What is Ray?¶

Ray provides a simple, universal API for building distributed applications.

Ray accomplishes this mission by:

Providing simple primitives for building and running distributed applications.
Enabling end users to parallelize single machine code, with little to zero code changes.
Including a large ecosystem of applications, libraries, and tools on top of the core Ray to enable complex applications.

Ray Core provides the simple primitives for application building.

On top of Ray Core are several libraries for solving problems in machine learning:

As well as libraries for taking ML and distributed apps to production:

There are also many community integrations with Ray, including Dask, MARS, Modin, Horovod, Hugging Face, Scikit-learn, and others. Check out the full list of Ray distributed libraries here.

Getting Started with Ray¶

Check out A Gentle Introduction to Ray to learn more about Ray and its ecosystem of libraries that enable things like distributed hyperparameter tuning, reinforcement learning, and distributed training.

Ray provides Python, Java, and EXPERIMENTAL C++ API. And Ray uses Tasks (functions) and Actors (Classes) to allow you to parallelize your code.

# First, run `pip install ray`.

import ray
ray.init()

@ray.remote
def f(x):
    return x * x

futures = [f.remote(i) for i in range(4)]
print(ray.get(futures)) # [0, 1, 4, 9]

@ray.remote
class Counter(object):
    def __init__(self):
        self.n = 0

    def increment(self):
        self.n += 1

    def read(self):
        return self.n

counters = [Counter.remote() for i in range(4)]
[c.increment.remote() for c in counters]
futures = [c.read.remote() for c in counters]
print(ray.get(futures)) # [1, 1, 1, 1]

First, add the ray-api and ray-runtime dependencies in your project.

import io.ray.api.ActorHandle;
import io.ray.api.ObjectRef;
import io.ray.api.Ray;
import java.util.ArrayList;
import java.util.List;
import java.util.stream.Collectors;

public class RayDemo {

  public static int square(int x) {
    return x * x;
  }

  public static class Counter {

    private int value = 0;

    public void increment() {
      this.value += 1;
    }

    public int read() {
      return this.value;
    }
  }

  public static void main(String[] args) {
    // Intialize Ray runtime.
    Ray.init();
    {
      List<ObjectRef<Integer>> objectRefList = new ArrayList<>();
      // Invoke the `square` method 4 times remotely as Ray tasks.
      // The tasks will run in parallel in the background.
      for (int i = 0; i < 4; i++) {
        objectRefList.add(Ray.task(RayDemo::square, i).remote());
      }
      // Get the actual results of the tasks with `get`.
      System.out.println(Ray.get(objectRefList));  // [0, 1, 4, 9]
    }

    {
      List<ActorHandle<Counter>> counters = new ArrayList<>();
      // Create 4 actors from the `Counter` class.
      // They will run in remote worker processes.
      for (int i = 0; i < 4; i++) {
        counters.add(Ray.actor(Counter::new).remote());
      }

      // Invoke the `increment` method on each actor.
      // This will send an actor task to each remote actor.
      for (ActorHandle<Counter> counter : counters) {
        counter.task(Counter::increment).remote();
      }
      // Invoke the `read` method on each actor, and print the results.
      List<ObjectRef<Integer>> objectRefList = counters.stream()
          .map(counter -> counter.task(Counter::read).remote())
          .collect(Collectors.toList());
      System.out.println(Ray.get(objectRefList));  // [1, 1, 1, 1]
    }
  }
}

The C++ Ray API is currently experimental with limited support. You can track its development here and report issues on GitHub.

Run the following commands to get started:

- Install ray with C++ API support and generate a bazel project with the ray command.

pip install ray[cpp]
mkdir ray-template && ray cpp --generate-bazel-project-template-to ray-template

- The project template comes with a simple example application. You can try this example out in 2 ways:

- 1. Run the example application directly, which will start a Ray cluster locally.

cd ray-template && sh run.sh

- 2. Connect the example application to an existing Ray cluster by specifying the RAY_ADDRESS env var.

ray start --head
RAY_ADDRESS=127.0.0.1:6379 sh run.sh

- Now you can build your own Ray C++ application based on this project template.

You can also get started by visiting our Tutorials. For the latest wheels (nightlies), see the installation page.

Ray v2.0.0.dev0

What is Ray?¶

Getting Started with Ray¶

Getting Involved¶

More Information¶

Blog and Press¶

Talks (Videos)¶

Slides¶

Papers¶