title: How does `MonitoredTrainingSession()` work with "restore" and "testing mode"?
body: <p>In Tensorflow, we could build and create multiple Tensorflow Sessions using <code>Between-graph Replication</code> for distributed training. <code>MonitoredTrainingSession()</code> coordinates multiple Tensorflow Sessions, and there is an argument <code>checkpoint_dir</code> for <code>MonitoredTrainingSession()</code> to restore the Tensorflow session/graph. Now I have following questions:</p>
  
  <ol>
  <li>We normally use the object of <code>tf.train.Saver()</code> to restore the Tensorflow graphs by <code>saver.restore(...)</code>. But how do we restore them by using <code>MonitoredTrainingSession()</code>?</li>
  <li>Since we run multiple processes and each process builds and creates a Tensorflow Session for training, I wonder if we also have to run multiple processes for testing (or prediction) after training. In other words, how does <code>MonitoredTrainingSession()</code> work with testing (or prediction) mode?</li>
  </ol>
  
  <p>I read Tensorflow Doc, but didn't find the answers for these 2 questions. I really appreciate if anyone has solutions. Thanks!</p>