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Lucas Hosseini a8e4c5e2d5 Move build to CMake (#1313) 
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1313

Reviewed By: mdouze

Differential Revision: D22948267

Pulled By: beauby

fbshipit-source-id: ec16fa0342f37672d46fb7886ecc55c7996011c4
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CMakeLists.txt
Move build to CMake (#1313)
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demo_auto_tune.py
fbshipit-source-id: 4f3cfa59471d548af93fe118d1b73d45bc648edf
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demo_client_server_ivf.py
Facebook sync 2020-07-31 (#1308)
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demo_imi_flat.cpp
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demo_imi_pq.cpp
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demo_ivfpq_indexing.cpp
Facebook sync (2019-09-10) (#943)
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demo_ondisk_ivf.py
fbshipit-source-id: 4f3cfa59471d548af93fe118d1b73d45bc648edf
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demo_sift1M.cpp
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demo_weighted_kmeans.cpp
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README.md
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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