Speaker
Description
Through its TMVA package, ROOT provides and connects to machine learning tools for data analysis at HEP experiments and beyond. In addition, ROOT provides through its powerful I/O system and RDataFrame analysis tools the capability to efficiently select and query input data from large data sets as typically used in HEP analysis. At the same time, several existing Machine Learning tools exist in a diversified landscape outside of ROOT.
In this talk, we present new developments in ROOT that bridge the gap between external Machine Learning tools and ROOT, by providing better interoperability between tools and facilitating the analysis workflows.
We present recently included features in TMVA allowing for generating batches of events for ROOT I/O and RDataFrame to train efficiently machine learning models using Python tools such as Tensorflow and PyTorch. This will facilitate direct access to the ROOT input data when training using external tools in particular for the case when all input data cannot be stored in memory.
We will present the new software tool and we will show examples on how to use it with some open datasets of the LHC experiments.
| Consider for long presentation | No |
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