Research
Educational PyTorch repo for distributed training from scratch: DP, FSDP, TP, FSDP+TP, and PP [P]
This Reddit post shares an educational PyTorch repository designed to teach distributed training techniques from the ground up, covering Data Parallelism (DP), Fully Sharded Data Parallel (FSDP), Tens
This Reddit post shares an educational PyTorch repository designed to teach distributed training techniques from the ground up, covering Data Parallelism (DP), Fully Sharded Data Parallel (FSDP), Tensor Parallelism (TP), a combined FSDP+TP hybrid approach, and Pipeline Parallelism (PP). The repo aims to help practitioners understand the mechanics behind each parallelism strategy — including how they handle memory, computation, and inter-GPU communication — by implementing them from scratch rather than relying on high-level abstractions. It serves as a hands-on learning resource for those looking to scale model training across multiple GPUs or nodes using native PyTorch distributed APIs.
Related
- AI Systems Performance Engineering by Chris Fregly - is it worth it? [D]
- FlashAttention (FA1–FA4) in PyTorch - educational implementations focused on algorithmic differences [P]
- Started a video series on building an orchestration layer for LLM post-training [P]
- [[p-building-a-llm-from-scratch-with-mary-shelleys-frankenstei|[P] Building a LLM from scratch with Mary Shelley's 'Frankenstein' (on Kaggle)]]
Source: r/MachineLearning | 2026-04-12