tfenv VS pyenv

You'll need the correct version of Terraform (the version of Terraform used for apply can be found in terraform_version at the beginning of the .tfstate file), to switch between versions I recommend tfenv.

For almost four years I'd been using a Macbook as my main work machine. One quality of life tool I'd gotten used to was tfenv, a lovely little tool for managing terraform versions & quickly switching between them.

For example terraform state files often have non backwards compatible changes. You should consider using something like tfenv so everyone on your team has identical versions of terraform.

I'm using the tool tfenv to manage Terraform versions. Other tools can do that. You can use asdf, too. I saw that asdf can do more than manage Terraform versions.

Documentacion TFENV

git clone https://github.com/tfutils/tfenv.git ~/.tfenv export PATH="$HOME/.tfenv/bin:$PATH" # install to appropriate shell startup file, e.g. $HOME/.bashrc echo 'export PATH="$HOME/.tfenv/bin:$PATH"' >> ~/.profile

For more information visit: pyenv repository

Note that virtual environments assume you are using the same global version of Python. Often, this is not the case and additional tools like pyenv can be used alongside virtual environments when you need to switch between versions of Python itself on your local machine.

Python version manager pyenv

First of all, install the Python virtual environment from these links: 1 and 2. I developed my GPT-based API in Python version 3.8.18. Pick any Python versions >= 3.7.

Note: Most modern systems will probably have a system environment that meets this requirement, but if yours does not or if you prefer not to install anything in your system environment (even if it's just PDM) check out asdf or pyenv to help install and manage additional Python environments.

When dealing with software development, reproducibility is key. This is why we encourage you to use Python virtual environments to set up an isolated environment for your project. Virtual environments allow the isolation of dependencies, which plays a crucial role to avoid breaking compatibility between different projects. We cannot cover all the details about virtual environments in this post, but we encourage you to learn more about venv, pyenv or conda for a better understanding on how to create and manage virtual environments.

For Python, with your use case I would avoid system packages, no matter the distro. It sounds like it would be worth setting up pyenv and working exclusively with virtual environments.

For OSX there is homebrew or pyenv (pyenv is another solution on Linux). As pyenv compiles from source it will require setting up XCode (the Apple IDE) tools to support this which can be pretty bulky. Windows users have chocolatey but the issue there is it works off the binaries. That means it won't have the latest security release available since those are source only. Conda is also another solution which can be picked up by Visual Studio Code as available versions of Python making development easier. In the end it might be best to consider using WSL on Windows for installing a Linux version and using that instead.