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Matthijs Douze c5975cda72 PQ4 fast scan benchmarks (#1555) 
Summary:
Code + scripts for Faiss benchmarks around the  Fast scan codes.

Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1555

Test Plan: buck test //faiss/tests/:test_refine

Reviewed By: wickedfoo

Differential Revision: D25546505

Pulled By: mdouze

fbshipit-source-id: 902486b7f47e36221a2671d124df8c114f25db58
2020-12-16 01:18:58 -08:00
..
bench_all_ivf.py
PQ4 fast scan benchmarks (#1555)
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bench_kmeans.py
PQ4 fast scan benchmarks (#1555)
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cmp_with_scann.py
PQ4 fast scan benchmarks (#1555)
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datasets.py
PQ4 fast scan benchmarks (#1555)
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make_groundtruth.py
PQ4 fast scan benchmarks (#1555)
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parse_bench_all_ivf.py
PQ4 fast scan benchmarks (#1555)
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README.md
Update README.md
2018-12-20 14:52:59 +01:00
run_on_cluster_generic.bash
PQ4 fast scan benchmarks (#1555)
2020-12-16 01:18:58 -08:00

README.md

Benchmark of IVF variants

This is a benchmark of IVF index variants, looking at compression vs. speed vs. accuracy. The results are in this wiki chapter

The code is organized as:

  • datasets.py: code to access the datafiles, compute the ground-truth and report accuracies

  • bench_all_ivf.py: evaluate one type of inverted file

  • run_on_cluster_generic.bash: call bench_all_ivf.py for all tested types of indices. Since the number of experiments is quite large the script is structued so that the benchmark can be run on a cluster.

  • parse_bench_all_ivf.py: make nice tradeoff plots from all the results.

The code depends on Faiss and can use 1 to 8 GPUs to do the k-means clustering for large vocabularies.

It was run in October 2018 for the results in the wiki.