title: Using tf.estimator.Estimator with save_checkpoint_steps leads to Tensorboard warnings
body: <p>When using <code>tf.estimator.train_and_evaluate(...)</code> with an <code>tf.estimator.Estimator</code> configurated with <code>tf.contrib.learn.RunConfig(save_checkpoints_steps=10, ...)</code>, a <code>CheckpointSaverHook</code> will be created automatically. This <code>CheckpointSaverHook</code> will save the graph and graph_def to the summary writer every time it is triggered (see <a href="https://github.com/tensorflow/tensorflow/blob/36b83257287148ddb4aab02c641d1ac9e192a136/tensorflow/python/training/basic_session_run_hooks.py#L440" rel="nofollow noreferrer">CheckpointSaverHook.before_run</a>). </p>
  
  <p>Basic code example:</p>
  
  <pre class="lang-py prettyprint-override"><code>estimator = tf.estimator.Estimator(
      model_fn, model_dir, params,
      config=tf.estimator.RunConfig(
          save_checkpoints_steps=100, 
          save_summary_steps=100
      )
  )
  train_spec = tf.estimator.TrainSpec(train_fn)
  eval_spec = tf.estimator.TrainSpec(eval_fn)
  tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
  </code></pre>
  
  <p>When starting Tensorboard on the written summary, it will output a hundreds of warnings because of multiple graph defs in the summary which I guess slows it down a lot on startup:</p>
  
  <pre class="lang-none prettyprint-override"><code>W0117 18:47:30.278879 Reloader tf_logging.py:86] Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.
  W0117 18:47:30.279753 Reloader tf_logging.py:86] Found more than one metagraph event per run. Overwriting the metagraph with the newest event.
  </code></pre>
  
  <p>I see there might be issues when using multiple graphs, but for a single graph this seems unpracticable. Are we using the estimator correctly here and is this behavior intended? Do we need to write a custom hook instead in order to prevent this from happening?</p>
  
  <p>Thanks for your advice!</p>
  
  <p>Edit: Due to suggestions from comments there is an issue now:  <a href="https://github.com/tensorflow/tensorflow/issues/17272" rel="nofollow noreferrer">https://github.com/tensorflow/tensorflow/issues/17272</a></p>