/usr/lib/python2.7/dist-packages/joblib/test/test_parallel.py is in python-joblib 0.10.3+git55-g660fe5d-1.
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Test the parallel module.
"""
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# Copyright (c) 2010-2011 Gael Varoquaux
# License: BSD Style, 3 clauses.
import time
import sys
import io
import os
from math import sqrt
import threading
import warnings
from multiprocessing import TimeoutError
from time import sleep
from joblib import parallel
from joblib.test.common import np, with_numpy
from joblib.test.common import with_multiprocessing
from joblib.testing import (assert_equal, assert_raises, check_subprocess_call,
SkipTest, skipif)
from joblib._compat import PY3_OR_LATER
try:
import cPickle as pickle
PickleError = TypeError
except ImportError:
import pickle
PickleError = pickle.PicklingError
if PY3_OR_LATER:
PickleError = pickle.PicklingError
try:
# Python 2/Python 3 compat
unicode('str')
except NameError:
unicode = lambda s: s
try:
from queue import Queue
except ImportError:
# Backward compat
from Queue import Queue
try:
import posix
except ImportError:
posix = None
from joblib._parallel_backends import SequentialBackend
from joblib._parallel_backends import ThreadingBackend
from joblib._parallel_backends import MultiprocessingBackend
from joblib._parallel_backends import SafeFunction
from joblib._parallel_backends import WorkerInterrupt
from joblib.parallel import Parallel, delayed
from joblib.parallel import register_parallel_backend, parallel_backend
from joblib.parallel import mp, cpu_count, BACKENDS, effective_n_jobs
from joblib.my_exceptions import JoblibException
ALL_VALID_BACKENDS = [None] + sorted(BACKENDS.keys())
if hasattr(mp, 'get_context'):
# Custom multiprocessing context in Python 3.4+
ALL_VALID_BACKENDS.append(mp.get_context('spawn'))
def division(x, y):
return x / y
def square(x):
return x ** 2
class MyExceptionWithFinickyInit(Exception):
"""An exception class with non trivial __init__
"""
def __init__(self, a, b, c, d):
pass
def exception_raiser(x, custom_exception=False):
if x == 7:
raise (MyExceptionWithFinickyInit('a', 'b', 'c', 'd')
if custom_exception else ValueError)
return x
def interrupt_raiser(x):
time.sleep(.05)
raise KeyboardInterrupt
def f(x, y=0, z=0):
""" A module-level function so that it can be spawn with
multiprocessing.
"""
return x ** 2 + y + z
def _active_backend_type():
return type(parallel.get_active_backend()[0])
###############################################################################
def test_cpu_count():
assert cpu_count() > 0
def test_effective_n_jobs():
assert effective_n_jobs() > 0
###############################################################################
# Test parallel
def check_simple_parallel(backend):
X = range(5)
for n_jobs in (1, 2, -1, -2):
assert ([square(x) for x in X] ==
Parallel(n_jobs=n_jobs, backend=backend)(
delayed(square)(x) for x in X))
try:
# To smoke-test verbosity, we capture stdout
orig_stdout = sys.stdout
orig_stderr = sys.stdout
if PY3_OR_LATER:
sys.stderr = io.StringIO()
sys.stderr = io.StringIO()
else:
sys.stdout = io.BytesIO()
sys.stderr = io.BytesIO()
for verbose in (2, 11, 100):
Parallel(n_jobs=-1, verbose=verbose, backend=backend)(
delayed(square)(x) for x in X)
Parallel(n_jobs=1, verbose=verbose, backend=backend)(
delayed(square)(x) for x in X)
Parallel(n_jobs=2, verbose=verbose, pre_dispatch=2,
backend=backend)(
delayed(square)(x) for x in X)
Parallel(n_jobs=2, verbose=verbose, backend=backend)(
delayed(square)(x) for x in X)
except Exception as e:
my_stdout = sys.stdout
my_stderr = sys.stderr
sys.stdout = orig_stdout
sys.stderr = orig_stderr
print(unicode(my_stdout.getvalue()))
print(unicode(my_stderr.getvalue()))
raise e
finally:
sys.stdout = orig_stdout
sys.stderr = orig_stderr
def test_simple_parallel():
for backend in ALL_VALID_BACKENDS:
yield check_simple_parallel, backend
def check_main_thread_renamed_no_warning(backend):
with warnings.catch_warnings(record=True) as caught_warnings:
warnings.simplefilter("always")
results = Parallel(n_jobs=2, backend=backend)(
delayed(square)(x) for x in range(3))
assert results == [0, 1, 4]
# The multiprocessing backend will raise a warning when detecting that is
# started from the non-main thread. Let's check that there is no false
# positive because of the name change.
assert caught_warnings == []
def test_main_thread_renamed_no_warning():
# Check that no default backend relies on the name of the main thread:
# https://github.com/joblib/joblib/issues/180#issuecomment-253266247
# Some programs use a different name for the main thread. This is the case
# for uWSGI apps for instance.
main_thread = threading.current_thread()
original_name = main_thread.name
try:
main_thread.name = "some_new_name_for_the_main_thread"
for backend in ALL_VALID_BACKENDS:
yield check_main_thread_renamed_no_warning, backend
finally:
main_thread.name = original_name
def nested_loop(backend):
Parallel(n_jobs=2, backend=backend)(
delayed(square)(.01) for _ in range(2))
def check_nested_loop(parent_backend, child_backend):
Parallel(n_jobs=2, backend=parent_backend)(
delayed(nested_loop)(child_backend) for _ in range(2))
def test_nested_loop():
for parent_backend in BACKENDS:
for child_backend in BACKENDS:
yield check_nested_loop, parent_backend, child_backend
def test_mutate_input_with_threads():
"""Input is mutable when using the threading backend"""
q = Queue(maxsize=5)
Parallel(n_jobs=2, backend="threading")(
delayed(q.put, check_pickle=False)(1) for _ in range(5))
assert q.full()
def test_parallel_kwargs():
"""Check the keyword argument processing of pmap."""
lst = range(10)
for n_jobs in (1, 4):
yield (assert_equal,
[f(x, y=1) for x in lst],
Parallel(n_jobs=n_jobs)(delayed(f)(x, y=1) for x in lst))
def check_parallel_as_context_manager(backend):
lst = range(10)
expected = [f(x, y=1) for x in lst]
with Parallel(n_jobs=4, backend=backend) as p:
# Internally a pool instance has been eagerly created and is managed
# via the context manager protocol
managed_backend = p._backend
if mp is not None:
assert managed_backend is not None
assert managed_backend._pool is not None
# We make call with the managed parallel object several times inside
# the managed block:
assert expected == p(delayed(f)(x, y=1) for x in lst)
assert expected == p(delayed(f)(x, y=1) for x in lst)
# Those calls have all used the same pool instance:
if mp is not None:
assert managed_backend._pool is p._backend._pool
# As soon as we exit the context manager block, the pool is terminated and
# no longer referenced from the parallel object:
if mp is not None:
assert p._backend._pool is None
# It's still possible to use the parallel instance in non-managed mode:
assert expected == p(delayed(f)(x, y=1) for x in lst)
if mp is not None:
assert p._backend._pool is None
def test_parallel_context_manager():
for backend in ['multiprocessing', 'threading']:
yield check_parallel_as_context_manager, backend
def test_parallel_pickling():
""" Check that pmap captures the errors when it is passed an object
that cannot be pickled.
"""
def g(x):
return x ** 2
try:
# pickling a local function always fail but the exception
# raised is a PickleError for python <= 3.4 and AttributeError
# for python >= 3.5
pickle.dumps(g)
except Exception as exc:
exception_class = exc.__class__
assert_raises(exception_class, Parallel(),
(delayed(g)(x) for x in range(10)))
def test_parallel_timeout_success():
# Check that timeout isn't thrown when function is fast enough
for backend in ['multiprocessing', 'threading']:
assert len(Parallel(n_jobs=2, backend=backend, timeout=10)(
delayed(sleep)(0.001) for x in range(10))) == 10
@with_multiprocessing
def test_parallel_timeout_fail():
# Check that timeout properly fails when function is too slow
for backend in ['multiprocessing', 'threading']:
assert_raises(TimeoutError,
Parallel(n_jobs=2, backend=backend, timeout=0.01),
(delayed(sleep)(10) for x in range(10)))
def test_error_capture():
# Check that error are captured, and that correct exceptions
# are raised.
if mp is not None:
# A JoblibException will be raised only if there is indeed
# multiprocessing
assert_raises(JoblibException, Parallel(n_jobs=2),
[delayed(division)(x, y)
for x, y in zip((0, 1), (1, 0))])
assert_raises(WorkerInterrupt, Parallel(n_jobs=2),
[delayed(interrupt_raiser)(x) for x in (1, 0)])
# Try again with the context manager API
with Parallel(n_jobs=2) as parallel:
assert parallel._backend._pool is not None
original_pool = parallel._backend._pool
assert_raises(JoblibException, parallel,
[delayed(division)(x, y)
for x, y in zip((0, 1), (1, 0))])
# The managed pool should still be available and be in a working
# state despite the previously raised (and caught) exception
assert parallel._backend._pool is not None
# The pool should have been interrupted and restarted:
assert parallel._backend._pool is not original_pool
assert ([f(x, y=1) for x in range(10)] ==
parallel(delayed(f)(x, y=1) for x in range(10)))
original_pool = parallel._backend._pool
assert_raises(WorkerInterrupt, parallel,
[delayed(interrupt_raiser)(x) for x in (1, 0)])
# The pool should still be available despite the exception
assert parallel._backend._pool is not None
# The pool should have been interrupted and restarted:
assert parallel._backend._pool is not original_pool
assert ([f(x, y=1) for x in range(10)] ==
parallel(delayed(f)(x, y=1) for x in range(10)))
# Check that the inner pool has been terminated when exiting the
# context manager
assert parallel._backend._pool is None
else:
assert_raises(KeyboardInterrupt, Parallel(n_jobs=2),
[delayed(interrupt_raiser)(x) for x in (1, 0)])
# wrapped exceptions should inherit from the class of the original
# exception to make it easy to catch them
assert_raises(ZeroDivisionError, Parallel(n_jobs=2),
[delayed(division)(x, y) for x, y in zip((0, 1), (1, 0))])
assert_raises(
MyExceptionWithFinickyInit,
Parallel(n_jobs=2, verbose=0),
(delayed(exception_raiser)(i, custom_exception=True)
for i in range(30)))
try:
# JoblibException wrapping is disabled in sequential mode:
ex = JoblibException()
Parallel(n_jobs=1)(
delayed(division)(x, y) for x, y in zip((0, 1), (1, 0)))
except Exception as ex:
assert not isinstance(ex, JoblibException)
class Counter(object):
def __init__(self, list1, list2):
self.list1 = list1
self.list2 = list2
def __call__(self, i):
self.list1.append(i)
assert len(self.list1) == len(self.list2)
def consumer(queue, item):
queue.append('Consumed %s' % item)
def check_dispatch_one_job(backend):
""" Test that with only one job, Parallel does act as a iterator.
"""
queue = list()
def producer():
for i in range(6):
queue.append('Produced %i' % i)
yield i
# disable batching
Parallel(n_jobs=1, batch_size=1, backend=backend)(
delayed(consumer)(queue, x) for x in producer())
assert_equal(queue, [
'Produced 0', 'Consumed 0',
'Produced 1', 'Consumed 1',
'Produced 2', 'Consumed 2',
'Produced 3', 'Consumed 3',
'Produced 4', 'Consumed 4',
'Produced 5', 'Consumed 5',
])
assert len(queue) == 12
# empty the queue for the next check
queue[:] = []
# enable batching
Parallel(n_jobs=1, batch_size=4, backend=backend)(
delayed(consumer)(queue, x) for x in producer())
assert_equal(queue, [
# First batch
'Produced 0', 'Produced 1', 'Produced 2', 'Produced 3',
'Consumed 0', 'Consumed 1', 'Consumed 2', 'Consumed 3',
# Second batch
'Produced 4', 'Produced 5', 'Consumed 4', 'Consumed 5',
])
assert len(queue) == 12
def test_dispatch_one_job():
for backend in BACKENDS:
yield check_dispatch_one_job, backend
def check_dispatch_multiprocessing(backend):
""" Check that using pre_dispatch Parallel does indeed dispatch items
lazily.
"""
if mp is None:
raise SkipTest()
manager = mp.Manager()
queue = manager.list()
def producer():
for i in range(6):
queue.append('Produced %i' % i)
yield i
Parallel(n_jobs=2, batch_size=1, pre_dispatch=3, backend=backend)(
delayed(consumer)(queue, 'any') for _ in producer())
# Only 3 tasks are dispatched out of 6. The 4th task is dispatched only
# after any of the first 3 jobs have completed.
first_four = list(queue)[:4]
# The the first consumption event can sometimes happen before the end of
# the dispatching, hence, pop it before introspecting the "Produced" events
first_four.remove('Consumed any')
assert_equal(first_four,
['Produced 0', 'Produced 1', 'Produced 2'])
assert len(queue) == 12
def test_dispatch_multiprocessing():
for backend in BACKENDS:
if backend != "sequential":
yield check_dispatch_multiprocessing, backend
def test_batching_auto_threading():
# batching='auto' with the threading backend leaves the effective batch
# size to 1 (no batching) as it has been found to never be beneficial with
# this low-overhead backend.
with Parallel(n_jobs=2, batch_size='auto', backend='threading') as p:
p(delayed(id)(i) for i in range(5000)) # many very fast tasks
assert p._backend.compute_batch_size() == 1
def test_batching_auto_multiprocessing():
with Parallel(n_jobs=2, batch_size='auto', backend='multiprocessing') as p:
p(delayed(id)(i) for i in range(5000)) # many very fast tasks
# It should be strictly larger than 1 but as we don't want heisen
# failures on clogged CI worker environment be safe and only check that
# it's a strictly positive number.
assert p._backend.compute_batch_size() > 0
def test_exception_dispatch():
"Make sure that exception raised during dispatch are indeed captured"
assert_raises(
ValueError,
Parallel(n_jobs=2, pre_dispatch=16, verbose=0),
(delayed(exception_raiser)(i) for i in range(30)))
def test_nested_exception_dispatch():
# Ensure TransportableException objects for nested joblib cases gets
# propagated.
assert_raises(
JoblibException,
Parallel(n_jobs=2, pre_dispatch=16, verbose=0),
(delayed(SafeFunction(exception_raiser))(i) for i in range(30)))
def _reload_joblib():
# Retrieve the path of the parallel module in a robust way
joblib_path = Parallel.__module__.split(os.sep)
joblib_path = joblib_path[:1]
joblib_path.append('parallel.py')
joblib_path = '/'.join(joblib_path)
module = __import__(joblib_path)
# Reload the module. This should trigger a fail
reload(module)
def test_multiple_spawning():
# Test that attempting to launch a new Python after spawned
# subprocesses will raise an error, to avoid infinite loops on
# systems that do not support fork
if not int(os.environ.get('JOBLIB_MULTIPROCESSING', 1)):
raise SkipTest()
assert_raises(ImportError, Parallel(n_jobs=2, pre_dispatch='all'),
[delayed(_reload_joblib)() for i in range(10)])
class FakeParallelBackend(SequentialBackend):
"""Pretends to run concurrently while running sequentially."""
def configure(self, n_jobs=1, parallel=None, **backend_args):
self.n_jobs = self.effective_n_jobs(n_jobs)
self.parallel = parallel
return n_jobs
def effective_n_jobs(self, n_jobs=1):
if n_jobs < 0:
n_jobs = max(mp.cpu_count() + 1 + n_jobs, 1)
return n_jobs
def test_invalid_backend():
assert_raises(ValueError, Parallel, backend='unit-testing')
def test_register_parallel_backend():
try:
register_parallel_backend("test_backend", FakeParallelBackend)
assert "test_backend" in BACKENDS
assert BACKENDS["test_backend"] == FakeParallelBackend
finally:
del BACKENDS["test_backend"]
def test_overwrite_default_backend():
assert _active_backend_type() == MultiprocessingBackend
try:
register_parallel_backend("threading", BACKENDS["threading"],
make_default=True)
assert _active_backend_type() == ThreadingBackend
finally:
# Restore the global default manually
parallel.DEFAULT_BACKEND = 'multiprocessing'
assert _active_backend_type() == MultiprocessingBackend
def check_backend_context_manager(backend_name):
with parallel_backend(backend_name, n_jobs=3):
active_backend, active_n_jobs = parallel.get_active_backend()
assert active_n_jobs == 3
assert effective_n_jobs(3) == 3
p = Parallel()
assert p.n_jobs == 3
if backend_name == 'multiprocessing':
assert type(active_backend) == MultiprocessingBackend
assert type(p._backend) == MultiprocessingBackend
elif backend_name == 'threading':
assert type(active_backend) == ThreadingBackend
assert type(p._backend) == ThreadingBackend
elif backend_name.startswith('test_'):
assert type(active_backend) == FakeParallelBackend
assert type(p._backend) == FakeParallelBackend
@with_multiprocessing
def test_backend_context_manager():
all_test_backends = ['test_backend_%d' % i for i in range(3)]
for test_backend in all_test_backends:
register_parallel_backend(test_backend, FakeParallelBackend)
all_backends = ['multiprocessing', 'threading'] + all_test_backends
try:
assert _active_backend_type() == MultiprocessingBackend
# check that this possible to switch parallel backends sequentially
for test_backend in all_backends:
# TODO: parametrize this block later
# yield check_backend_context_manager, test_backend
check_backend_context_manager(test_backend)
# The default backend is retored
assert _active_backend_type() == MultiprocessingBackend
# Check that context manager switching is thread safe:
Parallel(n_jobs=2, backend='threading')(
delayed(check_backend_context_manager)(b)
for b in all_backends if not b)
# The default backend is again retored
assert _active_backend_type() == MultiprocessingBackend
finally:
for backend_name in list(BACKENDS.keys()):
if backend_name.startswith('test_'):
del BACKENDS[backend_name]
class ParameterizedParallelBackend(SequentialBackend):
"""Pretends to run conncurrently while running sequentially."""
def __init__(self, param=None):
if param is None:
raise ValueError('param should not be None')
self.param = param
def test_parameterized_backend_context_manager():
register_parallel_backend('param_backend', ParameterizedParallelBackend)
try:
assert _active_backend_type() == MultiprocessingBackend
with parallel_backend('param_backend', param=42, n_jobs=3):
active_backend, active_n_jobs = parallel.get_active_backend()
assert type(active_backend) == ParameterizedParallelBackend
assert active_backend.param == 42
assert active_n_jobs == 3
p = Parallel()
assert p.n_jobs == 3
assert p._backend is active_backend
results = p(delayed(sqrt)(i) for i in range(5))
assert results == [sqrt(i) for i in range(5)]
# The default backend is again retored
assert _active_backend_type() == MultiprocessingBackend
finally:
del BACKENDS['param_backend']
def test_direct_parameterized_backend_context_manager():
assert _active_backend_type() == MultiprocessingBackend
# Check that it's possible to pass a backend instance directly,
# without registration
with parallel_backend(ParameterizedParallelBackend(param=43), n_jobs=5):
active_backend, active_n_jobs = parallel.get_active_backend()
assert type(active_backend) == ParameterizedParallelBackend
assert active_backend.param == 43
assert active_n_jobs == 5
p = Parallel()
assert p.n_jobs == 5
assert p._backend is active_backend
results = p(delayed(sqrt)(i) for i in range(5))
assert results == [sqrt(i) for i in range(5)]
# The default backend is again retored
assert _active_backend_type() == MultiprocessingBackend
###############################################################################
# Test helpers
def test_joblib_exception():
# Smoke-test the custom exception
e = JoblibException('foobar')
# Test the repr
repr(e)
# Test the pickle
pickle.dumps(e)
def test_safe_function():
safe_division = SafeFunction(division)
assert_raises(JoblibException, safe_division, 1, 0)
def test_invalid_batch_size():
assert_raises(ValueError, Parallel, batch_size=0)
assert_raises(ValueError, Parallel, batch_size=-1)
assert_raises(ValueError, Parallel, batch_size=1.42)
def check_same_results(params):
n_tasks = params.pop('n_tasks')
expected = [square(i) for i in range(n_tasks)]
results = Parallel(**params)(delayed(square)(i) for i in range(n_tasks))
assert results == expected
def test_dispatch_race_condition():
# Check that using (async-)dispatch does not yield a race condition on the
# iterable generator that is not thread-safe natively.
# This is a non-regression test for the "Pool seems closed" class of error
yield check_same_results, dict(n_tasks=2, n_jobs=2, pre_dispatch="all")
yield check_same_results, dict(n_tasks=2, n_jobs=2, pre_dispatch="n_jobs")
yield check_same_results, dict(n_tasks=10, n_jobs=2, pre_dispatch="n_jobs")
yield check_same_results, dict(n_tasks=517, n_jobs=2,
pre_dispatch="n_jobs")
yield check_same_results, dict(n_tasks=10, n_jobs=2, pre_dispatch="n_jobs")
yield check_same_results, dict(n_tasks=10, n_jobs=4, pre_dispatch="n_jobs")
yield check_same_results, dict(n_tasks=25, n_jobs=4, batch_size=1)
yield check_same_results, dict(n_tasks=25, n_jobs=4, batch_size=1,
pre_dispatch="all")
yield check_same_results, dict(n_tasks=25, n_jobs=4, batch_size=7)
yield check_same_results, dict(n_tasks=10, n_jobs=4,
pre_dispatch="2*n_jobs")
@with_multiprocessing
def test_default_mp_context():
p = Parallel(n_jobs=2, backend='multiprocessing')
context = p._backend_args.get('context')
if sys.version_info >= (3, 4):
start_method = context.get_start_method()
# Under Python 3.4+ the multiprocessing context can be configured
# by an environment variable
env_method = os.environ.get('JOBLIB_START_METHOD', '').strip() or None
if env_method is None:
# Check the default behavior
if sys.platform == 'win32':
assert start_method == 'spawn'
else:
assert start_method == 'fork'
else:
assert start_method == env_method
else:
assert context is None
@with_multiprocessing
@with_numpy
def test_no_blas_crash_or_freeze_with_multiprocessing():
if sys.version_info < (3, 4):
raise SkipTest('multiprocessing can cause BLAS freeze on old Python')
# Use the spawn backend that is both robust and available on all platforms
spawn_backend = mp.get_context('spawn')
# Check that on recent Python version, the 'spawn' start method can make
# it possible to use multiprocessing in conjunction of any BLAS
# implementation that happens to be used by numpy with causing a freeze or
# a crash
rng = np.random.RandomState(42)
# call BLAS DGEMM to force the initialization of the internal thread-pool
# in the main process
a = rng.randn(1000, 1000)
np.dot(a, a.T)
# check that the internal BLAS thread-pool is not in an inconsistent state
# in the worker processes managed by multiprocessing
Parallel(n_jobs=2, backend=spawn_backend)(
delayed(np.dot)(a, a.T) for i in range(2))
def test_parallel_with_interactively_defined_functions():
# When functions are defined interactively in a python/IPython
# session, we want to be able to use them with joblib.Parallel
if posix is None:
# This test pass only when fork is the process start method
raise SkipTest('Not a POSIX platform')
code = '\n\n'.join([
'from joblib import Parallel, delayed',
'def square(x): return x**2',
'print(Parallel(n_jobs=2)(delayed(square)(i) for i in range(5)))'])
check_subprocess_call([sys.executable, '-c', code],
stdout_regex=r'\[0, 1, 4, 9, 16\]')
def test_parallel_with_exhausted_iterator():
exhausted_iterator = iter([])
assert Parallel(n_jobs=2)(exhausted_iterator) == []
def check_memmap(a):
if not isinstance(a, np.memmap):
raise TypeError('Expected np.memmap instance, got %r',
type(a))
return a.copy() # return a regular array instead of a memmap
@with_numpy
@with_multiprocessing
def test_auto_memmap_on_arrays_from_generator():
# Non-regression test for a problem with a bad interaction between the
# GC collecting arrays recently created during iteration inside the
# parallel dispatch loop and the auto-memmap feature of Parallel.
# See: https://github.com/joblib/joblib/pull/294
def generate_arrays(n):
for i in range(n):
yield np.ones(10, dtype=np.float32) * i
# Use max_nbytes=1 to force the use of memory-mapping even for small
# arrays
results = Parallel(n_jobs=2, max_nbytes=1)(
delayed(check_memmap)(a) for a in generate_arrays(100))
for result, expected in zip(results, generate_arrays(len(results))):
np.testing.assert_array_equal(expected, result)
# TODO: Fix https://github.com/joblib/joblib/issues/413 and unskip this test
@with_multiprocessing
@skipif(True, reason='Uncertain CI failure (Issue #413)')
def test_nested_parallel_warnings():
# The warnings happen in child processes so
# warnings.catch_warnings can not be used for this tests that's
# why we use check_subprocess_call instead
if posix is None:
# This test pass only when fork is the process start method
raise SkipTest('Not a POSIX platform')
template_code = """
import sys
from joblib import Parallel, delayed
def func():
return 42
def parallel_func():
res = Parallel(n_jobs={inner_n_jobs})(delayed(func)() for _ in range(3))
return res
Parallel(n_jobs={outer_n_jobs})(delayed(parallel_func)() for _ in range(5))
"""
# no warnings if inner_n_jobs=1
code = template_code.format(inner_n_jobs=1, outer_n_jobs=2)
check_subprocess_call([sys.executable, '-c', code],
stderr_regex='^$')
# warnings if inner_n_jobs != 1
regex = ('Multiprocessing-backed parallel loops cannot '
'be nested')
code = template_code.format(inner_n_jobs=2, outer_n_jobs=2)
check_subprocess_call([sys.executable, '-c', code],
stderr_regex=regex)
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