title: Dask Event loop was unresponsive - work not parallelized
body: <p>This is a follow-up to this <a href="https://stackoverflow.com/questions/48067066/local-dask-worker-unable-to-connect-to-local-scheduler/48067814#48067814" title="this question">question</a>. I'm now trying to run Dask on multiple EC2 nodes on AWS. </p>
  
  <p>I'm able to start up the scheduler on the first machine:</p>
  
  <p><a href="https://i.stack.imgur.com/kmuIR.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/kmuIR.png" alt="enter image description here"></a></p>
  
  <p>I then start up workers on several other machines. From the other machines I'm able to access the scheduler using <code>nc -zv ${HOST} ${PORT}</code>, and the workers otherwise seem to be able to connect to the master, as evidenced by the worker's sysout: <code>Registered to: tcp://10.201.101.108:31001</code>, but almost immediately the worker complains about a timeout loop. </p>
  
  <p><a href="https://i.stack.imgur.com/hcFAS.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/hcFAS.png" alt="enter image description here"></a></p>
  
  <p>From the master node, in my Jupyter notebook I then connect to the scheduler:</p>
  
  <p><code>dask_client = Client('10.201.101.108:31001')</code></p>
  
  <p>But the work does not propagate to the worker nodes (worker-node CPU stays at &lt;1%) or even to the worker running on the same machine as the scheduler. This is a highly parallelized task and when running on a single machine (i.e., using <code>Client(processes=False)</code> consumes every core on the machine).  </p>