Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3531
**In this diff**
1. I have add bench_fw to bento faiss kernel target
2. First part of notebook is changed to analyze sift1M results
Reviewed By: algoriddle
Differential Revision: D58823037
fbshipit-source-id: a67d4638af4368f0575bd289ce7aff8cf1fcd38b
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3154
Using the benchmark to find Pareto optimal indices, in this case on BigANN as an example.
Separately optimize the coarse quantizer and the vector codec and use Pareto optimal configurations to construct IVF indices, which are then retested at various scales. See `optimize()` in `optimize.py` as the main function driving the process.
The results can be interpreted with `bench_fw_notebook.ipynb`, which allows:
* filtering by maximum code size
* maximum time
* minimum accuracy
* space or time Pareto optimal options
* and visualize the results and output them as a table.
This version is intentionally limited to IVF(Flat|HNSW),PQ|SQ indices...
Reviewed By: mdouze
Differential Revision: D51781670
fbshipit-source-id: 2c0f800d374ea845255934f519cc28095c00a51f
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3144
Visualize results of running the benchmark with Pareto optima filtering:
1. per index or across indices
2. for space, time or space & time
3. knn or range search, the latter @ specific precision
Reviewed By: mdouze
Differential Revision: D51552775
fbshipit-source-id: d4f29e3d46ef044e71b54439b3972548c86af5a7
Kumar Saurabh Arora
Gergely Szilvasy