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Dask and Ray Task Runners

Task runners provide an execution environment for tasks. In a flow decorator, you can specify a task runner to run the tasks called in that flow.

The default task runner is the ConcurrentTaskRunner.

Use .submit to run your tasks asynchronously

To run tasks asynchronously use the .submit method when you call them. If you call a task as you would normally in Python code it will run synchronously, even if you are calling the task within a flow that uses the ConcurrentTaskRunner, DaskTaskRunner, or RayTaskRunner.

Many real-world data workflows benefit from true parallel, distributed task execution. For these use cases, the following Prefect-developed task runners for parallel task execution may be installed as Prefect Integrations.

These task runners can spin up a local Dask cluster or Ray instance on the fly, or let you connect with a Dask or Ray environment you've set up separately. Then you can take advantage of massively parallel computing environments.

Use Dask or Ray in your flows to choose the execution environment that fits your particular needs.

To show you how they work, let's start small.

Remote storage

We recommend configuring remote file storage for task execution with DaskTaskRunner or RayTaskRunner. This ensures tasks executing in Dask or Ray have access to task result storage, particularly when accessing a Dask or Ray instance outside of your execution environment.

Configuring a task runner

You may have seen this briefly in a previous tutorial, but let's look a bit more closely at how you can configure a specific task runner for a flow.

Let's start with the SequentialTaskRunner. This task runner runs all tasks synchronously and may be useful when used as a debugging tool in conjunction with async code.

Let's start with this simple flow. We import the SequentialTaskRunner, specify a task_runner on the flow, and call the tasks with .submit().

from prefect import flow, task
from prefect.task_runners import SequentialTaskRunner

@task
def say_hello(name):
    print(f"hello {name}")

@task
def say_goodbye(name):
    print(f"goodbye {name}")

@flow(task_runner=SequentialTaskRunner())
def greetings(names):
    for name in names:
        say_hello.submit(name)
        say_goodbye.submit(name)

greetings(["arthur", "trillian", "ford", "marvin"])

Save this as sequential_flow.py and run it in a terminal. You'll see output similar to the following:

$ python sequential_flow.py
16:51:17.967 | INFO    | prefect.engine - Created flow run 'humongous-mink' for flow 'greetings'
16:51:17.967 | INFO    | Flow run 'humongous-mink' - Starting 'SequentialTaskRunner'; submitted tasks will be run sequentially...
16:51:18.038 | INFO    | Flow run 'humongous-mink' - Created task run 'say_hello-811087cd-0' for task 'say_hello'
16:51:18.038 | INFO    | Flow run 'humongous-mink' - Executing 'say_hello-811087cd-0' immediately...
hello arthur
16:51:18.060 | INFO    | Task run 'say_hello-811087cd-0' - Finished in state Completed()
16:51:18.107 | INFO    | Flow run 'humongous-mink' - Created task run 'say_goodbye-261e56a8-0' for task 'say_goodbye'
16:51:18.107 | INFO    | Flow run 'humongous-mink' - Executing 'say_goodbye-261e56a8-0' immediately...
goodbye arthur
16:51:18.123 | INFO    | Task run 'say_goodbye-261e56a8-0' - Finished in state Completed()
16:51:18.134 | INFO    | Flow run 'humongous-mink' - Created task run 'say_hello-811087cd-1' for task 'say_hello'
16:51:18.134 | INFO    | Flow run 'humongous-mink' - Executing 'say_hello-811087cd-1' immediately...
hello trillian
16:51:18.150 | INFO    | Task run 'say_hello-811087cd-1' - Finished in state Completed()
16:51:18.159 | INFO    | Flow run 'humongous-mink' - Created task run 'say_goodbye-261e56a8-1' for task 'say_goodbye'
16:51:18.159 | INFO    | Flow run 'humongous-mink' - Executing 'say_goodbye-261e56a8-1' immediately...
goodbye trillian
16:51:18.181 | INFO    | Task run 'say_goodbye-261e56a8-1' - Finished in state Completed()
16:51:18.190 | INFO    | Flow run 'humongous-mink' - Created task run 'say_hello-811087cd-2' for task 'say_hello'
16:51:18.190 | INFO    | Flow run 'humongous-mink' - Executing 'say_hello-811087cd-2' immediately...
hello ford
16:51:18.210 | INFO    | Task run 'say_hello-811087cd-2' - Finished in state Completed()
16:51:18.219 | INFO    | Flow run 'humongous-mink' - Created task run 'say_goodbye-261e56a8-2' for task 'say_goodbye'
16:51:18.219 | INFO    | Flow run 'humongous-mink' - Executing 'say_goodbye-261e56a8-2' immediately...
goodbye ford
16:51:18.237 | INFO    | Task run 'say_goodbye-261e56a8-2' - Finished in state Completed()
16:51:18.246 | INFO    | Flow run 'humongous-mink' - Created task run 'say_hello-811087cd-3' for task 'say_hello'
16:51:18.246 | INFO    | Flow run 'humongous-mink' - Executing 'say_hello-811087cd-3' immediately...
hello marvin
16:51:18.264 | INFO    | Task run 'say_hello-811087cd-3' - Finished in state Completed()
16:51:18.273 | INFO    | Flow run 'humongous-mink' - Created task run 'say_goodbye-261e56a8-3' for task 'say_goodbye'
16:51:18.273 | INFO    | Flow run 'humongous-mink' - Executing 'say_goodbye-261e56a8-3' immediately...
goodbye marvin
16:51:18.290 | INFO    | Task run 'say_goodbye-261e56a8-3' - Finished in state Completed()
16:51:18.321 | INFO    | Flow run 'humongous-mink' - Finished in state Completed('All states completed.')

If we take out the log messages and just look at the printed output of the tasks, you see they're executed in sequential order:

$ python sequential_flow.py
hello arthur
goodbye arthur
hello trillian
goodbye trillian
hello ford
goodbye ford
hello marvin
goodbye marvin

Running parallel tasks with Dask

You could argue that this simple flow gains nothing from parallel execution, but let's roll with it so you can see just how simple it is to take advantage of the DaskTaskRunner.

To configure your flow to use the DaskTaskRunner:

  1. Make sure the prefect-dask collection is installed by running pip install prefect-dask.
  2. In your flow code, import DaskTaskRunner from prefect_dask.task_runners.
  3. Assign it as the task runner when the flow is defined using the task_runner=DaskTaskRunner argument.
  4. Use the .submit method when calling functions.

This is the same flow as above, with a few minor changes to use DaskTaskRunner where we previously configured SequentialTaskRunner. Install prefect-dask, made these changes, then save the updated code as dask_flow.py.

from prefect import flow, task
from prefect_dask.task_runners import DaskTaskRunner

@task
def say_hello(name):
    print(f"hello {name}")

@task
def say_goodbye(name):
    print(f"goodbye {name}")

@flow(task_runner=DaskTaskRunner())
def greetings(names):
    for name in names:
        say_hello.submit(name)
        say_goodbye.submit(name)

if __name__ == "__main__":
    greetings(["arthur", "trillian", "ford", "marvin"])

Note that, because you're using DaskTaskRunner in a script, you must use if __name__ == "__main__": or you'll see warnings and errors.

Now run dask_flow.py. If you get a warning about accepting incoming network connections, that's okay - everything is local in this example.

$ python dask_flow.py
19:29:03.798 | INFO    | prefect.engine - Created flow run 'fine-bison' for flow 'greetings'

19:29:03.798 | INFO    | Flow run 'fine-bison' - Using task runner 'DaskTaskRunner'

19:29:04.080 | INFO    | prefect.task_runner.dask - Creating a new Dask cluster with `distributed.deploy.local.LocalCluster`
16:54:18.465 | INFO    | prefect.engine - Created flow run 'radical-finch' for flow 'greetings'
16:54:18.465 | INFO    | Flow run 'radical-finch' - Starting 'DaskTaskRunner'; submitted tasks will be run concurrently...
16:54:18.465 | INFO    | prefect.task_runner.dask - Creating a new Dask cluster with `distributed.deploy.local.LocalCluster`
16:54:19.811 | INFO    | prefect.task_runner.dask - The Dask dashboard is available at <http://127.0.0.1:8787/status>
16:54:19.881 | INFO    | Flow run 'radical-finch' - Created task run 'say_hello-811087cd-0' for task 'say_hello'
16:54:20.364 | INFO    | Flow run 'radical-finch' - Submitted task run 'say_hello-811087cd-0' for execution.
16:54:20.379 | INFO    | Flow run 'radical-finch' - Created task run 'say_goodbye-261e56a8-0' for task 'say_goodbye'
16:54:20.386 | INFO    | Flow run 'radical-finch' - Submitted task run 'say_goodbye-261e56a8-0' for execution.
16:54:20.397 | INFO    | Flow run 'radical-finch' - Created task run 'say_hello-811087cd-1' for task 'say_hello'
16:54:20.401 | INFO    | Flow run 'radical-finch' - Submitted task run 'say_hello-811087cd-1' for execution.
16:54:20.417 | INFO    | Flow run 'radical-finch' - Created task run 'say_goodbye-261e56a8-1' for task 'say_goodbye'
16:54:20.423 | INFO    | Flow run 'radical-finch' - Submitted task run 'say_goodbye-261e56a8-1' for execution.
16:54:20.443 | INFO    | Flow run 'radical-finch' - Created task run 'say_hello-811087cd-2' for task 'say_hello'
16:54:20.449 | INFO    | Flow run 'radical-finch' - Submitted task run 'say_hello-811087cd-2' for execution.
16:54:20.462 | INFO    | Flow run 'radical-finch' - Created task run 'say_goodbye-261e56a8-2' for task 'say_goodbye'
16:54:20.474 | INFO    | Flow run 'radical-finch' - Submitted task run 'say_goodbye-261e56a8-2' for execution.
16:54:20.500 | INFO    | Flow run 'radical-finch' - Created task run 'say_hello-811087cd-3' for task 'say_hello'
16:54:20.511 | INFO    | Flow run 'radical-finch' - Submitted task run 'say_hello-811087cd-3' for execution.
16:54:20.544 | INFO    | Flow run 'radical-finch' - Created task run 'say_goodbye-261e56a8-3' for task 'say_goodbye'
16:54:20.555 | INFO    | Flow run 'radical-finch' - Submitted task run 'say_goodbye-261e56a8-3' for execution.
hello arthur
goodbye ford
goodbye arthur
hello ford
goodbye marvin
goodbye trillian
hello trillian
hello marvin

DaskTaskRunner automatically creates a local Dask cluster, then starts executing all of the tasks in parallel. The results do not return in the same order as the sequential code above.

Notice what happens if you do not use the submit method when calling tasks:

from prefect import flow, task
from prefect_dask.task_runners import DaskTaskRunner


@task
def say_hello(name):
    print(f"hello {name}")


@task
def say_goodbye(name):
    print(f"goodbye {name}")


@flow(task_runner=DaskTaskRunner())
def greetings(names):
    for name in names:
        say_hello(name)
        say_goodbye(name)


if __name__ == "__main__":
    greetings(["arthur", "trillian", "ford", "marvin"])

$ python dask_flow.py

16:57:34.534 | INFO    | prefect.engine - Created flow run 'papaya-honeybee' for flow 'greetings'
16:57:34.534 | INFO    | Flow run 'papaya-honeybee' - Starting 'DaskTaskRunner'; submitted tasks will be run concurrently...
16:57:34.535 | INFO    | prefect.task_runner.dask - Creating a new Dask cluster with `distributed.deploy.local.LocalCluster`
16:57:35.715 | INFO    | prefect.task_runner.dask - The Dask dashboard is available at <http://127.0.0.1:8787/status>
16:57:35.787 | INFO    | Flow run 'papaya-honeybee' - Created task run 'say_hello-811087cd-0' for task 'say_hello'
16:57:35.788 | INFO    | Flow run 'papaya-honeybee' - Executing 'say_hello-811087cd-0' immediately...
hello arthur
16:57:35.810 | INFO    | Task run 'say_hello-811087cd-0' - Finished in state Completed()
16:57:35.820 | INFO    | Flow run 'papaya-honeybee' - Created task run 'say_goodbye-261e56a8-0' for task 'say_goodbye'
16:57:35.820 | INFO    | Flow run 'papaya-honeybee' - Executing 'say_goodbye-261e56a8-0' immediately...
goodbye arthur
16:57:35.840 | INFO    | Task run 'say_goodbye-261e56a8-0' - Finished in state Completed()
16:57:35.849 | INFO    | Flow run 'papaya-honeybee' - Created task run 'say_hello-811087cd-1' for task 'say_hello'
16:57:35.849 | INFO    | Flow run 'papaya-honeybee' - Executing 'say_hello-811087cd-1' immediately...
hello trillian
16:57:35.869 | INFO    | Task run 'say_hello-811087cd-1' - Finished in state Completed()
16:57:35.878 | INFO    | Flow run 'papaya-honeybee' - Created task run 'say_goodbye-261e56a8-1' for task 'say_goodbye'
16:57:35.878 | INFO    | Flow run 'papaya-honeybee' - Executing 'say_goodbye-261e56a8-1' immediately...
goodbye trillian
16:57:35.894 | INFO    | Task run 'say_goodbye-261e56a8-1' - Finished in state Completed()
16:57:35.907 | INFO    | Flow run 'papaya-honeybee' - Created task run 'say_hello-811087cd-2' for task 'say_hello'
16:57:35.907 | INFO    | Flow run 'papaya-honeybee' - Executing 'say_hello-811087cd-2' immediately...
hello ford
16:57:35.924 | INFO    | Task run 'say_hello-811087cd-2' - Finished in state Completed()
16:57:35.933 | INFO    | Flow run 'papaya-honeybee' - Created task run 'say_goodbye-261e56a8-2' for task 'say_goodbye'
16:57:35.933 | INFO    | Flow run 'papaya-honeybee' - Executing 'say_goodbye-261e56a8-2' immediately...
goodbye ford
16:57:35.951 | INFO    | Task run 'say_goodbye-261e56a8-2' - Finished in state Completed()
16:57:35.959 | INFO    | Flow run 'papaya-honeybee' - Created task run 'say_hello-811087cd-3' for task 'say_hello'
16:57:35.959 | INFO    | Flow run 'papaya-honeybee' - Executing 'say_hello-811087cd-3' immediately...
hello marvin
16:57:35.976 | INFO    | Task run 'say_hello-811087cd-3' - Finished in state Completed()
16:57:35.985 | INFO    | Flow run 'papaya-honeybee' - Created task run 'say_goodbye-261e56a8-3' for task 'say_goodbye'
16:57:35.985 | INFO    | Flow run 'papaya-honeybee' - Executing 'say_goodbye-261e56a8-3' immediately...
goodbye marvin
16:57:36.004 | INFO    | Task run 'say_goodbye-261e56a8-3' - Finished in state Completed()
16:57:36.289 | INFO    | Flow run 'papaya-honeybee' - Finished in state Completed('All states completed.')

The tasks are not submitted to the DaskTaskRunner and are run sequentially.

Running parallel tasks with Ray

To demonstrate the ability to flexibly apply the task runner appropriate for your workflow, use the same flow as above, with a few minor changes to use the RayTaskRunner where we previously configured DaskTaskRunner.

To configure your flow to use the RayTaskRunner:

  1. Make sure the prefect-ray collection is installed by running pip install prefect-ray.
  2. In your flow code, import RayTaskRunner from prefect_ray.task_runners.
  3. Assign it as the task runner when the flow is defined using the task_runner=RayTaskRunner argument.

Ray environment limitations

While we're excited about parallel task execution via Ray to Prefect, there are some inherent limitations with Ray you should be aware of:

  • Support for Python 3.11 is experimental.
  • Ray support for non-x86/64 architectures such as ARM/M1 processors with installation from pip alone and will be skipped during installation of Prefect. It is possible to manually install the blocking component with conda. See the Ray documentation for instructions.
  • Ray's Windows support is currently in beta.

See the Ray installation documentation for further compatibility information.

Save this code in ray_flow.py.

from prefect import flow, task
from prefect_ray.task_runners import RayTaskRunner

@task
def say_hello(name):
    print(f"hello {name}")

@task
def say_goodbye(name):
    print(f"goodbye {name}")

@flow(task_runner=RayTaskRunner())
def greetings(names):
    for name in names:
        say_hello.submit(name)
        say_goodbye.submit(name)

if __name__ == "__main__":
    greetings(["arthur", "trillian", "ford", "marvin"])

Now run ray_flow.py RayTaskRunner automatically creates a local Ray instance, then immediately starts executing all of the tasks in parallel. If you have an existing Ray instance, you can provide the address as a parameter to run tasks in the instance. See Running tasks on Ray for details.

Using multiple task runners

Many workflows include a variety of tasks, and not all of them benefit from parallel execution. You'll most likely want to use the Dask or Ray task runners and spin up their respective resources only for those tasks that need them.

Because task runners are specified on flows, you can assign different task runners to tasks by using subflows to organize those tasks.

This example uses the same tasks as the previous examples, but on the parent flow greetings() we use the default ConcurrentTaskRunner. Then we call a ray_greetings() subflow that uses the RayTaskRunner to execute the same tasks in a Ray instance.

from prefect import flow, task
from prefect_ray.task_runners import RayTaskRunner

@task
def say_hello(name):
    print(f"hello {name}")

@task
def say_goodbye(name):
    print(f"goodbye {name}")

@flow(task_runner=RayTaskRunner())
def ray_greetings(names):
    for name in names:
        say_hello.submit(name)
        say_goodbye.submit(name)

@flow()
def greetings(names):
    for name in names:
        say_hello.submit(name)
        say_goodbye.submit(name)
    ray_greetings(names)

if __name__ == "__main__":
    greetings(["arthur", "trillian", "ford", "marvin"])

If you save this as ray_subflow.py and run it, you'll see that the flow greetings runs as you'd expect for a concurrent flow, then flow ray-greetings spins up a Ray instance to run the tasks again.