prefect-ray
Prefect integrations with the Ray execution framework.
Decision gist · record as of 2026-08-14
Yes, if you are already using Prefect and need distributed task execution. The package is actively maintained, has low install friction, carries no licensing restrictions, and supports Python 3.10, 3.11, and 3.12. Install it when you have Prefect workflows that would benefit from Ray's parallel execution and you have Ray infrastructure available.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Ray cluster to be running and accessible; Python 3.10 or later (not 3.13).
- Low friction installation with a pure Python wheel.
- Actively maintained with recent releases.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 is permissive and poses no restrictions on commercial or private use, modification, or redistribution.
last release 2026-05-16 (90 days) · last repo commit 2026-08-14 · 23,623 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 143,073 downloads/mo, #11,184 on PyPI
Alternatives
Verify before relying
pip install prefect-ray
from prefect import flow, task
from prefect_ray import RayTaskRunner
@flow
def my_workflow():
pass
my_workflow(task_runner=RayTaskRunner())- Whether Ray cluster setup and configuration is documented within the package or requires external Ray knowledge.
- What specific Prefect workflow patterns or task types benefit most from Ray execution versus other executors.
- How the integration handles Ray cluster lifecycle management and fault recovery.
What it is and what it does
prefect-ray is an integration layer that connects Prefect's workflow orchestration engine to Ray, a distributed computing framework. It allows Prefect workflows to execute tasks in parallel across Ray clusters, enabling horizontal scaling of compute-intensive workloads. The package depends on both prefect and ray as runtime dependencies, meaning you need both libraries installed and configured to use it effectively.
Typically used when you have Prefect workflows that need to run tasks in parallel on multiple machines or cores, or when you want to leverage Ray's distributed computing capabilities within a Prefect-orchestrated pipeline. The integration handles the bridge between Prefect's task model and Ray's execution model.
Use it for
- Run CPU-intensive Prefect tasks in parallel across a Ray cluster to reduce total workflow execution time.
- Scale machine learning training or batch inference jobs orchestrated by Prefect to multiple nodes via Ray.
- Execute data processing pipelines with Prefect while distributing compute across Ray workers.
- Coordinate multi-stage workflows in Prefect where individual stages benefit from Ray's parallel execution.
- Integrate Ray's fault tolerance with Prefect's workflow scheduling and monitoring capabilities.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using Prefect and need distributed task execution.
The package is actively maintained, has low install friction, carries no licensing restrictions, and supports Python 3.10, 3.11, and 3.12. Install it when you have Prefect workflows that would benefit from Ray's parallel execution and you have Ray infrastructure available.
Install
prefect-ray on PyPI
Before you install
Low friction installation with a pure Python wheel. Actively maintained with recent releases. Requires Python 3.10 or later but excludes Python 3.13.
Requires Ray cluster to be running and accessible; Python 3.10 or later (not 3.13).
License in practice
Apache License 2.0 is permissive and poses no restrictions on commercial or private use, modification, or redistribution.
Quickstart
pip install prefect-ray
from prefect import flow, task
from prefect_ray import RayTaskRunner
@flow
def my_workflow():
pass
my_workflow(task_runner=RayTaskRunner())Verify before relying
- Whether Ray cluster setup and configuration is documented within the package or requires external Ray knowledge.
- What specific Prefect workflow patterns or task types benefit most from Ray execution versus other executors.
- How the integration handles Ray cluster lifecycle management and fault recovery.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Capped below the current Python release !=3.13,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesprefectray |
| Maintenance | Actively maintained 90 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 143,073 / month, #11,184 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 :: 3.12Topic :: Software Development :: Libraries |
Evidence: prefect_ray-0.5.0-py3-none-any.whl
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See also prefect-dask · prefect-docker · prefect-redis · ray · prefect-gcp · prefect-client · prefect-dbt · prefect · prefect-sqlalchemy · prefect-shell