Set up Amazon Web Services – Part 2
Home Run into the Cloud
DIY Python scripts run in container environments on Amazon's Lambda service – this snapshot example deploys an AI program for motion analysis in video surveillance recordings.
After some initial steps in a previous article  to set up an AWS account, an S3 storage server with a static web server, and the first Lambda function, I'll now show you how to set up an API server on Amazon to track down interesting scenes in videos from a surveillance camera.
The Lambda function triggered either by a web request from the browser or a command-line tool like
curl retrieves a video from the web, runs it through an artificial intelligence (AI) algorithm implemented by the OpenCV library, generates a motion profile, and returns the URL of a contact sheet generated as a JPEG with all the interesting movements from the recording (Figures 1 and 2).
Unlike Amazon's EC2 instances with their full-blooded (albeit virtual) Linux servers, the Lambda Service  provides only a containerized environment. Inside a container, Node.js, Python, or Java programs run in a sandbox, which Amazon pushes around at will between physical servers, eventually going as far as putting the container to sleep in case of inactivity – just to conjure it up again when next accessed. Leaving data on the virtual disk of the container and hoping to find it still there next time would thus result in an unstable application. Instead, Lambda functions communicate with AWS offerings such as S3 storage or the Dynamo database to secure data and are otherwise "stateless."
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