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Documentation for Umami#

Umami is a framework which can be used for training (most) machine-learning-based taggers used in ATLAS FTAG:

Umami is also a tagger, the Umami tagger (UT). Its architecture includes jet features (DL1 inputs) plus a DIPS-like block. The high-level tagger (UT) and the DIPS block are trained in a single training (different to e.g. DL1d developments).

Umami is hosted on CERN GitLab:

Docker images are available on CERN GitLab container registry and on Docker Hub:

An API reference can be found here.

Tutorial for Umami#

At the FTAG Workshop in 2022 in Amsterdam, we gave a tutorial how to work with Umami. You can find the slides together with a recording of the talk here. The corresponding step-by-step tutorial can be found in the FTAG docs webpage here

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