Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3876
Demo script for distributed kmeans. It provides a `DatasetAssign` object and shows how to run it with torch.distributed.
Reviewed By: asadoughi, pankajsingh88
Differential Revision: D63013820
fbshipit-source-id: 22c959f3afdc04fd4aa8b9aeed309ea6290b1328
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3872
The contrib.torch subdirectory is intended to receive modules in python that are useful for similarity search and that apply to CPU or GPU pytorch tensors.
The current version includes CPU clustering on torch tensors. To be added:
* implementation of PQ
Reviewed By: asadoughi
Differential Revision: D62759207
fbshipit-source-id: 87dbaa5083e3f2f4f60526815e22ded4e83e8559
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3873
The previous version required scipy to do the accumulation, which is replaced here with a nifty piece of numpy accumulation.
This removes the need for scipy for non-sparse data.
Reviewed By: junjieqi
Differential Revision: D62884307
fbshipit-source-id: 5443634e487387a2b518fd2a7f9a3d9a40abd4b4
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2217
This diff introduces a new Faiss contrib module that contains:
- generic k-means implemented in python (was in distributed_ondisk)
- the two-level clustering code, including a simple function that runs it on a Faiss IVF index.
- sparse clustering code (new)
The main idea is that that code is often re-used so better have it in contrib.
Reviewed By: beauby
Differential Revision: D34170932
fbshipit-source-id: cc297cc56d241b5ef421500ed410d8e2be0f1b77
Michael Norris
Matthijs Douze
Matthijs Douze