Ray Distributed Computing Framework

Ray is a unified framework for scaling Python applications and AI workloads from a single machine to a cluster of hundreds of nodes, providing distributed task execution, actor model computation, and native integration with machine learning libraries. Developed by Anyscale (founded by the RISELab at UC Berkeley) in 2017, with over 34,000 stars as of 2026, Ray is used by OpenAI, Uber, Shopify, and Shopify for large-scale ML training and serving. Key features include: distributed tasks (remote functions executed asynchronously on cluster workers with automatic serialization, fault tolerance, and result retrieval via object refs), actors (stateful distributed objects with method invocation, enabling stateful services, model serving, and parameter servers), object store (distributed in-memory object store using Apache Arrow for zero-copy data sharing between tasks and actors), Ray Data (distributed data loading and preprocessing pipeline for ML training with streaming execution and datasource connectors for Parquet, CSV, JSON, and databases), Ray Train (distributed model training framework supporting PyTorch, TensorFlow, and Hugging Face with GPU and multi-node training), Ray Tune (hyperparameter tuning with ASHA, PBT, BOHB, and Optuna integration for efficient model optimization), Ray Serve (scalable model serving with batching, autoscaling, and canary deployments), RLlib (distributed reinforcement learning library supporting PPO, DQN, SAC, IMPALA, and multi-agent algorithms), Ray Core scheduling (distributed scheduler with placement groups for topology-aware resource allocation), cluster management (Autoscaler for dynamic cluster scaling on AWS, GCP, Azure, and Kubernetes via KubeRay), fault tolerance (automatic task retry, lineage reconstruction, and node failure recovery), and observability (Ray Dashboard for cluster monitoring, task profiling, and resource visualization).