I’ve been attempting to work through the first lesson. I have an older Asus gaming laptop which has a 660M
CPU. Going through the many posts on the forums I’ve noticed that pytorch does not support GPUs lower than a compute compatibility of 5.0. The 660M is 3.0.
So, I’ve installed the fastai environment using the fastai-cpu yml file.
That being said, what do I do with the two python statements:
and the subsequent statements which actually trigger the GPU warning:
data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz))
learn = ConvLearner.pretrained(arch, data, precompute=True)
The pytorch package which is currently install is pytorch-0.3.1-py36_cuda80.
There are references to removing the currently installed pytorch and installing
the pytorch-cpu as follows:
conda uninstall pytorch conda install -c peterjc123 pytorch-cpu
and then doing a
conda env update
Would this be correct?
There are other solutions which get pytorch source and compile it
so that it could use the GPU but I’m not sure if it would work
on this machine with the low compatibility of 3.0.
Are there any other things that I should do to get this running?
I could run this on one my Linux boxes (preferred),
but I thought I would try the windows laptop with Nvidia card.
I just don’t want to spend another 1/2 day trying to make sure the environment is correct.
I just don’t have the bucks to use aws or to new buy hardware.
I have extremely limited resources at this time.