wehub-resource-sync
100 lines
4.6 KiB
Python
100 lines
4.6 KiB
Python
import datetime
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from dataclasses import dataclass
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from enum import Enum
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from typing import Callable, Optional, Union
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from ray.train.constants import _DEPRECATED_VALUE
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from ray.tune.experiment.trial import Trial
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from ray.tune.schedulers import TrialScheduler
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from ray.tune.search import SearchAlgorithm, Searcher
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from ray.util.annotations import DeveloperAPI, PublicAPI
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@dataclass
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@PublicAPI(stability="beta")
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class TuneConfig:
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"""Tune specific configs.
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Args:
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metric: Metric to optimize. This metric should be reported
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with `tune.report()`. If set, will be passed to the search
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algorithm and scheduler.
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mode: Must be one of [min, max]. Determines whether objective is
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minimizing or maximizing the metric attribute. If set, will be
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passed to the search algorithm and scheduler.
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search_alg: Search algorithm for optimization. Default to
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random search.
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scheduler: Scheduler for executing the experiment.
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Choose among FIFO (default), MedianStopping,
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AsyncHyperBand, HyperBand and PopulationBasedTraining. Refer to
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ray.tune.schedulers for more options.
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num_samples: Number of times to sample from the
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hyperparameter space. Defaults to 1. If `grid_search` is
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provided as an argument, the grid will be repeated
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`num_samples` of times. If this is -1, (virtually) infinite
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samples are generated until a stopping condition is met.
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max_concurrent_trials: Maximum number of trials to run
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concurrently. Must be non-negative. If None or 0, no limit will
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be applied. This is achieved by wrapping the ``search_alg`` in
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a :class:`ConcurrencyLimiter`, and thus setting this argument
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will raise an exception if the ``search_alg`` is already a
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:class:`ConcurrencyLimiter`. Defaults to None.
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time_budget_s: Global time budget in
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seconds after which all trials are stopped. Can also be a
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``datetime.timedelta`` object.
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reuse_actors: Whether to reuse actors between different trials
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when possible. This can drastically speed up experiments that start
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and stop actors often (e.g., PBT in time-multiplexing mode). This
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requires trials to have the same resource requirements.
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Defaults to ``False``.
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trial_name_creator: Optional function that takes in a Trial and returns
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its name (i.e. its string representation). Be sure to include some unique
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identifier (such as `Trial.trial_id`) in each trial's name.
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NOTE: This API is in alpha and subject to change.
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trial_dirname_creator: Optional function that takes in a trial and
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generates its trial directory name as a string. Be sure to include some
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unique identifier (such as `Trial.trial_id`) is used in each trial's
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directory name. Otherwise, trials could overwrite artifacts and checkpoints
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of other trials. The return value cannot be a path.
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NOTE: This API is in alpha and subject to change.
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chdir_to_trial_dir: Deprecated. Set the `RAY_CHDIR_TO_TRIAL_DIR` env var instead
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"""
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# Currently this is not at feature parity with `tune.run`, nor should it be.
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# The goal is to reach a fine balance between API flexibility and conciseness.
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# We should carefully introduce arguments here instead of just dumping everything.
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mode: Optional[str] = None
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metric: Optional[str] = None
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search_alg: Optional[Union[Searcher, SearchAlgorithm]] = None
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scheduler: Optional[TrialScheduler] = None
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num_samples: int = 1
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max_concurrent_trials: Optional[int] = None
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time_budget_s: Optional[Union[int, float, datetime.timedelta]] = None
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reuse_actors: bool = False
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trial_name_creator: Optional[Callable[[Trial], str]] = None
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trial_dirname_creator: Optional[Callable[[Trial], str]] = None
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chdir_to_trial_dir: bool = _DEPRECATED_VALUE
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@DeveloperAPI
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@dataclass
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class ResumeConfig:
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"""[Experimental] This config is used to specify how to resume Tune trials."""
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class ResumeType(Enum):
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"""An enumeration to define resume types for various trial states.
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Members:
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RESUME: Resume from the latest checkpoint.
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RESTART: Restart from the beginning (with no checkpoint).
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SKIP: Skip this trial when resuming by treating it as terminated.
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"""
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RESUME = "resume"
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RESTART = "restart"
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SKIP = "skip"
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finished: str = ResumeType.SKIP
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unfinished: str = ResumeType.RESUME
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errored: str = ResumeType.SKIP
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