Hyperparameter Search: Adaptive (Simple)¶
Adaptive (Simple) is deprecated and will be removed in a future release. We recommend using the state-of-the-art Adaptive (ASHA) searcher.
adaptive_simple searcher takes two intuitive configuration
options capturing how many configurations to search and a maximum
resource budget allowed per configuration:
max_trials: The maximum number of hyperparameter configurations that will be explored. Most of these configurations will not be trained to convergence; rather, the search method will use early-stopping to prune hyperparameter configurations that are not performing well.
max_length: The maximum training length (see Training Units) of any trial that survives to the end of the experiment. This quantity is domain-specific and should roughly reflect the training length needed for the model to converge on the data set. For users who would like to determine this number experimentally, train a model with reasonable hyperparameters using the single search method.