ts_scripts/push_nightly.sh
NAME_ARG = "--override-name"
if NAME_ARG in sys.argv:
idx = sys.argv.index(NAME_ARG)
name = sys.argv.pop(idx + 1)
sys.argv.pop(idx)
is_nightly = "nightly" in name
version = get_nightly_version() if is_nightly else detect_model_server_version()
print(f"-- {name} building version: {version}")
requirements = ["Pillow", "psutil", "packaging", "wheel"]
setup(
name=name,
version=version,
description="TorchServe is a tool for serving neural net models for inference",
author="PyTorch Serving team",
author_email="[email protected]",
long_description=pypi_description(),
long_description_content_type="text/x-rst",
url="https://github.com/pytorch/serve.git",
keywords="TorchServe PyTorch Serving Deep Learning Inference AI",
packages=pkgs,
cmdclass={
"build_frontend": BuildFrontEnd,
"build_plugins": BuildPlugins,
"build_py": BuildPy,
},
install_requires=requirements,
extras_require={
"onnx": ["numpy", "onnx", "onnx-runtime"],
"ipex": ["intel_extension_for_pytorch"],
},
entry_points={"console_scripts": ["torchserve=ts.model_server:start"]},
include_package_data=True,
license="Apache License Version 2.0",
)