def test_get_scale_up_kwargs(loop):
with LocalCluster(0, scheduler_port=0, silence_logs=False,
diagnostics_port=None, loop=loop) as cluster:
alc = Adaptive(cluster.scheduler, cluster, interval=100,
scale_factor=3)
assert alc.get_scale_up_kwargs() == {'n': 1}
with Client(cluster, loop=loop) as c:
future = c.submit(lambda x: x + 1, 1)
assert future.result() == 2
assert c.ncores()
assert alc.get_scale_up_kwargs() == {'n': 3}
def test_simple(c, s):
with MarathonCluster(s, cpus=1, mem=256) as mc:
ac = Adaptive(s, mc)
ac.adapt()
yield gen.sleep(0.1)
assert not s.ncores
futures = c.map(lambda x: x + 1, range(10))
start = time()
while not s.ready:
yield gen.sleep(0.01)
assert time() < start + 5
ac.adapt()
results = yield c._gather(futures)
assert s.transition_log
if s.worker_info:
tasks = mc.client.list_tasks(app_id=mc.app.id)
names = {d['name'] for d in s.worker_info.values()}
assert names == {t.id for t in tasks}
yield ac._retire_workers()
start = time()
while len(s.worker_info) > 1:
yield gen.sleep(0.01)
assert time() < start + 5
assert len(s.who_has) == len(futures)
def adapt(self):
""" Start up an Adaptive deployment if not already started
This makes the cluster request resources in accordance to current
demand on the scheduler """
from distributed.deploy import Adaptive
if self._adaptive:
return
else:
self._adaptive = Adaptive(self.scheduler, self, startup_cost=5,
key=lambda ws: ws.host)
def start_cluster(clustertype='local', n=8, Adaptive_deploy=False):
#add the json config file ?
if clustertype == 'local':
cluster = LocalCluster(n_workers=n)
elif clustertype == 'LSF':
cluster = MyLSFCluster(n_workers=n)
adapative_cluster = None
if Adaptive_deploy == True:
#turns on cluster size management
adapative_cluster = Adaptive(cluster.scheduler, cluster)
cluster.scheduler.start()
client = Client(cluster)
return client, cluster, adapative_cluster
def test_min_max():
loop = IOLoop.current()
cluster = yield LocalCluster(0, scheduler_port=0, silence_logs=False,
processes=False, diagnostics_port=None,
loop=loop, asynchronous=True)
yield cluster._start()
try:
adapt = Adaptive(cluster.scheduler, cluster, minimum=1, maximum=2,
interval=20)
c = yield Client(cluster, asynchronous=True, loop=loop)
start = time()
while not cluster.scheduler.workers:
yield gen.sleep(0.01)
assert time() < start + 1
yield gen.sleep(0.2)
assert len(cluster.scheduler.workers) == 1
assert frequencies(pluck(1, adapt.log)) == {'up': 1}
futures = c.map(slowinc, range(100), delay=0.1)
start = time()
while len(cluster.scheduler.workers) < 2:
yield gen.sleep(0.01)
assert time() < start + 1
assert len(cluster.scheduler.workers) == 2
yield gen.sleep(0.5)
assert len(cluster.scheduler.workers) == 2
assert len(cluster.workers) == 2
assert frequencies(pluck(1, adapt.log)) == {'up': 2}
del futures
start = time()
while len(cluster.scheduler.workers) != 1:
yield gen.sleep(0.01)
assert time() < start + 1
assert frequencies(pluck(1, adapt.log)) == {'up': 2, 'down': 1}
finally:
yield c._close()
yield cluster._close()
def test_adaptive_local_cluster_multi_workers():
loop = IOLoop.current()
cluster = LocalCluster(0,
scheduler_port=0,
silence_logs=False,
processes=False,
diagnostics_port=None,
loop=loop,
start=False)
try:
cluster.scheduler.allowed_failures = 1000
alc = Adaptive(cluster.scheduler, cluster, interval=100)
c = yield Client(cluster, asynchronous=True, loop=loop)
futures = c.map(slowinc, range(100), delay=0.01)
start = time()
while not cluster.scheduler.worker_info:
yield gen.sleep(0.01)
assert time() < start + 15
yield c._gather(futures)
del futures
start = time()
while cluster.workers:
yield gen.sleep(0.01)
assert time() < start + 5
assert not cluster.workers
assert not cluster.scheduler.workers
yield gen.sleep(0.2)
assert not cluster.workers
assert not cluster.scheduler.workers
futures = c.map(slowinc, range(100), delay=0.01)
yield c._gather(futures)
finally:
yield c._close()
yield cluster._close()
def test_adaptive_local_cluster_multi_workers():
loop = IOLoop.current()
cluster = LocalCluster(0,
scheduler_port=0,
silence_logs=False,
nanny=False,
diagnostic_port=None,
loop=loop,
start=False)
alc = Adaptive(cluster.scheduler, cluster, interval=100)
c = Client(cluster, start=False, loop=loop)
yield c._start()
for i in range(20):
futures = c.map(slowinc, range(100), delay=0.01)
yield c._gather(futures)
del futures
yield gen.sleep(0.1)
yield c._shutdown()
yield cluster._close()
def test_adaptive_local_cluster(loop):
with LocalCluster(0, scheduler_port=0, silence_logs=False,
diagnostics_port=None, loop=loop) as cluster:
alc = Adaptive(cluster.scheduler, cluster, interval=100)
with Client(cluster, loop=loop) as c:
assert not c.ncores()
future = c.submit(lambda x: x + 1, 1)
assert future.result() == 2
assert c.ncores()
sleep(0.1)
assert c.ncores() # still there after some time
del future
start = time()
while cluster.scheduler.ncores:
sleep(0.01)
assert time() < start + 5
assert not c.ncores()
def test_avoid_churn():
""" We want to avoid creating and deleting workers frequently
Instead we want to wait a few beats before removing a worker in case the
user is taking a brief pause between work
"""
cluster = yield LocalCluster(0, asynchronous=True, processes=False,
scheduler_port=0, silence_logs=False,
diagnostics_port=None)
client = yield Client(cluster, asynchronous=True)
try:
adapt = Adaptive(cluster.scheduler, cluster, interval=20, wait_count=5)
for i in range(10):
yield client.submit(slowinc, i, delay=0.040)
yield gen.sleep(0.040)
assert frequencies(pluck(1, adapt.log)) == {'up': 1}
finally:
yield client._close()
yield cluster._close()
def test_adapt(loop):
thread = Thread(target=loop.start)
thread.daemon = True
thread.start()
s = Scheduler(loop=loop)
s.start(0)
with MarathonCluster(s, cpus=1, mem=1000) as mc:
ac = Adaptive(s, mc)
with Client(s.address, loop=loop) as c:
assert not s.ncores
x = c.submit(lambda x: x + 1, 1)
x.result()
assert len(s.ncores) == 1
del x
start = time()
while s.ncores:
sleep(0.01)
assert time() < start + 5
def __init__(self, loop=None, nworkers=0, ip=None, scheduler_port=0,
diagnostics_port=8787, services={}, adaptive=False,
silence_logs=logging.CRITICAL,
**kwargs):
if silence_logs:
for l in ['distributed.scheduler',
'distributed.worker',
'distributed.core',
'distributed.nanny']:
logging.getLogger(l).setLevel(silence_logs)
self.loop = loop or IOLoop()
if not self.loop._running:
self._thread = Thread(target=self.loop.start)
self._thread.daemon = True
self._thread.start()
while not self.loop._running:
sleep(0.001)
if diagnostics_port is not None:
try:
from distributed.bokeh.scheduler import BokehScheduler
except ImportError:
logger.info('To start diagnostics server please install Bokeh')
else:
services[('bokeh', diagnostics_port)] = BokehScheduler
self.scheduler = Scheduler(loop=self.loop, services=services)
self.workers = MarathonWorkers(self.scheduler, **kwargs)
if adaptive:
self.adaptive = Adaptive(self.scheduler, self.workers)
if ip is None:
ip = '127.0.0.1'
self.scheduler_port = scheduler_port
self.scheduler.start((ip, scheduler_port))
self.workers.start(nworkers)
self.status = 'running'
logging.info('Scheduler address: {}'.format(self.scheduler.address))
def test_adaptive_local_cluster_multi_workers():
loop = IOLoop.current()
cluster = LocalCluster(0,
scheduler_port=0,
silence_logs=False,
nanny=False,
diagnostics_port=None,
loop=loop,
start=False)
cluster.scheduler.allowed_failures = 1000
alc = Adaptive(cluster.scheduler, cluster, interval=100)
c = Client(cluster, start=False, loop=loop)
yield c._start()
futures = c.map(slowinc, range(100), delay=0.01)
start = time()
while not cluster.workers:
yield gen.sleep(0.01)
assert time() < start + 5
yield c._gather(futures)
del futures
start = time()
while cluster.workers:
yield gen.sleep(0.01)
assert time() < start + 5
assert not cluster.workers
yield gen.sleep(0.2)
assert not cluster.workers
futures = c.map(slowinc, range(100), delay=0.01)
yield c._gather(futures)
yield c._shutdown()
yield cluster._close()
def dask_setup(scheduler):
cluster = KubeCluster()
adapative_cluster = Adaptive(scheduler, cluster)
def __init__(self, *args, **kwargs):
self.min_size = kwargs.pop("min_size", 0)
Adaptive.__init__(self, *args, **kwargs)
