Have you ever sat down with the code and data for an existing machine learning project, trained the same model, checked your results… and found that they were different from the original results? Not being able to reproduce someone else's results is super frustrating. Not being able to reproduce your own results is frustrating and embarrassing. And tracking down the exact reason that you aren't able to reproduce results can take ages; it took me a solid week to reproduce this NLP paper, even with the original authors' exact code and data. But there's good news: Reproducibility breaks down in three main places: the code, the data and the environment. I've put together this guide to help you narrow down where your reproducibility problems are, so you can focus on fixing them. Let's go through the three potential offenders one by one, talk about what kind of problems arise and then see how to fix them. Non-deterministic code I've called this section "non-deterministic code" rather than "differences in code" because in a lot of machine learning or statistical applications you can end up with completely different results from the same code.


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