1334d169b9
Summary: For the release, we want to standardize on Faiss instead of FAISS. Changed everything except the CHANGELOG.md which I assume should not be changed after it lands. This doesn't aim to fix any existing lints / errors. Those can be handled at another time. Reviewed By: junjieqi Differential Revision: D68842649 fbshipit-source-id: c0b60d5baa0e1f710db3638ffcc6f223fb3408ad |
||
|---|---|---|
| .. | ||
| offline_ivf | ||
| rocksdb_ivf | ||
| CMakeLists.txt | ||
| README.md | ||
| demo_auto_tune.py | ||
| demo_client_server_ivf.py | ||
| demo_distributed_kmeans_torch.py | ||
| demo_imi_flat.cpp | ||
| demo_imi_pq.cpp | ||
| demo_ivfpq_indexing.cpp | ||
| demo_nndescent.cpp | ||
| demo_ondisk_ivf.py | ||
| demo_qinco.py | ||
| demo_residual_quantizer.cpp | ||
| demo_sift1M.cpp | ||
| demo_weighted_kmeans.cpp | ||
| index_pq_flat_separate_codes_from_codebook.py | ||
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