def create_scheduler(
scheduler,
**kwargs,
):
"""Instantiate a scheduler based on the given string.
This is useful for swapping between different schedulers.
Args:
scheduler (str): The scheduler to use.
**kwargs: Scheduler parameters.
These keyword arguments will be passed to the initialization
function of the chosen scheduler.
Returns:
ray.tune.schedulers.trial_scheduler.TrialScheduler: The scheduler.
Example:
>>> scheduler = tune.create_scheduler('pbt', **pbt_kwargs)
"""
scheduler = scheduler.lower()
if scheduler not in SCHEDULER_IMPORT:
raise ValueError(f"The `scheduler` argument must be one of "
f"{list(SCHEDULER_IMPORT)}. "
f"Got: {scheduler}")
SchedulerClass = SCHEDULER_IMPORT[scheduler]
scheduler_args = get_function_args(SchedulerClass)
trimmed_kwargs = {k: v for k, v in kwargs.items() if k in scheduler_args}
return SchedulerClass(**trimmed_kwargs)
def create_searcher(
search_alg,
**kwargs,
):
"""Instantiate a search algorithm based on the given string.
This is useful for swapping between different search algorithms.
Args:
search_alg (str): The search algorithm to use.
metric (str): The training result objective value attribute. Stopping
procedures will use this attribute.
mode (str): One of {min, max}. Determines whether objective is
minimizing or maximizing the metric attribute.
**kwargs: Additional parameters.
These keyword arguments will be passed to the initialization
function of the chosen class.
Returns:
ray.tune.suggest.Searcher: The search algorithm.
Example:
>>> search_alg = tune.create_searcher('ax')
"""
search_alg = search_alg.lower()
if search_alg not in SEARCH_ALG_IMPORT:
raise ValueError(f"The `search_alg` argument must be one of "
f"{list(SEARCH_ALG_IMPORT)}. "
f"Got: {search_alg}")
SearcherClass = SEARCH_ALG_IMPORT[search_alg]()
search_alg_args = get_function_args(SearcherClass)
trimmed_kwargs = {k: v for k, v in kwargs.items() if k in search_alg_args}
return SearcherClass(**trimmed_kwargs)
def create_scheduler(
scheduler,
**kwargs,
):
"""Instantiate a scheduler based on the given string.
This is useful for swapping between different schedulers.
Args:
scheduler (str): The scheduler to use.
**kwargs: Scheduler parameters.
These keyword arguments will be passed to the initialization
function of the chosen scheduler.
Returns:
ray.tune.schedulers.trial_scheduler.TrialScheduler: The scheduler.
Example:
>>> scheduler = tune.create_scheduler('pbt', **pbt_kwargs)
"""
SCHEDULER_IMPORT = {
"fifo": FIFOScheduler,
"async_hyperband": AsyncHyperBandScheduler,
"asynchyperband": AsyncHyperBandScheduler,
"median_stopping_rule": MedianStoppingRule,
"medianstopping": MedianStoppingRule,
"hyperband": HyperBandScheduler,
"hb_bohb": HyperBandForBOHB,
"pbt": PopulationBasedTraining,
"pbt_replay": PopulationBasedTrainingReplay,
"pb2": _pb2_importer,
}
scheduler = scheduler.lower()
if scheduler not in SCHEDULER_IMPORT:
raise ValueError(
f"Search alg must be one of {list(SCHEDULER_IMPORT)}. "
f"Got: {scheduler}")
SchedulerClass = SCHEDULER_IMPORT[scheduler]
scheduler_args = get_function_args(SchedulerClass)
trimmed_kwargs = {k: v for k, v in kwargs.items() if k in scheduler_args}
return SchedulerClass(**trimmed_kwargs)
def create_searcher(
search_alg,
**kwargs,
):
"""Instantiate a search algorithm based on the given string.
This is useful for swapping between different search algorithms.
Args:
search_alg (str): The search algorithm to use.
metric (str): The training result objective value attribute. Stopping
procedures will use this attribute.
mode (str): One of {min, max}. Determines whether objective is
minimizing or maximizing the metric attribute.
**kwargs: Additional parameters.
These keyword arguments will be passed to the initialization
function of the chosen class.
Returns:
ray.tune.suggest.Searcher: The search algorithm.
Example:
>>> search_alg = tune.create_searcher('ax')
"""
def _import_variant_generator():
return BasicVariantGenerator
def _import_ax_search():
from ray.tune.suggest.ax import AxSearch
return AxSearch
def _import_dragonfly_search():
from ray.tune.suggest.dragonfly import DragonflySearch
return DragonflySearch
def _import_skopt_search():
from ray.tune.suggest.skopt import SkOptSearch
return SkOptSearch
def _import_hyperopt_search():
from ray.tune.suggest.hyperopt import HyperOptSearch
return HyperOptSearch
def _import_bayesopt_search():
from ray.tune.suggest.bayesopt import BayesOptSearch
return BayesOptSearch
def _import_bohb_search():
from ray.tune.suggest.bohb import TuneBOHB
return TuneBOHB
def _import_nevergrad_search():
from ray.tune.suggest.nevergrad import NevergradSearch
return NevergradSearch
def _import_optuna_search():
from ray.tune.suggest.optuna import OptunaSearch
return OptunaSearch
def _import_zoopt_search():
from ray.tune.suggest.zoopt import ZOOptSearch
return ZOOptSearch
def _import_sigopt_search():
from ray.tune.suggest.sigopt import SigOptSearch
return SigOptSearch
SEARCH_ALG_IMPORT = {
"variant_generator": _import_variant_generator,
"random": _import_variant_generator,
"ax": _import_ax_search,
"dragonfly": _import_dragonfly_search,
"skopt": _import_skopt_search,
"hyperopt": _import_hyperopt_search,
"bayesopt": _import_bayesopt_search,
"bohb": _import_bohb_search,
"nevergrad": _import_nevergrad_search,
"optuna": _import_optuna_search,
"zoopt": _import_zoopt_search,
"sigopt": _import_sigopt_search,
}
search_alg = search_alg.lower()
if search_alg not in SEARCH_ALG_IMPORT:
raise ValueError(
f"Search alg must be one of {list(SEARCH_ALG_IMPORT)}. "
f"Got: {search_alg}")
SearcherClass = SEARCH_ALG_IMPORT[search_alg]()
search_alg_args = get_function_args(SearcherClass)
trimmed_kwargs = {k: v for k, v in kwargs.items() if k in search_alg_args}
return SearcherClass(**trimmed_kwargs)