![]() ![]() Note: Head over to YouTube to view the video's description MacOS installation tutorial ⤵️ (If using Intel, just download the Intel installer script) If your choice is Conda, you will find the best resources to get up and going below. Below you will find tutorials on installing Conda for various platforms and how to use Conda in your day-to-day development work. My reasoning for preferring Conda is that it offers more options, and in some scenarios, like when using Tensorflow on an Apple Silicon Mac, Conda is your only choice. I stated that you should be using Conda in the article's title however, Virtualenv is still a good choice depending on your scenario. A slight learning curve for those that are new to Conda.For a price, Conda can meet corporate security policies.Conda allows you to choose which Python version is installed to your virtual environment.You don't need to remember the directory on your computer that contains your virtual environment.Anaconda provides a Matlab like experience, Miniconda for those that are happy with the command line, and Miniforge for running on non-standard hardware. You have choices to meet your specific niche.Conda is your only choice when performing machine learning on a Mac.As of January 2022, Miniforge is the best choice for Apple Silicon ( ).The Conda installer looks after your Python install. ![]() Note: The above options are hyperlinked to their respective download pages (Scroll down to the Tutorials section to see how to install) Miniforge - A fork of Miniconda with access to libraries optimized for the M1 Mac.Miniconda - Installs Python to your computer and includes the Conda package manager.Anaconda - Includes an IDE and comes preinstalled with many libraries.If you have decided to go with Conda, the question you are left with is what flavour of Conda should you install? Below, I've provided a summary of your choices. (macOS and Linux users, watch this video instead ) CondaĬonda vastly differs from Virtualenv in that it is so much more than a tool for setting up virtual environments for example, Conda's installer will also install Python along with a native package manager onto your computer. If your choice is Virtualenv, Corey Schafer has an easy to follow tutorial on using Virtualenv to set up virtual environments. Reliant on the PIP package manager (Sometimes the Conda package manager or its variants are your only option).Has a lower learning curve for those that already know how to install libraries with PIP.If you already have Python version 3.5 or newer installed on your machine, Virtualenv is easily installed via the PIP package manager. Virtualenvīefore talking about Conda, I should mention that Virtualenv is the default option for setting up separate virtual environments for your Python projects. With the importance of why your Python configuration will benefit from using virtual environments covered above, it's time to look at the available tools, their advantages, disadvantages, and why I feel Conda is the better choice. Configuring Python for Virtual Environments With each project isolated, installing or changing any of a projects' libraries will not mess with the dependencies of other projects, which is why virtual environments should be an essential facet of how Python is configured on your development machine. ![]() conflicting libraries), Python provides tools that allow a developer to isolate each Python project to its own native environment called a virtual environment. To avoid the problem of conflicting dependencies (I.e. In any scenario where you have multiple Python projects, you will eventually run into the conflicting library versions problem. As you may expect, these libraries are updated over time, and with each update, these libraries gain or can even lose functionality. Before diving into the topics of Python Configuration or Conda, I must answer what I'm referring to when I say "Python configuration." I will start with that the Python programming language allows for libraries that, once imported, add significant functionality to the Python language. ![]()
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