Uv vs. conda vs. virtualenv

Q&A
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Dear colleagues,

Have you used uv to manage your Python environment (https://github.com/astral-sh/uv)? It seems to work very fast, but I wonder if there are any challenges or downsides to using this package management system?
Thank you.

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the uvproject by Astral (creators of Ruff and other popular Python tools) is a fast Python package manager and virtual environment builder. It aims to replace tools like pip, virtualenv, pip-tools, and even parts of poetry or hatch.

Advantages of uv:

  1. Blazing fast,
  2. All-in-one tool: It combines multiple tools: dependency resolution, virtual environment management, installation, and caching. Avoids the need for pip-tools, virtualenv, venv, poetry, etc.
  3. better dependency,
  4. fully compatible with Python Enhancement Proposal (PEP) standard: Follows Python packaging standards like pyproject.toml, editable installs, etc.

Downsides of uv:

  1. Very new project, some IDEs and tools e.g. VS Code, PyCharm may not recognize uv generated env.
  2. If you’re used to pip and virtualenv, there’s a new CLI to learn (uv pip, uv venv, etc.).
  3. While growing quickly, it doesn’t yet have the same community maturity or depth of StackOverflow support as pip or poetry.
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I have no direct experience with uv, and it does look like something worth looking into. Using conda to manage an environment can be very bloated and this can chew up disk space in a hurry. Most sites provide users with very small home directory spaces, and by default conda like to write new environments into $HOME/.conda/envs. I redirect to a location with much more space, but even when doing so, I end up sacrificing disk space due to the inefficient ways conda creates these environments.

virtualenv is a better approach IMHO, although lately I’ve been exploring spinning up custom containers instead whenever I need a specific environment.

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