Hyperparameter Search: Population-based training

Population-based training (PBT) is loosely based on genetic algorithms; see the original paper or blog post for details. The motivation is that it makes sense to explore hyperparameter configurations that are known to perform well, since the performance of a model as a function of the hyperparameters is likely to show some continuity. The algorithm works by repeatedly replacing low-performing hyperparameter configurations with modified versions of high-performing ones.

Quick start

A typical set of configuration values for PBT:

  • population_size: 40

  • num_rounds, length_per_round: The product of these values is the total training length for a trial that survives to the end of the experiment; it should be chosen similarly to the value of max_length for Hyperparameter Search: Adaptive (Simple). For a given value of the product, decreasing length_per_round creates more opportunity for evaluation and selection of good configurations at the cost of higher variance and computational overhead.

  • replace_function:

    • truncate_fraction: 0.2

  • explore_function:

    • resample_probability: 0.2

    • perturb_factor: 0.2


At any time, the searcher maintains a fixed number of active trials (the population). Initially, each trial uses a randomly chosen hyperparameter configuration, just as with the random searcher. The difference is that, periodically, every trial stops training and evaluates the validation metric for the trial’s current state; some of the worst-performing trials are closed, while an equal number of the best-performing trials are cloned to replace them. Cloning a trial involves checkpointing it and creating a new trial that continues training from that checkpoint. The hyperparameters of the new trial are not generally equal to those of the original trial, but are derived from them in a particular way; see the description of available parameters for details.

There is an important constraint on the hyperparameters that are allowed to vary when PBT is in use: it must always be possible to load a checkpoint from a model that was created with any potential hyperparameter configuration into a model using any other configuration; otherwise, the cloning process could fail. This means that, for instance, the number of hidden units in a neural network layer cannot be such a hyperparameter. If it were, the models for different configurations could have weight matrices of different dimensions, so their checkpoints would not be compatible.


One round consists of a period of training followed by a validate/close/clone phase. During each round, each running trial does a fixed amount of training, determined by the experiment configuration.

  • population_size: The number of trials that should run at the same time.

  • num_rounds: The total number of rounds to run.

  • length_per_round: The training units to train each trial for during a

    round, in terms of records, batches or epochs (see Training Units).

The parameters for the cloning process are also configurable using two nested objects, called replace_function and explore_function, within the searcher fields of the experiment configuration file.

  • replace_function: The configuration for deciding which trials to close.

    • truncate_fraction: The fraction of the population that is closed and replaced by clones at the end of each round.

  • explore_function: The configuration for modifying hyperparameter configurations when cloning. Each hyperparameter is either resampled, meaning that it is replaced by a value drawn independently from the original configuration, or perturbed, meaning that it is multiplied by a configurable factor.

    • resample_probability: The probability that a hyperparameter is replaced with a new value sampled from the original distribution specified in the configuration.

    • perturb_factor: The amount by which hyperparameters that are not resampled are perturbed: each numerical hyperparameter is multiplied by either 1 + perturb_factor or 1 - perturb_factor with equal probability; categorical and const hyperparameters are left unchanged.