286 lines
10 KiB
Python
286 lines
10 KiB
Python
###############################################################################
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# Reusable ProcessPoolExecutor
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#
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# author: Thomas Moreau and Olivier Grisel
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#
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import time
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import warnings
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import threading
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import multiprocessing as mp
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from .process_executor import ProcessPoolExecutor, EXTRA_QUEUED_CALLS
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from .backend.context import cpu_count
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from .backend import get_context
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__all__ = ["get_reusable_executor"]
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# Singleton executor and id management
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_executor_lock = threading.RLock()
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_next_executor_id = 0
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_executor = None
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_executor_kwargs = None
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def _get_next_executor_id():
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"""Ensure that each successive executor instance has a unique, monotonic id.
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The purpose of this monotonic id is to help debug and test automated
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instance creation.
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"""
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global _next_executor_id
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with _executor_lock:
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executor_id = _next_executor_id
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_next_executor_id += 1
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return executor_id
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def get_reusable_executor(
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max_workers=None,
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context=None,
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timeout=10,
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kill_workers=False,
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reuse="auto",
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job_reducers=None,
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result_reducers=None,
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initializer=None,
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initargs=(),
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env=None,
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):
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"""Return the current ReusableExectutor instance.
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Start a new instance if it has not been started already or if the previous
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instance was left in a broken state.
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If the previous instance does not have the requested number of workers, the
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executor is dynamically resized to adjust the number of workers prior to
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returning.
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Reusing a singleton instance spares the overhead of starting new worker
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processes and importing common python packages each time.
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``max_workers`` controls the maximum number of tasks that can be running in
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parallel in worker processes. By default this is set to the number of
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CPUs on the host.
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Setting ``timeout`` (in seconds) makes idle workers automatically shutdown
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so as to release system resources. New workers are respawn upon submission
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of new tasks so that ``max_workers`` are available to accept the newly
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submitted tasks. Setting ``timeout`` to around 100 times the time required
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to spawn new processes and import packages in them (on the order of 100ms)
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ensures that the overhead of spawning workers is negligible.
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Setting ``kill_workers=True`` makes it possible to forcibly interrupt
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previously spawned jobs to get a new instance of the reusable executor
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with new constructor argument values.
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The ``job_reducers`` and ``result_reducers`` are used to customize the
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pickling of tasks and results send to the executor.
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When provided, the ``initializer`` is run first in newly spawned
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processes with argument ``initargs``.
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The environment variable in the child process are a copy of the values in
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the main process. One can provide a dict ``{ENV: VAL}`` where ``ENV`` and
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``VAL`` are string literals to overwrite the environment variable ``ENV``
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in the child processes to value ``VAL``. The environment variables are set
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in the children before any module is loaded. This only works with the
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``loky`` context.
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"""
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_executor, _ = _ReusablePoolExecutor.get_reusable_executor(
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max_workers=max_workers,
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context=context,
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timeout=timeout,
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kill_workers=kill_workers,
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reuse=reuse,
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job_reducers=job_reducers,
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result_reducers=result_reducers,
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initializer=initializer,
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initargs=initargs,
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env=env,
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)
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return _executor
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class _ReusablePoolExecutor(ProcessPoolExecutor):
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def __init__(
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self,
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submit_resize_lock,
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max_workers=None,
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context=None,
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timeout=None,
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executor_id=0,
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job_reducers=None,
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result_reducers=None,
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initializer=None,
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initargs=(),
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env=None,
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):
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super().__init__(
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max_workers=max_workers,
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context=context,
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timeout=timeout,
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job_reducers=job_reducers,
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result_reducers=result_reducers,
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initializer=initializer,
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initargs=initargs,
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env=env,
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)
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self.executor_id = executor_id
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self._submit_resize_lock = submit_resize_lock
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@classmethod
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def get_reusable_executor(
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cls,
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max_workers=None,
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context=None,
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timeout=10,
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kill_workers=False,
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reuse="auto",
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job_reducers=None,
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result_reducers=None,
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initializer=None,
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initargs=(),
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env=None,
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):
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with _executor_lock:
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global _executor, _executor_kwargs
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executor = _executor
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if max_workers is None:
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if reuse is True and executor is not None:
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max_workers = executor._max_workers
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else:
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max_workers = cpu_count()
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elif max_workers <= 0:
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raise ValueError(
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f"max_workers must be greater than 0, got {max_workers}."
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)
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if isinstance(context, str):
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context = get_context(context)
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if context is not None and context.get_start_method() == "fork":
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raise ValueError(
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"Cannot use reusable executor with the 'fork' context"
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)
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kwargs = dict(
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context=context,
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timeout=timeout,
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job_reducers=job_reducers,
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result_reducers=result_reducers,
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initializer=initializer,
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initargs=initargs,
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env=env,
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)
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if executor is None:
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is_reused = False
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mp.util.debug(
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f"Create a executor with max_workers={max_workers}."
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)
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executor_id = _get_next_executor_id()
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_executor_kwargs = kwargs
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_executor = executor = cls(
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_executor_lock,
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max_workers=max_workers,
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executor_id=executor_id,
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**kwargs,
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)
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else:
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if reuse == "auto":
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reuse = kwargs == _executor_kwargs
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if (
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executor._flags.broken
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or executor._flags.shutdown
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or not reuse
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):
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if executor._flags.broken:
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reason = "broken"
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elif executor._flags.shutdown:
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reason = "shutdown"
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else:
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reason = "arguments have changed"
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mp.util.debug(
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"Creating a new executor with max_workers="
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f"{max_workers} as the previous instance cannot be "
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f"reused ({reason})."
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)
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executor.shutdown(wait=True, kill_workers=kill_workers)
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_executor = executor = _executor_kwargs = None
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# Recursive call to build a new instance
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return cls.get_reusable_executor(
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max_workers=max_workers, **kwargs
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)
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else:
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mp.util.debug(
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"Reusing existing executor with "
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f"max_workers={executor._max_workers}."
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)
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is_reused = True
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executor._resize(max_workers)
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return executor, is_reused
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def submit(self, fn, *args, **kwargs):
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with self._submit_resize_lock:
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return super().submit(fn, *args, **kwargs)
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def _resize(self, max_workers):
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with self._submit_resize_lock:
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if max_workers is None:
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raise ValueError("Trying to resize with max_workers=None")
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elif max_workers == self._max_workers:
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return
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if self._executor_manager_thread is None:
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# If the executor_manager_thread has not been started
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# then no processes have been spawned and we can just
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# update _max_workers and return
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self._max_workers = max_workers
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return
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self._wait_job_completion()
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# Some process might have returned due to timeout so check how many
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# children are still alive. Use the _process_management_lock to
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# ensure that no process are spawned or timeout during the resize.
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with self._processes_management_lock:
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processes = list(self._processes.values())
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nb_children_alive = sum(p.is_alive() for p in processes)
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self._max_workers = max_workers
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for _ in range(max_workers, nb_children_alive):
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self._call_queue.put(None)
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while (
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len(self._processes) > max_workers and not self._flags.broken
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):
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time.sleep(1e-3)
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self._adjust_process_count()
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processes = list(self._processes.values())
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while not all(p.is_alive() for p in processes):
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time.sleep(1e-3)
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def _wait_job_completion(self):
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"""Wait for the cache to be empty before resizing the pool."""
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# Issue a warning to the user about the bad effect of this usage.
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if self._pending_work_items:
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warnings.warn(
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"Trying to resize an executor with running jobs: "
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"waiting for jobs completion before resizing.",
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UserWarning,
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)
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mp.util.debug(
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f"Executor {self.executor_id} waiting for jobs completion "
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"before resizing"
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)
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# Wait for the completion of the jobs
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while self._pending_work_items:
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time.sleep(1e-3)
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def _setup_queues(self, job_reducers, result_reducers):
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# As this executor can be resized, use a large queue size to avoid
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# underestimating capacity and introducing overhead
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queue_size = 2 * cpu_count() + EXTRA_QUEUED_CALLS
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super()._setup_queues(
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job_reducers, result_reducers, queue_size=queue_size
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)
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