DAPPLE
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About
DAPPLE is a synchronous training framework which combines data parallelism and pipeline parallelism for large DNN models. It features a novel parallelization strategy planner to solve the partition and placement problems, and explores the optimal hybrid strategy of data and pipeline parallelism. We also propose a new runtime scheduling algorithm. The framework, implemented on TensorFlow, significantly speeds up training of large models like GPT3-175B and is available on Arnold and VolcEngine platforms.
Platform
Features
• tensorflow implementation
• available as open-source code
• supports large dnn models (e.g., gpt3-175b)
• runtime scheduling algorithm
• hybrid parallelism strategy planning
• pipelined data parallelism
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