ray.tune.search.optuna.OptunaSearch#

class ray.tune.search.optuna.OptunaSearch(space: Optional[Union[Dict[str, None], List[Tuple], Callable[[None], Optional[Dict[str, Any]]]]] = None, metric: Optional[Union[str, List[str]]] = None, mode: Optional[Union[str, List[str]]] = None, points_to_evaluate: Optional[List[Dict]] = None, sampler: None = None, seed: Optional[int] = None, evaluated_rewards: Optional[List] = None)[source]#

Bases: ray.tune.search.searcher.Searcher

A wrapper around Optuna to provide trial suggestions.

Optuna is a hyperparameter optimization library. In contrast to other libraries, it employs define-by-run style hyperparameter definitions.

This Searcher is a thin wrapper around Optuna’s search algorithms. You can pass any Optuna sampler, which will be used to generate hyperparameter suggestions.

Multi-objective optimization is supported.

Parameters

space –
Hyperparameter search space definition for Optuna’s sampler. This can be either a dict with parameter names as keys and optuna.distributions as values, or a Callable - in which case, it should be a define-by-run function using optuna.trial to obtain the hyperparameter values. The function should return either a dict of constant values with names as keys, or None. For more information, see https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html.

Warning

No actual computation should take place in the define-by-run function. Instead, put the training logic inside the function or class trainable passed to tune.Tuner().
metric – The training result objective value attribute. If None but a mode was passed, the anonymous metric _metric will be used per default. Can be a list of metrics for multi-objective optimization.
mode – One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute. Can be a list of modes for multi-objective optimization (corresponding to metric).
points_to_evaluate – Initial parameter suggestions to be run first. This is for when you already have some good parameters you want to run first to help the algorithm make better suggestions for future parameters. Needs to be a list of dicts containing the configurations.
sampler –
Optuna sampler used to draw hyperparameter configurations. Defaults to MOTPESampler for multi-objective optimization with Optuna<2.9.0, and TPESampler in every other case. See https://optuna.readthedocs.io/en/stable/reference/samplers/index.html for available Optuna samplers.

Warning

Please note that with Optuna 2.10.0 and earlier default MOTPESampler/TPESampler suffer from performance issues when dealing with a large number of completed trials (approx. >100). This will manifest as a delay when suggesting new configurations. This is an Optuna issue and may be fixed in a future Optuna release.
seed – Seed to initialize sampler with. This parameter is only used when sampler=None. In all other cases, the sampler you pass should be initialized with the seed already.
evaluated_rewards –
If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same length as points_to_evaluate.

Warning

When using evaluated_rewards, the search space space must be provided as a dict with parameter names as keys and optuna.distributions instances as values. The define-by-run search space definition is not yet supported with this functionality.

Tune automatically converts search spaces to Optuna’s format:

from ray.tune.search.optuna import OptunaSearch

config = {
    "a": tune.uniform(6, 8)
    "b": tune.loguniform(1e-4, 1e-2)
}

optuna_search = OptunaSearch(
    metric="loss",
    mode="min")

tuner = tune.Tuner(
    trainable,
    tune_config=tune.TuneConfig(
        search_alg=optuna_search,
    ),
    param_space=config,
)
tuner.fit()

If you would like to pass the search space manually, the code would look like this:

from ray.tune.search.optuna import OptunaSearch
import optuna

space = {
    "a": optuna.distributions.FloatDistribution(6, 8),
    "b": optuna.distributions.FloatDistribution(1e-4, 1e-2, log=True),
}

optuna_search = OptunaSearch(
    space,
    metric="loss",
    mode="min")

tuner = tune.Tuner(
    trainable,
    tune_config=tune.TuneConfig(
        search_alg=optuna_search,
    ),
)
tuner.fit()

# Equivalent Optuna define-by-run function approach:

def define_search_space(trial: optuna.Trial):
    trial.suggest_float("a", 6, 8)
    trial.suggest_float("b", 1e-4, 1e-2, log=True)
    # training logic goes into trainable, this is just
    # for search space definition

optuna_search = OptunaSearch(
    define_search_space,
    metric="loss",
    mode="min")

tuner = tune.Tuner(
    trainable,
    tune_config=tune.TuneConfig(
        search_alg=optuna_search,
    ),
)
tuner.fit()

Multi-objective optimization is supported:

from ray.tune.search.optuna import OptunaSearch
import optuna

space = {
    "a": optuna.distributions.FloatDistribution(6, 8),
    "b": optuna.distributions.FloatDistribution(1e-4, 1e-2, log=True),
}

# Note you have to specify metric and mode here instead of
# in tune.TuneConfig
optuna_search = OptunaSearch(
    space,
    metric=["loss1", "loss2"],
    mode=["min", "max"])

# Do not specify metric and mode here!
tuner = tune.Tuner(
    trainable,
    tune_config=tune.TuneConfig(
        search_alg=optuna_search,
    ),
)
tuner.fit()

You can pass configs that will be evaluated first using points_to_evaluate:

from ray.tune.search.optuna import OptunaSearch
import optuna

space = {
    "a": optuna.distributions.FloatDistribution(6, 8),
    "b": optuna.distributions.FloatDistribution(1e-4, 1e-2, log=True),
}

optuna_search = OptunaSearch(
    space,
    points_to_evaluate=[{"a": 6.5, "b": 5e-4}, {"a": 7.5, "b": 1e-3}]
    metric="loss",
    mode="min")

tuner = tune.Tuner(
    trainable,
    tune_config=tune.TuneConfig(
        search_alg=optuna_search,
    ),
)
tuner.fit()

Avoid re-running evaluated trials by passing the rewards together with points_to_evaluate:

from ray.tune.search.optuna import OptunaSearch
import optuna

space = {
    "a": optuna.distributions.FloatDistribution(6, 8),
    "b": optuna.distributions.FloatDistribution(1e-4, 1e-2, log=True),
}

optuna_search = OptunaSearch(
    space,
    points_to_evaluate=[{"a": 6.5, "b": 5e-4}, {"a": 7.5, "b": 1e-3}]
    evaluated_rewards=[0.89, 0.42]
    metric="loss",
    mode="min")

tuner = tune.Tuner(
    trainable,
    tune_config=tune.TuneConfig(
        search_alg=optuna_search,
    ),
)
tuner.fit()

New in version 0.8.8.

Methods

`add_evaluated_trials`(trials_or_analysis, metric)	Pass results from trials that have been evaluated separately.
`restore_from_dir`(checkpoint_dir)	Restores the state of a searcher from a given checkpoint_dir.
`save_to_dir`(checkpoint_dir[, session_str])	Automatically saves the given searcher to the checkpoint_dir.
`set_max_concurrency`(max_concurrent)	Set max concurrent trials this searcher can run.

Attributes

`CKPT_FILE_TMPL`
`FINISHED`
`metric`	The training result objective value attribute.
`mode`	Specifies if minimizing or maximizing the metric.

Ray 2.7.0

ray.tune.search.optuna.OptunaSearch

ray.tune.search.optuna.OptunaSearch#