Source code for deephyper.evaluator._process_pool
import asyncio
import functools
import logging
from concurrent.futures import ProcessPoolExecutor
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 ProcessPoolEvaluator(Evaluator):
"""This evaluator uses the ``ProcessPoolExecutor`` as backend.
Args:
run_function (callable): functions to be executed by the ``Evaluator``.
num_workers (int, optional): Number of parallel processes 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: 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.sem = asyncio.Semaphore(num_workers)
# !creating the exector once here is crutial to avoid repetitive overheads
self.executor = ProcessPoolExecutor(max_workers=num_workers)
if hasattr(run_function, "__name__") and hasattr(run_function, "__module__"):
logger.info(
f"ProcessPool Evaluator will execute {self.run_function.__name__}() from module {self.run_function.__module__}"
)
else:
logger.info(f"ProcessPool Evaluator will execute {self.run_function}")
[docs] async def execute(self, job: Job) -> Job:
async with self.sem:
running_job = job.create_running_job(self._storage, self._stopper)
run_function = functools.partial(
job.run_function, running_job, **self.run_function_kwargs
)
output = await self.loop.run_in_executor(self.executor, run_function)
job.set_output(output)
return job