AI Frameworks · ray-project

ray

Ray is an open-source distributed computing framework for scaling Python and AI workloads from a laptop to a cluster. It provides core runtime abstractions (tasks, actors, objects) and specialized libraries for data processing, model training, hyperparameter tuning, reinforcement learning, and model serving.

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Source: GitHub — github.com/ray-project/ray

43.2k

GitHub stars

7.8k

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	ray-project/ray
Owner	ray-project
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	43.2k
Forks	7.8k
Open issues	3.5k
Latest release	ray-2.56.0 (2026-06-29)
Last updated	2026-07-08
Source	https://github.com/ray-project/ray

What ray is

Ray abstracts distributed execution across heterogeneous clusters via a task/actor/object model, with Python-native APIs. It includes specialized libraries (Data, Train, Tune, RLlib, Serve) for ML workflows and integrates with PyTorch, TensorFlow, and Kubernetes. The core runtime handles scheduling, fault tolerance, and object management across nodes.

Quickstart

Get the ray source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/ray-project/ray.git2cd ray3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Distributed hyperparameter tuning and AutoML

Ray Tune is purpose-built for large-scale hyperparameter optimization with support for Bayesian optimization, population-based training, and integration with ML frameworks. Ideal when you need to explore thousands of configurations across a cluster.

Scaling ML training pipelines

Ray Train simplifies distributed training for PyTorch, TensorFlow, and XGBoost. Use when you need to train large models on multi-node clusters without managing distributed boilerplate or when combining training with data preprocessing and tuning.

Real-time LLM and model serving at scale

Ray Serve handles production serving of LLMs and other models with built-in batching, canary deployments, and request routing. Well-suited for serving multiple models with variable latency/throughput requirements on shared infrastructure.

Implementation considerations

Cluster setup and configuration: Plan for node discovery, network topology, and resource allocation. Ray Auto Scaler can manage cloud clusters, but on-premises setups require manual orchestration.
Data locality and serialization: Large objects must be serialized/deserialized across the network. Design data pipelines to minimize object materialization and use Ray's object store efficiently.
Fault tolerance strategy: Ray provides fault recovery via task re-execution and actor restarts, but stateful actors require explicit checkpoint logic. Plan for transient and permanent failures.
Monitoring and debugging: Use Ray Dashboard for cluster visibility and the distributed debugger for multi-node issues. Single-node debugging is simpler; distributed debugging requires practice.
Library composition: Ray libraries (Tune, Train, Serve) are modular. Mixing libraries or custom Ray code requires understanding task/actor scheduling and potential resource contention.

When to avoid it — and what to weigh

You need strict real-time guarantees — Ray does not provide hard latency bounds or deterministic scheduling. Avoid if your application requires sub-millisecond or highly predictable latency (e.g., high-frequency trading, safety-critical systems).
Your primary workload is not Python-based — Ray's strength is scaling Python code. If your application is primarily Go, Rust, Java, or requires tight C/C++ integration, Kubernetes or language-specific frameworks may be more efficient.
You require minimal operational overhead — Ray clusters require setup, monitoring, and tuning. Avoid if you want a fully managed, hands-off experience; consider serverless ML platforms or cloud-native managed services instead.
Your workload fits comfortably on a single machine — Ray adds distributed complexity (serialization, network latency, debugging). Single-node workloads (small datasets, quick experiments) are often faster and simpler without it.

License & commercial use

Ray is licensed under Apache License 2.0 (Apache-2.0), a permissive open-source license that permits commercial use, modification, and distribution with limited liability and a requirement to include the license and copyright notice.

Apache-2.0 permits commercial use, modification, and closed-source deployment. No royalties or special permissions required. However, review your organization's license compliance policies and consider support contracts (via Anyscale) for production deployments. Warranty and liability exclusions in Apache-2.0 apply.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	High
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Security model is not explicitly documented in the provided data. Consider: Ray clusters are typically deployed within trusted networks; inter-node communication should use TLS/mutual auth in untrusted networks (configuration required). No formal security audit or CVE history visible in provided data; review Ray security advisories independently. Authentication/authorization for Ray objects and tasks is not built-in; implement at application layer or rely on network isolation. Requires independent security assessment before production use.

Alternatives to consider

Apache Spark

Spark excels at data processing and SQL workloads at scale but is less flexible for custom distributed computing patterns. Ray is more general-purpose and has better Python-first ML integration.

Kubernetes + custom orchestration

Kubernetes provides infrastructure abstraction but requires more boilerplate for ML workloads (job definitions, resource management). Ray abstracts these details for Python but trades operational control.

Managed ML platforms (SageMaker, Vertex AI, Lambda Labs)

Cloud-managed services eliminate operational overhead but lock you into a vendor and charge per usage. Ray gives you control and lower per-compute costs but requires cluster management.

Software development agency

Build on ray with DEV.co software developers

Start with Ray locally, then scale to a cluster. Review deployment architecture and operational requirements with your infrastructure team.

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ray FAQ

Does Ray require Kubernetes?

No. Ray runs on bare metal, VMs, on-premises clusters, and Kubernetes. A Kubernetes operator is available but not required. Local clusters are useful for development.

How does Ray compare to Dask?

Both scale Python workloads, but Ray is more general-purpose (tasks, actors) and excels at fine-grained parallelism and ML workflows. Dask is often simpler for dataframe/array operations. Ray Tune and Serve are purpose-built for ML and have no direct Dask equivalent.

Can I use Ray for production serving?

Yes, Ray Serve is designed for production model serving with features like batching, traffic splitting, and auto-scaling. It requires operational setup (monitoring, resource allocation) and is best suited for ML serving, not general web APIs.

What are Ray's resource requirements?

Minimum: a few GB RAM per node. Production clusters typically run 8+ nodes with 16+ GB RAM per node and GPU support for ML workloads. Exact requirements depend on your workload; start small and profile.

Work with a software development agency

DEV.co helps companies turn open-source tools like ray into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your ai frameworks stack.

Ready to scale your ML workloads?

Start with Ray locally, then scale to a cluster. Review deployment architecture and operational requirements with your infrastructure team.

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