500 lines
18 KiB
Python
500 lines
18 KiB
Python
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from __future__ import print_function, division, absolute_import
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import os
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import warnings
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import pytest
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from random import random
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from uuid import uuid4
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from time import sleep
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from .. import Parallel, delayed, parallel_config
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from ..parallel import ThreadingBackend, AutoBatchingMixin
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from .._dask import DaskDistributedBackend
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distributed = pytest.importorskip('distributed')
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dask = pytest.importorskip('dask')
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# These imports need to be after the pytest.importorskip hence the noqa: E402
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from distributed import Client, LocalCluster, get_client # noqa: E402
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from distributed.metrics import time # noqa: E402
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# Note: pytest requires to manually import all fixtures used in the test
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# and their dependencies.
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from distributed.utils_test import cluster, inc, cleanup # noqa: E402, F401
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def noop(*args, **kwargs):
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pass
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def slow_raise_value_error(condition, duration=0.05):
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sleep(duration)
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if condition:
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raise ValueError("condition evaluated to True")
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def count_events(event_name, client):
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worker_events = client.run(lambda dask_worker: dask_worker.log)
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event_counts = {}
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for w, events in worker_events.items():
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event_counts[w] = len([event for event in list(events)
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if event[1] == event_name])
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return event_counts
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def test_simple(loop):
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop) as client: # noqa: F841
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with parallel_config(backend='dask'):
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seq = Parallel()(delayed(inc)(i) for i in range(10))
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assert seq == [inc(i) for i in range(10)]
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with pytest.raises(ValueError):
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Parallel()(delayed(slow_raise_value_error)(i == 3)
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for i in range(10))
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seq = Parallel()(delayed(inc)(i) for i in range(10))
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assert seq == [inc(i) for i in range(10)]
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def test_dask_backend_uses_autobatching(loop):
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assert (DaskDistributedBackend.compute_batch_size
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is AutoBatchingMixin.compute_batch_size)
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop) as client: # noqa: F841
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with parallel_config(backend='dask'):
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with Parallel() as parallel:
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# The backend should be initialized with a default
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# batch size of 1:
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backend = parallel._backend
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assert isinstance(backend, DaskDistributedBackend)
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assert backend.parallel is parallel
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assert backend._effective_batch_size == 1
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# Launch many short tasks that should trigger
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# auto-batching:
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parallel(
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delayed(lambda: None)()
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for _ in range(int(1e4))
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)
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assert backend._effective_batch_size > 10
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def random2():
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return random()
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def test_dont_assume_function_purity(loop):
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop) as client: # noqa: F841
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with parallel_config(backend='dask'):
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x, y = Parallel()(delayed(random2)() for i in range(2))
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assert x != y
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@pytest.mark.parametrize("mixed", [True, False])
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def test_dask_funcname(loop, mixed):
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from joblib._dask import Batch
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if not mixed:
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tasks = [delayed(inc)(i) for i in range(4)]
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batch_repr = 'batch_of_inc_4_calls'
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else:
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tasks = [
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delayed(abs)(i) if i % 2 else delayed(inc)(i) for i in range(4)
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]
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batch_repr = 'mixed_batch_of_inc_4_calls'
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assert repr(Batch(tasks)) == batch_repr
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop) as client:
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with parallel_config(backend='dask'):
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_ = Parallel(batch_size=2, pre_dispatch='all')(tasks)
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def f(dask_scheduler):
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return list(dask_scheduler.transition_log)
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batch_repr = batch_repr.replace('4', '2')
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log = client.run_on_scheduler(f)
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assert all('batch_of_inc' in tup[0] for tup in log)
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def test_no_undesired_distributed_cache_hit():
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# Dask has a pickle cache for callables that are called many times. Because
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# the dask backends used to wrap both the functions and the arguments
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# under instances of the Batch callable class this caching mechanism could
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# lead to bugs as described in: https://github.com/joblib/joblib/pull/1055
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# The joblib-dask backend has been refactored to avoid bundling the
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# arguments as an attribute of the Batch instance to avoid this problem.
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# This test serves as non-regression problem.
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# Use a large number of input arguments to give the AutoBatchingMixin
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# enough tasks to kick-in.
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lists = [[] for _ in range(100)]
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np = pytest.importorskip('numpy')
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X = np.arange(int(1e6))
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def isolated_operation(list_, data=None):
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if data is not None:
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np.testing.assert_array_equal(data, X)
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list_.append(uuid4().hex)
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return list_
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cluster = LocalCluster(n_workers=1, threads_per_worker=2)
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client = Client(cluster)
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try:
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with parallel_config(backend='dask'):
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# dispatches joblib.parallel.BatchedCalls
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res = Parallel()(
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delayed(isolated_operation)(list_) for list_ in lists
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)
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# The original arguments should not have been mutated as the mutation
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# happens in the dask worker process.
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assert lists == [[] for _ in range(100)]
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# Here we did not pass any large numpy array as argument to
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# isolated_operation so no scattering event should happen under the
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# hood.
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counts = count_events('receive-from-scatter', client)
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assert sum(counts.values()) == 0
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assert all([len(r) == 1 for r in res])
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with parallel_config(backend='dask'):
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# Append a large array which will be scattered by dask, and
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# dispatch joblib._dask.Batch
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res = Parallel()(
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delayed(isolated_operation)(list_, data=X) for list_ in lists
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)
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# This time, auto-scattering should have kicked it.
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counts = count_events('receive-from-scatter', client)
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assert sum(counts.values()) > 0
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assert all([len(r) == 1 for r in res])
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finally:
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client.close(timeout=30)
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cluster.close(timeout=30)
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class CountSerialized(object):
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def __init__(self, x):
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self.x = x
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self.count = 0
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def __add__(self, other):
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return self.x + getattr(other, 'x', other)
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__radd__ = __add__
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def __reduce__(self):
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self.count += 1
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return (CountSerialized, (self.x,))
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def add5(a, b, c, d=0, e=0):
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return a + b + c + d + e
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def test_manual_scatter(loop):
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x = CountSerialized(1)
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y = CountSerialized(2)
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z = CountSerialized(3)
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop) as client: # noqa: F841
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with parallel_config(backend='dask', scatter=[x, y]):
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f = delayed(add5)
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tasks = [f(x, y, z, d=4, e=5),
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f(x, z, y, d=5, e=4),
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f(y, x, z, d=x, e=5),
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f(z, z, x, d=z, e=y)]
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expected = [func(*args, **kwargs)
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for func, args, kwargs in tasks]
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results = Parallel()(tasks)
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# Scatter must take a list/tuple
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with pytest.raises(TypeError):
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with parallel_config(backend='dask', loop=loop, scatter=1):
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pass
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assert results == expected
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# Scattered variables only serialized once
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assert x.count == 1
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assert y.count == 1
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# Depending on the version of distributed, the unscattered z variable
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# is either pickled 4 or 6 times, possibly because of the memoization
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# of objects that appear several times in the arguments of a delayed
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# task.
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assert z.count in (4, 6)
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# When the same IOLoop is used for multiple clients in a row, use
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# loop_in_thread instead of loop to prevent the Client from closing it. See
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# dask/distributed #4112
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def test_auto_scatter(loop_in_thread):
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np = pytest.importorskip('numpy')
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data1 = np.ones(int(1e4), dtype=np.uint8)
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data2 = np.ones(int(1e4), dtype=np.uint8)
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data_to_process = ([data1] * 3) + ([data2] * 3)
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop_in_thread) as client:
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with parallel_config(backend='dask'):
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# Passing the same data as arg and kwarg triggers a single
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# scatter operation whose result is reused.
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Parallel()(delayed(noop)(data, data, i, opt=data)
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for i, data in enumerate(data_to_process))
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# By default large array are automatically scattered with
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# broadcast=1 which means that one worker must directly receive
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# the data from the scatter operation once.
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counts = count_events('receive-from-scatter', client)
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assert counts[a['address']] + counts[b['address']] == 2
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop_in_thread) as client:
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with parallel_config(backend='dask'):
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Parallel()(delayed(noop)(data1[:3], i) for i in range(5))
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# Small arrays are passed within the task definition without going
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# through a scatter operation.
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counts = count_events('receive-from-scatter', client)
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assert counts[a['address']] == 0
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assert counts[b['address']] == 0
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@pytest.mark.parametrize("retry_no", list(range(2)))
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def test_nested_scatter(loop, retry_no):
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np = pytest.importorskip('numpy')
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NUM_INNER_TASKS = 10
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NUM_OUTER_TASKS = 10
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def my_sum(x, i, j):
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return np.sum(x)
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def outer_function_joblib(array, i):
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client = get_client() # noqa
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with parallel_config(backend="dask"):
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results = Parallel()(
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delayed(my_sum)(array[j:], i, j) for j in range(
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NUM_INNER_TASKS)
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)
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return sum(results)
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop) as _:
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with parallel_config(backend="dask"):
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my_array = np.ones(10000)
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_ = Parallel()(
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delayed(outer_function_joblib)(
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my_array[i:], i) for i in range(NUM_OUTER_TASKS)
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)
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def test_nested_backend_context_manager(loop_in_thread):
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def get_nested_pids():
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pids = set(Parallel(n_jobs=2)(delayed(os.getpid)() for _ in range(2)))
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pids |= set(Parallel(n_jobs=2)(delayed(os.getpid)() for _ in range(2)))
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return pids
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop_in_thread) as client:
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with parallel_config(backend='dask'):
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pid_groups = Parallel(n_jobs=2)(
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delayed(get_nested_pids)()
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for _ in range(10)
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)
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for pid_group in pid_groups:
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assert len(set(pid_group)) <= 2
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# No deadlocks
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with Client(s['address'], loop=loop_in_thread) as client: # noqa: F841
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with parallel_config(backend='dask'):
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pid_groups = Parallel(n_jobs=2)(
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delayed(get_nested_pids)()
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for _ in range(10)
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)
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for pid_group in pid_groups:
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assert len(set(pid_group)) <= 2
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def test_nested_backend_context_manager_implicit_n_jobs(loop):
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# Check that Parallel with no explicit n_jobs value automatically selects
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# all the dask workers, including in nested calls.
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def _backend_type(p):
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return p._backend.__class__.__name__
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def get_nested_implicit_n_jobs():
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with Parallel() as p:
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return _backend_type(p), p.n_jobs
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop) as client: # noqa: F841
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with parallel_config(backend='dask'):
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with Parallel() as p:
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assert _backend_type(p) == "DaskDistributedBackend"
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assert p.n_jobs == -1
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all_nested_n_jobs = p(
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delayed(get_nested_implicit_n_jobs)()
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for _ in range(2)
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)
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for backend_type, nested_n_jobs in all_nested_n_jobs:
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assert backend_type == "DaskDistributedBackend"
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assert nested_n_jobs == -1
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def test_errors(loop):
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with pytest.raises(ValueError) as info:
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with parallel_config(backend='dask'):
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pass
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assert "create a dask client" in str(info.value).lower()
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def test_correct_nested_backend(loop):
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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop) as client: # noqa: F841
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# No requirement, should be us
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with parallel_config(backend='dask'):
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result = Parallel(n_jobs=2)(
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delayed(outer)(nested_require=None) for _ in range(1))
|
||
|
|
assert isinstance(result[0][0][0], DaskDistributedBackend)
|
||
|
|
|
||
|
|
# Require threads, should be threading
|
||
|
|
with parallel_config(backend='dask'):
|
||
|
|
result = Parallel(n_jobs=2)(
|
||
|
|
delayed(outer)(nested_require='sharedmem')
|
||
|
|
for _ in range(1))
|
||
|
|
assert isinstance(result[0][0][0], ThreadingBackend)
|
||
|
|
|
||
|
|
|
||
|
|
def outer(nested_require):
|
||
|
|
return Parallel(n_jobs=2, prefer='threads')(
|
||
|
|
delayed(middle)(nested_require) for _ in range(1)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def middle(require):
|
||
|
|
return Parallel(n_jobs=2, require=require)(
|
||
|
|
delayed(inner)() for _ in range(1)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def inner():
|
||
|
|
return Parallel()._backend
|
||
|
|
|
||
|
|
|
||
|
|
def test_secede_with_no_processes(loop):
|
||
|
|
# https://github.com/dask/distributed/issues/1775
|
||
|
|
with Client(loop=loop, processes=False, set_as_default=True):
|
||
|
|
with parallel_config(backend='dask'):
|
||
|
|
Parallel(n_jobs=4)(delayed(id)(i) for i in range(2))
|
||
|
|
|
||
|
|
|
||
|
|
def _worker_address(_):
|
||
|
|
from distributed import get_worker
|
||
|
|
return get_worker().address
|
||
|
|
|
||
|
|
|
||
|
|
def test_dask_backend_keywords(loop):
|
||
|
|
with cluster() as (s, [a, b]):
|
||
|
|
with Client(s['address'], loop=loop) as client: # noqa: F841
|
||
|
|
with parallel_config(backend='dask', workers=a['address']):
|
||
|
|
seq = Parallel()(
|
||
|
|
delayed(_worker_address)(i) for i in range(10))
|
||
|
|
assert seq == [a['address']] * 10
|
||
|
|
|
||
|
|
with parallel_config(backend='dask', workers=b['address']):
|
||
|
|
seq = Parallel()(
|
||
|
|
delayed(_worker_address)(i) for i in range(10))
|
||
|
|
assert seq == [b['address']] * 10
|
||
|
|
|
||
|
|
|
||
|
|
def test_scheduler_tasks_cleanup(loop):
|
||
|
|
with Client(processes=False, loop=loop) as client:
|
||
|
|
with parallel_config(backend='dask'):
|
||
|
|
Parallel()(delayed(inc)(i) for i in range(10))
|
||
|
|
|
||
|
|
start = time()
|
||
|
|
while client.cluster.scheduler.tasks:
|
||
|
|
sleep(0.01)
|
||
|
|
assert time() < start + 5
|
||
|
|
|
||
|
|
assert not client.futures
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("cluster_strategy", ["adaptive", "late_scaling"])
|
||
|
|
@pytest.mark.skipif(
|
||
|
|
distributed.__version__ <= '2.1.1' and distributed.__version__ >= '1.28.0',
|
||
|
|
reason="distributed bug - https://github.com/dask/distributed/pull/2841")
|
||
|
|
def test_wait_for_workers(cluster_strategy):
|
||
|
|
cluster = LocalCluster(n_workers=0, processes=False, threads_per_worker=2)
|
||
|
|
client = Client(cluster)
|
||
|
|
if cluster_strategy == "adaptive":
|
||
|
|
cluster.adapt(minimum=0, maximum=2)
|
||
|
|
elif cluster_strategy == "late_scaling":
|
||
|
|
# Tell the cluster to start workers but this is a non-blocking call
|
||
|
|
# and new workers might take time to connect. In this case the Parallel
|
||
|
|
# call should wait for at least one worker to come up before starting
|
||
|
|
# to schedule work.
|
||
|
|
cluster.scale(2)
|
||
|
|
try:
|
||
|
|
with parallel_config(backend='dask'):
|
||
|
|
# The following should wait a bit for at least one worker to
|
||
|
|
# become available.
|
||
|
|
Parallel()(delayed(inc)(i) for i in range(10))
|
||
|
|
finally:
|
||
|
|
client.close()
|
||
|
|
cluster.close()
|
||
|
|
|
||
|
|
|
||
|
|
def test_wait_for_workers_timeout():
|
||
|
|
# Start a cluster with 0 worker:
|
||
|
|
cluster = LocalCluster(n_workers=0, processes=False, threads_per_worker=2)
|
||
|
|
client = Client(cluster)
|
||
|
|
try:
|
||
|
|
with parallel_config(backend='dask', wait_for_workers_timeout=0.1):
|
||
|
|
# Short timeout: DaskDistributedBackend
|
||
|
|
msg = "DaskDistributedBackend has no worker after 0.1 seconds."
|
||
|
|
with pytest.raises(TimeoutError, match=msg):
|
||
|
|
Parallel()(delayed(inc)(i) for i in range(10))
|
||
|
|
|
||
|
|
with parallel_config(backend='dask', wait_for_workers_timeout=0):
|
||
|
|
# No timeout: fallback to generic joblib failure:
|
||
|
|
msg = "DaskDistributedBackend has no active worker"
|
||
|
|
with pytest.raises(RuntimeError, match=msg):
|
||
|
|
Parallel()(delayed(inc)(i) for i in range(10))
|
||
|
|
finally:
|
||
|
|
client.close()
|
||
|
|
cluster.close()
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("backend", ["loky", "multiprocessing"])
|
||
|
|
def test_joblib_warning_inside_dask_daemonic_worker(backend):
|
||
|
|
cluster = LocalCluster(n_workers=2)
|
||
|
|
client = Client(cluster)
|
||
|
|
try:
|
||
|
|
|
||
|
|
def func_using_joblib_parallel():
|
||
|
|
# Somehow trying to check the warning type here (e.g. with
|
||
|
|
# pytest.warns(UserWarning)) make the test hang. Work-around:
|
||
|
|
# return the warning record to the client and the warning check is
|
||
|
|
# done client-side.
|
||
|
|
with warnings.catch_warnings(record=True) as record:
|
||
|
|
Parallel(n_jobs=2, backend=backend)(
|
||
|
|
delayed(inc)(i) for i in range(10))
|
||
|
|
|
||
|
|
return record
|
||
|
|
|
||
|
|
fut = client.submit(func_using_joblib_parallel)
|
||
|
|
record = fut.result()
|
||
|
|
|
||
|
|
assert len(record) == 1
|
||
|
|
warning = record[0].message
|
||
|
|
assert isinstance(warning, UserWarning)
|
||
|
|
assert "distributed.worker.daemon" in str(warning)
|
||
|
|
finally:
|
||
|
|
client.close(timeout=30)
|
||
|
|
cluster.close(timeout=30)
|