Installation

graphnetGraphNeT is available for Python 3.9 to Python 3.11.

Note

We recommend installing graphnetGraphNeT in a separate environment, e.g. using a Python virtual environment or Anaconda (see details on installation here). With conda installed, you can create a fresh environment like so

# Create the environment with minimal packages
conda create --name graphnet_env --no-default-packages python=3.10
conda activate graphnet_env

# Update central packaging libraries
pip install --upgrade setuptools packaging

# Verify that only wheel, packaging and setuptools are installed
pip list

# Now you're ready to proceed with the installation

Quick Start

PyTorch
Your OS
CUDA
Run:








When installation is completed, you should be able to run the examples.






Installation in CVMFS (IceCube)


You may want graphnetGraphNeT to be able to interface with IceTray, e.g., when converting I3 files to a deep learning friendly file format, or when deploying models as part of an IceTray chain. In these cases, you need to install graphnetGraphNeT in a Python runtime that has IceTray installed.


To achieve this, we recommend installing graphnetGraphNeT into a CVMFS with IceTray installed, like so:


# Download GraphNeT
git clone https://github.com/graphnet-team/graphnet.git
cd graphnet
# Open your favorite CVMFS distribution
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
# Update central utils
pip install --upgrade 'pip>=20'
pip install wheel setuptools==59.5.0
# Install graphnet into the CVMFS as a user
pip install --user -r requirements/torch_cpu.txt -e .[torch,develop]





Once installed, graphnetGraphNeT is available whenever you open the CVMFS locally.






Installation with km3io (KM3NeT)


This installation is only necessary if you want to process KM3NeT/ARCA or KM3NeT/ORCA files. Processing means converting them from a .root offline format into a suitable format for training using graphnet. If you already have your KM3NeT data in SQLite or parquet format and only want to train a model or perform inference on this database, this specific installation is not needed.


Note that this installation will add km3io ensuring it is built with a compatible versions. The steps below are provided for a conda environment, with an enviroment created in the same way it is done above in this page, but feel free to choose a different enviroment setup.


As mentioned, it is highly reommended to create a conda enviroment where your installation is done to do not mess up any dependecy. It can be done with the following commands:


# Create an environment with Python 3.10
conda create -p <full-path-to-env> --no-default-packages python=3.10 -y
# Activate the environment and move to the graphnet repository you just cloned. If using conda:
conda activate <full-path-to-env>





The isntallation of GraphNeT is then done by:


git clone https://github.com/graphnet-team/graphnet.git
cd graphnet





Choose the appropriate requirements file based on your system. Here there is just an example of installation with PyTorch-2.5.1 but check the matrix above for a full idea of all the versions can be installed.


For CPU-only enviroments:


pip3 install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cpu
pip3 install -e .[torch-25] -f https://data.pyg.org/whl/torch-2.5.1+cpu.html





For GPU enviroments with, for instance, CUDA 11.8 drivers:


pip3 install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu118
pip3 install -e .[torch-25] -f https://data.pyg.org/whl/torch-2.5.1+cu118.html





Downgrade setuptools for compatibility between km3io and GraphNeT.


pip3 install --force-reinstall setuptools==70.3.0
pip3 install km3io==1.2.0







Note


We recommend installing graphnetGraphNeT without GPU in clean metaprojects.