Skip to content

Home

Documentation for Umami#

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

The intended use is as a software using configuration files to carry out preprocessing, training and evaluation. It is not written with its use as a library in mind, as its core functionalities are provided by separate python modules which in turn can be installed with pip.

These are:

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.

Tutorials for Umami#

A general tutorial is provided as part of the Umami documentation. It makes use of the JetClass dataset and is provided here.

A corresponding step-by-step tutorial for Umami inside the ATLAS collaboration for jet flavour tagging is provided here

Additionally, 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. Please note that since that tutorial, the Umami software has been developed further and might slightly deviate from the version covered in the tutorial.

Back to top