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Introduction#

Welcome to the Salt framework!

Salt is a general-purpose framework to train multi-modal, multi-task models. It was developed for state-of-the art jet flavour tagging algorithms such as GN1 and GN2, but can be applied much more widely. For example, you could use Salt to classify or regress properties of objects or events, or all these things at once!

The code is hosted on the CERN GitLab: https://gitlab.cern.ch/atlas-flavor-tagging-tools/algorithms/salt

Features#

  • Built on Pytorch Lightning.
  • Support for multiple YAML-configurable input modalities and output tasks.
  • ONNX export support to use trained models in Athena.
  • Easily extensible: you can implement your own custom dataloaders and models.
  • Documented and tested.

Getting Started#

Below are some helpful links to get you started:

You can find out more about flavour tagging algorithms at the FTAG docs

There is a channel for the framework in the FTAG Mattermost workspace

A tutorial on how to use Salt can be found here and here

Contributions are welcome! Check out existing issues for inspiration, or open your own

You can become a Salt expert by checking out the API reference

Current Usage#

Salt is currently used for the following projects:

The framework is originally based on work from two previously existing projects: [1] [2].

Meeting Contributions#

Date Title Speakers
2024-09-24 Scaling Salt on Large Machines Nicholas Luongo
2024-07-09 Scaling Salt on Large Machines Update Nicholas Luongo
2024-05-23 Transformer updates in salt [10+10] Matthew Leigh
2024-05-21 Scaling Salt on Large Machines Update Nicholas Luongo
2024-01-16 Scaling salt on Large Machines Nicholas Luongo
2022-11-22 Update about Salt framework Samuel Van Stroud

search terms: ['salt']

date cutoff: 2022-11-19, for older meetings please check indico

Last update: November 7, 2024
Created: October 7, 2022