title: Using all cores in Dask
body: <p>I am working on a google cloud computing instance with 24 vCPUs. The code running is the following</p>
  
  <pre><code>import dask.dataframe as dd
  from distributed import Client
  client = Client()
  
  #read data
  logd = (dd.read_csv('vol/800000test', sep='\t', parse_dates=['Date'])
           .set_index('idHttp')
           .rename(columns={'User Agent Type':'UA'})
           .categorize())
  </code></pre>
  
  <p>When I run it (and this is also the case for the posterior data analysis I am doing after loading the data) I see 11 cores being used, sometimes 4.</p>
  
  <p><a href="https://i.stack.imgur.com/dDBvV.png" rel="noreferrer"><img src="https://i.stack.imgur.com/dDBvV.png" alt="enter image description here"></a></p>
  
  <p>Is there any way to control this better and make full use of cores?</p>