OrcaNet: A training organizer for Deep Learning in KM3NeT
OrcaNet is a deep learning framework based on tensorflow in order to simplify the training process of neural networks for astroparticle physics, in particular for Orca, Arca and Antares. It incorporates automated logging, plotting and validating during the training, as well as saving and continuing the training process. Additionally, it features easy management of multiple neural network inputs and the use of training data which is split over multiple files.
In this sense, it tackles many challenges that are usually found in astroparticle physics, like huge datasets.
Documentation is at https://ml.pages.km3net.de/OrcaNet/.
OrcaNet is a part of the Deep Learning efforts for the neutrino telescope KM3NeT. Find more information about KM3NeT on http://www.km3net.org .
OrcaNet is currently being developed at the official KM3NeT gitlab (https://git.km3net.de/ml/OrcaNet).
However, there’s also a github mirror that can be found at https://github.com/ViaFerrata/OrcaNet.
For studies done using orcanet, check out the wiki page of the machine learning working group https://wiki.km3net.de/index.php/Machine_Learning_working_group . You can find orcanet config files used in studies in the directory orcanet_contrib/configs of the orcanet repo.
OrcaNet can be installed via pip by running:
pip install orcanet
In order to make use of tensorflow’s GPU acceleration, you need cuda and cudnn installed. You can see which of these each tensorflow version needs here https://www.tensorflow.org/install/source#gpu
The easiest way to run OrcaNet is with singularity. A Singularity image of the latest stable version of OrcaNet with tensorflow and cuda/cudnn for GPUs is automatically uploaded to our sftp server. Download it e.g. via:
where v??? is the version, e.g. orcanet_v1.0.1.sif. Run it on a CPU e.g. via:
singularity shell orcanet_v???.sif
If you run it on a GPU, don’t forget the nv flag:
singularity shell --nv orcanet_v???.sif