mirror of
https://github.com/facebookresearch/faiss.git
synced 2026-10-12 02:13:36 +00:00
Lucas Hosseini
a8118acbc5
Changelog: - changed license: BSD+Patents -> MIT - propagates exceptions raised in sub-indexes of IndexShards and IndexReplicas - support for searching several inverted lists in parallel (parallel_mode != 0) - better support for PQ codes where nbit != 8 or 16 - IVFSpectralHash implementation: spectral hash codes inside an IVF - 6-bit per component scalar quantizer (4 and 8 bit were already supported) - combinations of inverted lists: HStackInvertedLists and VStackInvertedLists - configurable number of threads for OnDiskInvertedLists prefetching (including 0=no prefetch) - more test and demo code compatible with Python 3 (print with parentheses) - refactored benchmark code: data loading is now in a single file
46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
from __future__ import print_function
|
|
from setuptools import setup, find_packages
|
|
import os
|
|
import shutil
|
|
|
|
here = os.path.abspath(os.path.dirname(__file__))
|
|
|
|
check_fpath = os.path.join("_swigfaiss.so")
|
|
if not os.path.exists(check_fpath):
|
|
print("Could not find {}".format(check_fpath))
|
|
print("Have you run `make` and `make -C python`?")
|
|
|
|
# make the faiss python package dir
|
|
shutil.rmtree("faiss", ignore_errors=True)
|
|
os.mkdir("faiss")
|
|
shutil.copyfile("faiss.py", "faiss/__init__.py")
|
|
shutil.copyfile("swigfaiss.py", "faiss/swigfaiss.py")
|
|
shutil.copyfile("_swigfaiss.so", "faiss/_swigfaiss.so")
|
|
|
|
long_description="""
|
|
Faiss is a library for efficient similarity search and clustering of dense
|
|
vectors. It contains algorithms that search in sets of vectors of any size,
|
|
up to ones that possibly do not fit in RAM. It also contains supporting
|
|
code for evaluation and parameter tuning. Faiss is written in C++ with
|
|
complete wrappers for Python/numpy. Some of the most useful algorithms
|
|
are implemented on the GPU. It is developed by Facebook AI Research.
|
|
"""
|
|
setup(
|
|
name='faiss',
|
|
version='1.5.1',
|
|
description='A library for efficient similarity search and clustering of dense vectors',
|
|
long_description=long_description,
|
|
url='https://github.com/facebookresearch/faiss',
|
|
author='Matthijs Douze, Jeff Johnson, Herve Jegou, Lucas Hosseini',
|
|
author_email='[email protected]',
|
|
license='MIT',
|
|
keywords='search nearest neighbors',
|
|
|
|
install_requires=['numpy'],
|
|
packages=['faiss'],
|
|
package_data={
|
|
'faiss': ['*.so'],
|
|
},
|
|
|
|
)
|