Mosaic is a framework dedicated to the comparison of AI models. It is often very difficult to choose the best AI model for a specific problematic and multiple options are available, including choices over the model hyper-parameters. It is tempting to try different options and to compare them to get the best performance/resource ratio. But this kind of test can be pretty time consuming from the developper point of vue. The Mosaic framework eases the automation of the program generation and provides tools to help the study (Database, plot system…)

Mosaic is a python framework based on Pytorch. From a simple configuration file, a set of pipelines is generated including all the steps of the data treatment (data loading, formatting, normalization, post-treatment…) and the model training itself. The framework executes all these pipelines in a parallel way and store all the results in a database and in differents files. Some facility are offered to pause/resume and monitor the run. A plot module helps getting some compact and graphical representations to ease the interpretation of this data.

The Mosaic framework is open-source and freely available from pip and from github. A full documentation and a tutorial are also available. An article is submitted to JLMR and is available on arxiv.