Source code for ray.tune.experiment

import copy
import logging
import os
import six

from ray.tune.error import TuneError
from ray.tune.registry import register_trainable, get_trainable_cls
from ray.tune.result import DEFAULT_RESULTS_DIR
from ray.tune.sample import sample_from
from ray.tune.stopper import FunctionStopper, Stopper

logger = logging.getLogger(__name__)

def _raise_deprecation_note(deprecated, replacement, soft=False):
    """User notification for deprecated parameter.

        deprecated (str): Deprecated parameter.
        replacement (str): Replacement parameter to use instead.
        soft (bool): Fatal if True.
    error_msg = ("`{deprecated}` is deprecated. Please use `{replacement}`. "
                 "`{deprecated}` will be removed in future versions of "
                 "Ray.".format(deprecated=deprecated, replacement=replacement))
    if soft:
        raise DeprecationWarning(error_msg)

def _raise_on_durable(trainable_name, sync_to_driver, upload_dir):
    trainable_cls = get_trainable_cls(trainable_name)
    from ray.tune.durable_trainable import DurableTrainable
    if issubclass(trainable_cls, DurableTrainable):
        if sync_to_driver is not False:
            raise ValueError(
                "EXPERIMENTAL: DurableTrainable will automatically sync "
                "results to the provided upload_dir. "
                "Set `sync_to_driver=False` to avoid data inconsistencies.")
        if not upload_dir:
            raise ValueError(
                "EXPERIMENTAL: DurableTrainable will automatically sync "
                "results to the provided upload_dir. "
                "`upload_dir` must be provided.")

[docs]class Experiment: """Tracks experiment specifications. Implicitly registers the Trainable if needed. Examples: >>> experiment_spec = Experiment( >>> "my_experiment_name", >>> my_func, >>> stop={"mean_accuracy": 100}, >>> config={ >>> "alpha": tune.grid_search([0.2, 0.4, 0.6]), >>> "beta": tune.grid_search([1, 2]), >>> }, >>> resources_per_trial={ >>> "cpu": 1, >>> "gpu": 0 >>> }, >>> num_samples=10, >>> local_dir="~/ray_results", >>> checkpoint_freq=10, >>> max_failures=2) """ def __init__(self, name, run, stop=None, config=None, resources_per_trial=None, num_samples=1, local_dir=None, upload_dir=None, trial_name_creator=None, loggers=None, sync_to_driver=None, checkpoint_freq=0, checkpoint_at_end=False, sync_on_checkpoint=True, keep_checkpoints_num=None, checkpoint_score_attr=None, export_formats=None, max_failures=0, restore=None, repeat=None, trial_resources=None, sync_function=None): """Initialize a new Experiment. The args here take the same meaning as the command line flags defined in ``. """ if repeat: _raise_deprecation_note("repeat", "num_samples", soft=False) if trial_resources: _raise_deprecation_note( "trial_resources", "resources_per_trial", soft=False) if sync_function: _raise_deprecation_note( "sync_function", "sync_to_driver", soft=False) config = config or {} self._run_identifier = Experiment.register_if_needed(run) = name or self._run_identifier if upload_dir: self.remote_checkpoint_dir = os.path.join(upload_dir, else: self.remote_checkpoint_dir = None self._stopper = None stopping_criteria = {} if not stop: pass elif isinstance(stop, dict): stopping_criteria = stop elif callable(stop): if FunctionStopper.is_valid_function(stop): self._stopper = FunctionStopper(stop) elif issubclass(type(stop), Stopper): self._stopper = stop else: raise ValueError("Provided stop object must be either a dict, " "a function, or a subclass of " "`ray.tune.Stopper`.") else: raise ValueError("Invalid stop criteria: {}. Must be a " "callable or dict".format(stop)) _raise_on_durable(self._run_identifier, sync_to_driver, upload_dir) spec = { "run": self._run_identifier, "stop": stopping_criteria, "config": config, "resources_per_trial": resources_per_trial, "num_samples": num_samples, "local_dir": os.path.abspath( os.path.expanduser(local_dir or DEFAULT_RESULTS_DIR)), "upload_dir": upload_dir, "remote_checkpoint_dir": self.remote_checkpoint_dir, "trial_name_creator": trial_name_creator, "loggers": loggers, "sync_to_driver": sync_to_driver, "checkpoint_freq": checkpoint_freq, "checkpoint_at_end": checkpoint_at_end, "sync_on_checkpoint": sync_on_checkpoint, "keep_checkpoints_num": keep_checkpoints_num, "checkpoint_score_attr": checkpoint_score_attr, "export_formats": export_formats or [], "max_failures": max_failures, "restore": os.path.abspath(os.path.expanduser(restore)) if restore else None } self.spec = spec
[docs] @classmethod def from_json(cls, name, spec): """Generates an Experiment object from JSON. Args: name (str): Name of Experiment. spec (dict): JSON configuration of experiment. """ if "run" not in spec: raise TuneError("No trainable specified!") # Special case the `env` param for RLlib by automatically # moving it into the `config` section. if "env" in spec: spec["config"] = spec.get("config", {}) spec["config"]["env"] = spec["env"] del spec["env"] spec = copy.deepcopy(spec) run_value = spec.pop("run") try: exp = cls(name, run_value, **spec) except TypeError: raise TuneError("Improper argument from JSON: {}.".format(spec)) return exp
[docs] @classmethod def register_if_needed(cls, run_object): """Registers Trainable or Function at runtime. Assumes already registered if run_object is a string. Also, does not inspect interface of given run_object. Arguments: run_object (str|function|class): Trainable to run. If string, assumes it is an ID and does not modify it. Otherwise, returns a string corresponding to the run_object name. Returns: A string representing the trainable identifier. """ if isinstance(run_object, six.string_types): return run_object elif isinstance(run_object, sample_from): logger.warning("Not registering trainable. Resolving as variant.") return run_object elif isinstance(run_object, type) or callable(run_object): name = "DEFAULT" if hasattr(run_object, "__name__"): name = run_object.__name__ else: logger.warning( "No name detected on trainable. Using {}.".format(name)) register_trainable(name, run_object) return name else: raise TuneError("Improper 'run' - not string nor trainable.")
@property def stopper(self): return self._stopper @property def local_dir(self): return self.spec.get("local_dir") @property def checkpoint_dir(self): if self.local_dir: return os.path.join(self.local_dir, @property def run_identifier(self): """Returns a string representing the trainable identifier.""" return self._run_identifier
def convert_to_experiment_list(experiments): """Produces a list of Experiment objects. Converts input from dict, single experiment, or list of experiments to list of experiments. If input is None, will return an empty list. Arguments: experiments (Experiment | list | dict): Experiments to run. Returns: List of experiments. """ exp_list = experiments # Transform list if necessary if experiments is None: exp_list = [] elif isinstance(experiments, Experiment): exp_list = [experiments] elif type(experiments) is dict: exp_list = [ Experiment.from_json(name, spec) for name, spec in experiments.items() ] # Validate exp_list if (type(exp_list) is list and all(isinstance(exp, Experiment) for exp in exp_list)): if len(exp_list) > 1: logger.warning("All experiments will be " "using the same SearchAlgorithm.") else: raise TuneError("Invalid argument: {}".format(experiments)) return exp_list