This repository is a working example for the SISAP 2026 Indexing Challenge https://sisap-challenges.github.io/, working with Python and GitHub Actions.
git clone https://github.com/sisap-challenges/sisap26-python-baseline
cd sisap26-python-baselineThis repository requires Python 3.8+ and several dependencies. We provide a helper script for easy setup, or you can install manually.
Use the provided install script to set up a virtual environment and install dependencies (including CPU-optimized PyTorch):
chmod +x install.sh
./install.sh
source venv/bin/activate- Install base requirements:
pip install -r requirements.txt
- Install CPU-only PyTorch (to avoid large CUDA downloads):
pip install torch~=2.4.0 --index-url https://download.pytorch.org/whl/cpu
Build and run using Docker:
docker build -t sisap-baseline .
docker run sisap-baseline --task task3 --dataset nqRun the tasks on an example input using
python search.py --task {task1, task2, task3}For task3 (approximate nearest neighbor search on sparse data), specifically:
python search.py --task task3 --dataset nqIt will automatically take care of downloading the necessary example dataset.
python eval.py results.csvwill produce a summary file of the results with the computed recall against the ground truth data.
This csv file can be further processed to create plots (using python plot.py --task {task1, task2}) and show the fastest solutions above a certain recall threshold (using python show_operating_points.py).
You can fork this repository and polish it to create your solution. Please also take care of the ci workflow (see below).
You can monitor your runnings in the "Actions" tab of the GitHub panel: for instance, you can see some runs of this repository: https://github.com/sisap-challenges/sisap26-python-baseline/actions