title: What's the recommend way of restoring only parts model in distributed tensorflow
body: <p>When we finetune a model on a different task, only a part of vars in the model are restored from the pretrained task and others are left as initial values.</p>
  
  <p>As many docs recommends(<a href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim" rel="nofollow noreferrer">page1</a> <a href="https://github.com/tensorflow/models/blob/master/inception/inception/inception_train.py" rel="nofollow noreferrer">page2</a>), when training with a local graph, restoring the pretrained model after running the global init op(call restoring in "init_fn" if MonitoredSession or supervisor is included).</p>
  
  <p>But in the distributed case, does global init op <strong>make "model_ready" returns true before the restoring-model called</strong>? other non-chief nodes will use the "not ready" values.</p>