A new educational repository is now available to help individuals grasp the fundamentals of distributed training in PyTorch. Hosted on GitHub, this repository provides a comprehensive, from-scratch implementation of distributed training parallelism, including Data Parallelism (DP), Fully Sharded Data Parallelism (FSDP), Tensor Parallelism (TP), FSDP+TP, and Pipeline Parallelism (PP).

The code is intentionally designed to be straightforward and easy to follow, with clear forward and backward logic, as well as collectives. This allows users to visualize the algorithm in action, making it easier to understand the underlying concepts. A simple model, comprising repeated 2-matmul MLP blocks on a synthetic task, is utilized to demonstrate the communication patterns involved in distributed training.

This resource is perfect for those seeking a deeper understanding of the mathematical principles behind distributed training and its implementation in PyTorch, all without the complexity of navigating a large framework.

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