Ray

Ray is an open-source framework for managing, executing, and optimizing compute needs, supported by Anyscale. It is positioned as an "AI compute engine," with the goal of precisely orchestrating infrastructure for distributed workloads of any scale and on any accelerator, thereby helping teams achieve scale, performance, and efficiency when developing AI.

Purpose and Positioning Ray is aimed at AI, machine learning, and generative AI workloads. The official description states that AI is becoming increasingly complex, with data modalities and models, frameworks, and accelerators constantly emerging, and the lack of effective infrastructure leads to slow launches, low resource utilization, and surging costs. As an AI compute engine, Ray is designed to support any AI workload and address the "wall of AI complexity."

Main Capabilities

• Python-native and parallel Python code: Ray is Python-native and can scale and distribute arbitrary Python code, making it suitable for scenarios such as simulation and backtesting.
• Multimodal data processing: It can process structured and unstructured data, including images, video, audio, and more.
• Distributed training: With one line of code, it can run generative AI foundation models, time series models, and traditional AI/ML models such as XGBoost in a distributed manner, and is compatible with the framework chosen by the developer.
• Model deployment: Ray Serve is used to deploy models and business logic rather than instances, supports independent scaling and fractional resources, and supports various ML models from LLMs to Stable Diffusion models, object detection models, and more.
• Batch inference: It simplifies offline batch inference processes using heterogeneous compute, and can use CPUs and GPUs in the same pipeline, improving utilization and reducing costs.
• Reinforcement learning: Ray RLlib supports production-grade, highly distributed reinforcement learning workloads while maintaining a unified and simple API.
• Generative AI workflows: It supports end-to-end GenAI workflows such as multimodal models and RAG applications.
• LLM inference and fine-tuning: It supports online and batch inference for large language models and can scale seamlessly; it also supports large-scale fine-tuning of large language models (derived from the framework behind ChatGPT).

Key Concepts and Ecosystem Ray Core provides a small set of core primitives (tasks, actors, objects) for building and scaling distributed Python applications. Ray also provides developer-facing high-level ML libraries that can seamlessly scale any AI workload such as data processing, training, and serving. The complete ML ecosystem supports running end-to-end AI/ML workflows on Ray, providing deployment Easy-to-use tools for Ray clusters, debugging and optimizing applications, and integrating with common tools and frameworks.

Anyscale and Usage Anyscale is Ray's fully managed AI platform, built by the founding team of Ray, providing enterprise governance, advanced developer tools, and expert support. The official website provides a free trial entry point and recommends running Ray on Anyscale for a fully managed integrated experience. Users can also join the Ray Slack community for communication. The GitHub repository provides downloads.

Applicable Scenarios The officially listed scenarios include: large-scale model training, multimodal data processing, batch inference, reinforcement learning, LLM online and batch inference, LLM fine-tuning, RAG applications, and more. Industry leaders use Ray to accelerate AI operations, and the official website mentions some outcome data (such as more training data, CPU cores used for online model serving, number of parameters for foundation model training, reduced data processing costs, cost reduction from switching from Spark to Ray for GPU batch inference, improved GPU utilization and reduced costs, number of models trained per month, faster iterative delivery of 100+ production models, etc.), but does not provide specific customer names and detailed conditions.

Limitations and Missing Information Official materials do not provide specific pricing, details of free quotas, operating system or deployment environment limitations supported by the platform, or the complete test conditions for specific performance benchmarks. Some features (such as a specific framework compatibility list and cluster size limits) are not yet provided.