This advanced PyTorch course focuses on Distributed Data Parallel (DDP) for high-performance deep learning. Designed for experienced machine learning engineers and AI researchers, it provides a practical guide to scaling models across multiple GPUs and nodes.

The course starts with an introduction to DDP, explaining its architecture, benefits, and how it improves training efficiency. You will then explore multi-GPU training through hands-on code walkthroughs, including Torchrun-based workflows to simplify distributed training.

Advanced modules cover multi-node DDP setups, allowing you to scale large models across multiple machines. The course also includes a complete example of training a GPT-like model with DDP, demonstrating best practices for performance, memory optimization, and synchronization across GPUs and nodes.

By the end of this course, learners will be able to implement and optimize distributed training for large deep learning models, reduce training time dramatically, and confidently handle multi-GPU and multi-node setups for research or production environments.