Pytorch GPU utilization low and CPU utilization high?

(Alankar) #1

I have created the fast ai environment on my windows 10 laptop and everything installed properly. I was running the lesson-1.ipynb and found that my gpu utilization is low (about 8-10%) where as the CPU utilization goes even up to 75%. I don’t understand why is this happening.

torch.cuda.is_available() shows True when executed and torch.backends.cudnn.enabled also shows True

I looked into my task manager under the Performance section and found that dedicated gpu memory for pytorch is showing 1gb / 4 gb (I have a gtx 1050ti laptop), but I also have a tensorflow-gpu environment and when I run any model on that written in tensorflow / Keras ,the gpu utilization is 30-40% and dedicated memory is 3gb / 4gb. Is pytorch working normally or is somehing wrong?

Thanks in advance.



Can you run torch.cuda.is_available()?
If it is can you see what device you’re currently using torch.cuda.get_device_name(torch.cuda.current_device())


(Alankar) #3

Problem automatically got solved after a reboot (didn’t understand how) and it shows my gpu name ‘GeForce GTX 1050 Ti’. Thanks!


(Karl) #4

The image augmentation used in lesson 1 is very CPU heavy. That’s what the CPU usage is.

Since you have GPU memory to spare, try increase the batch size and see if the GPU usage goes up accordingly.


(Shreyas Verma) #5

I am running the same windows version and GPu model…when trying to run the lesson3-planets.ipynb, the GPU utilization is very less(utilization~5%; dedicated GPU memory - 2.8/4gb)…cpu utilization is ~30%…each epoch takes around 7min to run! while the notebook in the lecture ran 5 epochs in under 4min cant understand the underlying issue here