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Training too slow and not using full GPU, whats the training time ? #2
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From your screenshot, I would say that you didnt use gpu. Would you try to print some message after the line "if torch.cuda.is_available():" ? |
I do was using GPU, the 2nd image is showing that as the python process was running on GPU, its just that it was using only 300-400MB GPU instead of full 12GB, thus the issue. I had now deleted my project since there was no reply I thought this is a dead repo. |
Sorry for that. I was quite busy over the last months. |
Btw, if you want to train a model with 300 categories, increasing model's breadth and deepness is indispensable. |
New to all these terms, can you provide me the parameters to use for whole data set ? Also, the way I wanted to try it was for saved image files, like I can just load a saved .png .jpg file of a drawing, feed it to the network and get the result. Somehow, all the repos I have come across either use webcam approach, javascript or recording/storing drawing coordinates and none had a approach to use saved image files (whether hand drawings or even quick draw dataset as saved image files). I tried to pre-process the images to feed in network but the result is not the same. |
Using default parameters and all 300 categories, I feel its training quite slow even though I am using a AWS EC2 P2.xLarge instance with Nvidia K80 GPU.
Its using only 360MB of the GPU and I feel as if its stuck on that usage, its not using more or less from that number (checked via nvidia-smi command)
I tried calculating the time between each iteration and its 5-7 seconds, and calculating the total iterations with that time and 20 epochs, its results in more than 150 days.
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