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IceGraph is an end-to-end framework for building and deploying Graph Neural Networks for reconstruction and classification in IceCube. It covers the full path from raw detector files to a trained, deployable model through three workflows:
Processing converts events from source files to an ML-ready graph dataset.
Training fits a GNN to that dataset using the PyTorch framework.
Inference applies a trained model to new data to produce predictions.
All three are driven by YAML configuration and share a common substrate, so most of this documentation is organized around the framework’s objects and what each is responsible for.
To get started with scripting, see Usage.