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Follow requirement to allow finalizers to run #108

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Follow requirement to allow finalizers to run #108

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31 changes: 16 additions & 15 deletions cleanlab/pruning.py
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Expand Up @@ -29,6 +29,7 @@
from sklearn.preprocessing import MultiLabelBinarizer
import multiprocessing
from multiprocessing.sharedctypes import RawArray
from contextlib import contextmanager
import sys
import os
import time
Expand Down Expand Up @@ -56,19 +57,19 @@
# pruning, regardless if noise estimates are larger.
MIN_NUM_PER_CLASS = 5

# For python 2/3 compatibility, define pool context manager
# to support the 'with' statement in Python 2
if sys.version_info[0] == 2:
from contextlib import contextmanager


@contextmanager
def multiprocessing_context(*args, **kwargs):
pool = multiprocessing.Pool(*args, **kwargs)
yield pool
pool.terminate()
else:
multiprocessing_context = multiprocessing.Pool
# Coverage testing registers finalizers that don't run if
# `multiprocessing.Pool.terminate()` is called
# (`multiprocessing.Pool.__exit__()` also calls `terminate()`), so we use this
# alternative context manager that calls `join()` to ensure finalizers are run
# reliably.
#
# See https://pytest-cov.readthedocs.io/en/latest/subprocess-support.html#if-you-use-multiprocessing-pool
@contextmanager
def joining_pool(*args, **kwargs):
pool = multiprocessing.Pool(*args, **kwargs)
yield pool
pool.close()
pool.join()

# Globals to be shared across threads in multiprocessing
mp_params = {} # parameters passed to multiprocessing helper functions
Expand Down Expand Up @@ -392,7 +393,7 @@ def get_noise_indices(
# Operations are parallelized across all CPU processes
if prune_method == 'prune_by_class' or prune_method == 'both':
if n_jobs > 1: # parallelize
with multiprocessing_context(
with joining_pool(
n_jobs,
initializer=_init,
initargs=(_s, _s_counts, _prune_count_matrix,
Expand All @@ -417,7 +418,7 @@ def get_noise_indices(

if prune_method == 'prune_by_noise_rate' or prune_method == 'both':
if n_jobs > 1: # parallelize
with multiprocessing_context(
with joining_pool(
n_jobs,
initializer=_init,
initargs=(_s, _s_counts, _prune_count_matrix,
Expand Down