RAY: A Powerful Distributed Computing Framework for ML/AI

The rapidly advancing of artificial intelligence (AI) and machine learning (ML) are driving an unprecedented demand for tools that are not only efficient and scalable but also intuitive and user-friendly. This increasing demand has catalyzed the development of resilient frameworks capable of handling complex AI workloads. In this context, RAY emerges as a revolutionary solution, streamlining the AI development process by enhancing efficiency and scalability.

RAY: An Introduction

RAY is an open-source distributed computing framework designed to democratize AI development. It stands out as the first unified, distributed compute framework specifically tailored for scaling ML and AI workloads. By providing developers, data scientists, and engineers with access to powerful distributed computing capabilities, RAY simplifies the handling of intricate AI tasks, making these advanced technologies more accessible and efficient.

Unlike other distributed system frameworks, RAY is designed to address a broader range of challenges. It supports a variety of scalable programming paradigms, from actors to ML and data parallelism. RAY’s remote function and actor models make it a versatile development environment, extending its utility beyond the confines of big data applications.

Key Features of RAY

Scalability and Performance

RAY excels in scaling AI applications by leveraging its distributed architecture to efficiently distribute tasks across multiple nodes. This architecture not only saves time but also boosts performance, making RAY an invaluable tool for data-intensive AI projects. Its robust framework ensures that large-scale computations are handled smoothly and efficiently.

Ease of Use

One of RAY’s standout qualities is its user-centric approach. Featuring a high-level API, RAY opens the doors to parallel and distributed computing for developers without requiring specialized expertise in distributed systems. This intuitive interface accelerates the creation and deployment of AI applications, making it easier for developers to leverage advanced computing power.

Support for Various Workloads

RAY is designed to meet diverse needs, whether the focus is on machine learning, reinforcement learning, or hyperparameter tuning. Its comprehensive libraries and APIs cater to a wide range of AI and ML applications, making it a versatile choice for developers across various industries.

Cloud and Cluster Integration

RAY integrates seamlessly with well-established cloud platforms and cluster managers such as YARN and Kubernetes. This capability allows users to deploy applications effortlessly across different environments, from standalone machines to expansive cloud infrastructures, ensuring a versatile and straightforward experience.

Task Scheduling and Resource Efficiency

RAY’s dynamic and adaptable task scheduling mechanism optimizes resource utilization and minimizes latency in task execution. By efficiently managing CPU and memory usage, RAY ensures optimal performance for large-scale, data-intensive AI workloads, making it a critical asset for any AI-driven project.

Community and Support

RAY benefits from an active open-source community and regular updates, allowing users to customize and extend its capabilities to meet specific needs. This vibrant community ensures that RAY evolves continuously, incorporating the latest advancements and user feedback.

The RAY Framework

RAY simplifies the orchestration of distributed individual and end-to-end machine learning workflows through several key components:

Ray’s unified compute framework consists of three layers:

1. Ray AI Libraries -An open-source, Python, domain-specific set of libraries that equip ML engineers, data scientists, and researchers with a scalable and unified toolkit for ML applications.
2. Ray Core -An open-source, Python, general purpose, distributed computing library that enables ML engineers and Python developers to scale Python applications and accelerate machine learning workloads.
3. Ray Clusters -A set of worker nodes connected to a common Ray head node. Ray clusters can be fixed-size, or they can autoscale up and down according to the resources requested by applications running on the cluster.

RAY’s five native libraries are designed to handle specific machine-learning tasks:

1. Data: Provides scalable, framework-agnostic data loading and transformation for training, tuning, and prediction.
2. Train: Facilitates distributed multi-node and multi-core model training with fault tolerance, integrating seamlessly with popular training libraries.
3. Tune: Enables scalable hyperparameter tuning to optimize model performance.
4. Serve: Offers scalable and programmable model serving for online inference, with optional microbatching to enhance performance.
5. RLlib: Supports scalable distributed reinforcement learning workloads.

These libraries cater to both data scientists and ML engineers. Data scientists can use them to scale individual workloads and entire ML applications. ML engineers benefit from scalable platform abstractions, simplifying the integration of tools from the broader ML ecosystem.

For custom applications, the Ray Core library allows Python developers to easily create scalable, distributed systems that can run on laptops, clusters, clouds, or Kubernetes. This core library serves as the foundation for Ray’s AI libraries and third-party integrations within the Ray ecosystem.

RAY operates on any machine, cluster, cloud provider, or Kubernetes and boasts a growing ecosystem of community integrations.

Comparison with Other Frameworks

Here’s a comparison chart for Ray Framework with Spark and Flink:

While a detailed comparison with other frameworks like Spark and Flink is beyond the scope of this post, a brief overview highlights RAY’s unique advantages. RAY prioritizes user-friendliness and scalability for AI workloads in distributed computing. Spark is well-known for its in-memory processing and extensive ecosystem, while Flink excels in stream and stateful processing. The choice among these frameworks depends on the specific needs of your project, particularly regarding processing methods, scalability, and usability.

Ray Use Cases

This page indexes common Ray use cases for scaling ML. It contains highlighted references to blogs, examples, and tutorials also located elsewhere in the Ray documentation.

1. LLMs and Gen AI

Large language models (LLMs) and generative AI are rapidly changing industries, and demand compute at an astonishing pace. Ray provides a distributed compute framework for scaling these models, allowing developers to train and deploy models faster and more efficiently. With specialized libraries for data streaming, training, fine-tuning, hyperparameter tuning, and serving, Ray simplifies the process of developing and deploying large-scale AI models.

2. Batch Inference

Batch inference is the process of generating model predictions on a large “batch” of input data. Ray for batch inference works with any cloud provider and ML framework, and is fast and cheap for modern deep learning applications. It scales from single machines to large clusters with minimal code changes. As a Python-first framework, you can easily express and interactively develop your inference workloads in Ray. To learn more about running batch inference with Ray, see the batch inference guide.

3. Model Serving

Ray Serve is well suited for model composition, enabling you to build a complex inference service consisting of multiple ML models and business logic all in Python code.

It supports complex model deployment patterns requiring the orchestration of multiple Ray actors, where different actors provide inference for different models. Serve handles both batch and online inference and can scale to thousands of models in production.

4. Hyperparameter Tuning

The Ray Tune library enables any parallel Ray workload to be run under a hyperparameter tuning algorithm.

Running multiple hyperparameter tuning experiments is a pattern apt for distributed computing because each experiment is independent of one another. Ray Tune handles the hard bit of distributing hyperparameter optimization and makes available key features such as checkpointing the best result, optimizing scheduling, and specifying search patterns.

Distributed Training

The Ray Train library integrates many distributed training frameworks under a simple Trainer API, providing distributed orchestration and management capabilities out of the box.

In contrast to training many models, model parallelism partitions a large model across many machines for training. Ray Train has built-in abstractions for distributing shards of models and running training in parallel.

5. Reinforcement Learning

RLlib is an open-source library for reinforcement learning (RL). It provides support for highly distributed RL workloads at a production level while maintaining unified and simple APIs for a wide range of industry applications. RLlib is utilized by industry leaders in various verticals, including climate control, industrial control, manufacturing and logistics, finance, gaming, automobile, robotics, boat design, and others.

6. ML Platform

Ray and its AI libraries offer a unified compute runtime for teams aiming to simplify their ML platform. Ray’s libraries, including Ray Train, Ray Data, and Ray Serve, can be utilized to create end-to-end ML workflows. These libraries provide features and APIs for data preprocessing as part of training, and for transitioning from training to serving.

Conclusion

RAY stands as a transformative force in the realm of distributed computing, offering a versatile platform for a wide array of applications. From orchestrating end-to-end machine learning pipelines to handling individual tasks, RAY provides the foundation for building AI-driven systems that scale seamlessly from single laptops to expansive cloud environments. With its efficient task parallelism, distributed ML capabilities, and seamless scaling, RAY is a powerful ally in the pursuit of computational excellence.

Whether you are a developer, data scientist, or engineer, RAY offers the tools and support needed to elevate your AI and ML projects to new heights. Its comprehensive framework and active community ensure that RAY remains at the forefront of distributed computing innovation.

Originally published at https://blog.spheron.network on June 2, 2024.