Martin Aumüller
Martin Aumüller
As discussed in #225. Let's see what CI says to that.
The random blobs created from sklearn seem to be far too easy to solve, see screenshot below. I propose to bring back the old random datasets that use https://github.com/maumueller/random-inputs/. For...
Dear N2 team I am reaching out to you on behalf of the organizers of the [NeurIPS’21 competition on Billion-Scale Approximate Nearest Neighbor Search Challenge]( big-ann-benchmarks.com) to request your participation....
Dear all, Please add your thoughts on the future of this benchmark! Thank you very much for participating in our NeurIPS'21 competition. The competition will end with an event on...
Using `axel` to speed-up index downloads can basically only be run with `-q` (quiet) inside the docker container. It would be better if index downloading would happen outside of the...
This PR makes it possible to specify custom runners to run ann-benchmarks in containerized, non-Docker environments. My use case is that I (and many others) have to use SLURM on...
Since ann-benchmarks is **the meeting place** for people working in approximate nearest neighbor search, I'd like to use the chance to advertise an indexing challenge that provides a different angle...
[To keep track of my own tasks] The user has to decide if the results files are compared to public or private queries at the moment. We should rather use...
The current workflow to run algorithm X on dataset Y is something like this: 1. `python install.py` builds docker container 2. `python create_dataset.py` sets up datasets 3. `python run.py --dataset...
Hi all! Let me first thank you for making your source code available! I was interested in reproducing some of your runs (Table 1 and Figure 3) and found it...