title: Installing tensorflow-gpu via conda-forge results in using CPU-only tensorflow
body: <p>I am creating a conda environment solely for using the <code>tensorflow-gpu</code> package from the <code>conda-forge</code> channel</p>
  
  <pre class="lang-none prettyprint-override"><code>conda create -n tst -c conda-forge tensorflow-gpu
  </code></pre>
  
  <p>This results in both <code>tensorflow-gpu</code> <strong>and</strong> <code>tensorflow</code> packages to be installed:</p>
  
  <pre class="lang-none prettyprint-override"><code>The following NEW packages will be INSTALLED:
  
      _tflow_1100_select: 0.0.1-gpu
      ...
      tensorboard:        1.10.0-py36_0         conda-forge
      tensorflow:         1.10.0-py36_0         conda-forge
      tensorflow-gpu:     1.10.0-hf154084_0
      ...
  </code></pre>
  
  <p>Then when I import tensorflow, it does not see GPUs:</p>
  
  <pre><code>&gt;&gt;&gt; import tensorflow as tf
  &gt;&gt;&gt; tf.test.is_gpu_available()
  2018-09-20 15:29:21.778708: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
  False
  </code></pre>
  
  <p>Questions:</p>
  
  <ul>
  <li>why does conda install both <code>tensorflow-gpu</code> and <code>tensorflow</code> packages when only the former is required?</li>
  <li>can both packages co-exist peacefully, and if so, how to switch between the two?</li>
  <li>Bonus points: why does everything work fine when installing from the main channel, i.e. <code>conda create -n tst tensorflow-gpu</code>? (My uneducated guess is that in <code>conda-forge</code>, the <code>tensorflow-gpu</code> package actually comes from the main channel and thus has a lower priority during import).</li>
  </ul>