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feat: Add 'Reproducible ML for Scientists with Pixi' draft
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## Acknowledgements
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Matthew Feickert is supported by the U.S. National Science Foundation (NSF) under Cooperative Agreement PHY-2323298 (IRIS-HEP) and by the US Research Software Sustainability Institute (URSSI) via grant G-2022-19347 from the Sloan Foundation.
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Ruben Arts is supported by Prefix.dev.
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John Kirkham is supported by NVIDIA.

papers/matthew_feickert/banner.png

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## conda-forge
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Technical overview of conda-forge and conda packages.

papers/matthew_feickert/cuda.md

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## CUDA
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* Overview of CUDA 12 packages
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* Importance of the work
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* What it enables
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## Introduction
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"Reproducible" research can mean many things and is a multipronged problem.
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Focus primarily on computational reproducibility.
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Like all forms of reproducibility, there are multiple "levels" of reproducibility.
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justo vitae odio tincidunt tempus at vitae tortor.
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* Introduction
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* Computational reproducibility
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* Computational reproducibility vs. scientific reuse

papers/matthew_feickert/main.md

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---
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# Ensure that this title is the same as the one in `myst.yml`
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title: Reproducible Machine Learning Workflows for Scientists with Pixi
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abstract: |
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Scientific researchers need reproducible software environments for complex applications that can run across heterogeneous computing platforms.
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Modern open source tools, like Pixi, provide automatic reproducibility solutions for all dependencies while providing a high level interface well suited for researchers.
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Combined with the recent emergence of the entire CUDA software stack — from compilers to development libraries — being supported on conda-forge, researchers are now able to easily specify their exact hardware acceleration requirements and software dependencies and get portable computational environments locked down to the hash level.
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---
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:::{include} introduction.md
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:::
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:::{include} conda-forge.md
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:::
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:::{include} cuda.md
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:::
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:::{include} pixi.md
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:::
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:::{include} acknowledgements.md
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:::

papers/matthew_feickert/mybib.bib

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@software{pixi,
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author = {Arts, Ruben and Zalmstra, Bas and Vollprecht, Wolf and de Jager, Tim and Morcotilo, Nichita and Hofer, Julian},
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license = {BSD-3-Clause},
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title = {{pixi}},
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url = {https://github.com/prefix-dev/pixi/releases/tag/v0.48.0}
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}

papers/matthew_feickert/myst.yml

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version: 1
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extends: ../papers.yml
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project:
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# Update this to match `scipy-2025-<folder>` the folder should be `<firstname_surname>`
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id: scipy-2025-matthew_feickert
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# Ensure your title is the same as in your `main.md`
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title: Reproducible Machine Learning Workflows for Scientists with Pixi
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# subtitle:
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description: |
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Scientific researchers need reproducible software environments for complex applications that can run across heterogeneous computing platforms.
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Modern open source tools, like Pixi and the CUDA conda-forge packages, provide reproducibility solutions for while providing high level semantics well suited for researchers.
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# Authors should have affiliations, emails and ORCIDs if available
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authors:
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- name: Matthew Feickert
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orcid: 0000-0003-4124-7862
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affiliations:
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- University of Wisconsin&ndash;Madison
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corresponding: true
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- name: Ruben Arts
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affiliations:
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- Prefix.dev
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- name: John Kirkham
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affiliations:
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- NVIDIA
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keywords:
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- reproducible
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- machine learning
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- hardware acceleration
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- CUDA
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- conda-forge
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- Pixi
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# Add the abbreviations that you use in your paper here
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abbreviations:
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CUDA: Compute Unified Device Architecture
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exports:
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- id: pdf
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format: typst
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template: https://github.com/curvenote-templates/scipy.git
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article: main.md
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output: full_text.pdf

papers/matthew_feickert/pixi.md

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## Pixi
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* Modern manager [@pixi]
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* Automatic and non-optional hash level lock files upon workspace mutation
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* Multi-platform

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