---
id: prefect-dask
version: "0.3.7"
license: Apache License 2.0
license_treatment: permissive
maintenance: active
---

# prefect-dask — Prefect integrations with the Dask execution framework.

License: permissive · Maintenance: active · Downloads: 171.5K/mo

## What it is and what it does

prefect-dask bridges Prefect's workflow orchestration engine with Dask's distributed computing framework. It allows you to define Prefect flows and have their tasks executed in parallel across a Dask cluster instead of sequentially or on a single machine. The package provides task runners and other integration points that handle the communication between Prefect's task graph and Dask's scheduler.

This is useful when you have compute-intensive workflows that benefit from distributed execution. You define your workflow logic in Prefect as usual, then configure it to use Dask as the execution backend. The package handles the plumbing: submitting tasks to the Dask cluster, managing dependencies, and collecting results back into your Prefect flow.

Use it for:

- Run CPU-intensive data processing pipelines across multiple machines using Prefect workflows with Dask execution.
- Scale machine learning training or batch inference jobs by distributing Prefect tasks to a Dask cluster.
- Coordinate complex multi-stage ETL jobs where different stages can run in parallel on distributed workers.
- Combine Prefect's scheduling and monitoring with Dask's horizontal scaling for long-running analytical workloads.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Integrates Prefect workflow orchestration with Dask distributed computing, enabling Prefect flows to execute tasks across Dask clusters.

Yes, if you use Prefect and need distributed task execution. The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It fills a specific integration gap: Prefect users who want to leverage Dask's parallelism without managing two separate orchestration systems. Not necessary if you run Prefect flows on a single machine or already use Prefect's built-in execution options.

## Install

pip install prefect-dask
uv add prefect-dask
poetry add prefect-dask

## Installing prefect-dask

Before you install: Low install friction with a pure-Python wheel. Active maintenance with a recent release (70 days ago) and an established repository. Requires prefect and distributed as runtime dependencies.

License in practice: Apache License 2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions.

Quickstart:

pip install prefect-dask

from prefect import flow, task
from prefect_dask import DaskTaskRunner

@flow
def my_flow():
    pass

my_flow(task_runner=DaskTaskRunner())

Requires Python 3.10 or later; a Dask cluster (local or remote) must be available or created separately.

Verify before relying:

- Whether this package requires a running Dask cluster or can provision one automatically
- Specific Prefect version compatibility constraints beyond what requires_python indicates
- Performance characteristics or scaling limits when coordinating large Dask clusters

## Package facts

- License: Apache License 2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 171.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

prefect dask integration, distributed task execution prefect, dask cluster orchestration, prefect workflow dask, parallel task scheduling, dask executor prefect, workflow-orchestration, distributed-computing, task-scheduling

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