About
This skill automates the complex process of setting up distributed training architectures for large-scale machine learning models. It provides specialized guidance for implementing patterns like Distributed Data Parallel (DDP) and multi-node synchronization across PyTorch, TensorFlow, and other major ML frameworks. By generating production-ready configurations and validating environment setups, it ensures efficient resource utilization and minimizes the friction typically associated with scaling model training from a single device to distributed clusters.
