Introduction

Scaling machine learning workloads from a single laptop to a massive cluster often involves rewriting entire codebases or wrestling with complex infrastructure. Ray solves this by providing a unified compute engine that allows Python developers to scale their applications seamlessly without changing their core logic. With over 43k GitHub stars, Ray has become the industry standard for distributed AI, powering everything from small-scale experiments to the training of massive models like OpenAI’s ChatGPT.

What Is Ray?

Ray is an open-source AI compute engine that provides a core distributed runtime and a set of AI libraries designed to accelerate machine learning workloads. Written primarily in Python and C++, it allows developers to execute Python functions and classes across a cluster of machines with minimal overhead. Licensed under the Apache License 2.0, Ray is maintained by a global community and supported by Anyscale, ensuring it remains a general-purpose tool for any Python-based distributed workload.

At its heart, Ray transforms standard Python code into distributed tasks and actors, abstracting away the complexities of cluster management, object storage, and task scheduling. This makes it an ideal choice for developers who want the power of a distributed system without the steep learning curve of traditional big-data frameworks.

Why Ray Matters

The demand for AI compute has surged exponentially, while hardware acceleration has not kept pace. This gap creates a critical need for software that can efficiently orchestrate GPUs and CPUs across multiple nodes. Ray fills this gap by offering fine-grained scheduling that is far more flexible than the coarse-grained batch processing found in older distributed systems.

Unlike traditional frameworks, Ray is “Python-native.” This means it integrates deeply with the existing data science ecosystem, including PyTorch, TensorFlow, scikit-learn, and Pandas. Instead of forcing developers to learn a proprietary API, Ray allows them to use the libraries they already love while providing the infrastructure to scale them. This reduction in “glue code” significantly accelerates the time-to-market for AI products.

The traction is evident in its adoption by leading technology companies like Uber, Spotify, and Shopify. By providing a path from local development to production-scale clusters, Ray eliminates the friction between data scientists and ML engineers, creating a streamlined pipeline for AI deployment.

Key Features

Ray is structured as a foundational runtime (Ray Core) and a suite of high-level libraries that target specific ML stages of the pipeline.

Ray Core: The Distributed Foundation

• Distributed Tasks: Stateless functions that can be executed in parallel across the cluster. By adding a simple @ray.remote decorator, any Python function becomes a distributed task.
• Stateful Actors: Worker processes that maintain internal state across multiple method calls. This is critical for managing model weights or maintaining connection pools in a distributed environment.
• Distributed Object Store: A high-performance, shared-memory object store that allows tasks and actors to share large datasets without expensive serialization or copying.
• Dynamic Task Scheduling: A decentralized scheduler that can handle millions of tasks per second with microsecond-level latency, enabling complex, asynchronous workflows.

Ray AI Libraries: Accelerating the ML Lifecycle

• Ray Data: A library for scalable dataset loading and preprocessing. It handles sharding and streaming of data from storage to CPU/GPU, avoiding the common “data bottleneck” in ML training.
• Ray Train: Provides a unified API for distributed training of PyTorch, TensorFlow, and Horovod models across multiple GPUs and nodes.
• Ray Tune: A scalable hyperparameter tuning library that implements advanced algorithms like ASHA and PBT to find the best model configurations efficiently.
• Ray Serve: A programmable model serving library that allows you to deploy ML models as scalable REST APIs with complex routing and orchestration.
• Ray RLlib: An industry-leading library for reinforcement learning, providing a wide array of algorithms and a unified interface for training complex agents.

How Ray Compares

When choosing a distributed computing framework, the decision usually comes down to the nature of the workload: data engineering (ETL) versus AI/ML compute. Ray is specifically optimized for the latter.

Ray excels in task parallelism—where multiple independent tasks run concurrently. This is essential for reinforcement learning or hyperparameter tuning, where you might run hundreds of different model versions. Apache Spark, conversely, excels in data parallelism—applying the same operation to every element of a massive dataset. If your primary goal is large-scale SQL joins or ETL pipelines, Spark is the superior choice.

Dask is an excellent middle ground for teams that want to scale NumPy or Pandas workflows without the overhead of a full AI engine. However, Ray’s dedicated libraries for training and serving make it the more complete ecosystem for those building production-grade AI applications. The tradeoff is that Ray’s flexibility can lead to more complex debugging compared to the structured nature of Spark’s DataFrames.

Getting Started: Installation

Ray can be installed on Linux, macOS, and Windows (beta). It is highly recommended to use a virtual environment to avoid dependency conflicts.

Installation via pip

For a basic installation of Ray Core, use the following command:

pip install -U ray

For machine learning applications, it is recommended to install the bundle that includes the most common AI libraries:

pip install -U "ray[data,train,tune,serve]"

If you need reinforcement learning support, install RLlib specifically:

pip install -U "ray[rllib]"

Installation via Docker

Using Docker is the most reliable way to ensure environment consistency across a cluster. You can build your own image from the Ray repository or use prebuilt images from Docker Hub.

docker pull rayproject/ray:latest

To launch a Ray instance in a container:

docker run --shm-size=1024m -t -i rayproject/ray:latest

Note: The --shm-size flag is critical because Ray’s shared-memory object store requires significant shared memory to avoid crashes.

How to Use Ray

The beauty of Ray is that it requires very little change to existing Python code. The primary workflow involves initializing the Ray runtime and using decorators to define distributed components.

First, initialize Ray on your local machine or connect to an existing cluster:

import ray
ray.init()

Once initialized, you can turn any function into a Task (stateless) or any class into an Actor (stateful). Tasks are executed asynchronously and return a “future” (ObjectRef), which you can later resolve using ray.get().

For a simple parallel execution, you can define a remote function and call it multiple times. Ray will automatically distribute these calls across all available CPU cores or nodes in the cluster.

Code Examples

Example 1: Distributed Tasks

This example demonstrates how to execute a function in parallel across a cluster.

import ray

ray.init()

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

# Launch 10 tasks in parallel
# .remote() returns an ObjectRef (a future)
results_refs = [square.remote(i) for i in range(10)]

# Resolve the futures into actual values
results = ray.get(results_refs)
print(results)

Example 2: Stateful Actors

This example shows how to maintain state across multiple distributed calls.

import ray

ray.init()

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

    def increment(self):
        self.value += 1
        return self.value

# Create an actor instance
counter = Counter.remote()

# Call methods on the actor
print(counter.increment.remote())
print(counter.increment.remote())

# Resolve the results
# Note: we use ray.get() to resolve the result of the increment call
print(ray.get([counter.increment.remote() for _ in range(5)]))

Real-World Use Cases

Ray is most effective when workloads are heterogeneous and require fine-grained control over compute resources.

• Large-Scale LLM Training: AI researchers use Ray Train to distribute the training of Large Language Models across hundreds of GPUs, managing the model parallelism and data parallelism required for models with billions of parameters.
• Hyperparameter Optimization: ML engineers use Ray Tune to run thousands of parallel experiments to find the optimal learning rate, batch size, and architecture for a model, reducing the search time from days to hours.
• Distributed Model Serving: DevOps engineers use Ray Serve to deploy models as a graph of services, where a pre-processing step is handled by one actor and the model inference is handled by another, allowing each part of the 경로 to be scaled independently.
• Reinforcement Learning: Game AI developers use RLlib to train agents in complex simulated environments, where the agent interacts with the environment and collects experience in parallel across many workers.