Abstract¶
While advancements in software development practices across particle physics and adoption of Linux container technology have made substantial impact in the ease of replicability and reuse of analysis software stacks, the underlying software environments are still primarily bespoke builds that lack a full manifest to ensure reproducibility across time. Pixi is a new technology supporting the conda and Python packaging communities that allows for the declarative specification of dependencies across multi-platforms and automatic creation of fully specified and portable scientific computing environments. This applies to the Python ecosystem, hardware accelerated software, and the broader HEP and scientific open source ecosystems as well.
This talk will be structured as a practical hands on tutorial that will explore relevant use cases for the PyHEP community as well as provide participants with their own example repository for future reference.
Presented at PyHEP 2025 workshop on Monday October 27th, 2025.
References¶
This tutorial’s information is distilled from the following resources:
Matthew Feickert, Ruben Arts, John Kirkham, Reproducible Machine Learning Workflows for Scientists with Pixi, Proceedings of 24th International SciPy Conference — SciPy 2025, July, 2025. DOI: Feickert et al. (2025)
SciPy 2025 tutorial on Reproducible Machine Learning Workflows for Scientists with Pixi, by Matthew Feickert, Ruben Arts, John Kirkham.
Matthew Feickert, Reproducible Machine Learning Workflows for Scientists, 2025.
Rough Outline (50 minutes: 45 talk + 5 questions)¶
00:00 – 00:05 (5 min):
Install Pixi on your machine and get a repository setup.
00:05 – 00:20 (15 min):
Walk through Pixi manifest
Explain
workspacetableShow
featuresShow
environments
00:20 – 00:25 (5 min):
Explain lock files
00:25 – 00:30 (5 min):
Explain adding Python packages
Show adding from PyPI
Show adding local source
00:30 – 00:35 (5 min):
Show adding to
pyproject.tomlwith minimal example project
00:35 – 00:45 (10 min):
Show introduction to reproducible CUDA environments
- Matthew Feickert. (2025). matthewfeickert-talks/talk-pyhep-2025: PyHEP 2025. Zenodo. 10.5281/ZENODO.17471943
- Feickert, M., Arts, R., & Kirkham, J. (2025). Reproducible Machine Learning Workflows for Scientists with Pixi. Proceedings of the 24th Python in Science Conference, 232–244. 10.25080/nwuf8465