ray
Ray provides a simple, universal API for building distributed applications.
Decision gist · record as of 2026-08-14
Yes. Ray is a mature, actively maintained framework with strong community adoption (top 1000 PyPI packages) and no known vulnerabilities. It solves a genuine problem—scaling Python code from laptop to cluster—with a clean API and broad AI/ML library support. Medium install friction is acceptable for the capability gained. Permissive Apache 2.0 license poses no barrier. Install if you need distributed computing, parallel ML workloads, or cluster-scale Python execution.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; distributed execution benefits from a multi-node setup but can run on a single machine for development.
- Medium install friction with 8 runtime dependencies (click, filelock, jsonschema, msgpack, packaging, protobuf, pyyaml, requests).
- Actively maintained with a release 3 days old and 43514 GitHub stars.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary projects.
last release 2026-08-11 (3 days) · last repo commit 2026-08-14 · 43,514 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 63,307,310 downloads/mo, #494 on PyPI
Alternatives
Verify before relying
pip install ray
import ray
ray.init()
@ray.remote
def task():
return "result"
result = ray.get(task.remote())- Performance characteristics and scalability limits for specific workload types (tasks vs. actors vs. objects).
- Memory overhead and resource consumption patterns in production clusters.
- Compatibility and integration depth with specific cloud platforms beyond generic Kubernetes support.
What it is and what it does
Ray is a unified distributed computing framework designed to scale Python and AI applications from development laptops to production clusters. It provides a core runtime with three key abstractions: Tasks (stateless functions executed remotely), Actors (stateful worker processes), and Objects (immutable values shared across the cluster). On top of this foundation, Ray includes specialized AI libraries for Data (scalable datasets), Train (distributed training), Tune (hyperparameter tuning), RLlib (reinforcement learning), and Serve (model serving).
The framework handles the complexity of distributed execution—scheduling, communication, fault tolerance, and resource management—so developers can write Python code that runs identically on a laptop or a large cluster. Ray runs on any machine, cloud provider, or Kubernetes cluster, with an ecosystem of community integrations. It is actively maintained, supports modern Python versions (3.10–3.14), and carries no known security vulnerabilities.
Use it for
- Distribute hyperparameter tuning experiments across a cluster to accelerate model optimization.
- Scale machine learning training pipelines to process large datasets in parallel.
- Build reinforcement learning agents that train on distributed environments.
- Deploy and serve multiple ML models with dynamic scaling and load balancing.
- Run batch processing jobs that parallelize stateless computations across nodes.
- Coordinate stateful microservices using Actors for fault-tolerant, distributed state management.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Ray is a mature, actively maintained framework with strong community adoption (top 1000 PyPI packages) and no known vulnerabilities. It solves a genuine problem—scaling Python code from laptop to cluster—with a clean API and broad AI/ML library support. Medium install friction is acceptable for the capability gained. Permissive Apache 2.0 license poses no barrier. Install if you need distributed computing, parallel ML workloads, or cluster-scale Python execution.
Install
ray on PyPI
Before you install
Medium install friction with 8 runtime dependencies (click, filelock, jsonschema, msgpack, packaging, protobuf, pyyaml, requests). Actively maintained with a release 3 days old and 43514 GitHub stars. Supports Python 3.10–3.14 with prebuilt wheels for macOS, Linux, and Windows.
Requires Python 3.10 or later; distributed execution benefits from a multi-node setup but can run on a single machine for development.
License in practice
Apache 2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary projects.
Quickstart
pip install ray
import ray
ray.init()
@ray.remote
def task():
return "result"
result = ray.get(task.remote())Verify before relying
- Performance characteristics and scalability limits for specific workload types (tasks vs. actors vs. objects).
- Memory overhead and resource consumption patterns in production clusters.
- Compatibility and integration depth with specific cloud platforms beyond generic Kubernetes support.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 8 packagesclickfilelockjsonschemamsgpackpackagingprotobufpyyamlrequests |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 63,307,310 / month, #494 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: ray-2.57.0-cp310-cp310-macosx_12_0_arm64.whl; ray-2.57.0-cp310-cp310-manylinux2014_aarch64.whl; ray-2.57.0-cp310-cp310-manylinux2014_x86_64.whl; ray-2.57.0-cp310-cp310-win_amd64.whl; ray-2.57.0-cp311-cp311-macosx_12_0_arm64.whl; ray-2.57.0-cp311-cp311-manylinux2014_aarch64.whl; ray-2.57.0-cp311-cp311-manylinux2014_x86_64.whl; ray-2.57.0-cp311-cp311-win_amd64.whl; ray-2.57.0-cp312-cp312-macosx_12_0_arm64.whl; ray-2.57.0-cp312-cp312-manylinux2014_aarch64.whl; ray-2.57.0-cp312-cp312-manylinux2014_x86_64.whl; ray-2.57.0-cp312-cp312-win_amd64.whl; ray-2.57.0-cp313-cp313-macosx_12_0_arm64.whl; ray-2.57.0-cp313-cp313-manylinux2014_aarch64.whl; ray-2.57.0-cp313-cp313-manylinux2014_x86_64.whl; ray-2.57.0-cp314-cp314-macosx_12_0_arm64.whl; ray-2.57.0-cp314-cp314-manylinux2014_aarch64.whl; ray-2.57.0-cp314-cp314-manylinux2014_x86_64.whl
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See also xgboost-ray · cosmos-xenna · prefect-ray · ray-haproxy · raydp · dask-ml · embreex · daft · thespian · distributed