Usage Guide - Prefect

Use prefect-dask to speed up code with parallelization.

Run tasks on Dask

The DaskTaskRunner is a parallel task runner that submits tasks to the dask.distributed scheduler. By default, a temporary Dask cluster is created for the duration of the flow run. For example, this flow counts up to 10 in parallel (note that the output is not sequential).

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

If you already have a Dask cluster running, either local or cloud hosted, you can provide the connection URL via an address argument. To configure your flow to use the DaskTaskRunner:

Make sure the prefect-dask collection is installed as described earlier: pip install prefect-dask.
In your flow code, import DaskTaskRunner from prefect_dask.task_runners.
Assign it as the task runner when the flow is defined using the task_runner=DaskTaskRunner argument.

For example, this flow uses the DaskTaskRunner configured to access an existing Dask cluster at http://my-dask-cluster.

from prefect import flow
from prefect_dask.task_runners import DaskTaskRunner

@flow(task_runner=DaskTaskRunner(address="http://my-dask-cluster"))
def my_flow():
    ...

DaskTaskRunner accepts the following optional parameters:

Parameter	Description
address	Address of a currently running Dask scheduler.
cluster_class	The cluster class to use when creating a temporary Dask cluster. It can be either the full class name (for example, `"distributed.LocalCluster"`), or the class itself.
cluster_kwargs	Additional kwargs to pass to the `cluster_class` when creating a temporary Dask cluster.
adapt_kwargs	Additional kwargs to pass to `cluster.adapt` when creating a temporary Dask cluster. Note that adaptive scaling is only enabled if `adapt_kwargs` are provided.
client_kwargs	Additional kwargs to use when creating a `dask.distributed.Client`.

Multiprocessing safetyNote that, because the DaskTaskRunner uses multiprocessing, calls to flows in scripts must be guarded with if __name__ == "__main__": or you will encounter warnings and errors.

If you don’t provide the address of a Dask scheduler, Prefect creates a temporary local cluster automatically. The number of workers used is based on the number of cores available to your execution environment. The default provides a mix of processes and threads that should work well for most workloads. If you want to specify this explicitly, you can pass values for n_workers or threads_per_worker to cluster_kwargs.

# Use 4 worker processes, each with 2 threads
DaskTaskRunner(
    cluster_kwargs={"n_workers": 4, "threads_per_worker": 2}
)

Distributing Dask collections across workers

If you use a Dask collection, such as a dask.DataFrame or dask.Bag, to distribute the work across workers and achieve parallel computations, use one of the context managers get_dask_client or get_async_dask_client:

import dask
from prefect import flow, task
from prefect_dask import DaskTaskRunner, get_dask_client

@task
def compute_task():
    with get_dask_client() as client:
        df = dask.datasets.timeseries("2000", "2001", partition_freq="4w")
        summary_df = df.describe().compute()
    return summary_df

@flow(task_runner=DaskTaskRunner())
def dask_flow():
    prefect_future = compute_task.submit()
    return prefect_future.result()

dask_flow()

The context managers can be used the same way in both flow run contexts and task run contexts.

Resolving futures in sync clientNote, by default, dask_collection.compute() returns concrete values while client.compute(dask_collection) returns Dask Futures. Therefore, if you call client.compute, you must resolve all futures before exiting out of the context manager by either:

setting sync=True

with get_dask_client() as client:
    df = dask.datasets.timeseries("2000", "2001", partition_freq="4w")
    summary_df = client.compute(df.describe(), sync=True)

calling result()

with get_dask_client() as client:
    df = dask.datasets.timeseries("2000", "2001", partition_freq="4w")
    summary_df = client.compute(df.describe()).result()

For more information, visit the docs on Waiting on Futures.

There is also an equivalent context manager for asynchronous tasks and flows: get_async_dask_client.

import asyncio

import dask
from prefect import flow, task
from prefect_dask import DaskTaskRunner, get_async_dask_client

@task
async def compute_task():
    async with get_async_dask_client() as client:
        df = dask.datasets.timeseries("2000", "2001", partition_freq="4w")
        summary_df = await client.compute(df.describe())
    return summary_df

@flow(task_runner=DaskTaskRunner())
async def dask_flow():
    prefect_future = await compute_task.submit()
    return await prefect_future.result()

asyncio.run(dask_flow())

Resolving futures in async clientWith the async client, you do not need to set sync=True or call result().However, you must await client.compute(dask_collection) before exiting the context manager.To invoke compute from the Dask collection, set sync=False and call result() before exiting out of the context manager: await dask_collection.compute(sync=False).

Use a temporary cluster

The DaskTaskRunner is capable of creating a temporary cluster using any of Dask’s cluster-manager options. This can be useful when you want each flow run to have its own Dask cluster, allowing for per-flow adaptive scaling. To configure, you need to provide a cluster_class. This can be:

A string specifying the import path to the cluster class (for example, "dask_cloudprovider.aws.FargateCluster")
The cluster class itself
A function for creating a custom cluster

You can also configure cluster_kwargs, which takes a dictionary of keyword arguments to pass to cluster_class when starting the flow run. For example, to configure a flow to use a temporary dask_cloudprovider.aws.FargateCluster with 4 workers running with an image named my-prefect-image:

DaskTaskRunner(
    cluster_class="dask_cloudprovider.aws.FargateCluster",
    cluster_kwargs={"n_workers": 4, "image": "my-prefect-image"},
)

Connect to an existing cluster

Multiple Prefect flow runs can all use the same existing Dask cluster. You might manage a single long-running Dask cluster (maybe using the Dask Helm Chart) and configure flows to connect to it during execution. This has a few downsides when compared to using a temporary cluster (as described above):

All workers in the cluster must have dependencies installed for all flows you intend to run.
Multiple flow runs may compete for resources. Dask tries to do a good job sharing resources between tasks, but you may still run into issues.

That said, you may prefer managing a single long-running cluster. To configure a DaskTaskRunner to connect to an existing cluster, pass in the address of the scheduler to the address argument:

# Connect to an existing cluster running at a specified address
DaskTaskRunner(address="tcp://...")

Adaptive scaling

One nice feature of using a DaskTaskRunner is the ability to scale adaptively to the workload. Instead of specifying n_workers as a fixed number, this lets you specify a minimum and maximum number of workers to use, and the dask cluster will scale up and down as needed. To do this, you can pass adapt_kwargs to DaskTaskRunner. This takes the following fields:

maximum (int or None, optional): the maximum number of workers to scale to. Set to None for no maximum.
minimum (int or None, optional): the minimum number of workers to scale to. Set to None for no minimum.

For example, here we configure a flow to run on a FargateCluster scaling up to at most 10 workers.

DaskTaskRunner(
    cluster_class="dask_cloudprovider.aws.FargateCluster",
    adapt_kwargs={"maximum": 10}
)

Dask annotations

Dask annotations can be used to further control the behavior of tasks. For example, we can set the priority of tasks in the Dask scheduler:

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

@task
def show(x):
    print(x)


@flow(task_runner=DaskTaskRunner())
def my_flow():
    with dask.annotate(priority=-10):
        future = show(1)  # low priority task

    with dask.annotate(priority=10):
        future = show(2)  # high priority task

Another common use case is resource annotations:

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

@task
def show(x):
    print(x)

# Create a `LocalCluster` with some resource annotations
# Annotations are abstract in dask and not inferred from your system.
# Here, we claim that our system has 1 GPU and 1 process available per worker
@flow(
    task_runner=DaskTaskRunner(
        cluster_kwargs={"n_workers": 1, "resources": {"GPU": 1, "process": 1}}
    )
)
def my_flow():
    with dask.annotate(resources={'GPU': 1}):
        future = show(0)  # this task requires 1 GPU resource on a worker

    with dask.annotate(resources={'process': 1}):
        # These tasks each require 1 process on a worker; because we've 
        # specified that our cluster has 1 process per worker and 1 worker,
        # these tasks will run sequentially
        future = show(1)
        future = show(2)
        future = show(3)

​Run tasks on Dask

​Distributing Dask collections across workers

​Use a temporary cluster

​Connect to an existing cluster

​Adaptive scaling

​Dask annotations

Run tasks on Dask

Distributing Dask collections across workers

Use a temporary cluster

Connect to an existing cluster

Adaptive scaling

Dask annotations