Gradient Synchronization
A core communication step in distributed training in which participating nodes compute local gradients and then exchange and aggregate them to ensure that all model replicas are updated consistently. It is commonly implemented with All-Reduce or parameter server architectures.
Timeline (last 90 days)
Gradient synchronization in data-parallel training usually occurs every step, and the communication volume can reach hundreds of MB to GB level.
Gradient synchronization is the core communication step in distributed training: after each participating node computes gradients based on local data, it needs to exchange and aggregate gradients with other nodes to ensure all replica models maintain consistent updates.
依赖WiFi传输,网络不稳定环境下可能影响数据推送及时性
All Facts (2)
Gradient synchronization in data-parallel training usually occurs every step, and the communication volume can reach hundreds of MB to GB level.
50%UnverifiedGradient synchronization is the core communication step in distributed training: after each participating node computes gradients based on local data, it needs to exchange and aggregate gradients with other nodes to ensure all replica models maintain consistent updates.
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