faiss 1.7.3-2build1 source package in Ubuntu
Changelog
faiss (1.7.3-2build1) lunar; urgency=medium * No-change rebuild with Python 3.11 as default -- Graham Inggs <email address hidden> Sun, 25 Dec 2022 19:43:45 +0000
Upload details
- Uploaded by:
- Graham Inggs
- Uploaded to:
- Lunar
- Original maintainer:
- Debian Deep Learning Team
- Architectures:
- any
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
Series | Published | Component | Section | |
---|---|---|---|---|
Lunar | release | universe | misc |
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
faiss_1.7.3.orig.tar.gz | 823.9 KiB | 3e4fac26d8c9e9008ecea4ae5fc414c658998fce4ba75835058b1a71d3516002 |
faiss_1.7.3-2build1.debian.tar.xz | 6.5 KiB | a439d1f7b4e4ff4dd5d4a3500be7073df9300e6628216d2438bd358787266ab3 |
faiss_1.7.3-2build1.dsc | 2.2 KiB | 3c1945f75535426a0e9d8f22c1d8759cc08f9fd55947eeeceb6cabbeb2dd42c4 |
Available diffs
- diff from 1.7.3-2 (in Debian) to 1.7.3-2build1 (302 bytes)
Binary packages built by this source
- libfaiss-dev: 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.
.
This package contains the CPU-only version of the development files.
- python3-faiss: Python 3 module for 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.
.
This package contains the CPU-only version of the Python interface.
- python3-faiss-dbgsym: debug symbols for python3-faiss