Check out :ref:`gentle-intro` 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.
.. tabs::
.. group-tab:: Python
.. code-block:: python
# 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]
.. group-tab:: Java
First, add the `ray-api <https://mvnrepository.com/artifact/io.ray/ray-api>`__ and `ray-runtime <https://mvnrepository.com/artifact/io.ray/ray-runtime>`__ dependencies in your project.
.. code-block:: java
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]
}
}
}
.. group-tab:: C++ (EXPERIMENTAL)
| The C++ Ray API is currently experimental with limited support. You can track its development `here <https://github.com/ray-project/ray/milestone/17>`__ and report issues on GitHub.
| Run the following commands to get started:
| - Build ray from source with *bazel* as shown `here <https://docs.ray.io/en/master/development.html#building-ray-full>`__.
| - Modify `cpp/example/example.cc`.
| - Run `"bazel build //cpp:example"`.
| Option 1: run the example directly with a dynamic library path. It will start a Ray cluster automatically.
| - Run `"ray stop"`.
| - Run `"./bazel-bin/cpp/example/example --dynamic-library-path=bazel-bin/cpp/example/example.so"`
| Option 2: connect to an existing Ray cluster with a known redis address (e.g. `127.0.0.1:6379`).
| - Run `"ray stop"`.
| - Run `"ray start --head --port 6379 --redis-password 5241590000000000 --node-manager-port 62665"`.
| - Run `"./bazel-bin/cpp/example/example --dynamic-library-path=bazel-bin/cpp/example/example.so --redis-address=127.0.0.1:6379"`.
.. literalinclude:: ../../cpp/example/example.cc
:language: cpp
You can also get started by visiting our Tutorials. For the latest wheels (nightlies), see the installation page.
If you're interested in contributing to Ray, visit our page on :ref:`Getting Involved <getting-involved>` to read about the contribution process and see what you can work on!
Here are some talks, papers, and press coverage involving Ray and its libraries. Please raise an issue if any of the below links are broken, or if you'd like to add your own talk!
- Modern Parallel and Distributed Python: A Quick Tutorial on Ray
- Why Every Python Developer Will Love Ray
- Ray: A Distributed System for AI (BAIR)
- 10x Faster Parallel Python Without Python Multiprocessing
- Implementing A Parameter Server in 15 Lines of Python with Ray
- Ray Distributed AI Framework Curriculum
- RayOnSpark: Running Emerging AI Applications on Big Data Clusters with Ray and Analytics Zoo
- First user tips for Ray
- [Tune] Tune: a Python library for fast hyperparameter tuning at any scale
- [Tune] Cutting edge hyperparameter tuning with Ray Tune
- [RLlib] New Library Targets High Speed Reinforcement Learning
- [RLlib] Scaling Multi Agent Reinforcement Learning
- [RLlib] Functional RL with Keras and Tensorflow Eager
- [Modin] How to Speed up Pandas by 4x with one line of code
- [Modin] Quick Tip – Speed up Pandas using Modin
- Ray Blog
- Programming at any Scale with Ray | SF Python Meetup Sept 2019
- Ray for Reinforcement Learning | Data Council 2019
- Scaling Interactive Pandas Workflows with Modin
- Ray: A Distributed Execution Framework for AI | SciPy 2018
- Ray: A Cluster Computing Engine for Reinforcement Learning Applications | Spark Summit
- RLlib: Ray Reinforcement Learning Library | RISECamp 2018
- Enabling Composition in Distributed Reinforcement Learning | Spark Summit 2018
- Tune: Distributed Hyperparameter Search | RISECamp 2018
- Ray 1.0 Architecture whitepaper (new)
- Ray Design Patterns (new)
- RLlib paper
- RLlib flow paper
- Tune paper
Older papers:
.. toctree:: :hidden: :maxdepth: -1 :caption: Overview of Ray ray-overview/index.rst ray-libraries.rst installation.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: Ray Core walkthrough.rst using-ray.rst configure.rst ray-dashboard.rst Tutorial and Examples <auto_examples/overview.rst> package-ref.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: Ray Clusters/Autoscaler cluster/index.rst cluster/quickstart.rst cluster/reference.rst cluster/cloud.rst cluster/deploy.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: Ray Serve serve/index.rst serve/tutorial.rst serve/core-apis.rst serve/deployment.rst serve/ml-models.rst serve/advanced-traffic.rst serve/advanced.rst serve/performance.rst serve/architecture.rst serve/tutorials/index.rst serve/faq.rst serve/package-ref.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: Ray Tune tune/index.rst tune/key-concepts.rst tune/user-guide.rst tune/tutorials/overview.rst tune/examples/index.rst tune/api_docs/overview.rst tune/contrib.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: RLlib rllib.rst rllib-toc.rst rllib-training.rst rllib-env.rst rllib-models.rst rllib-algorithms.rst rllib-sample-collection.rst rllib-offline.rst rllib-concepts.rst rllib-examples.rst rllib-package-ref.rst rllib-dev.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: Ray SGD raysgd/raysgd.rst raysgd/raysgd_pytorch.rst raysgd/raysgd_tensorflow.rst raysgd/raysgd_dataset.rst raysgd/raysgd_ptl.rst raysgd/raysgd_tune.rst raysgd/raysgd_ref.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: Data Processing modin/index.rst dask-on-ray.rst mars-on-ray.rst raydp.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: More Libraries multiprocessing.rst joblib.rst iter.rst xgboost-ray.rst ray-client.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: Ray Observability ray-metrics.rst ray-debugging.rst ray-logging.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: Contributing getting-involved.rst
.. toctree:: :hidden: :maxdepth: -1 :caption: Development and Ray Internals development.rst whitepaper.rst debugging.rst profiling.rst fault-tolerance.rst