In this article, we will explore the newest methods to install or update to the latest version of Python on our Ubuntu system.
What is Python?
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It’s high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development and use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse.
“A No-code development platform allows programmers and non-programmers to create application software through graphical user interfaces and configuration instead of traditional computer programming1.” Various software tools and applications are being used all around us each day. You might be asking yourself, “How do they do it?”.
Jupyter Notebook is an extremely powerful open-source, web-based tool that facilitates the creation of documentation. There are many different avenues to provide technical documentation or demonstrations, but Jupyter Notebook makes it possible to embed visualizations and execute live code. It is useful to be able to utilize documentation to describe development concepts or planning, but providing working examples within documentation can be a more effective way of conveying information. This tutorial will cover how to install Jupyter Notebook on an Ubuntu 18.04 LTS server and connect to it remotely via an SSH tunnel.
Serverless computing (or serverless for short), is an execution model where the cloud provider manages and allocates resources dynamically without the need for infrastructure. Resource allocation is based on the as needed, real-time use of your application or website. When running this type of hosting, you are only charged for the amount of resources that our code uses.
Python is fast becoming one of the most popular programming languages worldwide. Its low entry barrier for new programmers and simple, elegant syntax makes it a fantastic language to start learning. Python is excellent for task automation, and thankfully most Linux distributions come with Python installed right out of the box. This is true of Ubuntu 18.04; however, the Python package distributed with Ubuntu 18.04 is version 3.6.8. This article will cover how to install a newer version of Python, specifically, the latest stable version 3.8.3.
The REST acronym is defined as a “REpresentational State Transfer” and is designed to take advantage of existing HTTP protocols when used for Web APIs. It is very flexible in that it is not tied to resources or methods and has the ability to handle different calls and data formats. Because REST API is not constrained to an XML format like SOAP, it can return multiple other formats depending on what is needed. If a service adheres to this style, it is considered a “RESTful” application. REST allows components to access and manage functions within another application.
In this tutorial, we will consider how to enable both Python 2 and Python 3 for use on CentOS 8. In earlier distributions of CentOS, an unversioned Python command was available by default.
When the CentOS installation was complete, it was possible to drop into a Python shell by simply running the “python” command in a terminal.
Paradoxically, CentOS 8 does not have an unversioned Python command by default. This begs the question, why? RedHat states that this choice is by design “to avoid locking users into a specific version of Python.” Currently, RedHat 8 utilizes Python 3.6 implicitly by default, although Python 2.7 is additionally provided to maintain existing software.
In this tutorial, we are going to take a look at how to get started with TensorFlow on CentOS. We will be covering two methods. First, we will take a look at installing TensorFlow in a Python virtual environment via the Python package manager pip. After that, we will walk through installing TensorFlow via the Anaconda package manager. Finally, we will cover building a TensorFlow pip package from source.
In this tutorial, we are going to cover how to set up a Python virtual environment on CentOS. A Python virtual environment makes it possible to install Python packages into a discreet Python ecosystem that is entirely separate from your system’s default Python framework. This means that you do not have to worry about overwriting the installation of any current packages that might be defaulted to the existing version of Python on your system.
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