8b05434a50
Summary: Removed an unused function that caused compile errors in some configurations. Added contrib function (exhaustive_search.knn) to compute the k nearest neighbors without constructing an index. Renamed the equivalent GPU function as exhaustive_search.knn_gpu (it does not make much sense to mention numpy in the name as all functions take numpy arguments by default). Reviewed By: beauby Differential Revision: D24215427 fbshipit-source-id: 6d8e1eafa7c57593304b7b76f83b3015e4d2a2bb
The contrib modules
The contrib directory contains helper modules for Faiss for various tasks.
Code structure
The contrib directory gets compiled in the module faiss.contrib. Note that although some of the modules may depend on additional modules (eg. GPU Faiss, pytorch, hdf5), they are not necessarily compiled in to avoid adding dependencies. It is the user's responsibility to provide them.
In contrib, we are progressively dropping python2 support.
List of contrib modules
rpc.py
A very simple Remote Procedure Call library, where function parameters and results are pickled, for use with client_server.py
client_server.py
The server handles requests to a Faiss index. The client calls the remote index. This is mainly to shard datasets over several machines, see Distributd index
ondisk.py
Encloses the main logic to merge indexes into an on-disk index. See On-disk storage
exhaustive_search.py
Computes the ground-truth search results for a dataset that possibly does not fit in RAM. Uses GPU if available.
Tested in tests/test_contrib.TestComputeGT
gpu.py
(requires GPU Faiss)
Interoperability functions for pytorch and Faiss: pass GPU data without copying back to CPU.
Tested in gpu/test/test_pytorch_faiss
datasets.py
(may require h5py)
Defintion of how to access data for some standard datsets.