Pixi for Project and Environment Management

[AndrewSazonov]

We use Pixi as the recommended tool for environment management, dependency management, and task execution across all EasyScience projects.

Main Benefits

• Ease of use
  One tool handles Python versions, environments, dependencies, and tasks, reducing setup friction.
• Python version management
  Python versions are explicitly declared per project, ensuring consistent behavior.
• Isolated environments
  Each project gets its own isolated environment with no risk of conflicts.
• PyPI + Conda support
  Pixi installs packages from both ecosystems cleanly, avoiding fragile pip + conda mixing.
• Performance
  Fast dependency resolution and environment creation compared to traditional workflows.
• Task management
  Common tasks (tests, docs, formatting, CI commands) can be declared once and run consistently via pixi run.

Integration with Existing Projects

Important: In our projects, Python dependencies must remain defined in pyproject.toml to allow building and distributing the package independently of Pixi.

Therefore:

• Python dependencies continue to live in pyproject.toml, which ensures:
  • standard Python packaging
  • PyPI builds independent of Pixi
• Pixi configuration focuses on:
  • environment definition
  • platform-specific or development-only dependencies
  • development and CI tasks

This approach keeps Pixi as an environment and workflow management tool, not a replacement for standard Python packaging.

How to Get Started

Pixi configuration is managed through the EasyScience Copier templates:
https://github.com/easyscience/templates

A step-by-step guide for using Pixi in EasyScience projects is available in the templates README:
https://github.com/easyscience/templates/blob/master/README.md

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Links to the related discussions: #43