title: Managing worker memory on a dask localcluster
body: <p>I am trying to load a dataset with dask but when it is time to compute my dataset I keep getting problems like this:</p>
  
  <blockquote>
    <p>WARNING - Worker exceeded 95% memory budget. Restarting.</p>
  </blockquote>
  
  <p>I am just working on my local machine, initiating dask as follows:</p>
  
  <pre><code>if __name__ == '__main__':
      libmarket.config.client = Client()  # use dask.distributed by default
  </code></pre>
  
  <p>Now in my error messages I keep seeing a reference to a 'memory_limit=' keyword parameter. However I've searched the dask documentation thoroughly and I can't figure out how to increase the bloody worker memory-limit in a single-machine configuration. I have 256GB of RAM and I'm removing the majority of the future's columns (a 20GB csv file) before converting it back into a pandas dataframe, so I know it will fit in memory. I just need to increase the per-worker memory limit from my code (not using dask-worker) so that I can process it.</p>
  
  <p>Please, somebody help me.</p>