prefect-dask
Prefect integrations with the Dask execution framework.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; a Dask cluster (local or remote) must be available or created separately.
- Low install friction with a pure-Python wheel.
- Active maintenance with a recent release (70 days ago) and an established repository.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions.
last release 2026-06-05 (70 days) · last repo commit 2026-08-14 · 23,623 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 171,494 downloads/mo, #10,364 on PyPI
Alternatives
Verify before relying
pip install prefect-dask
from prefect import flow, task
from prefect_dask import DaskTaskRunner
@flow
def my_flow():
pass
my_flow(task_runner=DaskTaskRunner())- 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
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 on this page. Verify before relying on it.
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
prefect-dask on PyPI
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.
Requires Python 3.10 or later; a Dask cluster (local or remote) must be available or created separately.
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())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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesprefectdistributed |
| Maintenance | Actively maintained 70 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 171,494 / month, #10,364 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: System AdministratorsLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Software Development :: Libraries |
Evidence: prefect_dask-0.3.7-py3-none-any.whl
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See also prefect-gcp · prefect-ray · prefect-redis · prefect-dbt · prefect · prefect-docker · distributed · prefect-sqlalchemy · prefect-shell · prefect-cloud