information necessary to compute the gradient of the model parameters.information necessary to backpropagate the error (gradients of the activation w.r.t.So where does this need for memory comes from? Below I present the two main high-level reasons why a deep learning training need to store information: ![]() In this first part, I will explain how a deep learning models that use a few hundred MB for its parameters can crash a GPU with more than 10GB of memory during their training ! Shedding some light on the causes behind CUDA out of memory ERROR, and an example on how to reduce by 80% your memory footprint with a few lines of code in Pytorch Understanding memory usage in deep learning models training ![]() Understanding memory usage in deep learning models training
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