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prefect-dask

Dask helps you run your flow in parallel and distribute runs over multiple machines. The prefect-dask integration makes it easy speed up your flow runs by integrating Dask into your code.

Getting started

Prerequisites

Install prefect-dask

pip install -U prefect-dask

Integrate with Prefect flows

Say your flow downloads many images to train a machine learning model. It can take longer than you'd like for the code to run because the code runs sequentially.

Parallelize your flow code with prefect-dask in three steps:

  1. Add the import: from prefect_dask import DaskTaskRunner
  2. Specify the task runner in the flow decorator: @flow(task_runner=DaskTaskRunner)
  3. Submit tasks to the flow's task runner: a_task.submit(*args, **kwargs)

Below is code with and without the DaskTaskRunner:

# Completed in 15.2 seconds

from typing import List
from pathlib import Path

import httpx
from prefect import flow, task

URL_FORMAT = (
    "https://www.cpc.ncep.noaa.gov/products/NMME/archive/"
    "{year:04d}{month:02d}0800/current/images/nino34.rescaling.ENSMEAN.png"
)

@task
def download_image(year: int, month: int, directory: Path) -> Path:
    # download image from URL
    url = URL_FORMAT.format(year=year, month=month)
    resp = httpx.get(url)

    # save content to directory/YYYYMM.png
    file_path = (directory / url.split("/")[-1]).with_stem(f"{year:04d}{month:02d}")
    file_path.write_bytes(resp.content)
    return file_path

@flow
def download_nino_34_plumes_from_year(year: int) -> List[Path]:
    # create a directory to hold images
    directory = Path("data")
    directory.mkdir(exist_ok=True)

    # download all images
    file_paths = []
    for month in range(1, 12 + 1):
        file_path = download_image(year, month, directory)
        file_paths.append(file_path)
    return file_paths

if __name__ == "__main__":
    download_nino_34_plumes_from_year(2022)

# Completed in 5.7 seconds

from typing import List
from pathlib import Path

import httpx
from prefect import flow, task
from prefect_dask import DaskTaskRunner

URL_FORMAT = (
    "https://www.cpc.ncep.noaa.gov/products/NMME/archive/"
    "{year:04d}{month:02d}0800/current/images/nino34.rescaling.ENSMEAN.png"
)

@task
def download_image(year: int, month: int, directory: Path) -> Path:
    # download image from URL
    url = URL_FORMAT.format(year=year, month=month)
    resp = httpx.get(url)

    # save content to directory/YYYYMM.png
    file_path = (directory / url.split("/")[-1]).with_stem(f"{year:04d}{month:02d}")
    file_path.write_bytes(resp.content)
    return file_path

@flow(task_runner=DaskTaskRunner(cluster_kwargs={"processes": False}))
def download_nino_34_plumes_from_year(year: int) -> List[Path]:
    # create a directory to hold images
    directory = Path("data")
    directory.mkdir(exist_ok=True)

    # download all images
    file_paths = []
    for month in range(1, 12 + 1):
        file_path = download_image.submit(year, month, directory)
        file_paths.append(file_path)
    return file_paths

if __name__ == "__main__":
    download_nino_34_plumes_from_year(2022)

In our tests, the sequential flow run took 15.2 seconds. The version with DaskTaskRunner reduced the runtime to 5.7 seconds!

For additional time savings with Dask, you could distribute large workloads over multiple machines.

Integrate with Dask client/cluster

Suppose you have an existing Dask client/cluster such as a dask.dataframe.DataFrame, and you want to add observability.

With prefect-dask, it takes just a few steps:

  1. Add imports
  2. Add task and flow decorators
  3. Use get_dask_client context manager to distribute work across Dask workers
  4. Specify the task runner and client's address in the flow decorator
  5. Submit the tasks to the flow's task runner
import dask.dataframe
import dask.distributed


client = dask.distributed.Client()

def read_data(start: str, end: str) -> dask.dataframe.DataFrame:
    df = dask.datasets.timeseries(start, end, partition_freq="4w")
    return df


def process_data(df: dask.dataframe.DataFrame) -> dask.dataframe.DataFrame:

    df_yearly_avg = df.groupby(df.index.year).mean()
    return df_yearly_avg.compute()


def dask_pipeline():
    df = read_data("1988", "2022")
    df_yearly_average = process_data(df)
    return df_yearly_average

if __name__ == "__main__":
    dask_pipeline()

import dask.dataframe
import dask.distributed
from prefect import flow, task
from prefect_dask import DaskTaskRunner, get_dask_client

client = dask.distributed.Client()

@task
def read_data(start: str, end: str) -> dask.dataframe.DataFrame:
    df = dask.datasets.timeseries(start, end, partition_freq="4w")
    return df

@task
def process_data(df: dask.dataframe.DataFrame) -> dask.dataframe.DataFrame:
    with get_dask_client():
        df_yearly_avg = df.groupby(df.index.year).mean()
        return df_yearly_avg.compute()

@flow(task_runner=DaskTaskRunner(address=client.scheduler.address))
def dask_pipeline():
    df = read_data.submit("1988", "2022")
    df_yearly_average = process_data.submit(df)
    return df_yearly_average

if __name__ == "__main__":
    dask_pipeline()

Run the code and check how long it takes each task to complete.

Resources

For additional examples, check out the Usage Guide. Refer to the prefect-dask API documentation linked in the sidebar to explore all the capabilities of the prefect-dask library.

For assistance using Dask, consult the Dask documentation.