Pipeline Parallelism: An Assembly Line for Your Model
Think of training a huge model like an assembly line. Pipeline parallelism splits a model's layers into stages across multiple GPUs, allowing you to train models too large for one device.
Think of training a huge model like an assembly line. Pipeline parallelism partitions a model's layers into sequential stages, assigning each stage to a different GPU. This is crucial for training models too large for a single GPU's memory; a batch is split into micro-batches that flow through the GPU stages. The main footgun is pipeline 'bubbles'—idle time on GPUs—if the compute load across stages is unbalanced, creating a bottleneck.
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