Hi,
I am using cuda for a simple model in the mnist example with
model=torch.nn.DataParallel(model, device_ids=[0,1]).cuda()
The program than hangs with 100% GPU-Util on  GPUs 1 and 2 under nvidia-smi, although it runs fine with one GPU.
It seems similar to a previous posting. However, I am using 1080ti, which seems to work fine for other users.
Raw code and stack trace are here:

  
      gist.github.com
  
  
    https://gist.github.com/sam186/ce30eb715ab8197f6da24066db1c7cea#file-stacktrace 48
mnist.py
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable


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stacktrace
#0  0x00007ffff7bc9827 in futex_abstimed_wait_cancelable (private=0, abstime=0x0, expected=0, futex_word=0x7fff94000c10)
    at ../sysdeps/unix/sysv/linux/futex-internal.h:205
#1  do_futex_wait (sem=sem@entry=0x7fff94000c10, abstime=0x0) at sem_waitcommon.c:111
#2  0x00007ffff7bc98d4 in __new_sem_wait_slow (sem=0x7fff94000c10, abstime=0x0) at sem_waitcommon.c:181
#3  0x00007ffff7bc997a in __new_sem_wait (sem=<optimized out>) at sem_wait.c:29
#4  0x000055555563c516 in PyThread_acquire_lock_timed ()
#5  0x00005555556d63ac in lock_PyThread_acquire_lock ()
#6  0x00005555556616e4 in _PyCFunction_FastCallDict ()
#7  0x00005555556ef4ec in call_function ()
#8  0x0000555555713134 in _PyEval_EvalFrameDefault ()
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__Python VERSION: 3.6.2 |Anaconda custom (64-bit)| (default, Sep 22 2017, 02:03:08)
[GCC 7.2.0]
__PyTorch VERSION: 0.2.0_4
__CUDNN VERSION: 6021
__Number CUDA Devices: 4