title: How should I treat joblib multiprocessing in an AWS lambda implementation?
body: <p>I have a relatively simple linear regression lambda in AWS. Each instance the function is called the logs display the following:</p>
  
  <pre><code>/opt/python/sklearn/externals/joblib/_multiprocessing_helpers.py:38: UserWarning: [Errno 38] Function not implemented. joblib will operate in serial mode
  </code></pre>
  
  <pre><code>warnings.warn('%s. joblib will operate in serial mode' % (e,))
  </code></pre>
  
  <p>I suspect this is due to sklearn running on a lambda (i.e. 'serverless') and trying to determine it's multi-processing capabilities as per <a href="https://stackoverflow.com/questions/2009278/python-multiprocessing-permission-denied">this question</a> and <a href="https://github.com/joblib/joblib/issues/391" rel="nofollow noreferrer">this GH issue</a>.</p>
  
  <p>I am also understanding from the GH that this is not a 'fixable' issue, it will always happen when deploying with these dependencies on this hardware. I am getting back my expected results (even though I am currently maxing out the default, minimum lambda memory of 128mb).</p>
  
  <p>I aim to control the warnings and would know if there is a way to either:</p>
  
  <ul>
  <li>stop sklearn looking for multiprocessing, so preventing the warning from issuing</li>
  <li>capture <em>this specific</em> warning and prevent it from being passed from my function into the cloudwatch logs </li>
  <li>if both are possible, which would be preferable from a aws architecture/pythonic opinion? </li>
  </ul>