ray-project/ray

Project overview

Ray provides a unified framework for scaling Python and AI applications from a laptop to a cluster. It offers distributed abstractions for tasks and actors, alongside libraries for data processing, training, tuning, reinforcement learning, and serving.

Project type

Model Development · Model Runtime · Data Processing · Infrastructure

Deployment

Refer to project documentation

License

Apache-2.0

Best for

• AI engineers and developers who need to scale compute-intensive Python applications from a laptop to a cluster.

Key capabilities

• Provides distributed abstractions for stateless tasks, stateful actors, and immutable objects accessible across a cluster.
• Provides scalable datasets for machine learning workloads.
• Provides distributed training for machine learning workloads.
• Provides scalable hyperparameter tuning.
• Provides scalable reinforcement learning.
• Provides scalable and programmable serving.
• Monitors Ray applications and clusters.
• Debugs Ray applications.

Limitations and risks

• Coding is required to use the framework, which functions as a Python library.
• GPU requirements, data boundaries, cost dependencies, minimum hardware, and telemetry are not documented in the available facts.

Getting started

• Install Ray using pip install ray. The supplied documentation does not provide a complete first-success workflow beyond package installation.

Evidence and sources

• GitHub project description: Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
• README: Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:
• README: Today's ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.
• README: Ray is a unified way to scale Python and AI applications from a laptop to a cluster.
• README: - `Tasks`_: Stateless functions executed in the cluster. - `Actors`_: Stateful worker processes created in the cluster. - `Objects`_: Immutable values accessible across the cluste…

Reviewed at: 2026-07-17T07:57:18Z