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Hash: SHA512
Format: 3.0 (quilt)
Source: faiss
Binary: libfaiss-dev, python3-faiss
Architecture: any
Version: 1.9.0-2
Maintainer: Debian Deep Learning Team <debian-ai@lists.debian.org>
Uploaders: Mo Zhou <lumin@debian.org>
Homepage: https://github.com/facebookresearch/faiss
Description: efficient similarity search and clustering of dense vectors
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.
Standards-Version: 4.7.0
Vcs-Browser: https://salsa.debian.org/deeplearning-team/faiss
Vcs-Git: https://salsa.debian.org/deeplearning-team/faiss.git
Testsuite: autopkgtest
Testsuite-Triggers: build-essential, c++-compiler, cmake, g++, libgtest-dev, ninja-build, python3-all
Build-Depends: cmake, debhelper-compat (= 13), dh-python, dh-sequence-numpy3, dh-sequence-python3, libblas-dev, libgtest-dev, liblapack-dev, libpython3-all-dev, python3-all-dev:any, python3-numpy, python3-setuptools, swig
Package-List:
libfaiss-dev deb libdevel optional arch=any
python3-faiss deb python optional arch=any
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Checksums-Sha256:
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Files:
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