ray-train

What problem does it solve?

This Skill simplifies and scales machine learning model training across multiple GPUs and nodes, making distributed training accessible and efficient.

Core Features & Use Cases

• Distributed Training Orchestration: Seamlessly scales PyTorch, TensorFlow, and HuggingFace models from a single GPU to thousands of nodes.
• Hyperparameter Tuning: Integrates with Ray Tune for large-scale, distributed hyperparameter optimization.
• Fault Tolerance & Elastic Scaling: Automatically handles worker failures and allows adding/removing nodes during training.
• Use Case: Train a large language model on a cluster of 100 GPUs, or run a hyperparameter sweep for a complex computer vision model across 32 nodes.

Quick Start

Install Ray Train using pip install -U "ray[train]" and then use the TorchTrainer to scale your PyTorch training function across 4 GPUs.