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The current bench-ann scripts and workflow is pretty low-level and not as simple to use as it could be. One of the standards for benchmarking ANN algorithms in Python is the popular ann-benchmarks library- the scripts are very easy to use, the workflow is very straightforward (specifying training/search params, querying/using available algorithms and datasets, plotting). I think our bench-ann has all the available features but they could presented in a manner which is more friendly to the end-user who just wants to compare the perf of our algorithms with others and doesn't necessarily want to jump through hoops to do so.
I propose we write scripts that mimic the ann-benchmarks as much as possible so that users already familiar with that repository can more easily use our stuff. This is somewhat how the big-ann-benchmarks scripts functioned as well.
The text was updated successfully, but these errors were encountered:
The current bench-ann scripts and workflow is pretty low-level and not as simple to use as it could be. One of the standards for benchmarking ANN algorithms in Python is the popular
ann-benchmarks
library- the scripts are very easy to use, the workflow is very straightforward (specifying training/search params, querying/using available algorithms and datasets, plotting). I think our bench-ann has all the available features but they could presented in a manner which is more friendly to the end-user who just wants to compare the perf of our algorithms with others and doesn't necessarily want to jump through hoops to do so.I propose we write scripts that mimic the
ann-benchmarks
as much as possible so that users already familiar with that repository can more easily use our stuff. This is somewhat how thebig-ann-benchmarks
scripts functioned as well.The text was updated successfully, but these errors were encountered: