mirror of
https://github.com/facebookresearch/faiss.git
synced 2026-10-12 02:13:36 +00:00
Lucas Hosseini
7f5b22b0ff
+ Add conda packages metadata (now building Faiss using conda's toolchain); + add Dockerfile for building conda packages (for all CUDA versions); + add working Dockerfile building faiss on Centos7; + simplify GPU build; + avoid falling back to CPU-only version (python); + simplify TravisCI config; + update INSTALL.md; + add configure flag for specifying target architectures (--with-cuda-arch); + fix Makefile for gpu tests; + fix various Makefile issues; + remove stale file (gpu/utils/DeviceUtils.cpp).
46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
from __future__ import print_function
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from setuptools import setup, find_packages
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import os
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import shutil
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here = os.path.abspath(os.path.dirname(__file__))
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check_fpath = os.path.join("_swigfaiss.so")
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if not os.path.exists(check_fpath):
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print("Could not find {}".format(check_fpath))
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print("Have you run `make` and `make -C python`?")
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# make the faiss python package dir
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shutil.rmtree("faiss", ignore_errors=True)
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os.mkdir("faiss")
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shutil.copyfile("faiss.py", "faiss/__init__.py")
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shutil.copyfile("swigfaiss.py", "faiss/swigfaiss.py")
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shutil.copyfile("_swigfaiss.so", "faiss/_swigfaiss.so")
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long_description="""
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Faiss is a library for efficient similarity search and clustering of dense
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vectors. It contains algorithms that search in sets of vectors of any size,
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up to ones that possibly do not fit in RAM. It also contains supporting
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code for evaluation and parameter tuning. Faiss is written in C++ with
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complete wrappers for Python/numpy. Some of the most useful algorithms
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are implemented on the GPU. It is developed by Facebook AI Research.
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"""
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setup(
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name='faiss',
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version='1.5.1',
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description='A library for efficient similarity search and clustering of dense vectors',
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long_description=long_description,
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url='https://github.com/facebookresearch/faiss',
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author='Matthijs Douze, Jeff Johnson, Herve Jegou, Lucas Hosseini',
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author_email='[email protected]',
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license='BSD',
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keywords='search nearest neighbors',
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install_requires=['numpy'],
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packages=['faiss'],
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package_data={
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'faiss': ['*.so'],
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},
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)
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