LUMIN aims to become a deep-learning and data-analysis ecosystem for High-Energy Physics, and perhaps other scientific domains in the future. Similar to Keras and fastai it is a wrapper framework for a graph computation library (PyTorch), but includes many useful functions to handle domain-specific requirements and problems. It also intends to provide easy access to to state-of-the-art methods, but still be flexible enough for users to inherit from base classes and override methods to meet their own demands.
In this talk I will be introducing the library, discussing some of its distinguishing characteristics, and going through an example workflow. There will also be a general invitation for people to test out the library and provide feedback, suggestions, or contributions.
|Preferred contribution length||20 minutes|