title: Python memory consumption when using pickle.load vs process memory usage
body: <p>I'm not very familiar with Python memory management ou profiling but I'm in a weird situation and can't find any clear explication.</p>
  <p>I'm trying to load a large ML Model (1.4 GB) using pickle in my Python code (3.7). When I call pickle.load, my process ram usage increase of around 8GB (according my task manager). I can easily understand that the pickle version on disk should be smaller than the object fully loaded in Python but the difference seems me excessive so I used Pympler to check the size of my model in my Python code and I got around 5GB.</p>
  <p>I would like to understand why I get this 3GB difference:</p>
  <ul>
  <li>There is a problem with Pickle?</li>
  <li>Pympler doesn't count all the objects accurately?</li>
  <li>My task manager doesn't report the ram consumption the same way Pympler count it?</li>
  <li>...</li>
  </ul>
  <p>If you have another ideas to profile my code, feel free to answer.</p>
  <p>Thanks</p>