title: Anaconda reading wrong CUDA version
body: <p>I have a conda environment with PyTorch and Tensorflow, which both require CUDA 9.0 (~cudatoolkit 9.0 from conda). After installing pytorch with torchvision and the cudatoolkit (like they provided on their website) I wanted to install Tensorflow, the problem here is that I get this error:</p>
  <pre><code>Collecting package metadata (current_repodata.json): done
  Solving environment: failed with initial frozen solve. Retrying with flexible solve.
  Collecting package metadata (repodata.json): done
  Solving environment: failed with initial frozen solve. Retrying with flexible solve.
  Solving environment: / 
  Found conflicts! Looking for incompatible packages.
  This can take several minutes.  Press CTRL-C to abort.
  failed                                                                                                   
  
  UnsatisfiableError: The following specifications were found
  to be incompatible with the existing python installation in your environment:
  
  Specifications:
  
    - tensorflow==1.12.0 -&gt; python[version='2.7.*|3.6.*']
    - tensorflow==1.12.0 -&gt; python[version='&gt;=2.7,&lt;2.8.0a0|&gt;=3.6,&lt;3.7.0a0']
  
  Your python: python=3.5
  
  If python is on the left-most side of the chain, that's the version you've asked for.
  When python appears to the right, that indicates that the thing on the left is somehow
  not available for the python version you are constrained to. Note that conda will not
  change your python version to a different minor version unless you explicitly specify
  that.
  
  The following specifications were found to be incompatible with your system:
  
    - feature:/linux-64::__cuda==10.2=0
    - feature:|@/linux-64::__cuda==10.2=0
  
  Your installed version is: 10.2
  </code></pre>
  <p>If I run <code>nvcc</code> or <code>nvidia-smi</code> on my host or the activated conda environment, I get that I have installed CUDA 10.2, even though <code>conda list</code> shows me that cudatoolkit 9.0 is installed. Any solution to this?</p>
  <p>EDIT:</p>
  <p>When running this code sample:</p>
  <pre><code># setting device on GPU if available, else CPU
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  print('Using device:', device)
  print()
  
  #Additional Info when using cuda
  if device.type == 'cuda':
      print(torch.cuda.get_device_name(0))
      print('Memory Usage:')
      print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')
      print('Cached:   ', round(torch.cuda.memory_cached(0)/1024**3,1), 'GB')
  
  
  print(torch.version.cuda)
  </code></pre>
  <p>I get this output:</p>
  <pre><code>GeForce GTX 1050
  Memory Usage:
  Allocated: 0.0 GB
  Cached:    0.0 GB
  9.0.176
  </code></pre>
  <p>So PyTorch does get the correct CUDA version, I just cant get tensorflow-gpu installed.</p>