本技术指南和课程大纲深入探讨了流水线并行的机制，这是训练超出单张 GPU 显存容量的大规模 AI 模型的关键技术。内容遵循结构化的教学路径：从单体多层感知器（MLP）基准开始，逐步进行手动模型分区和分布式通信原语的实现。核心内容侧重于流水线调度策略的演进，从基础的“停止并等待”方法，发展到行业标准的 GPipe（含微批次处理）以及高效的交错式 1F1B（一前向一后向）算法。它将理论推导与异步通信的手动实现相结合。
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. Kian Kyars created this course.
Here are the sections in this course:
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Introduction, Repository Setup & Syllabus
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Step 0: The Monolith Baseline
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Step 1: Manual Model Partitioning
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Step 2: Distributed Communication Primitives
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Step 3: Distributed Ping Pong Lab
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Step 4: Building the Sharded Model
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Step 5: The Main Training Orchestrator
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Step 6a: Naive Pipeline Parallelism
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Step 6b: GPipe & Micro-batching
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Step 6c: 1F1B Theory & Spreadsheet Derivation
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Step 6c: Implementing 1F1B & Async Sends
Watch the full course on the freeCodeCamp.org YouTube channel (3-hour watch).