ray-train

What problem does it solve?

This Skill guides distributed training orchestration across clusters, enabling scalable experiments with Ray Train, Ray Tune, and elastic resource management.

Core Features & Use Cases

• Distributed Training: Runs PyTorch/TensorFlow/HuggingFace across multiple nodes with minimal code changes.
• Hyperparameter Tuning: Integrated Ray Tune for scalable hyperparameter optimization.
• Fault Tolerance & Elasticity: Automatic handling of worker failures and dynamic resource scaling.
• Multi-Framework Support: Work across PyTorch, TensorFlow, and HuggingFace ecosystems.
• Multi-Node Scaling: Easy configuration for cross‑node deployments.

Quick Start

Basic PyTorch training on a single node can be extended to multi-node clusters with Ray Train by wiring a TorchTrainer and a ScalingConfig.