<p>some good holiday reading on the latest research in this area:  </p>

<p><a href="https://arxiv.org/abs/1706.04972" rel="nofollow noreferrer">https://arxiv.org/abs/1706.04972</a></p>
TensorFlow device placement algorithm and graph partitioning
<p>1) I have seen that unless stated otherwise, a subgraph in simple_placer.cc is placed on task 0 (mapped to device 0), but, before that, it occurs graph partitioning. So, if after this operation we have two subgraphs, then they are going to be mapped to different tasks/devices?</p>

<p>2) Is there a way to have automatic device placement for model parallelism considering distributed execution or should I have to set it manually? Broadly speaking, not only model parallelism, but automatic task parallelism?</p>