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331 lines
12 KiB
Python
331 lines
12 KiB
Python
from __future__ import print_function, division, absolute_import
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import os
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import pytest
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from random import random
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from time import sleep
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from .. import Parallel, delayed, parallel_backend
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from ..parallel import ThreadingBackend
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from .._dask import DaskDistributedBackend, TimeoutError
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distributed = pytest.importorskip('distributed')
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from distributed import Client, LocalCluster
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from distributed.metrics import time
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from distributed.utils_test import cluster, inc
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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 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_backend('dask') as (ba, _):
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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 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_backend('dask') as (ba, _):
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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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def test_dask_funcname(loop):
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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_backend('dask') as (ba, _):
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x, y = Parallel()(delayed(inc)(i) for i in range(2))
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def f(dask_scheduler):
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return list(dask_scheduler.transition_log)
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log = client.run_on_scheduler(f)
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assert all(tup[0].startswith('inc-batch') for tup in log)
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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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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 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_backend('dask', scatter=[x, y]) as (ba, _):
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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_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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assert z.count == 4
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def test_auto_scatter(loop):
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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] * 4) + ([data2] * 4)
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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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with cluster() as (s, [a, b]):
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with Client(s['address'], loop=loop) as client:
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with parallel_backend('dask') as (ba, _):
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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) as client:
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with parallel_backend('dask') as (ba, _):
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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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def test_nested_backend_context_manager(loop):
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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) as client:
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with parallel_backend('dask') as (ba, _):
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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) as client: # noqa: F841
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with parallel_backend('dask') as (ba, _):
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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_backend('dask') as (ba, _):
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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_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_backend('dask') as (ba, _):
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result = Parallel(n_jobs=2)(
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delayed(outer)(nested_require=None) for _ in range(1))
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assert isinstance(result[0][0][0], DaskDistributedBackend)
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# Require threads, should be threading
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with parallel_backend('dask') as (ba, _):
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result = Parallel(n_jobs=2)(
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delayed(outer)(nested_require='sharedmem')
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for _ in range(1))
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assert isinstance(result[0][0][0], ThreadingBackend)
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def outer(nested_require):
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return Parallel(n_jobs=2, prefer='threads')(
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delayed(middle)(nested_require) for _ in range(1)
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)
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def middle(require):
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return Parallel(n_jobs=2, require=require)(
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delayed(inner)() for _ in range(1)
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)
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def inner():
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return Parallel()._backend
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def test_secede_with_no_processes(loop):
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# https://github.com/dask/distributed/issues/1775
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with Client(loop=loop, processes=False, set_as_default=True):
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with parallel_backend('dask'):
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Parallel(n_jobs=4)(delayed(id)(i) for i in range(2))
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def _worker_address(_):
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from distributed import get_worker
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return get_worker().address
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def test_dask_backend_keywords(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_backend('dask', workers=a['address']) as (ba, _):
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seq = Parallel()(
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delayed(_worker_address)(i) for i in range(10))
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assert seq == [a['address']] * 10
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with parallel_backend('dask', workers=b['address']) as (ba, _):
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seq = Parallel()(
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delayed(_worker_address)(i) for i in range(10))
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assert seq == [b['address']] * 10
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def test_cleanup(loop):
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with Client(processes=False, loop=loop) as client:
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with parallel_backend('dask'):
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Parallel()(delayed(inc)(i) for i in range(10))
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start = time()
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while client.cluster.scheduler.tasks:
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sleep(0.01)
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assert time() < start + 5
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assert not client.futures
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@pytest.mark.parametrize("cluster_strategy", ["adaptive", "late_scaling"])
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@pytest.mark.skipif(
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distributed.__version__ <= '2.1.1' and distributed.__version__ >= '1.28.0',
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reason="distributed bug - https://github.com/dask/distributed/pull/2841")
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def test_wait_for_workers(cluster_strategy):
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cluster = LocalCluster(n_workers=0, processes=False, threads_per_worker=2)
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client = Client(cluster)
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if cluster_strategy == "adaptive":
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cluster.adapt(minimum=0, maximum=2)
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elif cluster_strategy == "late_scaling":
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# Tell the cluster to start workers but this is a non-blocking call
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# and new workers might take time to connect. In this case the Parallel
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# call should wait for at least one worker to come up before starting
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# to schedule work.
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cluster.scale(2)
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try:
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with parallel_backend('dask'):
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# The following should wait a bit for at least one worker to
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# become available.
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Parallel()(delayed(inc)(i) for i in range(10))
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finally:
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client.close()
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cluster.close()
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def test_wait_for_workers_timeout():
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# Start a cluster with 0 worker:
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cluster = LocalCluster(n_workers=0, processes=False, threads_per_worker=2)
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client = Client(cluster)
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try:
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with parallel_backend('dask', wait_for_workers_timeout=0.1):
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# Short timeout: DaskDistributedBackend
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msg = "DaskDistributedBackend has no worker after 0.1 seconds."
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with pytest.raises(TimeoutError, match=msg):
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Parallel()(delayed(inc)(i) for i in range(10))
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with parallel_backend('dask', wait_for_workers_timeout=0):
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# No timeout: fallback to generic joblib failure:
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msg = "DaskDistributedBackend has no active worker"
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with pytest.raises(RuntimeError, match=msg):
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Parallel()(delayed(inc)(i) for i in range(10))
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finally:
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client.close()
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cluster.close()
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