You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
74 lines
2.8 KiB
Python
74 lines
2.8 KiB
Python
5 years ago
|
"""Utility function to construct a loky.ReusableExecutor with custom pickler.
|
||
|
|
|
||
|
|
This module provides efficient ways of working with data stored in
|
||
|
|
shared memory with numpy.memmap arrays without inducing any memory
|
||
|
|
copy between the parent and child processes.
|
||
|
|
"""
|
||
|
|
# Author: Thomas Moreau <thomas.moreau.2010@gmail.com>
|
||
|
|
# Copyright: 2017, Thomas Moreau
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
import random
|
||
|
|
from .disk import delete_folder
|
||
|
|
from ._memmapping_reducer import get_memmapping_reducers
|
||
|
|
from .externals.loky.reusable_executor import get_reusable_executor
|
||
|
|
|
||
|
|
|
||
|
|
_executor_args = None
|
||
|
|
|
||
|
|
|
||
|
|
def get_memmapping_executor(n_jobs, timeout=300, initializer=None, initargs=(),
|
||
|
|
env=None, **backend_args):
|
||
|
|
"""Factory for ReusableExecutor with automatic memmapping for large numpy
|
||
|
|
arrays.
|
||
|
|
"""
|
||
|
|
global _executor_args
|
||
|
|
# Check if we can reuse the executor here instead of deferring the test to
|
||
|
|
# loky as the reducers are objects that changes at each call.
|
||
|
|
executor_args = backend_args.copy()
|
||
|
|
executor_args.update(env if env else {})
|
||
|
|
executor_args.update(dict(
|
||
|
|
timeout=timeout, initializer=initializer, initargs=initargs))
|
||
|
|
reuse = _executor_args is None or _executor_args == executor_args
|
||
|
|
_executor_args = executor_args
|
||
|
|
|
||
|
|
id_executor = random.randint(0, int(1e10))
|
||
|
|
job_reducers, result_reducers, temp_folder = get_memmapping_reducers(
|
||
|
|
id_executor, **backend_args)
|
||
|
|
_executor = get_reusable_executor(n_jobs, job_reducers=job_reducers,
|
||
|
|
result_reducers=result_reducers,
|
||
|
|
reuse=reuse, timeout=timeout,
|
||
|
|
initializer=initializer,
|
||
|
|
initargs=initargs, env=env)
|
||
|
|
# If executor doesn't have a _temp_folder, it means it is a new executor
|
||
|
|
# and the reducers have been used. Else, the previous reducers are used
|
||
|
|
# and we should not change this attibute.
|
||
|
|
if not hasattr(_executor, "_temp_folder"):
|
||
|
|
_executor._temp_folder = temp_folder
|
||
|
|
else:
|
||
|
|
delete_folder(temp_folder)
|
||
|
|
return _executor
|
||
|
|
|
||
|
|
|
||
|
|
class _TestingMemmappingExecutor():
|
||
|
|
"""Wrapper around ReusableExecutor to ease memmapping testing with Pool
|
||
|
|
and Executor. This is only for testing purposes.
|
||
|
|
"""
|
||
|
|
def __init__(self, n_jobs, **backend_args):
|
||
|
|
self._executor = get_memmapping_executor(n_jobs, **backend_args)
|
||
|
|
self._temp_folder = self._executor._temp_folder
|
||
|
|
|
||
|
|
def apply_async(self, func, args):
|
||
|
|
"""Schedule a func to be run"""
|
||
|
|
future = self._executor.submit(func, *args)
|
||
|
|
future.get = future.result
|
||
|
|
return future
|
||
|
|
|
||
|
|
def terminate(self):
|
||
|
|
self._executor.shutdown()
|
||
|
|
delete_folder(self._temp_folder)
|
||
|
|
|
||
|
|
def map(self, f, *args):
|
||
|
|
res = self._executor.map(f, *args)
|
||
|
|
return list(res)
|