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Summary: Demonstrate IndexLSH does not need training or codebook serialization Pull Request resolved: https://github.com/facebookresearch/faiss/pull/4009 Reviewed By: junjieqi Differential Revision: D65274645 Pulled By: asadoughi fbshipit-source-id: c9af463757edbd07cc07b1cf607b88373fa334c4
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