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[Neurips23] NLE submission for the sparse track #176
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Thanks for the submission. could you please run.sh (perhaps add it as comments in the PR instead) and add a CI test for your algorithm and sparse-small dataset in .github/neurips23.yaml? |
Hi @harsha-simhadri, from what I understood I removed the run.sh and added to the workflow, but I'm not sure how to test if the adding to the workflow worked. |
I see the CI test for NLE is green. Thanks for that. |
These are the results I see on the competition spec VM nle,pisa,sparse-full,10,2993.964456425476,0.0,1000000.0,10371632.0,3464.1800699206683,0,0,sparse,0.8624498567335243 Does this agree with your observations? IF so, I can merge this PR |
Hi @harsha-simhadri , the numbers are smaller than what I had expected and I found a bug on the number of threads (it was hardcoded to 4 threads instead of CPU count), could you please run the new version? |
This is what I see with the latest commit nle,pisa,sparse-full,10,3024.789817796044,0.0,1000000.0,4133388.0,1366.5042032612087,0,0,sparse,0.8624498567335243 |
Hi @harsha-simhadri, sorry for this, the result seem weird because there was no difference between 4 hard-coded threads and what was supposed to be 8. I've hardcoded it to 8 now and if there's no difference I probably had hardcoded somewhere to 8 before and I'm not finding (code is not as clean as it could be as you're seeing). Can you please try with the newest commit and if there's no difference I think I will just leave as is. |
@cadurosar Let me merge this to make way for other submissions. Feel free to investigate further and submit any config changes before Oct 31 deadline. |
Hi, this is our NLE submission for the sparse track. We have added two configs (changed the names after the figure, nle is NLE-10 and NLE-FULL is NLE), nle (the official one, with the 10 configurations for the end competition) and a NLE-Full with more configs to test different configurations. nle takes around 12 hours to run completely (sparse-small, sparse-1M and sparse-full) and indexing could be made much faster, this was just the first one we got to work.
For running nle quicker, we make all the indexes available here, just need to extract in big-ann-benchmarks/results/indexes
We have tested our solution on three machines:
Azure (Standard D4s v5 (4 vcpus, 16 GiB memory)
Mac M1 (16G, linscan could not run due to lack of memory, I never got the machine to be with more than 11G free...)
Internal server (64 CPU - Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz, linscan does not work right a lot of threads, while our method is bottlenecked by 200ms to preprocess query data)