title: Pytorch: no CUDA-capable device is detected on Linux
body: <p>When trying to run some Pytorch code I get this error:</p>
  
  <pre><code>THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=74 error=38 : no CUDA-capable device is detected
  Traceback (most recent call last):
  File "demo.py", line 173, in test
  pca = torch.FloatTensor( np.load('../basics/U_lrw1.npy')[:,:6]).cuda()
  RuntimeError: cuda runtime error (38) : no CUDA-capable device is detected at /pytorch/aten/src/THC/THCGeneral.cpp:74
  </code></pre>
  
  <p>I a running a cloud virtual machine using the 'Google Deep Learning VM'
  Version: tf-gpu.1-13.m25
  Based on: Debian GNU/Linux 9.9 (stretch) (GNU/Linux 4.9.0-9-amd64 x86_64\n)
  Linux tf-gpu-interruptible 4.9.0-9-amd64 #1 SMP Debian 4.9.168-1 (2019-04-12) x86_64</p>
  
  <p>Environment info:</p>
  
  <pre><code>$ nvidia-smi
  Sun May 26 05:32:33 2019       
  +-----------------------------------------------------------------------------+
  | NVIDIA-SMI 410.72       Driver Version: 410.72       CUDA Version: 10.0     |
  |-------------------------------+----------------------+----------------------+
  | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
  | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
  |===============================+======================+======================|
  |   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |
  | N/A   42C    P0    74W / 149W |      0MiB / 11441MiB |    100%      Default |
  +-------------------------------+----------------------+----------------------+
  
  +-----------------------------------------------------------------------------+
  | Processes:                                                       GPU Memory |
  |  GPU       PID   Type   Process name                             Usage      |
  |=============================================================================|
  |  No running processes found                                                 |
  +-----------------------------------------------------------------------------+
  
   $ echo $CUDA_PATH
  
   $ echo $LD_LIBRARY_PATH
  /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64
  
  $ env | grep CUDA
  CUDA_VISIBLE_DEVICES=0
  
  $ pip freeze
  DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.
  7 won't be maintained after that date. A future version of pip will drop support for Python 2.7.
  audioread==2.1.7
  backports.functools-lru-cache==1.5
  certifi==2019.3.9
  chardet==3.0.4
  cloudpickle==1.1.1
  cycler==0.10.0
  dask==1.2.2
  decorator==4.4.0
  dlib==19.17.0
  enum34==1.1.6
  filelock==3.0.12
  funcsigs==1.0.2
  future==0.17.1
  gdown==3.8.1
  idna==2.8
  joblib==0.13.2
  kiwisolver==1.1.0
  librosa==0.6.3
  llvmlite==0.28.0
  </code></pre>