Source code for ray.tune.tune_config

import datetime
from dataclasses import dataclass
from enum import Enum
from typing import Callable, Optional, Union

from ray.train.constants import _DEPRECATED_VALUE
from ray.tune.experiment.trial import Trial
from ray.tune.schedulers import TrialScheduler
from ray.tune.search import SearchAlgorithm, Searcher
from ray.util.annotations import DeveloperAPI, PublicAPI


@dataclass
@PublicAPI(stability="beta")
class TuneConfig:
    """Tune specific configs.

    Args:
        metric: Metric to optimize. This metric should be reported
            with `tune.report()`. If set, will be passed to the search
            algorithm and scheduler.
        mode: Must be one of [min, max]. Determines whether objective is
            minimizing or maximizing the metric attribute. If set, will be
            passed to the search algorithm and scheduler.
        search_alg: Search algorithm for optimization. Default to
            random search.
        scheduler: Scheduler for executing the experiment.
            Choose among FIFO (default), MedianStopping,
            AsyncHyperBand, HyperBand and PopulationBasedTraining. Refer to
            ray.tune.schedulers for more options.
        num_samples: Number of times to sample from the
            hyperparameter space. Defaults to 1. If `grid_search` is
            provided as an argument, the grid will be repeated
            `num_samples` of times. If this is -1, (virtually) infinite
            samples are generated until a stopping condition is met.
        max_concurrent_trials: Maximum number of trials to run
            concurrently. Must be non-negative. If None or 0, no limit will
            be applied. This is achieved by wrapping the ``search_alg`` in
            a :class:`ConcurrencyLimiter`, and thus setting this argument
            will raise an exception if the ``search_alg`` is already a
            :class:`ConcurrencyLimiter`. Defaults to None.
        time_budget_s: Global time budget in
            seconds after which all trials are stopped. Can also be a
            ``datetime.timedelta`` object.
        reuse_actors: Whether to reuse actors between different trials
            when possible. This can drastically speed up experiments that start
            and stop actors often (e.g., PBT in time-multiplexing mode). This
            requires trials to have the same resource requirements.
            Defaults to ``False``.
        trial_name_creator: Optional function that takes in a Trial and returns
            its name (i.e. its string representation). Be sure to include some unique
            identifier (such as `Trial.trial_id`) in each trial's name.
            NOTE: This API is in alpha and subject to change.
        trial_dirname_creator: Optional function that takes in a trial and
            generates its trial directory name as a string. Be sure to include some
            unique identifier (such as `Trial.trial_id`) is used in each trial's
            directory name. Otherwise, trials could overwrite artifacts and checkpoints
            of other trials. The return value cannot be a path.
            NOTE: This API is in alpha and subject to change.
        chdir_to_trial_dir: Deprecated. Set the `RAY_CHDIR_TO_TRIAL_DIR` env var instead
    """

    # Currently this is not at feature parity with `tune.run`, nor should it be.
    # The goal is to reach a fine balance between API flexibility and conciseness.
    # We should carefully introduce arguments here instead of just dumping everything.
    mode: Optional[str] = None
    metric: Optional[str] = None
    search_alg: Optional[Union[Searcher, SearchAlgorithm]] = None
    scheduler: Optional[TrialScheduler] = None
    num_samples: int = 1
    max_concurrent_trials: Optional[int] = None
    time_budget_s: Optional[Union[int, float, datetime.timedelta]] = None
    reuse_actors: bool = False
    trial_name_creator: Optional[Callable[[Trial], str]] = None
    trial_dirname_creator: Optional[Callable[[Trial], str]] = None
    chdir_to_trial_dir: bool = _DEPRECATED_VALUE


@DeveloperAPI
@dataclass
class ResumeConfig:
    """[Experimental] This config is used to specify how to resume Tune trials."""

    class ResumeType(Enum):
        """An enumeration to define resume types for various trial states.

        Members:
            RESUME: Resume from the latest checkpoint.
            RESTART: Restart from the beginning (with no checkpoint).
            SKIP: Skip this trial when resuming by treating it as terminated.
        """

        RESUME = "resume"
        RESTART = "restart"
        SKIP = "skip"

    finished: str = ResumeType.SKIP
    unfinished: str = ResumeType.RESUME
    errored: str = ResumeType.SKIP