<p>Not by default. Grid search is easy to use and easy to understand but it suffers from the curse of <a href="https://en.wikipedia.org/wiki/Curse_of_dimensionality" rel="nofollow noreferrer">dimensionality problem</a>. Instead of grid search, Google Cloud ML Engine uses a <a href="https://cloud.google.com/blog/big-data/2017/08/hyperparameter-tuning-in-cloud-machine-learning-engine-using-bayesian-optimization" rel="nofollow noreferrer">Bayesian optimization technique</a> that based on an algorithm called <a href="https://arxiv.org/abs/1012.2599" rel="nofollow noreferrer">Gaussian process bandits</a>. </p>

<p>The underlying technology used by Cloud ML Engine is from a Google Research project <a href="https://research.google.com/pubs/pub46180.html" rel="nofollow noreferrer">Vizier</a> which is a Google-internal service for performing black-box optimization that has become the de facto parameter tuning engine at Google.</p>

<p>However, if you really want to use grid search, you can force Cloud ML Engine to use it by specifying the "algorithm" parameter in your hyperparameter yaml file as described in the <a href="https://cloud.google.com/ml-engine/docs/tensorflow/hyperparameter-tuning-overview#search_algorithms" rel="nofollow noreferrer">Cloud ML Engine documentation</a></p>
Does Google Cloud ML Engine use grid search for tuning hyperparameters?
<p>The grid search technique is an easy to use and an embarrassingly parallel approach for finding the best set of hyperparameters for machine learning models. Does Google Cloud Machine Learning (ML) Engine use grid search?</p>