title: How to publish custom (non-tensorflow) models using tensorflow-serving?
body: <p>I've read the <a href="https://github.com/tensorflow/serving/blob/master/tensorflow_serving/g3doc/serving_basic.md" rel="noreferrer">basic</a> and <a href="https://www.tensorflow.org/serving/serving_advanced" rel="noreferrer">advanced</a> tensorflow-serving tutorials but I am still unclear for how to build support in tensorflow-serving for the following:</p>
  
  <ol>
  <li>models built in Python (like xgboost or scikit-learn)</li>
  <li>models built in R (like xgboost or lightgbm)</li>
  </ol>
  
  <p>Considered using TFBT available in <code>tf.contrib</code>, but according to <a href="https://nicolovaligi.com/gradient-boosting-tensorflow-xgboost.html" rel="noreferrer">this</a>, <a href="https://arxiv.org/abs/1710.11555" rel="noreferrer">TensorFlow Boosted Trees (TFBT)</a> takes much longer to train compared to xgboost and observed that it had worse precision. </p>
  
  <p>Any help or suggestions would be appreciated...</p>