ray.tune.search.Repeater

class ray.tune.search.Repeater(searcher: ray.tune.search.searcher.Searcher, repeat: int = 1, set_index: bool = True)

Bases: ray.tune.search.searcher.Searcher

A wrapper algorithm for repeating trials of same parameters.

Set tune.TuneConfig(num_samples=…) to be a multiple of repeat. For example, set num_samples=15 if you intend to obtain 3 search algorithm suggestions and repeat each suggestion 5 times. Any leftover trials (num_samples mod repeat) will be ignored.

It is recommended that you do not run an early-stopping TrialScheduler simultaneously.

Parameters

• searcher – Searcher object that the Repeater will optimize. Note that the Searcher will only see 1 trial among multiple repeated trials. The result/metric passed to the Searcher upon trial completion will be averaged among all repeats.

• repeat – Number of times to generate a trial with a repeated configuration. Defaults to 1.

• set_index – Sets a tune.search.repeater.TRIAL_INDEX in Trainable/Function config which corresponds to the index of the repeated trial. This can be used for seeds. Defaults to True.

Example:

from ray.tune.search import Repeater

search_alg = BayesOptSearch(...)
re_search_alg = Repeater(search_alg, repeat=10)

# Repeat 2 samples 10 times each.
tuner = tune.Tuner(
    trainable,
    tune_config=tune.TuneConfig(
        search_alg=re_search_alg,
        num_samples=20,
    ),
)
tuner.fit()

PublicAPI: This API is stable across Ray releases.

suggest(trial_id: str) → Optional[Dict]

Queries the algorithm to retrieve the next set of parameters.

Parameters

trial_id – Trial ID used for subsequent notifications.

Returns

Configuration for a trial, if possible.

If FINISHED is returned, Tune will be notified that no more suggestions/configurations will be provided. If None is returned, Tune will skip the querying of the searcher for this step.

Return type

dict | FINISHED | None

on_trial_complete(trial_id: str, result: Optional[Dict] = None, **kwargs)

Stores the score for and keeps track of a completed trial.

Stores the metric of a trial as nan if any of the following conditions are met:

1. result is empty or not provided.

2. result is provided but no metric was provided.

save(checkpoint_path: str)

Save state to path for this search algorithm.

Parameters

checkpoint_path – File where the search algorithm state is saved. This path should be used later when restoring from file.

Example:

search_alg = Searcher(...)

tuner = tune.Tuner(
    cost,
    tune_config=tune.TuneConfig(
        search_alg=search_alg,
        num_samples=5
    ),
    param_space=config
)
results = tuner.fit()

search_alg.save("./my_favorite_path.pkl")

Changed in version 0.8.7: Save is automatically called by Tuner().fit(). You can use Tuner().restore() to restore from an experiment directory such as /ray_results/trainable.

restore(checkpoint_path: str)

Restore state for this search algorithm

Parameters

checkpoint_path – File where the search algorithm state is saved. This path should be the same as the one provided to “save”.

Example:

search_alg.save("./my_favorite_path.pkl")

search_alg2 = Searcher(...)
search_alg2 = ConcurrencyLimiter(search_alg2, 1)
search_alg2.restore(checkpoint_path)
tuner = tune.Tuner(
    cost,
    tune_config=tune.TuneConfig(
        search_alg=search_alg2,
        num_samples=5
    ),
)
tuner.fit()

set_search_properties(metric: Optional[str], mode: Optional[str], config: Dict, **spec) → bool

Pass search properties to searcher.

This method acts as an alternative to instantiating search algorithms with their own specific search spaces. Instead they can accept a Tune config through this method. A searcher should return True if setting the config was successful, or False if it was unsuccessful, e.g. when the search space has already been set.

Parameters

• metric – Metric to optimize

• mode – One of [“min”, “max”]. Direction to optimize.

• config – Tune config dict.

• **spec – Any kwargs for forward compatiblity. Info like Experiment.PUBLIC_KEYS is provided through here.