Experiment - an abstraction that contains information about the experiment - a model, a criterion, an optimizer, a scheduler, and their hyperparameters. It also holds information about the data and transformations to apply. The Experiment knows what you would like to run.

Each deep learning project has several main components. These primitives define what we want to use during the experiment:

  • the data

  • the model(s)

  • the optimizer(s)

  • the loss(es)

  • and the scheduler(s) if we need them.

That are the abstractions that Experiment covers in Catalyst, with a few modifications for easier experiment monitoring and hyperparameters logging. For each stage of our experiment, the Experiment provides interfaces to all primitives above + the callbacks.


If you haven’t found the answer for your question, feel free to join our slack for the discussion.