ModelHub: Lifecycle Management for Deep Learning

Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle contains a rich set of artifacts, such as learned parameters and training logs, and frequently conducted tasks, e.g., to understand the model behaviors and to try out new models. Dealing with such artifacts and tasks is cumbersome and left to the users. To address these issues in a comprehensive manner, we propose ModelHub, which includes a novel model versioning system (dlv); a domain specific language for searching through model space (DQL); and a hosted service (ModelHub) to store developed models, explore existing models, enumerate new models and share models with others.