Traceback (most recent call last):
File "main.py", line 201, in <module>
loss_list, lr_epoch, mu_epoch = train(epoch)
File "main.py", line 132, in train
outputs = net(inputs)
File "/home/lala/miniconda2/lib/python2.7/site-packages/torch/nn/modules/module.py", line 224, in __call__
result = self.forward(*input, **kwargs)
File "/home/lala/miniconda2/lib/python2.7/site-packages/torch/nn/parallel/data_parallel.py", line 59, in forward
replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
File "/home/lala/miniconda2/lib/python2.7/site-packages/torch/nn/parallel/data_parallel.py", line 64, in replicate
return replicate(module, device_ids)
File "/home/lala/miniconda2/lib/python2.7/site-packages/torch/nn/parallel/replicate.py", line 12, in replicate
param_copies = Broadcast(devices)(*params)
File "/home/lala/miniconda2/lib/python2.7/site-packages/torch/nn/parallel/_functions.py", line 19, in forward
outputs = comm.broadcast_coalesced(inputs, self.target_gpus)
File "/home/lala/miniconda2/lib/python2.7/site-packages/torch/cuda/comm.py", line 54, in broadcast_coalesced
results = broadcast(_flatten_tensors(chunk), devices)
File "/home/lala/miniconda2/lib/python2.7/site-packages/torch/cuda/comm.py", line 24, in broadcast
nccl.broadcast(tensors)
File "/home/lala/miniconda2/lib/python2.7/site-packages/torch/cuda/nccl.py", line 190, in broadcast
data_type, root, comm[i], cudaStream()))
File "/home/lala/miniconda2/lib/python2.7/site-packages/torch/cuda/nccl.py", line 118, in check_error
raise NcclError(status)
torch.cuda.nccl.NcclError: Unhandled Cuda Error (1)
When I run without GPU, the code is fine. On v0.1.12 it is fine on GPU and CPU.
Lines with issues I believe
if use_cuda:
net.cuda()
net = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count()))
cudnn.benchmark = True