syne_tune.optimizer.schedulers.pbt module

class syne_tune.optimizer.schedulers.pbt.PBTTrialState(trial, last_score=None, last_checkpoint=None, last_perturbation_time=0, stopped=False)

Bases: object

Internal PBT state tracked per-trial.

trial: Trial

last_score: float = None

last_checkpoint: int = None

last_perturbation_time: int = 0

stopped: bool = False

class syne_tune.optimizer.schedulers.pbt.PopulationBasedTraining(config_space, custom_explore_fn=None, **kwargs)

Bases: FIFOScheduler

Implements the Population Based Training (PBT) algorithm. This is an adapted version of the Ray Tune implementation:

https://docs.ray.io/en/latest/tune/tutorials/tune-advanced-tutorial.html

PBT was originally presented in the following paper:

Jaderberg et. al.

Population Based Training of Neural Networks

https://arxiv.org/abs/1711.09846

Population based training (PBT) maintains a population of models spread across an asynchronous set of workers and dynamically adjust their hyperparameters during training. Every time a worker reaches a user-defined milestone, it returns the performance of the currently evaluated network. If the network is within the top percentile of the population, the worker resumes its training until the next milestone. If not, PBT selects a model from the top percentile uniformly at random. The worker now continues with the latest checkpoint of this new model but mutates the hyperparameters.

The mutation happens as following. For each hyperparameter, we either resample its value uniformly at random, or otherwise increment (multiply by 1.2) or decrement (multiply by 0.8) the value (probability 0.5 each). For categorical hyperparameters, the value is always resampled uniformly.

Note: While this is implemented as child of FIFOScheduler, we require searcher="random" (default), since the current code only supports a random searcher.

Additional arguments on top of parent class FIFOScheduler.

Parameters:

• resource_attr (str) – Name of resource attribute in results obtained via on_trial_result, defaults to “time_total_s”

• population_size (int, optional) – Size of the population, defaults to 4

• perturbation_interval (float, optional) – Models will be considered for perturbation at this interval of resource_attr. Note that perturbation incurs checkpoint overhead, so you shouldn’t set this to be too frequent. Defaults to 60

• quantile_fraction (float, optional) – Parameters are transferred from the top quantile_fraction fraction of trials to the bottom quantile_fraction fraction. Needs to be between 0 and 0.5. Setting it to 0 essentially implies doing no exploitation at all. Defaults to 0.25

• resample_probability (float, optional) – The probability of resampling from the original distribution when applying _explore(). If not resampled, the value will be perturbed by a factor of 1.2 or 0.8 if continuous, or changed to an adjacent value if discrete. Defaults to 0.25

• custom_explore_fn (function, optional) – Custom exploration function. This function is invoked as f(config) instead of the built-in perturbations, and should return config updated as needed. If this is given, resample_probability is not used

on_trial_add(trial)

Called when a new trial is added to the trial runner.

Additions are normally triggered by suggest.

Parameters:

trial (Trial) – Trial to be added

on_trial_result(trial, result)

We simply relay result to the searcher. Other decisions are done in on_trial_complete.

Return type:

str