This is the Windows app named Faiss whose latest release can be downloaded as v1.7.3.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Faiss with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS
Ad
Faiss
DESCRIPTION
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. Faiss contains several methods for similarity search. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 (Euclidean) distances or dot products. Vectors that are similar to a query vector are those that have the lowest L2 distance or the highest dot product with the query vector. It also supports cosine similarity, since this is a dot product on normalized vectors.
Features
- The library is mostly implemented in C++, with optional GPU support provided via CUDA, and an optional Python interface
- Faiss handles collections of vectors of a fixed dimensionality d, typically a few 10s to 100s
- The CPU version requires a BLAS library. It compiles with a Makefile and can be packaged in a docker image
- Faiss is built around an index type that stores a set of vectors, and provides a function to search in them with L2 and/or dot product vector comparison
- The optional GPU implementation provides what is likely the fastest exact and approximate nearest neighbor search implementation for high-dimensional vectors
- Faiss is built around the Index object. It encapsulates the set of database vectors, and optionally preprocesses them to make searching efficient
Programming Language
C++
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/faiss.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.