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As the dynamic computational graph is widely supported by many machine learning frameworks, GPU memory utilization for training on a dynamic computational graph becomes a key specification of these...

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Source: Synced | AI Technology & Industry Review

As the dynamic computational graph is widely supported by many machine learning frameworks, GPU memory utilization for training on a dynamic computational graph becomes a key specification of these frameworks. In the recently released v1.4, MegEngine provides a way to reduce the GPU memory usage by additional computation using Dynamic Tensor Rematerialization (DTR) technique and further engineering optimization, which makes large batch size training on a single GPU possible.

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