[ericfrederich]

I don't see any meaningful speedup. The 10x claims are not reproducible. He's also comparing it to the much older style of requirements.txt projects and not a poetry project with a lockfile.

I detailed this in another comment but pip (via requirements.txt): 8.1s, poetry: 3.7s, uv: 2.1s.

Not even 10x against pip and certainly not against poetry.

[nchmy]

You must be holding it wrong, because everyone else raves about uv

[cpburns2009]

Usually uv pip is only about x2 as fast as regular pip for me. Occasionally I'll have some combination of dependencies that will cause pip to take 2-5 minutes to resolve that uv will handle in 10-20 seconds.

[nchmy]

They said "no meaningful speedup". 2x is meaningful

[cpburns2009]

The impact of a 2x speedup is relative. For a quick test on one of my projects it's 10 seconds with pip and 4 seconds with uv. That's roughly in line with my previous testing. It's a nice minor speedup on average. It really shines when pip does some non-optimal resolving in the background that takes a minute or more.

[bckr]

How complex are the requirements for this project?

[ericfrederich]

https://github.com/nickjj/docker-django-example/blob/bbe3486...

[bckr]

I see. I encourage you to try it with larger projects and see if it makes a difference.

That said, the speed is only one reason I use it. I find its ergonomics are the best in the Python tools I’ve tried. For example it has better dependency resolution than poetry in my estimation, and you can use the uv run —-with command to try things before adding them to your environment.