<p>I'm looking for an efficient and easy way to adapt my current Theano model so it can scale for prediction. I'm also looking for a way to easily train lots of models with different parameters.</p>

<p>It's seems that there is two main ways to do it. The first is to use Spark and the second is to use Docker and Kubernetes.</p>

<p>My experience with both is fairly limited, so, I have no idea if there are correct way to solve my problem and what are the differences between each solutions.</p>

<p>That is two thing between Kuberbetes and Spark,
Kubernets is a Paas, it provide you the platform to run your application,
Spark is used to run your algorithm and compute distributed ,but you need to build Spark in a cluster
So kubernetes can help you to do this things</p>

<p>How to build Spark with kubernetes? You can see the <a href="https://github.com/kubernetes/examples/tree/master/staging/spark" rel="nofollow noreferrer">reference</a></p>

<p>Good Luck!</p>
