Ray

Overview

Ray is an open-source distributed computing framework designed to simplify the development and scaling of Python applications, with particular emphasis on AI and machine learning workloads. Initiated in 2017, the project aims to provide a unified API that abstracts away the complexities of distributed systems, allowing developers to write parallel and distributed code using familiar Python constructs Ray documentation overview. This framework addresses challenges such as managing state across multiple nodes, handling fault tolerance, and optimizing communication between distributed tasks.

Developers use Ray to build a range of distributed AI applications, from scaling conventional machine learning training jobs and hyperparameter tuning to deploying real-time model serving pipelines. Its architecture supports various types of distributed computation through a consistent API, which includes features for task parallelism, actor-based concurrency, and shared memory management. This design enables efficient data processing and model training across clusters of machines, whether running on a single multi-core server or a large cloud infrastructure.

The Ray ecosystem comprises several libraries and components tailored for specific AI and ML use cases. Key components include Ray Core for basic distributed execution, Ray Data for scalable data ingestion and processing, Ray Train for distributed model training, and Ray Serve for scalable model deployment. For reinforcement learning research, RLlib provides a unified API for a variety of algorithms and environments. By integrating these specialized libraries, Ray aims to offer a comprehensive platform for the entire machine learning lifecycle in a distributed setting.

Ray's ability to unify various ML libraries and frameworks under a single distributed runtime simplifies the development of complex AI systems that often require multiple stages, such as data preparation, model training, hyperparameter tuning, and serving. This approach can reduce the operational overhead associated with integrating disparate tools and managing their distributed execution. For example, a common use case involves using Ray Train to distribute PyTorch or TensorFlow model training, followed by Ray Tune for hyperparameter optimization, and finally deploying the trained model with Ray Serve Ray AI Runtime documentation. This integrated workflow helps accelerate research and development cycles for AI engineers and data scientists.

Key features

• Ray Core: Provides the foundational primitives for distributed computing, including tasks for stateless functions and actors for stateful computation across a cluster Ray Core documentation.
• Ray AIR (AI Runtime): A unified set of libraries for common ML workloads, including data ingestion (Ray Data), distributed training (Ray Train), hyperparameter tuning (Ray Tune), and model serving (Ray Serve) Ray AIR overview.
• Ray Data: Offers a scalable data processing library for ingesting, transforming, and exchanging data within Ray applications, supporting common data formats and integrations with other data systems.
• Ray Train: Facilitates distributed training of machine learning models using popular frameworks like PyTorch, TensorFlow, and Hugging Face Transformers, enabling scaling across multiple GPUs and nodes Ray Train documentation.
• Ray Tune: A library for hyperparameter tuning that supports various search algorithms, fault tolerance, and integrations with distributed training frameworks to optimize model performance Ray Tune documentation.
• Ray Serve: Enables the deployment of machine learning models and arbitrary Python functions as scalable, production-ready microservices, supporting real-time inference and complex model compositions Ray Serve documentation.
• RLlib: A library for reinforcement learning that provides scalable implementations of various algorithms and supports integration with different simulation environments RLlib documentation.
• Pythonic API: Designed to feel like standard Python, minimizing the learning curve for developers accustomed to single-machine programming.

Pricing

Ray itself is an open-source framework available under an Apache 2.0 license. Anyscale, the company founded by the creators of Ray, offers a managed platform for running Ray clusters in the cloud. As of 2026-05-07, Anyscale provides custom enterprise pricing for its managed Ray platform. Specific pricing details are available upon request from their sales team Anyscale pricing page.

Common integrations

• Machine Learning Frameworks: Integrates with PyTorch Ray PyTorch integration, TensorFlow Ray TensorFlow integration, and Hugging Face Transformers for distributed training and inference.
• Data Processing Libraries: Compatible with Pandas, NumPy, and Dask for data manipulation tasks within Ray applications.
• Cloud Providers: Can be deployed and managed on major cloud platforms including AWS, Google Cloud, and Azure, often through Kubernetes or cloud-specific orchestration tools.
• MLflow: Integrates for experiment tracking and model lifecycle management Ray Tune MLflow example.
• Kubernetes: Supports deployment on Kubernetes clusters for containerized and orchestrated distributed workloads Ray on Kubernetes documentation.

Alternatives

• Apache Spark: A unified analytics engine for large-scale data processing, with modules for SQL, streaming, machine learning (MLlib), and graph processing Apache Spark homepage.
• Dask: A flexible library for parallel computing in Python, offering data structures like DataFrames and arrays that scale to out-of-core and distributed datasets Dask homepage.
• Kubeflow: An open-source platform for deploying and managing machine learning workflows on Kubernetes, providing components for training, serving, and pipeline orchestration Kubeflow homepage.

Getting started

To begin using Ray, you can install it via pip. The following example demonstrates a basic distributed task in Ray, showcasing how to define a remote function and execute it in parallel across a cluster.

import ray
import time

# Initialize Ray
ray.init()

# Define a remote function
@ray.remote
def my_remote_function(x):
    time.sleep(1) # Simulate some work
    return x * x

# Call the remote function multiple times
futures = [my_remote_function.remote(i) for i in range(10)]

# Retrieve the results
results = ray.get(futures)

print(f"Results: {results}")

# Shut down Ray (optional, done automatically on script exit in many cases)
ray.shutdown()

This example initializes a local Ray instance, defines a function my_remote_function that can be executed as a Ray task, and then calls this function 10 times in parallel. The .remote() suffix transforms a regular Python function into a Ray task, returning a future object. ray.get() is used to retrieve the actual results once the tasks complete. For more detailed installation instructions and advanced usage, refer to the Ray installation guide.