ray distributed computing
**Ray Distributed Computing Framework: Actor Model and Unified ML Platform — enabling flexible task and stateful distributed computing**
Ray provides a unified compute framework balancing task parallelism and stateful computation (actors). Unlike Spark (immutable RDDs) and Dask (functional task graphs), Ray's actor model manages stateful distributed objects, enabling new application classes.
**Actor Model and Task Parallelism**
Actors are long-lived distributed objects initialized on workers. Remote method calls serialize arguments, ship to actor location, execute, and return results. State persists across calls, enabling stateful services (model servers, caches, databases). Tasks execute remote functions without actor infrastructure, simpler than actors for stateless parallelism.
**Ray Tune for Hyperparameter Search**
Ray Tune distributes hyperparameter search across workers, supporting multiple schedulers (Population-Based Training, Hyperband, BOHB). Trial-level parallelism: each trial runs independently, training models with distinct hyperparameters. Population-based training enables dynamic scheduling: low-performing trials cease, resources reallocate to promising trials. This adaptive approach outperforms static grid/random search.
**Ray Serve for Model Serving**
Ray Serve manages model serving infrastructure: load balancing requests across replicas, batching for throughput, autoscaling based on request rate. Multiple models coexist, with traffic splitting for A/B testing. Integration with Ray enables end-to-end ML pipelines: Ray Train trains models (distributed GPU training), Ray Tune searches hyperparameters, Ray Serve deploys winners.
**Ray Data for Streaming Pipelines**
Ray Data provides distributed data processing: shuffle, groupby, aggregation operators. Streaming mode enables processing datasets larger than cluster memory via windowing and iterative processing.
**Ray Train and Distributed ML**
Ray Train provides distributed training for TensorFlow, PyTorch, XGBoost via parameter server and all-reduce backends. Automatic fault recovery (checkpointing) enables training large models across unreliable clusters. Integration with Ray Tune enables seamless hyperparameter optimization during training.
**Ray Cluster Autoscaling**
Ray clusters autoscale based on pending tasks: insufficient resources queue tasks; autoscaler launches new nodes. On-demand and spot instances mixed for cost optimization. Kubernetes and cloud-native integration (AWS, GCP, Azure) enable elastic scaling.