Description
Issue Description
A clear and concise description of the issue. If it's a feature request, please add [Feature Request] to the title.
Not able to get all the data into 'device (CUDA)'. Facing problem at 'loss = -dist_y.log_prob(data).mean()'. Looks like data cant be transferred to GPU.
Do we need to regester data as buffer and work around it?
Error: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:1! (when checking argument for argument mat1 in method wrapper_addmm)
Steps to Reproduce
Please provide steps to reproduce the issue attaching any error messages and stack traces.
dataset = torch.tensor(data_train, dtype=torch.float)
trainloader = torch.utils.data.DataLoader(dataset, batch_size=1024)
for steps in range(t_steps):
step_loss=0
for i, data in enumerate(trainloader):
data = data.to(device)
if i==0:
print(data.shape)
#p_getsizeof(data)
try:
optimizer.zero_grad()
loss = -dist_y.log_prob(data).mean()
loss.backward()
optimizer.step()
except ValueError as e:
print('Error')
print('Skipping thatbatch')
Expected Behavior
What did you expect to happen?
Matrices should be computated in the CUDA device and not show a conflit of data being at 2 different place.
System Info
Please provide information about your setup
- PyTorch Version (run
print(torch.__version__) - Python version