def test_more_clients(rsds_env):
url = rsds_env.start([1])
client1 = Client(url)
client2 = Client(url)
f1 = client1.submit(comp_fn1, 10)
f2 = client2.submit(comp_fn1, 20)
r2 = client2.gather(f2)
r1 = client1.gather(f1)
assert r1 == 100
assert r2 == 200
def test_submit_gather(rsds_env):
url = rsds_env.start([1])
client = Client(url)
f1 = client.submit(comp_fn1, 10)
f2 = client.submit(comp_fn1, 13)
r1, r2 = client.gather([f1, f2])
assert r1 == 100
assert r2 == 130
f1, f2 = client.map(comp_fn1, [20, 30])
r1, r2 = client.gather([f1, f2])
assert r1 == 200
assert r2 == 300
r1 = delayed_fn2(delayed_fn1(2), delayed_fn1(3)).compute()
assert r1 == 50
def test_same_input(rsds_env):
url = rsds_env.start([1])
client = Client(url)
f1 = client.submit(comp_fn1, 10)
f2 = client.submit(comp_fn2, f1, f1)
r2 = client.gather(f2)
assert r2 == 0
def test_gather_already_finished(rsds_env):
url = rsds_env.start([1])
client = Client(url)
x = client.submit(inc, 10)
assert not x.done()
assert isinstance(x, Future)
assert x.client is client
result = client.gather(x)
assert result == 11
assert x.done()
y = client.submit(inc, 20)
z = client.submit(add, x, y)
result = client.gather(z)
assert result == 11 + 21
def test_scatter(rsds_env):
url = rsds_env.start([1])
client = Client(url)
client.wait_for_workers(1)
metadata = client.scheduler_info()
worker = list(metadata["workers"].keys())[0]
futures = client.scatter(range(10), workers=[worker])
fut = client.submit(comp_fn3, futures)
assert client.gather(fut) == list(range(1, 11))
def test_recompute_existing(rsds_env):
url = rsds_env.start([1])
client = Client(url)
# assert delayed_fn1(10).compute() == 100
# assert delayed_fn1(10).compute() == 100
f1 = client.submit(comp_fn1, 10)
f2 = client.submit(comp_fn1, 10)
r1, r2 = client.gather([f1, f2])
assert r1 == 100
assert r2 == 100
def test_dask_cv_single(self):
test_cluster = LocalCluster(1)
test_client = Client(test_cluster)
iris = load_iris()
reg = tree.DecisionTreeClassifier()
cv_score = test_client.submit(cross_val_score, reg, iris.data,
iris.target)
self.assertGreater(cv_score.result().mean(), 0)
test_cluster.scale_up(4)
_cv_results = {
'reg_%i':
test_client.submit(cross_val_score,
tree.DecisionTreeClassifier(min_samples_leaf=i),
iris.data, iris.target)
for i in range(5)
}
cv_results = test_client.gather(list(_cv_results.values()))
for cv_result in cv_results:
self.assertGreaterEqual(cv_result.mean(), 0)
