ray.tune.TuneConfig#

class ray.tune.TuneConfig(mode: str | None = None, metric: str | None = None, search_alg: Searcher | SearchAlgorithm | None = None, scheduler: TrialScheduler | None = None, num_samples: int = 1, max_concurrent_trials: int | None = None, time_budget_s: int | float | timedelta | None = None, reuse_actors: bool = False, trial_name_creator: Callable[[Trial], str] | None = None, trial_dirname_creator: Callable[[Trial], str] | None = None, chdir_to_trial_dir: bool = 'DEPRECATED')[source]#

Tune specific configs.

Parameters:

  • 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 ConcurrencyLimiter, and thus setting this argument will raise an exception if the search_alg is already a 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

PublicAPI (beta): This API is in beta and may change before becoming stable.

Methods

Attributes

chdir_to_trial_dir

max_concurrent_trials

metric

mode

num_samples

reuse_actors

scheduler

search_alg

time_budget_s

trial_dirname_creator

trial_name_creator