Amazon Web Services (AWS) include many different computational tools, ranging from storage systems and virtual servers to databases and analytical tools. For us R-programmers, being familiar and experienced with these tools can be extremely beneficial in terms of efficiency, style, money-saving and more.
In this post we present a step-by-step screenshot tutorial that will get you to know Amazon EC2 service. We will set up an EC2 instance (Amazon virtual server), install an Rstudio server on it and use our beloved Rstudio via browser (all for free!). The slides below will also include an introduction to linux commands (basic), instructions for connecting to a remote server via ssh and more. No previous knowledge is required.
Set up an AWS account (do not worry about the credit card details, you will not be charged for any of our actions) – the steps are presented in the slides below.
Windows users: download MobaXterm (or any other ssh client software). Mac users: make sure you are familiar with the terminal (cause I’m not).
Disclaimer: This post is not intended to be a comprehensive review, but more of a “getting started guide”. If I did not mention an important tool or package I apologize, and invite readers to contribute in the comments.
I have recently had the delight to participate in a “Brain Hackathon” organized as part of the OHBM2013 conference. Being supported by Amazon, the hackathon participants were provided with Amazon credit in order to promote the analysis using Amazon’s Web Services (AWS). We badly needed this computing power, as we had 14*109 p-values to compute in order to localize genetic associations in the brain leading to Figure 1.
Figure 1- Brain volumes significantly associated to genotype.
While imaging genetics is an interesting research topic, and the hackathon was a great idea by itself, it is the AWS I wish to present in this post. Starting with the conclusion:
Storing your data and analyzing it on the cloud, be it AWS, Azure, Rackspace or others, is a quantum leap in analysis capabilities. I fell in love with my new cloud powers and I strongly recommend all statisticians and data scientists get friendly with these services. I will also note that if statisticians do not embrace these new-found powers, we should not be surprised if data analysis becomes synonymous with Machine Learning and not with Statistics (if you have no idea what I am talking about, read this excellent post by Larry Wasserman).
As motivation for analysis in the cloud consider:
The ability to do your analysis from any device, be it a PC, tablet or even smartphone.
The ability to instantaneously augment your CPU and memory to any imaginable configuration just by clicking a menu. Then scaling down to save costs once you are done.
The ability to instantaneously switch between operating systems and system configurations.
The ability to launch hundreds of machines creating your own cluster, parallelizing your massive job, and then shutting it down once done.
Here is a quick FAQ before going into the setup stages.