async def test_start():
with dask.config.set({
'labextension.defaults.kwargs': {
'processes': False
}, # for speed
'labextension.initial': [],
}):
async with DaskClusterManager() as manager:
# add cluster
model = await manager.start_cluster()
assert not model.get('adapt')
# close cluster
assert len(manager.list_clusters()) == 1
await manager.close_cluster(model['id'])
# add cluster with adaptive configuration
model = await manager.start_cluster(
configuration={'adapt': {
'minimum': 1,
'maximum': 3
}})
assert model['adapt'] == {'minimum': 1, 'maximum': 3}
await manager.close()
async def test_scale():
with dask.config.set({
'labextension.defaults.kwargs': {
'processes': False
}, # for speed
'labextension.initial': [],
}):
async with DaskClusterManager() as manager:
# add cluster with number of workers configuration
model = await manager.start_cluster(configuration={'workers': 3})
start = time()
while model['workers'] != 3:
await sleep(0.01)
model = manager.get_cluster(model['id'])
assert time() < start + 10, model['workers']
await sleep(0.2) # let workers settle # TODO: remove need for this
# rescale the cluster
model = manager.scale_cluster(model['id'], 6)
start = time()
while model['workers'] != 6:
await sleep(0.01)
model = manager.get_cluster(model['id'])
assert time() < start + 10, model['workers']
async def test_get():
with dask.config.set(config):
async with DaskClusterManager() as manager:
# start a cluster
model = await manager.start_cluster()
# return None if a nonexistent cluster is requested
assert not manager.get_cluster('fake')
# get the cluster by id
assert model == manager.get_cluster(model['id'])
async def test_adapt():
with dask.config.set(config):
async with DaskClusterManager() as manager:
# add a new cluster
model = await manager.start_cluster()
assert not model.get('adapt')
model = manager.adapt_cluster(model['id'], 0, 4)
adapt = model.get('adapt')
assert adapt
assert adapt['minimum'] == 0
assert adapt['maximum'] == 4
async def test_close():
with dask.config.set(config):
async with DaskClusterManager() as manager:
# start a cluster
model = await manager.start_cluster()
# return None if a nonexistent cluster is closed
assert not await manager.close_cluster('fake')
# close the cluster
await manager.close_cluster(model['id'])
assert not manager.list_clusters()
async def test_initial():
with dask.config.set({
'labextension': {
"initial": [{"name": "foo"}],
"default": {},
'factory': {
"module": "dask.distributed",
"class": "LocalCluster",
"kwargs": {"processes": False},
"args": []
}
}
}):
# Test asynchronous starting of clusters via a context
async with DaskClusterManager() as manager:
clusters = manager.list_clusters()
assert len(clusters) == 1
assert clusters[0]["name"] == 'foo'
# Test asynchronous starting of clusters outside of a context
manager = DaskClusterManager()
assert len(manager.list_clusters()) == 0
await manager
clusters = manager.list_clusters()
assert len(clusters) == 1
assert clusters[0]["name"] == 'foo'
await manager.close()
manager = await DaskClusterManager()
clusters = manager.list_clusters()
assert len(clusters) == 1
assert clusters[0]["name"] == 'foo'
await manager.close()
async def test_initial():
with dask.config.set({
'labextension.defaults.kwargs': {
'processes': False
}, # for speed
'labextension.initial': [{
'name': 'foo'
}],
}):
# Test asynchronous starting of clusters via a context
async with DaskClusterManager() as manager:
clusters = manager.list_clusters()
assert len(clusters) == 1
assert clusters[0]["name"] == 'foo'
# Test asynchronous starting of clusters outside of a context
manager = DaskClusterManager()
assert len(manager.list_clusters()) == 0
await manager
clusters = manager.list_clusters()
assert len(clusters) == 1
assert clusters[0]["name"] == 'foo'
await manager.close()
manager = await DaskClusterManager()
clusters = manager.list_clusters()
assert len(clusters) == 1
assert clusters[0]["name"] == 'foo'
await manager.close()
async def test_list():
with dask.config.set(config):
async with DaskClusterManager() as manager:
# start with an empty list
assert not manager.list_clusters()
# start clusters
model1 = await manager.start_cluster()
model2 = await manager.start_cluster()
models = manager.list_clusters()
assert len(models) == 2
assert model1 in models
assert model2 in models
async def test_get():
with dask.config.set({
'labextension.defaults.kwargs': {
'processes': False
}, # for speed
'labextension.initial': [],
}):
async with DaskClusterManager() as manager:
# start a cluster
model = await manager.start_cluster()
# return None if a nonexistent cluster is requested
assert not manager.get_cluster('fake')
# get the cluster by id
assert model == manager.get_cluster(model['id'])
async def test_adapt():
with dask.config.set({
'labextension.defaults.kwargs': {
'processes': False
}, # for speed
'labextension.initial': [],
}):
async with DaskClusterManager() as manager:
# add a new cluster
model = await manager.start_cluster()
assert not model.get('adapt')
model = manager.adapt_cluster(model['id'], 0, 4)
adapt = model.get('adapt')
assert adapt
assert adapt['minimum'] == 0
assert adapt['maximum'] == 4
async def test_start():
with dask.config.set(config):
async with DaskClusterManager() as manager:
# add cluster
model = await manager.start_cluster()
assert not model.get('adapt')
# close cluster
assert len(manager.list_clusters()) == 1
await manager.close_cluster(model['id'])
# add cluster with adaptive configuration
model = await manager.start_cluster(
configuration={'adapt': {'minimum': 1, 'maximum': 3}}
)
assert model['adapt'] == {'minimum': 1, 'maximum': 3}
await manager.close()
async def test_list():
with dask.config.set({
'labextension.defaults.kwargs': {
'processes': False
}, # for speed
'labextension.initial': [],
}):
async with DaskClusterManager() as manager:
# start with an empty list
assert not manager.list_clusters()
# start clusters
model1 = await manager.start_cluster()
model2 = await manager.start_cluster()
models = manager.list_clusters()
assert len(models) == 2
assert model1 in models
assert model2 in models
async def test_scale():
with dask.config.set(config):
async with DaskClusterManager() as manager:
# add cluster with number of workers configuration
model = await manager.start_cluster(configuration={'workers': 3})
start = time()
while model['workers'] != 3:
await sleep(0.01)
model = manager.get_cluster(model['id'])
assert time() < start + 10, model['workers']
await sleep(0.2) # let workers settle # TODO: remove need for this
# rescale the cluster
model = await manager.scale_cluster(model['id'], 6)
start = time()
while model['workers'] != 6:
await sleep(0.01)
model = manager.get_cluster(model['id'])
assert time() < start + 10, model['workers']