Usage	Development

🚀 About

GraphNeT is an open-source Python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using deep learning (DL). GraphNeT makes it fast and easy to train complex models that can provide event reconstruction with state-of-the-art performance, for arbitrary detector configurations, with inference times that are orders of magnitude faster than traditional reconstruction techniques.

Feel free to join the GraphNeT Slack group!

Publications using GraphNeT

Type	Title	DOI
Proceeding	Extending the IceCube search for neutrino point sources in the Northern sky with additional years of data
Proceeding	Sensitivity of the IceCube Upgrade to Atmospheric Neutrino Oscillations
Paper	GraphNeT: Graph neural networks for neutrino telescope event reconstruction
Paper	Graph Neural Networks for low-energy event classification & reconstruction in IceCube

⚙️ Install

We recommend installing graphnet in a separate environment, e.g. using a Python virtual environment or Anaconda (see details on installation here). Below we prove installation instructions for different setups.

Installing with IceTray

You may want graphnet to be able to interface with IceTray, e.g., when converting I3 files to an intermediate file format for training GNN models (e.g., SQLite or parquet),¹ or when running GNN inference as part of an IceTray chain. In these cases, you need to install graphnet in a Python runtime that has IceTray installed.

To achieve this, we recommend running the following commands in a clean bash shell:

$ eval `/cvmfs/icecube.opensciencegrid.org/py3-v4.2.1/setup.sh`
$ /cvmfs/icecube.opensciencegrid.org/py3-v4.2.1/RHEL_7_x86_64/metaprojects/icetray/v1.5.1/env-shell.sh

Optionally, you can alias these commands or save them as a bash script for convenience, as you will have to run these commands every time you want to use IceTray (with graphnet) in a clean shell.

With the IceTray environment active, you can now install graphnet, either at a user level or in a Python virtual environment. You can either install a light-weight version of graphnet without the torch extras, i.e., without the machine learning packages (pytorch and pytorch-geometric); this is useful when you just want to convert data from I3 files to, e.g., SQLite, and won't be running inference on I3 files later on. In this case, you don't need to specify a requirements file. If you want torch, you do.

Install without torch

$ pip install --user -e .[develop]  # Without torch, i.e. only for file conversion

Install with torch

$ pip install --user -r requirements/torch_cpu.txt -e .[develop,torch]  # CPU-only torch
$ pip install --user -r requirements/torch_gpu.txt -e .[develop,torch]  # GPU support

This should allow you to run the I3 conversion scripts in examples/ with your preferred I3 files.

Installing stand-alone

If you don't need to interface with IceTray (e.g., for reading data from I3 files or running inference on these), the following commands should provide a fast way to get up and running on most UNIX systems:

$ git clone [email protected]:<your-username>/graphnet.git
$ cd graphnet
$ conda create --name graphnet python=3.8 gcc_linux-64 gxx_linux-64 libgcc cudatoolkit=11.5 -c conda-forge -y  # Optional
$ conda activate graphnet  # Optional
(graphnet) $ pip install -r requirements/torch_cpu.txt -e .[develop,torch]  # CPU-only torch
(graphnet) $ pip install -r requirements/torch_gpu.txt -e .[develop,torch]  # GPU support

This should allow you to e.g. run the scripts in examples/ out of the box.

A stand-alone installation requires specifying a supported Python version (see above), ensuring that the C++ compilers (gcc) are up to date, and possibly installing the CUDA Toolkit. Here, we have installed recent C++ compilers using conda (gcc_linux-64 gxx_linux-64 libgcc), but if your system already has a recent version ($gcc --version should be > 5, at least) you should be able to omit these from the setup. If you install the CUDA Toolkit and/or newer compilers using the above command, you should add one of:

$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/anaconda3/lib/
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/miniconda3/lib/
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/anaconda3/envs/graphnet/lib/
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/miniconda3/envs/graphnet/lib/

depending on your setup to your .bashrc script or similar to make sure that the corresponding library files are accessible. Check which one of the above paths contains the .so-files you're looking to use, and add that path.

Running in Docker

If you want to run GraphNeT (with IceTray), and don't intend to contribute to the package, consider using the provided Docker image. With Docker, you can then run GraphNeT as:

$ docker run --rm -it asogaard/graphnet:latest
🐳 graphnet@dc423315742c ❯ ~/graphnet $ python examples/01_icetray/01_convert_i3_files.py sqlite icecube-upgrade
graphnet: INFO     2023-01-24 13:41:27 - Logger.__init__ - Writing log to logs/graphnet_20230124-134127.log
(...)
graphnet: INFO     2023-01-24 13:41:46 - SQLiteDataConverter.info - Saving results to /root/graphnet/data/examples/outputs/convert_i3_files/ic86
graphnet: INFO     2023-01-24 13:41:46 - SQLiteDataConverter.info - Processing 1 I3 file(s) in main thread (not multiprocessing)
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:39<00:00, 39.79s/file(s)]
graphnet: INFO     2023-01-24 13:42:26 - SQLiteDataConverter.info - Merging files output by current instance.
graphnet: INFO     2023-01-24 13:42:26 - SQLiteDataConverter.info - Merging 1 database files
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 413.88it/s]

This should allow you to run all provided examples (excluding the specialised ones requiring PISA) out of the box, and to start working on your own analysis scripts.

You can use any of the following Docker image tags:

• main: Image corresponding to the latest push to the main branch.
• latest: Image corresponding to the latest named tagged version of graphnet.
• vX.Y.Z: Image corresponding to the specific named tagged version of graphnet.

🪐 Use cases

Below is an incomplete list of potential use cases for Deep Learning in neutrino telescopes. These are categorised as either "Reconstruction challenges" that are considered common and that may benefit several experiments physics analyses; and those same "Experiments" and "Physics analyses".

Reconstruction challenges

Title	Status	People	Materials
Low-energy neutrino classification and reconstruction	Done	Rasmus Ørsøe	https://arxiv.org/abs/2209.03042
High-energy neutrino classification and reconstruction	Active	Rasmus Ørsøe
Pulse noise cleaning	Paused	Rasmus Ørsøe, Kaare Iversen (past), Morten Holm
(In-)elasticity reconstruction	Paused	Marc Jacquart (past)
Multi-class event classification	Active	Morten Holm, Peter Andresen
Data/MC difference mitigation
Systematic uncertainty mitigation

Experiments

Title	Status	People	Materials
IceCube	Active	(...)
IceCube-Upgrade	Active	(...)
IceCube-Gen2	Active	(...)
P-ONE		(...)
KM3NeT-ARCA		(...)
KM3NeT-ORCA		(...)

Physics analyses

Title	Status	People	Materials
Neutrino oscillations
Point source searches
Low-energy cosmic alerts
High-energy cosmic alerts
Moon pointing
Muon decay asymmetry
Spectra measurements

🤝 Contributing

To make sure that the process of contributing is as smooth and effective as possible, we provide a few guidelines in the contributing guide that we encourage contributors to follow.

In short, everyone who wants to contribute to this project is more than welcome to do so! Contributions are handled through pull requests, that should be linked to a GitHub issue describing the feature to be added or bug to be fixed. Pull requests will be reviewed by the project maintainers and merged into the main branch when accepted.

📝 License

GraphNeT has an Apache 2.0 license, as found in the LICENSE file.

🙌 Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 890778, and the PUNCH4NFDI consortium via DFG fund “NFDI39/1”.

Footnotes

1. Examples of this are shown in the examples/01_icetray/01_convert_i3_files.py script ↩