tune.searcher.suggestion

Searcher Objects

class Searcher()

Abstract class for wrapping suggesting algorithms. Custom algorithms can extend this class easily by overriding the suggest method provide generated parameters for the trials. Any subclass that implements __init__ must also call the constructor of this class: super(Subclass, self).__init__(...). To track suggestions and their corresponding evaluations, the method suggest will be passed a trial_id, which will be used in subsequent notifications. Not all implementations support multi objectives.

Arguments:

metric str or list - The training result objective value attribute. If list then list of training result objective value attributes
mode str or list - If string One of {min, max}. If list then list of max and min, determines whether objective is minimizing or maximizing the metric attribute. Must match type of metric.

class ExampleSearch(Searcher):
    def __init__(self, metric="mean_loss", mode="min", **kwargs):
        super(ExampleSearch, self).__init__(
            metric=metric, mode=mode, **kwargs)
        self.optimizer = Optimizer()
        self.configurations = {}
    def suggest(self, trial_id):
        configuration = self.optimizer.query()
        self.configurations[trial_id] = configuration
    def on_trial_complete(self, trial_id, result, **kwargs):
        configuration = self.configurations[trial_id]
        if result and self.metric in result:
            self.optimizer.update(configuration, result[self.metric])
tune.run(trainable_function, search_alg=ExampleSearch())

set_search_properties

def set_search_properties(metric: Optional[str], mode: Optional[str], config: Dict) -> 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.

Arguments:

metric str - Metric to optimize
mode str - One of ["min", "max"]. Direction to optimize.
config dict - Tune config dict.

on_trial_result

def on_trial_result(trial_id: str, result: Dict)

Optional notification for result during training. Note that by default, the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process.

Arguments:

trial_id str - A unique string ID for the trial.
result dict - Dictionary of metrics for current training progress. Note that the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process.

metric

@property
def metric() -> str

The training result objective value attribute.

mode

@property
def mode() -> str

Specifies if minimizing or maximizing the metric.

ConcurrencyLimiter Objects

class ConcurrencyLimiter(Searcher)

A wrapper algorithm for limiting the number of concurrent trials.

Arguments:

searcher Searcher - Searcher object that the ConcurrencyLimiter will manage.
max_concurrent int - Maximum concurrent samples from the underlying searcher.
batch bool - Whether to wait for all concurrent samples to finish before updating the underlying searcher.

Example:

from ray.tune.suggest import ConcurrencyLimiter  # ray version < 2
search_alg = HyperOptSearch(metric="accuracy")
search_alg = ConcurrencyLimiter(search_alg, max_concurrent=2)
tune.run(trainable, search_alg=search_alg)

validate_warmstart

def validate_warmstart(parameter_names: List[str], points_to_evaluate: List[Union[List, Dict]], evaluated_rewards: List, validate_point_name_lengths: bool = True)

Generic validation of a Searcher's warm start functionality. Raises exceptions in case of type and length mismatches between parameters. If validate_point_name_lengths is False, the equality of lengths between points_to_evaluate and parameter_names will not be validated.

OptunaSearch Objects

class OptunaSearch(Searcher)

A wrapper around Optuna to provide trial suggestions.

Optuna <https://optuna.org/>_ 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.

Arguments:

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.run.
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.
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.suggest.optuna import OptunaSearch

config = {
```
"a" - tune.uniform(6, 8)

"b" - tune.loguniform(1e-4, 1e-2) }

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

tune.run(trainable, config=config, search_alg=optuna_search)


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

```python
from ray.tune.suggest.optuna import OptunaSearch
import optuna

space = {

"a" - optuna.distributions.UniformDistribution(6, 8),
"b" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), }
optuna_search = OptunaSearch( space, metric="loss", mode="min")
tune.run(trainable, search_alg=optuna_search)
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")
tune.run(trainable, search_alg=optuna_search)
```
Multi-objective optimization is supported:

```python
from ray.tune.suggest.optuna import OptunaSearch
import optuna

space = {
```
"a" - optuna.distributions.UniformDistribution(6, 8),
"b" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), }
Note you have to specify metric and mode here instead of
in tune.run
optuna_search = OptunaSearch( space, metric=["loss1", "loss2"], mode=["min", "max"])
Do not specify metric and mode here!
tune.run( trainable, search_alg=optuna_search )
```
You can pass configs that will be evaluated first using
``points_to_evaluate``:

```python
from ray.tune.suggest.optuna import OptunaSearch
import optuna

space = {
```
"a" - optuna.distributions.UniformDistribution(6, 8),
"b" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), }
optuna_search = OptunaSearch( space,

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

tune.run(trainable, search_alg=optuna_search)


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

```python
from ray.tune.suggest.optuna import OptunaSearch
import optuna

space = {

"a" - optuna.distributions.UniformDistribution(6, 8),
"b" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), }
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")
tune.run(trainable, search_alg=optuna_search)

Searcher Objects​

set_search_properties​

on_trial_result​

metric​

mode​

ConcurrencyLimiter Objects​

validate_warmstart​

OptunaSearch Objects​

Searcher Objects

set_search_properties

on_trial_result

metric

mode

ConcurrencyLimiter Objects

validate_warmstart

OptunaSearch Objects