title: Will scikit-learn utilize GPU?
body: <p>Reading implementation of scikit-learn in TensorFlow: <a href="http://learningtensorflow.com/lesson6/" rel="noreferrer">http://learningtensorflow.com/lesson6/</a> and scikit-learn: <a href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html" rel="noreferrer">http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html</a> I'm struggling to decide which implementation to use.</p>
  <p>scikit-learn is installed as part of the tensorflow docker container so can use either implementation.</p>
  <p>Reason to use scikit-learn :</p>
  <blockquote>
  <p>scikit-learn contains less boilerplate than the tensorflow
  implementation.</p>
  </blockquote>
  <p>Reason to use tensorflow :</p>
  <blockquote>
  <p>If running on Nvidia GPU the algorithm will be run against in parallel
  , I'm not sure if scikit-learn will utilize all available GPUs?</p>
  </blockquote>
  <p>Reading <a href="https://www.quora.com/What-are-the-main-differences-between-TensorFlow-and-SciKit-Learn" rel="noreferrer">https://www.quora.com/What-are-the-main-differences-between-TensorFlow-and-SciKit-Learn</a></p>
  <blockquote>
  <p>TensorFlow is more  low-level; basically, the Lego bricks that help
  you to implement machine learning algorithms whereas scikit-learn
  offers you off-the-shelf algorithms, e.g., algorithms for
  classification such as SVMs, Random Forests, Logistic Regression, and
  many, many more. TensorFlow shines if you want to implement
  deep learning algorithms, since it allows you to take advantage of
  GPUs for more efficient training.</p>
  </blockquote>
  <p>This statement re-enforces my assertion that &quot;scikit-learn contains less boilerplate than the tensorflow implementation&quot; but also suggests scikit-learn will not utilize all available GPUs?</p>