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setup.py
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import os
from setuptools import setup, Extension
from setuptools.command.build_ext import build_ext
import sys
import setuptools
__version__ = '1.7.2'
libdir = os.path.join(".", "nmslib", "similarity_search")
library_file = os.path.join(libdir, "release", "libNonMetricSpaceLib.a")
source_files = ['nmslib.cc']
libraries = []
extra_objects = []
if os.path.exists(library_file):
# if we have a prebuilt nmslib library file, use that.
extra_objects.append(library_file)
else:
# Otherwise build all the files here directly (excluding extras which need eigen/boost)
exclude_files = set("""lsh.cc lsh_multiprobe.cc lsh_space.cc falconn.cc nndes.cc space_sqfd.cc
dummy_app.cc main.cc""".split())
for root, subdirs, files in os.walk(os.path.join("nmslib", "similarity_search", "src")):
source_files.extend(os.path.join(root, f) for f in files
if f.endswith(".cc") and f not in exclude_files)
if sys.platform.startswith('linux'):
lshkit = os.path.join(libdir, "release", "liblshkit.a")
if os.path.isfile(lshkit):
extra_objects.append(lshkit)
libraries.extend(['gsl', 'gslcblas', 'boost_program_options'])
ext_modules = [
Extension(
'nmslib',
source_files,
include_dirs=[os.path.join(libdir, "include")],
libraries=libraries,
language='c++',
extra_objects=extra_objects,
),
]
# As of Python 3.6, CCompiler has a `has_flag` method.
# cf http://bugs.python.org/issue26689
def has_flag(compiler, flagname):
"""Return a boolean indicating whether a flag name is supported on
the specified compiler.
"""
import tempfile
with tempfile.NamedTemporaryFile('w', suffix='.cpp') as f:
f.write('int main (int argc, char **argv) { return 0; }')
try:
compiler.compile([f.name], extra_postargs=[flagname])
except setuptools.distutils.errors.CompileError:
return False
return True
def cpp_flag(compiler):
"""Return the -std=c++[11/14] compiler flag.
The c++14 is prefered over c++11 (when it is available).
"""
if has_flag(compiler, '-std=c++14'):
return '-std=c++14'
elif has_flag(compiler, '-std=c++11'):
return '-std=c++11'
else:
raise RuntimeError('Unsupported compiler -- at least C++11 support '
'is needed!')
class BuildExt(build_ext):
"""A custom build extension for adding compiler-specific options."""
c_opts = {
'msvc': ['/EHsc', '/openmp', '/O2'],
'unix': ['-O3', '-march=native'],
}
link_opts = {
'unix': [],
'msvc': [],
}
if sys.platform == 'darwin':
c_opts['unix'] += ['-stdlib=libc++', '-mmacosx-version-min=10.7']
link_opts['unix'] += ['-stdlib=libc++', '-mmacosx-version-min=10.7']
else:
c_opts['unix'].append("-fopenmp")
link_opts['unix'].extend(['-fopenmp', '-pthread'])
def build_extensions(self):
ct = self.compiler.compiler_type
opts = self.c_opts.get(ct, [])
if ct == 'unix':
opts.append('-DVERSION_INFO="%s"' % self.distribution.get_version())
opts.append(cpp_flag(self.compiler))
if has_flag(self.compiler, '-fvisibility=hidden'):
opts.append('-fvisibility=hidden')
elif ct == 'msvc':
opts.append('/DVERSION_INFO=\\"%s\\"' % self.distribution.get_version())
# extend include dirs here (don't assume numpy/pybind11 are installed when first run, since
# pip could have installed them as part of executing this script
import pybind11
import numpy as np
for ext in self.extensions:
ext.extra_compile_args.extend(opts)
ext.extra_link_args.extend(self.link_opts.get(ct, []))
ext.include_dirs.extend([
# Path to pybind11 headers
pybind11.get_include(),
pybind11.get_include(True),
# Path to numpy headers
np.get_include()
])
build_ext.build_extensions(self)
setup(
name='nmslib',
version=__version__,
description='Non-Metric Space Library (NMSLIB)',
author='B. Naidan, L. Boytsov, Yu. Malkov, B. Frederickson, D. Novak, et al.',
url='https://github.com/searchivarius/nmslib',
long_description="""Non-Metric Space Library (NMSLIB) is an efficient cross-platform
similarity search library and a toolkit for evaluation of similarity search methods. The
goal of the project is to create an effective and comprehensive toolkit for searching in
generic non-metric spaces. Being comprehensive is important, because no single method is
likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in
high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods.""",
ext_modules=ext_modules,
install_requires=['pybind11>=2.0', 'numpy'],
cmdclass={'build_ext': BuildExt},
test_suite="tests",
zip_safe=False,
)