> For the complete documentation index, see [llms.txt](https://lwang010.gitbook.io/longw/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lwang010.gitbook.io/longw/mlops/chap-5.-lessons-learnt-from-paper-reproduction-1/takeaway/a-checklist-for-open-sourcing-your-code-for-reproducibility.md).

# A checklist for open-sourcing your code for reproducibility

* Start with a fresh computer
* Document in detail / document hierarchically
* Use docker or anaconda

Reproducibility in machine learning;

* Use yaml for hyperparameters; .yaml better for comments
* Have separate config file for each experiment
* Save within epochs
* Log all features of training and evaluation
* Experiment management; pytorch lightnight; scared; mlflow
* Set seed and  save it in config

pip freeze > requirements.txt

* Test and validate (test the setup on a separate machine to ensure reproducible build;)
* Make sure dependencies are ok and not hardcoded paths exist in the code
* Checklist in the readme:
* \- dependencies
* \- training script
* \- evaluation script
* \- pretrained models
* \- results 
* Good to have
* \- contributing guide
* \- blog post