Ray Train

If you're scaling PyTorch or TensorFlow training beyond a single machine, this handles the distributed orchestration without rewriting your code. Wrap your training function, specify worker count, and Ray manages GPU allocation, fault tolerance, and metric aggregation across nodes. The real win is tight integration with Ray Tune for distributed hyperparameter sweeps. Setup is genuinely simpler than raw PyTorch DDP or Horovod, though you're buying into the Ray ecosystem. Works well for large model training or running dozens of experiments in parallel across a cluster.