(gentle-intro)=
# Getting Started Guide
This tutorial will give you a quick tour of Ray's features.
To get started, we'll start by installing Ray.
Most of the examples in this guide are based on Python, but we'll also show you how to user Ray Core in Java.
````{panels}
:container: text-center
:column: col-lg-6 px-2 py-2
:card:
Python
^^^
To use Ray in Python, install it with
```
pip install ray
```
---
Java
^^^
To use Ray in 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.
````
Want to build Ray from source or with docker? Need more details?
Check out our detailed [installation guide](installation.rst).
## Ray ML Quick Start
Ray has a rich ecosystem of libraries and frameworks built on top of it.
Simply click on the dropdowns below to see examples of our most popular libraries.
`````{dropdown} 
Data: Creating and Transforming Datasets
:animate: fade-in-slide-down
Ray Datasets are the standard way to load and exchange data in Ray libraries and applications.
Datasets provide basic distributed data transformations such as `map`, `filter`, and `repartition`.
They are compatible with a variety of file formats, datasources, and distributed frameworks.
````{note}
To get started with this example install Ray Data as follows.
```bash
pip install "ray[data]" dask
```
````
Get started by creating Datasets from synthetic data using ``ray.data.range()`` and ``ray.data.from_items()``.
Datasets can hold either plain Python objects (schema is a Python type), or Arrow records (schema is Arrow).
```{literalinclude} ../data/doc_code/quick_start.py
:language: python
:start-after: __data_setup_begin__
:end-before: __data_setup_end__
```
Datasets can be created from files on local disk or remote datasources such as S3. Any filesystem
[supported by pyarrow](http://arrow.apache.org/docs/python/generated/pyarrow.fs.FileSystem.html) can be used to specify file locations.
You can also create a ``Dataset`` from existing data in the Ray object store or Ray-compatible distributed DataFrames:
```{literalinclude} ../data/doc_code/quick_start.py
:language: python
:start-after: __data_load_begin__
:end-before: __data_load_end__
```
Datasets can be transformed in parallel using ``.map()``.
Transformations are executed *eagerly* and block until the operation is finished.
Datasets also supports ``.filter()`` and ``.flat_map()``.
```{literalinclude} ../data/doc_code/quick_start.py
:language: python
:start-after: __data_transform_begin__
:end-before: __data_transform_end__
```
```{link-button} ../data/dataset
:type: ref
:text: Learn more about Ray Data
:classes: btn-outline-primary btn-block
```
`````
`````{dropdown} Train: Distributed Model Training
:animate: fade-in-slide-down
Ray Train abstracts away the complexity of setting up a distributed training
system. Let's take following simple examples:
````{tabbed} PyTorch
This example shows how you can use Ray Train with PyTorch.
First, set up your dataset and model.
```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
:language: python
:start-after: __torch_setup_begin__
:end-before: __torch_setup_end__
```
Now define your single-worker PyTorch training function.
```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
:language: python
:start-after: __torch_single_begin__
:end-before: __torch_single_end__
```
This training function can be executed with:
```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
:language: python
:start-after: __torch_single_run_begin__
:end-before: __torch_single_run_end__
```
Now let's convert this to a distributed multi-worker training function!
All you have to do is use the ``ray.train.torch.prepare_model`` and
``ray.train.torch.prepare_data_loader`` utility functions to
easily setup your model & data for distributed training.
This will automatically wrap your model with ``DistributedDataParallel``
and place it on the right device, and add ``DistributedSampler`` to your DataLoaders.
```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
:language: python
:start-after: __torch_distributed_begin__
:end-before: __torch_distributed_end__
```
Then, instantiate a ``Trainer`` that uses a ``"torch"`` backend
with 4 workers, and use it to run the new training function!
```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
:language: python
:start-after: __torch_trainer_begin__
:end-before: __torch_trainer_end__
```
````
````{tabbed} TensorFlow
This example shows how you can use Ray Train to set up `Multi-worker training
with Keras `_.
First, set up your dataset and model.
```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
:language: python
:start-after: __tf_setup_begin__
:end-before: __tf_setup_end__
```
Now define your single-worker TensorFlow training function.
```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
:language: python
:start-after: __tf_single_begin__
:end-before: __tf_single_end__
```
This training function can be executed with:
```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
:language: python
:start-after: __tf_single_run_begin__
:end-before: __tf_single_run_end__
```
Now let's convert this to a distributed multi-worker training function!
All you need to do is:
1. Set the *global* batch size - each worker will process the same size
batch as in the single-worker code.
2. Choose your TensorFlow distributed training strategy. In this example
we use the ``MultiWorkerMirroredStrategy``.
```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
:language: python
:start-after: __tf_distributed_begin__
:end-before: __tf_distributed_end__
```
Then, instantiate a ``Trainer`` that uses a ``"tensorflow"`` backend
with 4 workers, and use it to run the new training function!
```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
:language: python
:start-after: __tf_trainer_begin__
:end-before: __tf_trainer_end__
```
````
```{link-button} ../train/train
:type: ref
:text: Learn more about Ray Train
:classes: btn-outline-primary btn-block
```
`````
`````{dropdown} Tune: Hyperparameter Tuning at Scale
:animate: fade-in-slide-down
[Tune](../tune/index.rst) is a library for hyperparameter tuning at any scale.
With Tune, you can launch a multi-node distributed hyperparameter sweep in less than 10 lines of code.
Tune supports any deep learning framework, including PyTorch, TensorFlow, and Keras.
````{note}
To run this example, you will need to install the following:
```bash
pip install "ray[tune]"
```
````
This example runs a small grid search with an iterative training function.
```{literalinclude} ../../../python/ray/tune/tests/example.py
:end-before: __quick_start_end__
:language: python
:start-after: __quick_start_begin__
```
If TensorBoard is installed, automatically visualize all trial results:
```bash
tensorboard --logdir ~/ray_results
```
```{link-button} ../tune/index
:type: ref
:text: Learn more about Ray Tune
:classes: btn-outline-primary btn-block
```
`````
`````{dropdown} Serve: Scalable Model Serving
:animate: fade-in-slide-down
[Ray Serve](../serve/index) is a scalable model-serving library built on Ray.
````{note}
To run this example, you will need to install the following libraries.
```{code-block} bash
pip install "ray[serve]" scikit-learn
```
````
This example runs serves a scikit-learn gradient boosting classifier.
```{literalinclude} ../serve/_examples/doc_code/quick_start.py
:language: python
:start-after: __serve_example_begin__
:end-before: __serve_example_end__
```
As a result you will see `{"result": "versicolor"}`.
```{link-button} ../serve/index
:type: ref
:text: Learn more about Ray Serve
:classes: btn-outline-primary btn-block
```
`````
`````{dropdown} RLlib: Industry-Grade Reinforcement Learning
:animate: fade-in-slide-down
[RLlib](../rllib/index.rst) is an industry-grade library for reinforcement learning (RL) built on top of Ray.
RLlib offers high scalability and unified APIs for a variety of industry- and research applications.
````{note}
To run this example, you will need to install `rllib` and either `tensorflow` or `pytorch`.
```bash
pip install "ray[rllib]" tensorflow # or torch
```
````
```{literalinclude} ../../../rllib/examples/documentation/rllib_on_ray_readme.py
:end-before: __quick_start_end__
:language: python
:start-after: __quick_start_begin__
```
```{link-button} ../rllib/index
:type: ref
:text: Learn more about Ray RLlib
:classes: btn-outline-primary btn-block
```
`````
## Ray Core Quick Start
Ray Core provides simple primitives for building and running distributed applications.
Below you find examples that show you how to turn your functions and classes easily into Ray tasks and actors,
for both Python and Java.
`````{dropdown} Core: Parallelizing Functions with Ray Tasks
:animate: fade-in-slide-down
````{tabbed} Python
First, you import Ray and and initialize it with `ray.init()`.
Then you decorate your function with ``@ray.remote`` to declare that you want to run this function remotely.
Lastly, you call that function with ``.remote()`` instead of calling it normally.
This remote call yields a future, a so-called Ray _object reference_, that you can then fetch with ``ray.get``.
```{code-block} python
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]
```
````
````{tabbed} Java
First, use `Ray.init` to initialize Ray runtime.
Then you can use `Ray.task(...).remote()` to convert any Java static method into a Ray task.
The task will run asynchronously in a remote worker process. The `remote` method will return an ``ObjectRef``,
and you can then fetch the actual result with ``get``.
```{code-block} java
import io.ray.api.ObjectRef;
import io.ray.api.Ray;
import java.util.ArrayList;
import java.util.List;
public class RayDemo {
public static int square(int x) {
return x * x;
}
public static void main(String[] args) {
// Intialize Ray runtime.
Ray.init();
List> 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.
System.out.println(Ray.get(objectRefList)); // [0, 1, 4, 9]
}
}
````
In the above code block we defined some Ray Tasks. While these are great for stateless operations, sometimes you
must maintain the state of your application. You can do that with Ray Actors.
```{link-button} ../ray-core/walkthrough
:type: ref
:text: Learn more about Ray Core
:classes: btn-outline-primary btn-block
```
`````
`````{dropdown} Core: Parallelizing Classes with Ray Actors
:animate: fade-in-slide-down
Ray provides actors to allow you to parallelize an instance of a class in Python or Java.
When you instantiate a class that is a Ray actor, Ray will start a remote instance
of that class in the cluster. This actor can then execute remote method calls and
maintain its own internal state.
````{tabbed} Python
```{code-block} python
import ray
ray.init() # Only call this once.
@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]
```
````
````{tabbed} Java
```{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 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> 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 : counters) {
counter.task(Counter::increment).remote();
}
// Invoke the `read` method on each actor, and print the results.
List> objectRefList = counters.stream()
.map(counter -> counter.task(Counter::read).remote())
.collect(Collectors.toList());
System.out.println(Ray.get(objectRefList)); // [1, 1, 1, 1]
}
}
````
```{link-button} ../ray-core/walkthrough
:type: ref
:text: Learn more about Ray Core
:classes: btn-outline-primary btn-block
```
`````
## Ray Cluster Quick Start
You can deploy your applications on Ray clusters, often with minimal code changes to your existing code.
See an example of this below.
`````{dropdown} Clusters: Launching a Ray Cluster on AWS
:animate: fade-in-slide-down
Ray programs can run on a single machine, or seamlessly scale to large clusters.
Take this simple example that waits for individual nodes to join the cluster.
````{dropdown} example.py
:animate: fade-in-slide-down
```{literalinclude} ../../yarn/example.py
:language: python
```
````
You can also download this example from our [GitHub repository](https://github.com/ray-project/ray/blob/master/doc/yarn/example.py).
Go ahead and store it locally in a file called `example.py`.
To execute this script in the cloud, just download [this configuration file](https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/aws/example-full.yaml),
or copy it here:
````{dropdown} cluster.yaml
:animate: fade-in-slide-down
```{literalinclude} ../../../python/ray/autoscaler/aws/example-full.yaml
:language: yaml
```
````
Assuming you have stored this configuration in a file called `cluster.yaml`, you can now launch an AWS cluster as follows:
```bash
ray submit cluster.yaml example.py --start
```
```{link-button} cluster-cloud
:type: ref
:text: Learn more about launching Ray Clusters
:classes: btn-outline-primary btn-block
```
`````
```{include} learn-more.md
```