Faiss 1.7.4-foss-2023a-CUDA-12.1.1

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 primarily at Meta's Fundamental AI Research group.

Accessing Faiss 1.7.4-foss-2023a-CUDA-12.1.1

To load the module for Faiss 1.7.4-foss-2023a-CUDA-12.1.1 please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load bear-apps/2023a
module load Faiss/1.7.4-foss-2023a-CUDA-12.1.1

BEAR Apps Version

2023a

Architectures

EL8-icelake (GPUs: NVIDIA A100, NVIDIA A30)

The listed architectures consist of two parts: OS-CPU. The OS used is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.

More Information

For more information visit the Faiss website.

Dependencies

This version of Faiss has a direct dependency on: CUDA/12.1.1 foss/2023a Python/3.11.3-GCCcore-12.3.0 PyTorch/2.1.2-foss-2023a-CUDA-12.1.1 SciPy-bundle/2023.07-gfbf-2023a

Required By

This version of Faiss is a direct dependent of: tsne-cuda/3.0.1-foss-2023a-CUDA-12.1.1

Last modified on 3rd March 2025