Unfortunately, there’s been a new release of Pandas in the interim-and with a new release, there are differences: Now, time passes, and you want to rerun the same analysis, with just a minor change to the code. You install Python and Pandas with conda env create, write some code, it runs correctly, all is well with the world. Name : example channels : - conda-forge dependencies : - python - pandas You create an environment.yml for your dependencies: Let’s say your application depends on Python and Pandas. Let’s see how different ways of specifying dependencies can achieve these goals. Upgradability: We should be able to change versions of our dependencies without having to fight the packaging system.Reproducibility: If we reinstall the code, we should get the same libraries.We have two goals, reproducibility and upgradability we will limit our discussion to just dependencies, keeping in mind that both goals require more work than the limited focus of this article. How to use a third-party tool, conda-lock, to easily maintain these two different files.Why in practice you want to have two different dependency files.Three ways of specifying your dependencies, and how they impede and/or enable reproducibility and upgrades.Ideally you’d be able to both have a consistent, reproducible build, and still be able to quickly change your dependencies.Īnd you can do this-with a little understanding, and a bit more work. On the other hand, once you’ve pinned everything, upgrades become difficult: you’ll start encountering the infamous The following specifications were found to be incompatible with each other error. On the one hand, you want to pin all your dependencies to specific versions, so you get reproducible builds. If your application uses Conda to manage dependencies, you face a dilemma.
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