Source code for deephyper.evaluator._serial
import logging
from typing import Callable, Hashable
from deephyper.evaluator._evaluator import Evaluator
from deephyper.evaluator._job import Job
from deephyper.evaluator.storage import Storage
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.
run_function_kwargs (dict, optional): Static keyword arguments to pass to the ``run_function`` when executed.
storage (Storage, optional): Storage used by the evaluator. Defaults to ``MemoryStorage``.
search_id (Hashable, optional): The id of the search to use in the corresponding storage. If ``None`` it will create a new search identifier when initializing the search.
"""
def __init__(
self,
run_function: Callable,
num_workers: int = 1,
callbacks: list = None,
run_function_kwargs: dict = None,
storage: Storage = None,
search_id: Hashable = None,
):
super().__init__(
run_function=run_function,
num_workers=num_workers,
callbacks=callbacks,
run_function_kwargs=run_function_kwargs,
storage=storage,
search_id=search_id,
)
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: Job) -> Job:
running_job = job.create_running_job(self._storage, self._stopper)
output = self.run_function(running_job, **self.run_function_kwargs)
job.set_output(output)
return job