ray-train

To scale PyTorch training across multiple machines, you can orchestrate distributed training from a single GPU to multi-node clusters using Ray Train, which manages the underlying execution and scaling logic automatically.

Yes, distributed training with Ray supports HuggingFace transformers natively. It orchestrates training across clusters for PyTorch, TensorFlow, and HuggingFace models to handle large-scale workloads efficiently.

You can run hyperparameter tuning alongside distributed training by integrating Ray Tune, enabling efficient hyperparameter optimization while simultaneously executing your training scripts across allocated cluster resources.

To run multi-node machine learning training, you need to install the ray[train] package alongside specific machine learning libraries like torch and transformers, depending on the framework your models use.

Fault tolerance during distributed machine learning training ensures that your execution can automatically recover from unexpected node failures, preventing the loss of training progress across your cluster.

You can dynamically add or remove compute nodes during model training through elastic scaling, allowing you to optimize resource usage by adjusting cluster capacity based on current workload demands.