What It Does
Ray is a distributed computing framework that makes it easy to scale Python applications from a single machine to large clusters. For ML, it provides Ray Train for distributed training, Ray Serve for model serving, and Ray Tune for hyperparameter optimization.
Key Features
Ray offers distributed training integration (PyTorch, TensorFlow, JAX), online serving with autoscaling, hyperparameter tuning with schedulers, data processing with Ray Data, reinforcement learning with RLlib, workflow orchestration, and multi-node GPU cluster management.
When To Use It
Use Ray when you need to scale ML workloads beyond a single machine, orchestrate complex ML pipelines, combine training and serving in one platform, or when you need autoscaling model serving with minimal infrastructure management.
Alternatives
Dask provides simpler distributed DataFrames. Spark MLlib offers big data ML at scale. Horovod focuses specifically on distributed training. For pure serving, vLLM or Triton may be more specialized.