Concepts and Terms

An experiment is a collection of one or more deep learning (DL) training tasks that corresponds to a unified DL workflow, e.g., exploring a user-defined hyperparameter space.

Each training task in an experiment is called a trial. A trial is a full-specified training task with a fixed dataset and a deep learning model with all hyperparameters set. PEDL executes the training process associated with a trial as a sequence of steps, where each step corresponds to a fixed number of model updates.

To create an experiment, users must provide two resources. The first is called the experiment configuration file and specifies the hyperparameter search space, the location of the dataset, and other experiment-level settings. The second is called the model definition and specifies the deep learning model, e.g., via TensorFlow or Keras.

A search method is an algorithm for exploring the hyperparameter space of an experiment. Examples of search algorithms include adaptive search and random search.