Data Parallelism
PremiumUnderstanding communication primitives and DDP's gradient synchronization mechanism
Get code accessThe first step in training a large model is to get multiple GPUs working together. Data Parallelism is the most intuitive approach: each GPU holds a complete copy of the model, processes different data independently, and finally aggregates the gradients. In this chapter, we first understand the memory bottleneck of a single GPU, then learn the basics of multi-GPU communication, and finally dive into the implementation of DDP.
Memory Composition of Single-GPU Training
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