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Pipeline parallelism speeds up training of AI models by splitting a massive model across multiple GPUs and processing data like an assembly line, ensuring no single device has to hold the entire model in memory.
This course teaches pipeline parallelism from scratch, building a distributed training system step-by-step. Starting with a simple monolithic MLP, you’ll learn to manually partition models, implement distributed communication primitives, and progressively build three pipeline schedules: naive stop-and-wait, GPipe with micro-batching, and the interleaved 1F1B algorithm.
❤️ Support for this channel comes from our friends at Scrimba – the coding platform that’s reinvented interactive learning: https://scrimba.com/freecodecamp
⭐️ Contents ⭐️
– 0:00:00 Introduction, Repository Setup & Syllabus
– 0:05:36 Step 0: The Monolith Baseline
– 0:11:34 Step 1: Manual Model Partitioning
– 0:37:17 Step 2: Distributed Communication Primitives
– 0:55:35 Step 3: Distributed Ping Pong Lab
– 1:09:54 Step 4: Building the Sharded Model
– 1:16:32 Step 5: The Main Training Orchestrator
– 1:35:02 Step 6a: Naive Pipeline Parallelism
– 2:00:32 Step 6b: GPipe & Micro-batching
– 2:29:42 Step 6c: 1F1B Theory & Spreadsheet Derivation
– 2:50:34 Step 6c: Implementing 1F1B & Async Sends
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