Created
December 30, 2019 23:28
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cuda cheat sheet
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#check if pytorch is using the GPU | |
python -c 'import torch; print(torch.cuda.is_available())' #should print True | |
python -c 'import torch; print(torch.rand(2,3).cuda())' | |
watch -n 2 nvidia-smi | |
========================================================================= | |
import torch | |
dev = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") | |
t1 = torch.randn(1,2) | |
t2 = torch.randn(1,2).to(dev) | |
print(t1) # tensor([[-0.2678, 1.9252]]) | |
print(t2) # tensor([[ 0.5117, -3.6247]], device='cuda:0') | |
t1.to(dev) | |
print(t1) # tensor([[-0.2678, 1.9252]]) | |
print(t1.is_cuda) # False | |
t1=t1.to(dev) | |
print(t1) # tensor([[-0.2678, 1.9252]], device='cuda:0') | |
print(t1.is_cuda) # True | |
model = M() # not on cuda | |
model.to(dev) # is on cuda (all parameters) | |
print(next(model.parameters()).is_cuda) #True | |
========================================================================= | |
# setting device on GPU if available, else CPU | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
print('Using device:', device) | |
print() | |
#Additional Info when using cuda | |
if device.type == 'cuda': | |
print(torch.cuda.get_device_name(0)) | |
print('Memory Usage:') | |
print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB') | |
print('Cached: ', round(torch.cuda.memory_cached(0)/1024**3,1), 'GB') | |
========================================================================= |
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