Avocado is a multi-scale deep tensor factorization method for learning a latent representation of the human epigenome. The purpose of this model is two fold; first, to impute epigenomic experiments that have not yet been performed, and second, to learn a latest representation of the human epigenome that can be used as input for machine learning models in the place of epigenomic data itself. This is the source code used for the Avocado companion papers. The current version of the code and documentation can be found on the GitHub repository https://github.com/jmschrei/avocado.