Improving Shogun Observable Framework

Abstract

Being able to monitor model internals of Machine Learning algorithms at runtime is a crucial tool for both scientific investigations of data, and for debugging or development of new algorithms. Many Machine Learning frameworks are written in a high level, often interpreted languages (such as Python), which allow this type of monitoring in a relatively simple manner. Once algorithms are implemented in a lower (more efficient) level, however, observing parameter changes is much more challenging. For example, Google developed the Tensorboard framework as a tool to monitor, visualise and analyse internals of tensorflow programs (which are, while often instantiated in Python, implemented in C++). In previous Google Summer of Code projects [1][2], techniques from reactive programming have been used to build the foundations of an Observable framework -which allows for monitoring state changes at runtime- in Shogun. While functional, the new features have not been deployed to the code base in a systematic manner. The purpose of this project is to deploy, improve and extend Shogun’s Observable architecture, and to provide glueing code for frameworks such as Tensorboard. The project will also provide crucial documentation and real-world use-cases of how those features improve scientific workflows.

Blog Posts

PRs Merged

Trello Board: https://trello.com/b/vSRsje9A/observable-shoguns-models