SciPy 2025

Reproducible Science Made Easy: Package Management with Pixi
07-10, 15:00–15:30 (US/Pacific), Ballroom

Reproducibility is a major underpinning of the scientific method. In scientific computing, this also includes the ability to reproduce your dependencies. Yet, in 2025 this still remains a challenging topic.

Pixi is a modern package manager built on the Conda ecosystem. It integrates very well with all existing packages on conda-forge. Pixi makes package management reproducible, fast and painless – so that scientists can go back to coding instead of dealing with “dependency hell”. Pixi improves the mix Conda and PyPI package management by integrating with uv by astral.sh and streamlines automation with a cross-platform task runner. These features combined with a powerful lockfile make creating reproducible projects trivial.

This talk is for people who are interested in new, fast ways to set up their software (dev) environments on different systems – think your coworker's computer, CI, containers, and more.

Pixi is a new way of managing Conda and PyPI packages in a project. It's build on all the knowledge gained from building Mamba, and it draws inspiration from, among others, Cargo (Rust), PNPM (Node.js), and Poetry, combining their best features with a cross-platform task system. This allows users to build projects that work seamlessly on Linux, macOS and Windows. Pixi can complement Docker or even replace it in many cases, reducing overhead – which is especially important in cases like large data projects and HPC.

With Pixi and the underlying technology, we have completely rebuilt the Conda ecosystem in the Rust programming language, making it faster and more maintainable for the future. It's 100% Open-Source with a permissive BSD-3-Clause License.

The key features of pixi are:

  • pixi global: globally install your favorite tools and applications
  • pixi project: separate, isolated projects that come with lockfiles, task descriptions, and their own isolated dependencies. Add the pixi.toml and pixi.lock files to your git repository for perfect reproducibility of your scientific projects.
  • pixi build: because we know that scientists deal with hairy, compiled dependencies, pixi build will deal with it for you. Compile Fortran, C/C++, Rust projects and Python bindings by using the vast compiler ecosystem from conda-forge.

The performance increase over conda with pip in the worst case is 200% but more often close to 1000% faster or more [1][2].

This talk will cover the following topics:

  • Introduction: What is package management, conda vs. pip, …
  • Pixi overview: features and benefits of pixi for the scientific use case
  • Live demo: Pixi in action by looking at big projects like scipy as an example.
  • Practical takeaways: fast workflows for data scientist, and reliable environments for DevOps.

Audience

This talk will be of interest to everyone that uses or builds software in the scientific or data-science community. Especially those dealing with running their environments in multiple machines, e.g. CI, HPC, Docker. Whether you are building libraries, application or using them for running notebooks, this talk can kickstart a more efficient workflow for you.

[1]: Simple pixi vs conda cached installation benchmark on GitHub: https://github.com/ruben-arts/pixi_bench/actions/runs/13334032029

[2]: Blog post on faster repodata fetching: https://prefix.dev/blog/sharded_repodata