Efficient similarity search library from Facebook AI Research.
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.
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.
To install libfaiss, run the following command in macOS terminal (Applications->Utilities->Terminal)
sudo port install libfaiss
To see what files were installed by libfaiss, run:
port contents libfaiss
To later upgrade libfaiss, run:
sudo port selfupdate && sudo port upgrade libfaiss
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