syne_tune.optimizer.schedulers.fifo module

class syne_tune.optimizer.schedulers.fifo.FIFOScheduler(config_space, **kwargs)

Bases: TrialSchedulerWithSearcher

Scheduler which executes trials in submission order.

This is the most basic scheduler template. It can be configured to many use cases by choosing searcher along with search_options.

Parameters:

• config_space (Dict[str, Any]) – Configuration space for evaluation function

• searcher (str or BaseSearcher) – Searcher for get_config decisions. String values are passed to searcher_factory() along with search_options and extra information. Supported values: SUPPORTED_SEARCHERS_FIFO. Defaults to “random” (i.e., random search)

• search_options (Dict[str, Any], optional) – If searcher is str, these arguments are passed to searcher_factory()

• metric (str or List[str]) – Name of metric to optimize, key in results obtained via on_trial_result. For multi-objective schedulers, this can also be a list

• mode (str or List[str], optional) – “min” if metric is minimized, “max” if metric is maximized, defaults to “min”. This can also be a list if metric is a list

• points_to_evaluate (List[dict], optional) – List of configurations to be evaluated initially (in that order). Each config in the list can be partially specified, or even be an empty dict. For each hyperparameter not specified, the default value is determined using a midpoint heuristic. If not given, this is mapped to [dict()], a single default config determined by the midpoint heuristic. If [] (empty list), no initial configurations are specified. Note: If searcher is of type BaseSearcher, points_to_evaluate must be set there.

• random_seed (int, optional) – Master random seed. Generators used in the scheduler or searcher are seeded using RandomSeedGenerator. If not given, the master random seed is drawn at random here.

• max_resource_attr (str, optional) – Key name in config for fixed attribute containing the maximum resource. If this is given, max_t is not needed. We recommend to use max_resource_attr over max_t. If given, we use it to infer max_resource_level. It is also used to limit trial executions in promotion-based multi-fidelity schedulers (see class:HyperbandScheduler, type="promotion").

• max_t (int, optional) – Value for max_resource_level. Needed for schedulers which make use of intermediate reports via on_trial_result. If this is not given, we try to infer its value from config_space (see ResourceLevelsScheduler). checking config_space["epochs"], config_space["max_t"], and config_space["max_epochs"]. If max_resource_attr is given, we use the value config_space[max_resource_attr]. But if max_t is given here, it takes precedence.

• time_keeper (TimeKeeper, optional) – This will be used for timing here (see _elapsed_time). The time keeper has to be started at the beginning of the experiment. If not given, we use a local time keeper here, which is started with the first call to _suggest(). Can also be set after construction, with set_time_keeper(). Note: If you use SimulatorBackend, you need to pass its time_keeper here.

property searcher: BaseSearcher | None

set_time_keeper(time_keeper)

Assign time keeper after construction.

This is possible only if the time keeper was not assigned at construction, and the experiment has not yet started.

Parameters:

time_keeper (TimeKeeper) – Time keeper to be used

on_trial_result(trial, result)

We simply relay result to the searcher. Other decisions are done in on_trial_complete.

Return type:

str

metric_names()

Return type:

List[str]

Returns:

List of metric names. The first one is the target metric optimized over, unless the scheduler is a genuine multi-objective metric (for example, for sampling the Pareto front)

metric_mode()

Return type:

Union[str, List[str]]

Returns:

“min” if target metric is minimized, otherwise “max”. Here, “min” should be the default. For a genuine multi-objective scheduler, a list of modes is returned

is_multiobjective_scheduler()

Return True if a scheduler is multi-objective.

Return type:

bool