Comments (2)
Hi @ParsaMokhtariHessar , thank you for your interest in gossipy
. It seems that what you have done is correct, so it's hard to say what is wrong. Have you tried working with TPUs using a neural net outside gossipy? So to be sure that the problem comes from the framework. Anyway, (this happens also with GPUs), in these simulations there are a bunch of models and only one of them is loaded into the GPU/TPU memory at a time, and what happens is that the time overhead to move the models in/out of the memory usually overcomes the benefits of using GPU/TPUs. This is my take on this, but I honestly have to say that I did not dig into it.
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I see! And I guess it would be too much memory to attempt to move all calculations to the TPU VRAM. Nonetheless the "Loading trip" is significantly faster in the case of a GPU. I am going to attempt to time a simple MNIST train with CPU, GPU, and TPU and get back to you.
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Related Issues (7)
- Issue Getting the code to utilize google collab GPU HOT 2
- Are you open to contributions ? HOT 3
- Same code for Linear Regression and Logistic Regression ? HOT 2
- Why use PyTorch for simple machine learning models (linear regression, logistic regression, ...) instead of scikit-learn ? HOT 1
- Questions about the implementation of an algorithm HOT 1
- Issue when the network is composed of one node HOT 2
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