Clearing GPU Memory - PyTorch

[m.mansour]

I am trying to run the first lesson locally on a machine with GeForce GTX 760 which has 2GB of memory.

After executing this block of code:

arch = resnet34
data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz))
learn = ConvLearner.pretrained(arch, data, precompute=True)
learn.fit(0.01, 2)

The GPU memory jumped from 350MB to 700MB, going on with the tutorial and executing more blocks of code which had a training operation in them caused the memory consumption to go larger reaching the maximum of 2GB after which I got a run time error indicating that there isn’t enough memory.

I know for this particular case, this can be avoided by skipping the previous blocks of code which had a training operation in them and just executing the one where I ran out of memory, but how else could this be solved? I tried executing del learn but that doesn’t seem to free any memory.

[cedric]

Try to restart the Jupyter kernel.

How to free-up GPU memory in pyTorch 0.2.x?

Yeah I just restart the kernel.

Or, we can free this memory without needing to restart the kernel. See the following thread for more info.

GPU memory not being freed after training is over

I was checking my GPU usage using nvidia-smi command and noticed that its memory is being used even after I finished the running all the code in lesson 1 as shown in the figure bellow [Capture] The memory is only freed once I restart the jupyter kernel. Is this the usual behavior? How can I free this memory without needing to restart the kernel? Thanks

[sam2]

If you did del some_object

follow it up with torch.cuda.empty_cache()

This will allow the reusable memory to be freed (You may have read that pytorch reuses memory after a del some _object)

This way you can see what memory is truly avalable