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 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__"): f"ProcessPool Evaluator will execute {self.run_function.__name__}() from module {self.run_function.__module__}" ) else:"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