Pyenv is a fantastic tool for installing and managing multiple Python versions. It enables a developer to quickly gain access to newer versions of Python and keeps the system clean and free of unnecessary package bloat. It also offers the ability to quickly switch from one version of Python to another, as well as specify the version of Python a given project uses and can automatically switch to that version. This tutorial covers how to install pyenv on Ubuntu 18.04.
Virtualenv is a tool that creates an isolated environment separate from other projects. In this instance, we will be installing different Python versions, including their dependencies. Creating a virtual environment allows us to work on a Python project without affecting other projects that also use Python. It will utilize Python’s core files on the global environment to run, thus saving you disk space while providing the freedom to use different Python versions for separate apps or projects.
Reading Time: 2minutesArguably one of the easiest tools to use for installing and managing Python packages, Pip has earned its notoriety by the number of applications utilizing this tool. Fancied for its capabilities in handling binary packages over the easy_installed packages manager, Pip enables 3rd party package installations. Though Python does sometimes come with Pip as a default, this tutorial will show how to install, check its version as well as some basic commands for using Pip on Ubuntu 16.04.
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.
The SQLAlchemy Toolkit and Object Relational Mapper is an extensive set of utilities for working with Python and databases. This toolkit provides a package full of popular persistence patterns, designed for economical and robust database accessibility. SQLAlchemy allows a developer to use simple SQL statements (unlike other Object Relational Mapping tools) which provide a helpful method to connect database tables with user-defined Python classes. The SQLAlchemy Object Relational Mapping tool is primarily centered on using the SQL Expression language.
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.
The CentOS 7 Linux distribution includes Python 2 by default. However, Python 2 is going to reach EOL on January 1, 2020. While some legacy applications might require access to Python 2 for various reasons, it’s vitally important to kick start new projects in Python 3.
In this tutorial, we are going to take a look at how to get up and running with Python 3 on a CentOS 7 server. Specifically, we will take a look at how to install Python 3 via the CentOS 7 package manager Yum as well as from source.