o cin@sddlZddlmZddlmZddlmZmZmZddl m Z ddl m Z ddl mZddlmZmZdd lmZmZeed d Gd d d ZeeGdddZdS)N) dataclass)Enum)CallableOptionalUnion)_DEPRECATED_VALUE)Trial)TrialScheduler)SearchAlgorithmSearcher) DeveloperAPI PublicAPIbeta) stabilityc@seZdZUdZdZeeed<dZeeed<dZ ee e e fed<dZ eeed<dZeed<dZeeed <dZee eeejfed <d Zeed <dZeeegefed <dZeeegefed<eZeed<dS) TuneConfiga 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 Nmodemetric search_alg scheduler num_samplesmax_concurrent_trials time_budget_sF reuse_actorstrial_name_creatortrial_dirname_creatorchdir_to_trial_dir)__name__ __module__ __qualname____doc__rrstr__annotations__rrrr r rr rintrrfloatdatetime timedeltarboolrrrrrrr(r(H/home/ubuntu/.local/lib/python3.10/site-packages/ray/tune/tune_config.pyr s 2  rc@sLeZdZUdZGdddeZejZee d<ej Z ee d<ejZ ee d<dS) ResumeConfigzH[Experimental] This config is used to specify how to resume Tune trials.c@seZdZdZdZdZdZdS)zResumeConfig.ResumeTypea%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. resumerestartskipN)rrrr RESUMERESTARTSKIPr(r(r(r) ResumeTypeTs r1finished unfinishederroredN) rrrr rr1r0r2r!r"r.r3r4r(r(r(r)r*Os  r*)r% dataclassesrenumrtypingrrrray.train.constantsrray.tune.experiment.trialrray.tune.schedulersr ray.tune.searchr r ray.util.annotationsr r rr*r(r(r(r)s     @