KaiserSozo commented on issue #10532: NDArray failed to allocate CPU memory URL: https://github.com/apache/incubator-mxnet/issues/10532#issuecomment-382621382 As I understand now memory consumes here: weights.set_data(weights.data() + output). Where weights is the Parameter with grad_req {'null'}. I've choosen such way of parameter updating because: 1. I already have needed delta for gradient descent from output = net(data) 2. (The most valuable) Trainer.step takes 1.5x more time approx than modyfiing parameter directly. So here are 3 questions: 1. How to modify parameter directly and do not cause memory leak? 2. How to optimize performance of data saving? I measured time for data calculations and it is about 190 seconds, while direct data saving to the parameter takes about 500s and Trainer.step takes about 740s. 3. Is it usual that gradient descent (data saving) takes so much time compared to calculation time?
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