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