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History

Matthijs Douze a996a4a052 Put idx_t in the faiss namespace (#2582 )

Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2582

A few more or less cosmetic improvements
* Index::idx_t was in the Index object, which does not make much sense, this diff moves it to faiss::idx_t
* replace multiprocessing.dummy with multiprocessing.pool
* add Alexandr as a core contributor of Faiss in the README ;-)

```
for i in $( find . -name \*.cu -o -name \*.cuh -o -name \*.h -o -name \*.cpp ) ; do
  sed -i s/Index::idx_t/idx_t/ $i
done
```

For the fbcode deps:
```
for i in $( fbgs Index::idx_t --exclude fbcode/faiss -l ) ; do
   sed -i s/Index::idx_t/idx_t/ $i
done
```

Reviewed By: algoriddle

Differential Revision: D41437507

fbshipit-source-id: 8300f2a3ae97cace6172f3f14a9be3a83999fb89

2022-11-30 08:25:30 -08:00

CMakeLists.txt

Demo of residual quantizer distance computer for LaserKNN (#2283 )

2022-04-06 12:42:24 -07:00

demo_auto_tune.py

…

demo_client_server_ivf.py

Fix faiss_contrib (#1478 )

2020-10-20 04:35:19 -07:00

demo_imi_flat.cpp

Put idx_t in the faiss namespace (#2582 )

2022-11-30 08:25:30 -08:00

demo_imi_pq.cpp

Put idx_t in the faiss namespace (#2582 )

2022-11-30 08:25:30 -08:00

demo_ivfpq_indexing.cpp

Put idx_t in the faiss namespace (#2582 )

2022-11-30 08:25:30 -08:00

demo_nndescent.cpp

Put idx_t in the faiss namespace (#2582 )

2022-11-30 08:25:30 -08:00

demo_ondisk_ivf.py

Fix faiss_contrib (#1478 )

2020-10-20 04:35:19 -07:00

demo_residual_quantizer.cpp

Put idx_t in the faiss namespace (#2582 )

2022-11-30 08:25:30 -08:00

demo_sift1M.cpp

Put idx_t in the faiss namespace (#2582 )

2022-11-30 08:25:30 -08:00

demo_weighted_kmeans.cpp

Put idx_t in the faiss namespace (#2582 )

2022-11-30 08:25:30 -08:00

README.md

…

README.md

Demos for a few Faiss functionalities

demo_auto_tune.py

Demonstrates the auto-tuning functionality of Faiss

demo_ondisk_ivf.py

Shows how to construct a Faiss index that stores the inverted file data on disk, eg. when it does not fit in RAM. The script works on a small dataset (sift1M) for demonstration and proceeds in stages:

0: train on the dataset

1-4: build 4 indexes, each containing 1/4 of the dataset. This can be done in parallel on several machines

5: merge the 4 indexes into one that is written directly to disk (needs not to fit in RAM)

6: load and test the index