Source code for ray.tune.suggest.suggestion

import copy
import glob
import logging
import os
import warnings
from typing import Dict, Optional, List, Union, Any, TYPE_CHECKING

from ray.tune.suggest.util import set_search_properties_backwards_compatible
from ray.util.debug import log_once

if TYPE_CHECKING:
    from ray.tune.trial import Trial
    from ray.tune.analysis import ExperimentAnalysis

logger = logging.getLogger(__name__)

UNRESOLVED_SEARCH_SPACE = str(
    "You passed a `{par}` parameter to {cls} that contained unresolved search "
    "space definitions. {cls} should however be instantiated with fully "
    "configured search spaces only. To use Ray Tune's automatic search space "
    "conversion, pass the space definition as part of the `config` argument "
    "to `tune.run()` instead."
)

UNDEFINED_SEARCH_SPACE = str(
    "Trying to sample a configuration from {cls}, but no search "
    "space has been defined. Either pass the `{space}` argument when "
    "instantiating the search algorithm, or pass a `config` to "
    "`tune.run()`."
)

UNDEFINED_METRIC_MODE = str(
    "Trying to sample a configuration from {cls}, but the `metric` "
    "({metric}) or `mode` ({mode}) parameters have not been set. "
    "Either pass these arguments when instantiating the search algorithm, "
    "or pass them to `tune.run()`."
)


[docs]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. Args: metric: The training result objective value attribute. If list then list of training result objective value attributes mode: 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. .. code-block:: python 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()) """ FINISHED = "FINISHED" CKPT_FILE_TMPL = "searcher-state-{}.pkl" def __init__( self, metric: Optional[str] = None, mode: Optional[str] = None, ): self._metric = metric self._mode = mode if not mode or not metric: # Early return to avoid assertions return assert isinstance( metric, type(mode) ), "metric and mode must be of the same type" if isinstance(mode, str): assert mode in ["min", "max"], "if `mode` is a str must be 'min' or 'max'!" elif isinstance(mode, list): assert len(mode) == len(metric), "Metric and mode must be the same length" assert all( mod in ["min", "max", "obs"] for mod in mode ), "All of mode must be 'min' or 'max' or 'obs'!" else: raise ValueError("Mode most either be a list or string")
[docs] def set_search_properties( self, 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. Args: 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. """ return False
[docs] def on_trial_result(self, trial_id: str, result: Dict) -> None: """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. Args: trial_id: A unique string ID for the trial. result: 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. """ pass
[docs] def on_trial_complete( self, trial_id: str, result: Optional[Dict] = None, error: bool = False ) -> None: """Notification for the completion of trial. Typically, this method is used for notifying the underlying optimizer of the result. Args: trial_id: A unique string ID for the trial. result: 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. Upon errors, this may also be None. error: True if the training process raised an error. """ raise NotImplementedError
[docs] def suggest(self, trial_id: str) -> Optional[Dict]: """Queries the algorithm to retrieve the next set of parameters. Arguments: trial_id: Trial ID used for subsequent notifications. Returns: dict | FINISHED | None: 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. """ raise NotImplementedError
[docs] def add_evaluated_point( self, parameters: Dict, value: float, error: bool = False, pruned: bool = False, intermediate_values: Optional[List[float]] = None, ): """Pass results from a point that has been evaluated separately. This method allows for information from outside the suggest - on_trial_complete loop to be passed to the search algorithm. This functionality depends on the underlying search algorithm and may not be always available. Args: parameters: Parameters used for the trial. value: Metric value obtained in the trial. error: True if the training process raised an error. pruned: True if trial was pruned. intermediate_values: List of metric values for intermediate iterations of the result. None if not applicable. """ raise NotImplementedError
[docs] def add_evaluated_trials( self, trials_or_analysis: Union["Trial", List["Trial"], "ExperimentAnalysis"], metric: str, ): """Pass results from trials that have been evaluated separately. This method allows for information from outside the suggest - on_trial_complete loop to be passed to the search algorithm. This functionality depends on the underlying search algorithm and may not be always available (same as ``add_evaluated_point``.) Args: trials_or_analysis: Trials to pass results form to the searcher. metric: Metric name reported by trials used for determining the objective value. """ if self.add_evaluated_point == Searcher.add_evaluated_point: raise NotImplementedError # lazy imports to avoid circular dependencies from ray.tune.trial import Trial from ray.tune.analysis import ExperimentAnalysis from ray.tune.result import DONE if isinstance(trials_or_analysis, Trial): trials_or_analysis = [trials_or_analysis] elif isinstance(trials_or_analysis, ExperimentAnalysis): trials_or_analysis = trials_or_analysis.trials any_trial_had_metric = False def trial_to_points(trial: Trial) -> Dict[str, Any]: nonlocal any_trial_had_metric has_trial_been_pruned = ( trial.status == Trial.TERMINATED and not trial.last_result.get(DONE, False) ) has_trial_finished = ( trial.status == Trial.TERMINATED and trial.last_result.get(DONE, False) ) if not any_trial_had_metric: any_trial_had_metric = ( metric in trial.last_result and has_trial_finished ) if Trial.TERMINATED and metric not in trial.last_result: return None return dict( parameters=trial.config, value=trial.last_result.get(metric, None), error=trial.status == Trial.ERROR, pruned=has_trial_been_pruned, intermediate_values=None, # we do not save those ) for trial in trials_or_analysis: kwargs = trial_to_points(trial) if kwargs: self.add_evaluated_point(**kwargs) if not any_trial_had_metric: warnings.warn( "No completed trial returned the specified metric. " "Make sure the name you have passed is correct. " )
[docs] def save(self, checkpoint_path: str): """Save state to path for this search algorithm. Args: checkpoint_path: File where the search algorithm state is saved. This path should be used later when restoring from file. Example: .. code-block:: python search_alg = Searcher(...) analysis = tune.run( cost, num_samples=5, search_alg=search_alg, name=self.experiment_name, local_dir=self.tmpdir) search_alg.save("./my_favorite_path.pkl") .. versionchanged:: 0.8.7 Save is automatically called by `tune.run`. You can use `restore_from_dir` to restore from an experiment directory such as `~/ray_results/trainable`. """ raise NotImplementedError
[docs] def restore(self, checkpoint_path: str): """Restore state for this search algorithm Args: checkpoint_path: File where the search algorithm state is saved. This path should be the same as the one provided to "save". Example: .. code-block:: python search_alg.save("./my_favorite_path.pkl") search_alg2 = Searcher(...) search_alg2 = ConcurrencyLimiter(search_alg2, 1) search_alg2.restore(checkpoint_path) tune.run(cost, num_samples=5, search_alg=search_alg2) """ raise NotImplementedError
[docs] def set_max_concurrency(self, max_concurrent: int) -> bool: """Set max concurrent trials this searcher can run. This method will be called on the wrapped searcher by the ``ConcurrencyLimiter``. It is intended to allow for searchers which have custom, internal logic handling max concurrent trials to inherit the value passed to ``ConcurrencyLimiter``. If this method returns False, it signifies that no special logic for handling this case is present in the searcher. Args: max_concurrent: Number of maximum concurrent trials. """ return False
def get_state(self) -> Dict: raise NotImplementedError def set_state(self, state: Dict): raise NotImplementedError
[docs] def save_to_dir(self, checkpoint_dir: str, session_str: str = "default"): """Automatically saves the given searcher to the checkpoint_dir. This is automatically used by tune.run during a Tune job. Args: checkpoint_dir: Filepath to experiment dir. session_str: Unique identifier of the current run session. """ tmp_search_ckpt_path = os.path.join(checkpoint_dir, ".tmp_searcher_ckpt") success = True try: self.save(tmp_search_ckpt_path) except NotImplementedError: if log_once("suggest:save_to_dir"): logger.warning("save not implemented for Searcher. Skipping save.") success = False if success and os.path.exists(tmp_search_ckpt_path): os.replace( tmp_search_ckpt_path, os.path.join(checkpoint_dir, self.CKPT_FILE_TMPL.format(session_str)), )
[docs] def restore_from_dir(self, checkpoint_dir: str): """Restores the state of a searcher from a given checkpoint_dir. Typically, you should use this function to restore from an experiment directory such as `~/ray_results/trainable`. .. code-block:: python experiment_1 = tune.run( cost, num_samples=5, search_alg=search_alg, verbose=0, name=self.experiment_name, local_dir="~/my_results") search_alg2 = Searcher() search_alg2.restore_from_dir( os.path.join("~/my_results", self.experiment_name) """ pattern = self.CKPT_FILE_TMPL.format("*") full_paths = glob.glob(os.path.join(checkpoint_dir, pattern)) if not full_paths: raise RuntimeError( "Searcher unable to find checkpoint in {}".format(checkpoint_dir) ) # TODO most_recent_checkpoint = max(full_paths) self.restore(most_recent_checkpoint)
@property def metric(self) -> str: """The training result objective value attribute.""" return self._metric @property def mode(self) -> str: """Specifies if minimizing or maximizing the metric.""" return self._mode
[docs]class ConcurrencyLimiter(Searcher): """A wrapper algorithm for limiting the number of concurrent trials. Certain Searchers have their own internal logic for limiting the number of concurrent trials. If such a Searcher is passed to a ``ConcurrencyLimiter``, the ``max_concurrent`` of the ``ConcurrencyLimiter`` will override the ``max_concurrent`` value of the Searcher. The ``ConcurrencyLimiter`` will then let the Searcher's internal logic take over. Args: searcher: Searcher object that the ConcurrencyLimiter will manage. max_concurrent: Maximum concurrent samples from the underlying searcher. batch: Whether to wait for all concurrent samples to finish before updating the underlying searcher. Example: .. code-block:: python from ray.tune.suggest import ConcurrencyLimiter search_alg = HyperOptSearch(metric="accuracy") search_alg = ConcurrencyLimiter(search_alg, max_concurrent=2) tune.run(trainable, search_alg=search_alg) """ def __init__(self, searcher: Searcher, max_concurrent: int, batch: bool = False): assert type(max_concurrent) is int and max_concurrent > 0 self.searcher = searcher self.max_concurrent = max_concurrent self.batch = batch self.live_trials = set() self.num_unfinished_live_trials = 0 self.cached_results = {} self._limit_concurrency = True if not isinstance(searcher, Searcher): raise RuntimeError( f"The `ConcurrencyLimiter` only works with `Searcher` " f"objects (got {type(searcher)}). Please try to pass " f"`max_concurrent` to the search generator directly." ) self._set_searcher_max_concurrency() super(ConcurrencyLimiter, self).__init__( metric=self.searcher.metric, mode=self.searcher.mode ) def _set_searcher_max_concurrency(self): # If the searcher has special logic for handling max concurrency, # we do not do anything inside the ConcurrencyLimiter self._limit_concurrency = not self.searcher.set_max_concurrency( self.max_concurrent ) def set_max_concurrency(self, max_concurrent: int) -> bool: # Determine if this behavior is acceptable, or if it should # raise an exception. self.max_concurrent = max_concurrent return True def set_search_properties( self, metric: Optional[str], mode: Optional[str], config: Dict, **spec ) -> bool: self._set_searcher_max_concurrency() return set_search_properties_backwards_compatible( self.searcher.set_search_properties, metric, mode, config, **spec ) def suggest(self, trial_id: str) -> Optional[Dict]: if not self._limit_concurrency: return self.searcher.suggest(trial_id) assert ( trial_id not in self.live_trials ), f"Trial ID {trial_id} must be unique: already found in set." if len(self.live_trials) >= self.max_concurrent: logger.debug( f"Not providing a suggestion for {trial_id} due to " "concurrency limit: %s/%s.", len(self.live_trials), self.max_concurrent, ) return suggestion = self.searcher.suggest(trial_id) if suggestion not in (None, Searcher.FINISHED): self.live_trials.add(trial_id) self.num_unfinished_live_trials += 1 return suggestion def on_trial_complete( self, trial_id: str, result: Optional[Dict] = None, error: bool = False ): if not self._limit_concurrency: return self.searcher.on_trial_complete(trial_id, result=result, error=error) if trial_id not in self.live_trials: return elif self.batch: self.cached_results[trial_id] = (result, error) self.num_unfinished_live_trials -= 1 if self.num_unfinished_live_trials <= 0: # Update the underlying searcher once the # full batch is completed. for trial_id, (result, error) in self.cached_results.items(): self.searcher.on_trial_complete( trial_id, result=result, error=error ) self.live_trials.remove(trial_id) self.cached_results = {} self.num_unfinished_live_trials = 0 else: return else: self.searcher.on_trial_complete(trial_id, result=result, error=error) self.live_trials.remove(trial_id) self.num_unfinished_live_trials -= 1 def on_trial_result(self, trial_id: str, result: Dict) -> None: self.searcher.on_trial_result(trial_id, result) def add_evaluated_point( self, parameters: Dict, value: float, error: bool = False, pruned: bool = False, intermediate_values: Optional[List[float]] = None, ): return self.searcher.add_evaluated_point( parameters, value, error, pruned, intermediate_values ) def get_state(self) -> Dict: state = self.__dict__.copy() del state["searcher"] return copy.deepcopy(state) def set_state(self, state: Dict): self.__dict__.update(state) def save(self, checkpoint_path: str): self.searcher.save(checkpoint_path) def restore(self, checkpoint_path: str): self.searcher.restore(checkpoint_path)