prefect_dask.task_runners
¶
Interface and implementations of the Dask Task Runner. Task Runners in Prefect are responsible for managing the execution of Prefect task runs. Generally speaking, users are not expected to interact with task runners outside of configuring and initializing them for a flow.
Example
import time
from prefect import flow, task
@task
def shout(number):
time.sleep(0.5)
print(f"#{number}")
@flow
def count_to(highest_number):
for number in range(highest_number):
shout.submit(number)
if __name__ == "__main__":
count_to(10)
# outputs
#0
#1
#2
#3
#4
#5
#6
#7
#8
#9
Switching to a DaskTaskRunner
:
import time
from prefect import flow, task
from prefect_dask import DaskTaskRunner
@task
def shout(number):
time.sleep(0.5)
print(f"#{number}")
@flow(task_runner=DaskTaskRunner)
def count_to(highest_number):
for number in range(highest_number):
shout.submit(number)
if __name__ == "__main__":
count_to(10)
# outputs
#3
#7
#2
#6
#4
#0
#1
#5
#8
#9
DaskTaskRunner
¶
Bases: BaseTaskRunner
A parallel task_runner that submits tasks to the dask.distributed
scheduler.
By default a temporary distributed.LocalCluster
is created (and
subsequently torn down) within the start()
contextmanager. To use a
different cluster class (e.g.
dask_kubernetes.KubeCluster
), you can
specify cluster_class
/cluster_kwargs
.
Alternatively, if you already have a dask cluster running, you can provide
the cluster object via the cluster
kwarg or the address of the scheduler
via the address
kwarg.
Multiprocessing safety
Note that, because the DaskTaskRunner
uses multiprocessing, calls to flows
in scripts must be guarded with if __name__ == "__main__":
or warnings will
be displayed.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
cluster |
Cluster
|
Currently running dask cluster;
if one is not provider (or specified via |
None
|
address |
string
|
Address of a currently running dask
scheduler. Defaults to |
None
|
cluster_class |
string or callable
|
The cluster class to use
when creating a temporary dask cluster. Can be either the full
class name (e.g. |
None
|
cluster_kwargs |
dict
|
Additional kwargs to pass to the
|
None
|
adapt_kwargs |
dict
|
Additional kwargs to pass to |
None
|
client_kwargs |
dict
|
Additional kwargs to use when creating a
|
None
|
Examples:
Using a temporary local dask cluster:
from prefect import flow
from prefect_dask.task_runners import DaskTaskRunner
@flow(task_runner=DaskTaskRunner)
def my_flow():
...
Using a temporary cluster running elsewhere. Any Dask cluster class should work, here we use dask-cloudprovider:
DaskTaskRunner(
cluster_class="dask_cloudprovider.FargateCluster",
cluster_kwargs={
"image": "prefecthq/prefect:latest",
"n_workers": 5,
},
)
Connecting to an existing dask cluster:
DaskTaskRunner(address="192.0.2.255:8786")
Source code in prefect_dask/task_runners.py
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|
duplicate
¶
Create a new instance of the task runner with the same settings.
Source code in prefect_dask/task_runners.py
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