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?”.
Apache Spark is a distributed open-source, general-purpose framework for clustered computing. It is designed with computational speed in mind, from machine learning to stream processing to complex SQL queries. It can easily process and distribute work on large datasets across multiple computers.
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.
Artificial intelligence (AI) is a term to describe a branch of computer science that is dedicated to creating intelligent machines that would learn to work and react like humans. The field of Artificial Intelligence is not limited to creating only high levels of machine learning, but also to utilize data input in a manner required to produce a needed result. AI has multiple varieties and characteristics. Some of these types are noted as follows.
Keras is a Python-based high-level neural networks API that is capable of running on top TensorFlow, CNTK, or Theano frameworks used for machine learning. It can be said that Keras acts as the Python Deep Learning Library. Keras was created with emphasis on being user-friendly since the main principle behind it is “designed for human beings, not machines.” The core data structure of Keras is a model, or a way to organize layers.
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.