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(This article was first published on <strong><a href="http://rsnippets.blogspot.com/2016/10/deep-learning-in-cloud-with-mxnet.html"> R snippets</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Last Friday together with <a target="_blank" rel="nofollow" href="https://www.linkedin.com/in/przemys%C5%82aw-szufel-720a4411b">Przemysław Szufel</a> and <a target="_blank" rel="nofollow" href="https://www.linkedin.com/in/jakuczunwit">Wit Jakuczun</a> we were giving a live demo on introduction to deep learning at <a target="_blank" rel="nofollow" href="http://www.digitalchampions.pl/">Digital Champions</a> conference.</p>
<p>The objective of the workshop was to show how to build a simple predictive model using MXNet library in a few minutes. In the example, we train a model on GPUs using a standard <span>MNIST database of handwritten digits.</span><br><span><br></span><span>Our post-presentation thoughts are that many people did not have experience with using GPUs in the cloud. Our point is that it is actually simpler and cheaper than having the infrastructure on-premises. Running of the whole tutorial cost us under 1$. Using </span>bitfusion.io <a target="_blank" rel="nofollow" href="https://aws.amazon.com/marketplace/seller-profile?id=3b372560-86bf-4e3d-9ec0-016892a64bed">Scientific Computing AMI</a> we were able to painlessly get the whole computing environment running on AWS in a few minutes.<br><span><br></span><span>On <a target="_blank" rel="nofollow" href="https://github.com/WLOGSolutions/digital_champions_deeplearning_r_mxnet">GitHub</a> you can find the instructions how to set up computations in the cloud and the R source code allowing to build the model using MXNnet.</span></p>
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