This repository contains the framework used for developing, testing and presenting the GNN-based ITk track reconstruction project GNN4ITk.
Related work can be found here:
- https://arxiv.org/abs/2103.06995
- https://www.epj-conferences.org/articles/epjconf/abs/2021/05/epjconf_chep2021_03047/epjconf_chep2021_03047.html
- https://cds.cern.ch/record/2815578?ln=en.
This repository is still under development and may be subject to breaking changes.
To get started, run the setup commands (Install instructions section below), then take a look at the examples in the examples directory. Instructions and further details about the framework are available under the subdirectory of interest - examples, acorn/stages or acorn/core.
IMPORTANT! Please use the dev branch to run all Examples: it is the latest version and is fully supported!
To install ACORN, assuming GPU capability with cuda version >= 12.2, run the following commands.
git clone --recurse-submodules ssh://[email protected]:7999/gnn4itkteam/acorn.git && cd acorn
conda create --name acorn python=3.10 && conda activate acorn
pip install torch==2.1.0 && pip install --no-cache-dir -r requirements.txt
pip install -e .To check if the installation is successful, run python check_acorn.py. If you see (approximately) the following output, you are good to go!
python interpreter: path-to-conda-env/bin/python
torch: 2.1.1+cu121
torch cuda: True
torch cuda device count: 1
torch cuda device name: NVIDIA A100-PCIE-40GB
torch cuda device capability: (8, 0)
torch distributed : True
pytorch_lightning: 2.1.2
pyg: 2.4.0
frnn found
cugraph: 23.12.00
cudf: 23.12.01
torch_scatter: 2.1.2
Test scatter_max in cuda.
out: tensor([[0, 0, 4, 3, 2, 0],
[2, 4, 3, 0, 0, 0]], device='cuda:0')
argmax: tensor([[5, 5, 3, 4, 0, 1],
[1, 4, 3, 5, 5, 5]], device='cuda:0')
For NERSC Perlmutter HPC users, the default cudatoolkit is 11.7 as of writing (12/13/2023). Please switch it to 12.2 by running module load cudatoolkit/12.2, then run the following commands.
It is optional to install FRNN for faster (~3x) GPU nearest neighbor search. To do so, run
pip install git+https://github.com/asnaylor/prefix_sum.git
pip install git+https://github.com/xju2/FRNN.gitIMPORTANT! On December 2, 2023 a major refactoring of the code was merged on dev
If you have previously installed a version of 'acorn' (formerly known as 'gnn4itk_cf' or GNN4ITK CommonFramework) prior to December 2, 2023, it's important to update your setup. Follow the steps below:
git checkout dev
git pull dev
conda create --name acorn --clone gnn4itk
conda activate acorn
pip uninstall gnn4itk_cf
pip install -e .
You may temporarily retain the existing 'gnn4itk' conda environment for branches still in development with the previous version. If you have any active development branches from the previous setup, swiftly switch them to the 'dev' branch for updates
The new setup introduces the following changes:
- Renames the conda environment from
gnn4itktoacorn - Updates the command line to use
acorn [train|infer|eval] - Old commands
g4i-train,g4i-infer, andg4i-evalremain available for backward compatibility.
Please see the documentation for more details, examples and tutorials.
If this work is useful for your research, please cite our vCHEP2021 and CTD2022 proceedings:
@ARTICLE{YourReferenceHere,
author = {{Ju}, Xiangyang and {Murnane}, Daniel and {Calafiura}, Paolo and {Choma}, Nicholas and {Conlon}, Sean and {Farrell}, Steve and {Xu}, Yaoyuan and {Spiropulu}, Maria and {Vlimant}, Jean-Roch and {Aurisano}, Adam and {Hewes}, Jeremy and {Cerati}, Giuseppe and {Gray}, Lindsey and {Klijnsma}, Thomas and {Kowalkowski}, Jim and {Atkinson}, Markus and {Neubauer}, Mark and {DeZoort}, Gage and {Thais}, Savannah and {Chauhan}, Aditi and {Schuy}, Alex and {Hsu}, Shih-Chieh and {Ballow}, Alex},
title = "{Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking}",
journal = {arXiv e-prints},
keywords = {Physics - Data Analysis, Statistics and Probability, Computer Science - Machine Learning, High Energy Physics - Experiment},
year = 2021,
month = mar,
eid = {arXiv:2103.06995},
pages = {arXiv:2103.06995},
doi = {10.48550/arXiv.2103.06995},
archivePrefix = {arXiv},
eprint = {2103.06995},
primaryClass = {physics.data-an},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210306995J},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{YourReferenceHere,
author = {{Biscarat, Catherine} and {Caillou, Sylvain} and {Rougier, Charline} and {Stark, Jan} and {Zahreddine, Jad}},
title = {Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC},
DOI= "10.1051/epjconf/202125103047",
url= "https://doi.org/10.1051/epjconf/202125103047",
journal = {EPJ Web Conf.},
year = 2021,
volume = 251,
pages = "03047",
}
@techreport{YourReferenceHere,
author = "Caillou, Sylvain and Calafiura, Paolo and Farrell, Steven
Andrew and Ju, Xiangyang and Murnane, Daniel Thomas and
Rougier, Charline and Stark, Jan and Vallier, Alexis",
collaboration = "ATLAS",
title = "{ATLAS ITk Track Reconstruction with a GNN-based
pipeline}",
institution = "CERN",
reportNumber = "ATL-ITK-PROC-2022-006",
address = "Geneva",
year = "2022",
url = "https://cds.cern.ch/record/2815578",
}
If you use this code in your work, please cite the gnn4itk framework:
@misc{YourReferenceHere,
author = {Atkinson, Markus Julian and Caillou, Sylvain and Clafiura, Paolo and Collard, Christophe and Farrell, Steven Andrew and Huth, Benjamin and Ju, Xiangyang and Liu, Ryan and Minh Pham, Tuan and Murnane, Daniel (corresponding author) and Neubauer, Mark and Rougier, Charline and Stark, Jan and Torres, Heberth and Vallier, Alexis},
title = {gnn4itk},
url = {https://github.com/GNN4ITkTeam/CommonFramework}
}
(Optional)
Pre-commit hooks are available for running linting and code formatting. To set them up, run
pre-commit install
pre-commit run
(If you are using a conda environment, you may need to run pip install pre-commit first)