Ray

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads across distributed clusters with Ray Train, Ray Data, and Ray Serve.

When to Use

• Distributed Python Applications: Scaling compute-heavy Python tasks seamlessly from a single laptop to large cloud clusters.
• Distributed ML Training with Ray Train: Orchestrating multi-node PyTorch, XGBoost, and LightGBM training jobs.
• Production Model Serving with Ray Serve: Serving LLMs and multi-model microservice pipelines with dynamic autoscaling.
• Hyperparameter Optimization with Ray Tune: Running Bayesian, Optuna, and population-based training sweeps across hundreds of workers.

Quick Start

import ray

ray.init()