838612c9d7
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 |
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| .. | ||
| 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 | ||
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