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.

Scientific researchers need reproducible software environments for complex applications that can run across heterogeneous computing platforms. Modern open source tools, like pixi, provide automatic reproducibility solutions for all dependencies while providing a high level interface well suited for researchers.

This tutorial will provide a practical introduction to using pixi to easily create scientific and AI/ML environments that benefit from hardware acceleration, across multiple machines and platforms. The focus will be on applications using the PyTorch and JAX Python machine learning libraries with CUDA enabled, as well as deploying these environments to production settings in Linux container images.