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