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Ensuring Reproducibility with Pixi: A Live Demo and Discussion

In this live demo, we dive into the essential role of reproducibility in science, data science, ML, and AI, focusing on how Pixi helps maintain consistency across different environments. The discussion features a live demonstration of Pixi's lock file functionality, cloning the LlamaBot project, and configuring various environments. The demo showcases how to run JupyterLab and build documentation using Pixi, emphasizing the tool's speed and efficiency. The episode also explores containerization, illustrating how to create and run CUDA-enabled environments within Docker containers using Pixi. Real-world use cases highlight the importance of this setup for reproducibility and collaboration among colleagues using different hardware configurations. 00:00 Introduction to Reproducibility in Data Science 00:20 Demo: Setting Up LlamaBot with Pixi 01:16 Exploring Pixi's Configuration and Features 02:45 Running JupyterLab and Documentation with Pixi 05:37 Ensuring Reproducibility Across Different Machines 10:12 Containerization and CUDA Environments with Pixi 12:52 Building and Running Docker Containers with Pixi 17:59 Conclusion and Additional Resources Join us on Discord to escape dependency hell:   / discord   And check Pixi out on github! https://github.com/prefix-dev/pixi