wehub-resource-sync
150 lines
4.2 KiB
Python
150 lines
4.2 KiB
Python
from ray._common.utils import get_function_args
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from ray.tune.search.basic_variant import BasicVariantGenerator
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from ray.tune.search.concurrency_limiter import ConcurrencyLimiter
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from ray.tune.search.repeater import Repeater
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from ray.tune.search.search_algorithm import SearchAlgorithm
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from ray.tune.search.search_generator import SearchGenerator
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from ray.tune.search.searcher import Searcher
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from ray.tune.search.variant_generator import grid_search
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from ray.util import PublicAPI
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def _import_variant_generator():
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return BasicVariantGenerator
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def _import_ax_search():
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from ray.tune.search.ax.ax_search import AxSearch
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return AxSearch
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def _import_hyperopt_search():
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from ray.tune.search.hyperopt.hyperopt_search import HyperOptSearch
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return HyperOptSearch
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def _import_bayesopt_search():
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from ray.tune.search.bayesopt.bayesopt_search import BayesOptSearch
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return BayesOptSearch
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def _import_bohb_search():
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from ray.tune.search.bohb.bohb_search import TuneBOHB
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return TuneBOHB
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def _import_nevergrad_search():
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from ray.tune.search.nevergrad.nevergrad_search import NevergradSearch
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return NevergradSearch
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def _import_optuna_search():
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from ray.tune.search.optuna.optuna_search import OptunaSearch
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return OptunaSearch
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def _import_zoopt_search():
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from ray.tune.search.zoopt.zoopt_search import ZOOptSearch
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return ZOOptSearch
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def _import_hebo_search():
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from ray.tune.search.hebo.hebo_search import HEBOSearch
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return HEBOSearch
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SEARCH_ALG_IMPORT = {
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"variant_generator": _import_variant_generator,
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"random": _import_variant_generator,
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"ax": _import_ax_search,
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"hyperopt": _import_hyperopt_search,
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"bayesopt": _import_bayesopt_search,
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"bohb": _import_bohb_search,
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"nevergrad": _import_nevergrad_search,
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"optuna": _import_optuna_search,
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"zoopt": _import_zoopt_search,
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"hebo": _import_hebo_search,
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}
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@PublicAPI(stability="beta")
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def create_searcher(
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search_alg: str,
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**kwargs,
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):
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"""Instantiate a search algorithm based on the given string.
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This is useful for swapping between different search algorithms.
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Args:
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search_alg: The search algorithm to use.
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**kwargs: Additional parameters (e.g. ``metric`` and ``mode``).
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These keyword arguments will be passed to the initialization
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function of the chosen class.
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Returns:
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ray.tune.search.Searcher: The search algorithm.
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Example:
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>>> from ray import tune # doctest: +SKIP
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>>> search_alg = tune.create_searcher('ax') # doctest: +SKIP
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"""
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search_alg = search_alg.lower()
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if search_alg not in SEARCH_ALG_IMPORT:
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raise ValueError(
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f"The `search_alg` argument must be one of "
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f"{list(SEARCH_ALG_IMPORT)}. "
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f"Got: {search_alg}"
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)
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SearcherClass = SEARCH_ALG_IMPORT[search_alg]()
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search_alg_args = get_function_args(SearcherClass)
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trimmed_kwargs = {k: v for k, v in kwargs.items() if k in search_alg_args}
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return SearcherClass(**trimmed_kwargs)
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UNRESOLVED_SEARCH_SPACE = str(
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"You passed a `{par}` parameter to {cls} that contained unresolved search "
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"space definitions. {cls} should however be instantiated with fully "
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"configured search spaces only. To use Ray Tune's automatic search space "
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"conversion, pass the space definition as part of the `param_space` argument "
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"to `tune.Tuner()` instead."
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)
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UNDEFINED_SEARCH_SPACE = str(
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"Trying to sample a configuration from {cls}, but no search "
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"space has been defined. Either pass the `{space}` argument when "
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"instantiating the search algorithm, or pass a `param_space` to "
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"`tune.Tuner()`."
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)
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UNDEFINED_METRIC_MODE = str(
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"Trying to sample a configuration from {cls}, but the `metric` "
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"({metric}) or `mode` ({mode}) parameters have not been set. "
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"Either pass these arguments when instantiating the search algorithm, "
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"or pass them to `tune.TuneConfig()`."
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)
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__all__ = [
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"SearchAlgorithm",
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"Searcher",
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"ConcurrencyLimiter",
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"Repeater",
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"BasicVariantGenerator",
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"grid_search",
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"SearchGenerator",
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"UNRESOLVED_SEARCH_SPACE",
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"UNDEFINED_SEARCH_SPACE",
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"UNDEFINED_METRIC_MODE",
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]
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