class llc4320_benchmarks():
"""Zarr GCP tests on LLC4320 Datasets
"""
timer = timeit.default_timer
timeout = 3600
repeat = 1
number = 1
warmup_time = 0.0
run_nums = np.arange(1, RUNS + 1)
params = (['GCS'], [1], [60, 80, 100, 120, 140, 160], run_nums)
#params = (['GCS'], [1], [60], run_nums)
#params = getTestConfigValue("gcp_kubernetes_read_zarr.llc4320_benchmarks")
param_names = ['backend', 'z_chunksize', 'n_workers', 'run_num']
@bmt.test_gcp
def setup(self, backend, z_chunksize, n_workers, run_num):
self.cluster = KubeCluster(n_workers=n_workers)
self.client = Client(self.cluster)
bmt.cluster_wait(self.client, n_workers)
self.target = target_zarr.ZarrStore(backend=backend, dask=True)
# Open Zarr DS
self.ds_zarr = self.target.open_store(DS_STORE)
self.ds_zarr_theta = self.ds_zarr.Theta
@bmt.test_gcp
def time_read(self, backend, z_chunksize, n_workers, run_num):
self.ds_zarr_theta.max().load(retries=RETRIES)
@bmt.test_gcp
def teardown(self, backend, z_chunksize, n_workers, run_num):
del self.ds_zarr_theta
self.cluster.close()
class llc4320_benchmarks():
"""netCDF GCP tests on LLC4320 Datasets
"""
timer = timeit.default_timer
timeout = 3600
repeat = 1
number = 1
warmup_time = 0.0
run_nums = np.arange(1, RUNS + 1)
#params = (['FUSE'], [90], [60, 80, 100, 120, 140, 160], run_nums)
params = (['FUSE'], [10], [100], run_nums)
param_names = ['backend', 'z_chunksize', 'n_workers', 'run_num']
@bmt.test_gcp
def setup(self, backend, z_chunksize, n_workers, run_num):
self.cluster = KubeCluster(n_workers=n_workers)
self.client = Client(self.cluster)
bmt.cluster_wait(self.client, n_workers)
self.target = target_zarr.ZarrStore(backend=backend, dask=True)
# Open netCDF DS
self.ds_netcdf = xr.open_mfdataset(DS_FILES,
decode_cf=False,
autoclose=True,
chunks={'Z': z_chunksize})
self.ds_netcdf_theta = self.ds_netcdf.Theta
@bmt.test_gcp
def time_read(self, backend, z_chunksize, n_workers, run_num):
self.ds_netcdf_theta.max().load(retries=RETRIES)
@bmt.test_gcp
def teardown(self, backend, z_chunksize, n_workers, run_num):
del self.ds_netcdf_theta
self.cluster.close()
def setup(self, backend, z_chunksize, n_workers, run_num):
self.cluster = KubeCluster(n_workers=n_workers)
self.client = Client(self.cluster)
bmt.cluster_wait(self.client, n_workers)
self.target = target_zarr.ZarrStore(backend=backend, dask=True)
# Open Zarr DS
self.ds_zarr = self.target.open_store(DS_STORE)
self.ds_zarr_theta = self.ds_zarr.Theta
def test_namespace(pod_spec, loop, ns):
with KubeCluster(pod_spec, loop=loop, namespace=ns) as cluster:
assert 'dask' in cluster.name
assert getpass.getuser() in cluster.name
with KubeCluster(pod_spec, loop=loop, namespace=ns) as cluster2:
assert cluster.name != cluster2.name
cluster2.scale(1)
[pod] = cluster2.pods()
def test_bad_args():
with pytest.raises(TypeError) as info:
KubeCluster("myfile.yaml")
assert "KubeCluster.from_yaml" in str(info.value)
with pytest.raises((ValueError, TypeError)) as info:
KubeCluster({"kind": "Pod"})
assert "KubeCluster.from_dict" in str(info.value)
async def test_namespace(k8s_cluster, pod_spec):
async with KubeCluster(pod_spec, **cluster_kwargs) as cluster:
assert "dask" in cluster.name
assert getpass.getuser() in cluster.name
async with KubeCluster(pod_spec, **cluster_kwargs) as cluster2:
assert cluster.name != cluster2.name
cluster2.scale(1)
while len(await cluster2.pods()) != 1:
await asyncio.sleep(0.1)
def test_bad_args(loop):
with pytest.raises(TypeError) as info:
KubeCluster('myfile.yaml')
assert 'KubeCluster.from_yaml' in str(info.value)
with pytest.raises((ValueError, TypeError)) as info:
KubeCluster({'kind': 'Pod'})
assert 'KubeCluster.from_dict' in str(info.value)
def cluster(self):
if not self._cluster:
try:
from dask_kubernetes import KubeCluster
except ImportError as e:
print(
'missing dask_kubernetes, please run "pip install dask_kubernetes"'
)
raise e
self._cluster = KubeCluster(self.to_pod())
return self._cluster
def setup(self, backend, z_chunksize, n_workers, run_num):
self.cluster = KubeCluster(n_workers=n_workers)
self.client = Client(self.cluster)
bmt.cluster_wait(self.client, n_workers)
self.target = target_zarr.ZarrStore(backend=backend, dask=True)
# Open netCDF DS
self.ds_netcdf = xr.open_mfdataset(DS_FILES,
decode_cf=False,
autoclose=True,
chunks={'Z': z_chunksize})
self.ds_netcdf_theta = self.ds_netcdf.Theta
def setup(self, backend, z_chunksize, n_workers, run_num):
self.cluster = KubeCluster(n_workers=n_workers)
self.client = Client(self.cluster)
bmt.cluster_wait(self.client, n_workers)
self.chunks = (3000, 3000, z_chunksize)
self.da = da.random.normal(10, 0.1, size=DS_DIM, chunks=self.chunks)
self.target = target_zarr.ZarrStore(backend=backend,
dask=True,
chunksize=self.chunks,
shape=self.da.shape,
dtype=self.da.dtype)
self.target.get_temp_filepath()
def make_kube(pod_spec, **kws):
"""Create a dask_kubernetes.KubeCluster.
pod_spec is either the name of a YAML file containg the worker pod
specification or a dict containing the specification directly.
kws is passed to KubeCluster.from_yaml or .from_dict.
"""
from dask_kubernetes import KubeCluster
if isistance(pod_spec, str):
return KubeCluster.from_yaml(pod_spec, **kws)
else:
return KubeCluster.from_dict(pod_spec, **kws)
def cluster(self, scale=0):
if not self._cluster:
try:
from dask_kubernetes import KubeCluster
from dask.distributed import Client
except ImportError as e:
print('missing dask_kubernetes, please run "pip install dask_kubernetes"')
raise e
self._cluster = KubeCluster(self.to_pod())
if not scale:
self._cluster.adapt()
else:
self._cluster.scale(scale)
Client(self._cluster)
return self._cluster
def test_pod_template_minimal_dict(docker_image, loop):
spec = {
"spec": {
"containers": [
{
"args": [
"dask-worker",
"$(DASK_SCHEDULER_ADDRESS)",
"--nthreads",
"1",
"--death-timeout",
"60",
],
"command": None,
"image": docker_image,
"imagePullPolicy": "IfNotPresent",
"name": "worker",
}
]
}
}
with KubeCluster(spec, loop=loop) as cluster:
cluster.adapt()
with Client(cluster, loop=loop) as client:
future = client.submit(lambda x: x + 1, 10)
result = future.result()
assert result == 11
def run_flow(self) -> None:
"""
Run the flow from specified flow_file_path location using a Dask executor
"""
from prefect.engine import get_default_flow_runner_class
from prefect.engine.executors import DaskExecutor
from dask_kubernetes import KubeCluster
with open(path.join(path.dirname(__file__),
"worker_pod.yaml")) as pod_file:
worker_pod = yaml.safe_load(pod_file)
worker_pod = self._populate_worker_pod_yaml(yaml_obj=worker_pod)
cluster = KubeCluster.from_dict(worker_pod)
cluster.adapt(minimum=1, maximum=1)
# Load serialized flow from file and run it with a DaskExecutor
with open(
prefect.context.get("flow_file_path",
"/root/.prefect/flow_env.prefect"),
"rb",
) as f:
flow = cloudpickle.load(f)
executor = DaskExecutor(address=cluster.scheduler_address)
runner_cls = get_default_flow_runner_class()
runner_cls(flow=flow).run(executor=executor)
class DaskDelegateConfig(DelegateConfig):
redis_port = 6379
redis_address = os.environ.get("REDIS_ADDRESS")
redis_db = 0
redis_cache_ttl = 60 * 60
redis_vault_dir = "vault"
dask_cluster_port = 8786
dask_cluster_address = "localhost"
dask_use_remote_cluster = False
dask_worker_count = 1
dask_worker_threads = 2
dask_worker_memory_limit = "4GB"
dask_dashboard_port = 8788
dask_dashboard_address = "localhost"
dask_dashboard_enabled = False
kube_dask_worker_spec = os.environ.get("WORKER_SPEC_PATH")
kube_cluster = None
cache_provider: type[CacheProvider] = SimpleDiskCache(SimpleDiskCacheConfig())
if kube_dask_worker_spec is not None:
kube_cluster = KubeCluster(pod_template=kube_dask_worker_spec, n_workers=1)
def test_extra_container_config_merge(image_name, loop):
"""
Test that our container config merging process works recursively fine
"""
with KubeCluster(
make_pod_spec(
image_name,
extra_container_config={
"env": [{
"name": "BOO",
"value": "FOO"
}],
"args": ["last-item"],
},
),
loop=loop,
n_workers=0,
env={"TEST": "HI"},
) as cluster:
pod = cluster.pod_template
assert pod.spec.containers[0].env == [
{
"name": "TEST",
"value": "HI"
},
{
"name": "BOO",
"value": "FOO"
},
]
assert pod.spec.containers[0].args[-1] == "last-item"
async def test_escape_name(pod_spec, auth, ns):
async with KubeCluster(pod_spec,
namespace=ns,
name="foo@bar",
auth=auth,
**cluster_kwargs) as cluster:
assert "@" not in str(cluster.pod_template)
async def remote_cluster(pod_spec, ns, auth):
async with KubeCluster(pod_spec,
namespace=ns,
deploy_mode="remote",
auth=auth,
**cluster_kwargs) as cluster:
yield cluster
def test_escape_username(pod_spec, loop, ns, monkeypatch):
monkeypatch.setenv('LOGNAME', 'foo!')
with KubeCluster(pod_spec, loop=loop, namespace=ns) as cluster:
assert 'foo' in cluster.name
assert '!' not in cluster.name
assert 'foo' in cluster.pod_template.metadata.labels['user']
def test_automatic_startup(image_name, loop, ns):
test_yaml = {
"kind": "Pod",
"metadata": {
"labels": {
"foo": "bar",
}
},
"spec": {
"containers": [{
"args": [
"dask-worker", "$(DASK_SCHEDULER_ADDRESS)", "--nthreads",
"1"
],
"image":
image_name,
"name":
"dask-worker"
}]
}
}
with tmpfile(extension='yaml') as fn:
with open(fn, mode='w') as f:
yaml.dump(test_yaml, f)
with dask.config.set({'kubernetes.worker-template-path': fn}):
with KubeCluster(loop=loop, namespace=ns) as cluster:
assert cluster.pod_template.metadata.labels['foo'] == 'bar'
def test_pod_from_yaml_expand_env_vars(image_name, loop, ns):
try:
os.environ["FOO_IMAGE"] = image_name
test_yaml = {
"kind": "Pod",
"metadata": {
"labels": {
"app": "dask",
"component": "dask-worker"
}
},
"spec": {
"containers": [{
"args": [
"dask-worker",
"$(DASK_SCHEDULER_ADDRESS)",
"--nthreads",
"1"
],
"image": '${FOO_IMAGE}',
'imagePullPolicy': 'IfNotPresent',
"name": "dask-worker"
}]
}
}
with tmpfile(extension='yaml') as fn:
with open(fn, mode='w') as f:
yaml.dump(test_yaml, f)
with KubeCluster.from_yaml(f.name, loop=loop, namespace=ns) as cluster:
assert cluster.pod_template.spec.containers[0].image == image_name
finally:
del os.environ['FOO_IMAGE']
async def test_automatic_startup(image_name, ns, auth):
test_yaml = {
"kind": "Pod",
"metadata": {"labels": {"foo": "bar"}},
"spec": {
"containers": [
{
"args": [
"dask-worker",
"$(DASK_SCHEDULER_ADDRESS)",
"--nthreads",
"1",
],
"image": image_name,
"name": "dask-worker",
}
]
},
}
with tmpfile(extension="yaml") as fn:
with open(fn, mode="w") as f:
yaml.dump(test_yaml, f)
with dask.config.set({"kubernetes.worker-template-path": fn}):
async with KubeCluster(
namespace=ns, auth=auth, **cluster_kwargs
) as cluster:
assert cluster.pod_template.metadata.labels["foo"] == "bar"
async def test_pod_from_yaml_expand_env_vars(image_name, ns, auth):
try:
os.environ["FOO_IMAGE"] = image_name
test_yaml = {
"kind": "Pod",
"metadata": {"labels": {"app": "dask", "component": "dask-worker"}},
"spec": {
"containers": [
{
"args": [
"dask-worker",
"$(DASK_SCHEDULER_ADDRESS)",
"--nthreads",
"1",
],
"image": "${FOO_IMAGE}",
"imagePullPolicy": "IfNotPresent",
"name": "dask-worker",
}
]
},
}
with tmpfile(extension="yaml") as fn:
with open(fn, mode="w") as f:
yaml.dump(test_yaml, f)
async with KubeCluster.from_yaml(
f.name, namespace=ns, auth=auth, **cluster_kwargs
) as cluster:
assert cluster.pod_template.spec.containers[0].image == image_name
finally:
del os.environ["FOO_IMAGE"]
def test_default_toleration_preserved(image_name):
pod_spec = make_pod_spec(
image=image_name,
extra_pod_config={
"tolerations": [{
"key": "example.org/toleration",
"operator": "Exists",
"effect": "NoSchedule",
}]
},
)
cluster = KubeCluster(pod_spec)
tolerations = cluster.pod_template.to_dict()["spec"]["tolerations"]
assert {
"key": "k8s.dask.org/dedicated",
"operator": "Equal",
"value": "worker",
"effect": "NoSchedule",
"toleration_seconds": None,
} in tolerations
assert {
"key": "k8s.dask.org_dedicated",
"operator": "Equal",
"value": "worker",
"effect": "NoSchedule",
"toleration_seconds": None,
} in tolerations
assert {
"key": "example.org/toleration",
"operator": "Exists",
"effect": "NoSchedule",
} in tolerations
def test_escape_username(pod_spec, monkeypatch):
monkeypatch.setenv("LOGNAME", "Foo!")
with KubeCluster(pod_spec) as cluster:
assert "foo" in cluster.name
assert "!" not in cluster.name
assert "foo" in cluster.pod_template.metadata.labels["user"]
def run_flow(self) -> None:
"""
Run the flow from specified flow_file_path location using a Dask executor
"""
try:
from prefect.engine import get_default_flow_runner_class
from prefect.engine.executors import DaskExecutor
from dask_kubernetes import KubeCluster
with open(path.join(path.dirname(__file__),
"worker_pod.yaml")) as pod_file:
worker_pod = yaml.safe_load(pod_file)
worker_pod = self._populate_worker_pod_yaml(
yaml_obj=worker_pod)
cluster = KubeCluster.from_dict(
worker_pod, namespace=prefect.context.get("namespace"))
cluster.adapt(minimum=1, maximum=1)
# Load serialized flow from file and run it with a DaskExecutor
with open(
prefect.context.get("flow_file_path",
"/root/.prefect/flow_env.prefect"),
"rb",
) as f:
flow = cloudpickle.load(f)
executor = DaskExecutor(address=cluster.scheduler_address)
runner_cls = get_default_flow_runner_class()
runner_cls(flow=flow).run(executor=executor)
sys.exit(0) # attempt to force resource cleanup
except Exception as exc:
self.logger.error(
"Unexpected error raised during flow run: {}".format(exc))
raise exc
def test_pod_from_dict(image_name, loop, ns):
spec = {
'metadata': {},
'restartPolicy': 'Never',
'spec': {
'containers': [{
'args': ['dask-worker', '$(DASK_SCHEDULER_ADDRESS)',
'--nthreads', '1',
'--death-timeout', '60'],
'command': None,
'image': image_name,
'imagePullPolicy': 'IfNotPresent',
'name': 'dask-worker',
}]
}
}
with KubeCluster.from_dict(spec, loop=loop, namespace=ns) as cluster:
cluster.scale(2)
with Client(cluster) as client:
future = client.submit(lambda x: x + 1, 10)
result = future.result()
assert result == 11
while len(cluster.scheduler.workers) < 2:
sleep(0.1)
# Ensure that inter-worker communication works well
futures = client.map(lambda x: x + 1, range(10))
total = client.submit(sum, futures)
assert total.result() == sum(map(lambda x: x + 1, range(10)))
assert all(client.has_what().values())
async def test_pod_from_minimal_dict(image_name, ns, auth):
spec = {
"spec": {
"containers": [
{
"args": [
"dask-worker",
"$(DASK_SCHEDULER_ADDRESS)",
"--nthreads",
"1",
"--death-timeout",
"60",
],
"command": None,
"image": image_name,
"imagePullPolicy": "IfNotPresent",
"name": "worker",
}
]
}
}
async with KubeCluster.from_dict(
spec, namespace=ns, auth=auth, **cluster_kwargs
) as cluster:
cluster.adapt()
async with Client(cluster, asynchronous=True) as client:
future = client.submit(lambda x: x + 1, 10)
result = await future
assert result == 11
def test_pod_from_minimal_dict(image_name, loop, ns):
spec = {
'spec': {
'containers': [{
'args': [
'dask-worker', '$(DASK_SCHEDULER_ADDRESS)', '--nthreads',
'1', '--death-timeout', '60'
],
'command':
None,
'image':
image_name,
'imagePullPolicy':
'IfNotPresent',
'name':
'worker'
}]
}
}
with KubeCluster.from_dict(spec, loop=loop, namespace=ns) as cluster:
cluster.adapt()
with Client(cluster) as client:
future = client.submit(lambda x: x + 1, 10)
result = future.result()
assert result == 11
async def test_start_with_workers(pod_spec, ns, auth):
async with KubeCluster(
pod_spec, n_workers=2, namespace=ns, auth=auth, **cluster_kwargs
) as cluster:
async with Client(cluster, asynchronous=True) as client:
while len(cluster.scheduler_info["workers"]) != 2:
await asyncio.sleep(0.1)
def _get_kubernetes_cluster(worker_template_path=WORKER_TEMPLATE_PATH):
from dask_kubernetes import KubeCluster
cluster = KubeCluster.from_yaml(worker_template_path)
return Client(cluster)
