Scale generic Python code with simple, foundational primitives that enable a high degree of control for building distributed applications or custom platforms.

Parallel Python Code Ray is Python-native. Scale and distribute any Python code for use cases like simulation, backtesting, and more.

For custom applications, the Ray Core library enables Python developers to easily build scalable, distributed systems that can run on a laptop, cluster, cloud, or Kubernetes.

Ray is an open source unified framework for scaling AI and Python applications. It provides a simple, universal API for building distributed applications that can scale from a laptop to a cluster.

The Ray API builds on concepts such as decorators, functions, and classes, that are familiar to Python programmers. It is a universal programming interface for distributed computing.

All Python versions supported by Ray are available, e.g. py310, py311 and py312. If unspecified, the tag points to an image of the lowest Python version that the Ray version supports.

This page discusses the various ways to configure Ray, both from the Python API and from the command line. Take a look at the ray.init documentation for a complete overview of the configurations.

Tasks # Ray enables arbitrary functions to be executed asynchronously on separate worker processes. Such functions are called Ray remote functions and their asynchronous invocations are called Ray …

The decorator converts Translator from a Python class into a Ray Serve Deployment object. Each deployment stores a single Python function or class that you write and uses it to serve requests.

As a Python-first framework, you can easily express and interactively develop your inference workloads in Ray. To learn more about running batch inference with Ray, see the batch inference guide.