Source code for deephyper.evaluator._serial

import copy
import logging

from deephyper.evaluator._evaluator import Evaluator

ray_initializer = None

logger = logging.getLogger(__name__)


[docs]class SerialEvaluator(Evaluator): """This evaluator run evaluations one after the other (not parallel). Args: run_function (callable): functions to be executed by the ``Evaluator``. num_workers (int, optional): Number of parallel Ray-workers used to compute the ``run_function``. Defaults to 1. callbacks (list, optional): A list of callbacks to trigger custom actions at the creation or completion of jobs. Defaults to None. """ def __init__( self, run_function, num_workers: int = 1, callbacks: list = None, run_function_kwargs: dict = None, ): super().__init__(run_function, num_workers, callbacks, run_function_kwargs) self.num_workers = num_workers if hasattr(run_function, "__name__") and hasattr(run_function, "__module__"): logger.info( f"Serial Evaluator will execute {self.run_function.__name__}() from module {self.run_function.__module__}" ) else: logger.info(f"Serial Evaluator will execute {self.run_function}")
[docs] async def execute(self, job): sol = self.run_function(copy.deepcopy(job.config), **self.run_function_kwargs) job.result = sol return job