DualPipe
A bidirectional pipeline parallel algorithm for overlapping computation and communication in V3/R1 training.
Product Details
DualPipe is an innovative bidirectional pipeline parallel algorithm developed by the DeepSeek-AI team. This algorithm significantly reduces pipeline bubbles and improves training efficiency by optimizing the overlap of calculation and communication. It performs well in large-scale distributed training and is especially suitable for deep learning tasks that require efficient parallelization. DualPipe is developed based on PyTorch and is easy to integrate and expand. It is suitable for developers and researchers who require high-performance computing.
Main Features
How to Use
Target Users
This algorithm is suitable for deep learning tasks that require efficient parallelization, especially large-scale distributed training scenarios. It is suitable for developers and researchers who have high performance requirements and can help them achieve faster model training with limited resources.
Examples
In large-scale language model training, using the DualPipe algorithm significantly reduces training time.
In computer vision tasks, the convergence speed of the model is improved by optimizing the parallel strategy.
In a multi-node distributed training environment, DualPipe reduces communication overhead and improves overall efficiency.
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