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Distributed Computing with Python

Distributed Computing with Python

By : Pierfederici
4.3 (3)
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Distributed Computing with Python

Distributed Computing with Python

4.3 (3)
By: Pierfederici

Overview of this book

CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications. This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.
Table of Contents (10 chapters)
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9
Index

Amazon elastic beanstalk


Amazon Elastic Beanstalk (EB) is a simple way of deploying our applications to AWS without having to worry about the various moving parts, such as EC2 and S3, individually. Amazon EB is a sophisticated tool and has great support for Python.

EB is best used from the command line (using the awsebcli package) within a Python virtual environment. The gist of it is that you create a virtual environment for the Python application that you want to deploy to AWS. The application itself is contained in a single directory that serves as a way to package the code to be deployed.

Using the eb command-line tool (part of awsebcli), one creates an initial deployment configuration (eb init), and potentially (that is, usually) customizes this initial configuration by writing additional configuration files (in a directory called .ebextensions), specifying options, such as any environment variable needed, or any post installation actions to be performed.

Once the application has been...

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