title: GPU Support of Tensorflow on Linux Mint/Ubuntu 14.04
body: <p>So I installed tensorflow and the CPU Version works fine but I can't seem to get the GPU to work.
  I installed Cuda by downloading the .deb from Nvidia.
  I copied the cudNN content</p>
  
  <pre><code>cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
  #define CUDNN_MAJOR      6
  #define CUDNN_MINOR      0
  #define CUDNN_PATCHLEVEL 20
  --
  #define CUDNN_VERSION    (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 +     CUDNN_PATCHLEVEL)
  
  #include "driver_types.h"
  </code></pre>
  
  <p>I entered the path in ~/.profile</p>
  
  <pre><code>export CUDA_HOME=/usr/local/cuda
  export CUDA_ROOT=/usr/local/cuda
  export PATH=$PATH:$CUDA_ROOT/bin
  export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64
  </code></pre>
  
  <p>Oh and Nvidia-smi shows:</p>
  
  <pre><code>| NVIDIA-SMI 375.39                 Driver Version: 375.39                                            |
  |-------------------------------+----------------------+----------------------+
  | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
  | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
  |===============================+======================+======================|
  |   0  GeForce GTX 1080    Off  | 0000:01:00.0      On |                  N/A |
  | 10%   54C    P0    42W / 200W |    591MiB /  8105MiB |      4%      Default |
  +-------------------------------+----------------------+----------------------+
  
  +-----------------------------------------------------------------------------+
  | Processes:                                                       GPU Memory |
  |  GPU       PID  Type  Process name                               Usage      |
  |=============================================================================|
  |    0      1368    G   /usr/bin/X                                     344MiB |
  |    0      3078    G   cinnamon                                       129MiB |
  |    0      6549    G   /usr/lib/virtualbox/VirtualBox                  20MiB |
  |    0     15491    G   ...bleH2AndQuicRequests/Enabled/*NetworkTime    96MiB |
  </code></pre>
  
  <p>Yet still using Tensorflow I get:</p>
  
  <pre><code>&gt;&gt;&gt; python
  &gt;&gt;&gt; import tensorflow as tf
  &gt;&gt;&gt; sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
  
  W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
  W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
  W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
  W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
  W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
  W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
  Device mapping: no known devices.
  I tensorflow/core/common_runtime/direct_session.cc:257] Device mapping:
  </code></pre>
  
  <p>I hope you can tell me what else I can do.
  Best regards and thanks in advance</p>