/explore
|
You are here 
|
techtldr.com | ||
| | | | |
janakiev.com
Python's built-in venv module makes it easy to create virtual environments for your Python projects. Virtual environments are isolated spaces where your Python packages and their dependencies live. This means that each project can have its own dependencies, regardless of what other projects are doing. |
|
| | | | | ||
| | | | |
www.simplygeek.co.uk
|
|
| | | | | ||
| | | | |
ljvmiranda.micro.blog
I'm now a uv convert. I haven't really updated my Python tooling and workflow for the past two years. In fact, I've been using the same workflow ever since I started working: python -m venv venv source /venv/bin/activate # While in venv pip install -r requirements.txt But there's a lot of limitations in this workflow. For example, I'm limited by the Python version installed in my machine or it's hard to just install ad-hoc dependencies for my one-off scripts. |
|
| | | | | ||
| | | | |
vxlabs.com
The new Python package and project manager uv is in fact amazing. I say that, because it's really fast, but more importantly because this single tool does a whole lot, really fast: Installing Python binaries, installing and running packages in self-contained environments like pipx, managing virtual environments. However, I've been avoiding it so far due to one flaw: uv defaults to installing its virtual environment and all dependencies into the .venv sub-directory of your project, almost exactly like the... |
|
| | | |||
