How to print memory usage of each layer?

Hi All,
I am a beginner of pytorch, I tried to print memory cost (or variable shape/size) of each layer, not only the model._params and model._buffers, but also some mid-variable in model.backward(), how can I do it?



I would also like to know the answer.

Specifically, how to get the memory footprint of a Tensor? sys.getsizeof() gives size of the metadata of the Tensor. I would like to know how much Memory is it actually occupying.


Any updates on this? Did you find a way to do that @anand.saha ?

Nope, @EKami. Had moved on to other things …

okay np :slight_smile:

If you use master instead of 0.3.1, there is torch.cuda.memory_allocated(). This gives you all the allocated cuda memory, so you can instrument your code with it. I found it very helpful.


That’s perfect thanks a lot!

Anyone knows if there is any equivalent for CPU execution?
I came across this picture in this great paper and I would like to replicate

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It could be done by simply printing sys.getsizeof() after each layer, right?

Have you guys looked at the profiler?