Deep learning has improved the state-of-the-art results in an ever-growing number of domains. This success heavily relies on the development and training of deep learning models–an experimental, iterative process that produces tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of tools and frameworks that aim at facilitating deep learning, the process of managing the models and their artifacts is still surprisingly challenging and time-consuming. Existing model-management solutions are either tailored for commercial platforms or require significant code changes. Moreover, most of the existing solutions address a single phase of the modeling lifecycle, such as experiment monitoring, while ignoring other essential tasks, such as model deployment. In this paper, we present a software system to facilitate and accelerate the deep learning lifecycle, named ModelKB. ModelKB can automatically manage the modeling lifecycle end-to-end, including (1) monitoring and tracking experiments; (2) visualizing, searching for, and comparing models and experiments; (3) deploying models locally and on the cloud; and (4) sharing and publishing trained models. Moreover, our system provides a stepping-stone for enhanced reproducibility. ModelKB currently supports TensorFlow 2.0, Keras, and PyTorch, and it can be extended to other deep learning frameworks easily.

深度学习改善了领域不断增长的最新成果。成功与否很大程度上取决于深度学习模型的开发和训练，而深度学习模型是一种实验性的迭代过程，可以在生成满意结果之前生成数十至数百个模型。尽管旨在促进深度学习的工具和框架数量激增，但是管理模型及其工件的过程仍然令人惊讶且耗时。现有的模型管理解决方案要么针对商业平台量身定制，要么需要进行重大代码更改。而且，大多数现有解决方案都解决了建模生命周期的单个阶段（例如实验监视），而忽略了其他重要任务（例如模型部署）。在本文中，我们提出了一个名为ModelKB的软件系统，以促进和加快深度学习的生命周期。ModelKB可以端到端自动管理建模生命周期，包括（1）监视和跟踪实验；（2）可视化，搜索和比较模型和实验；（3）在本地和云上部署模型；（4）共享和发布经过训练的模型。此外，我们的系统为增强可重复性提供了垫脚石。ModelKB当前支持TensorFlow 2.0，Keras和PyTorch，并且可以轻松扩展到其他深度学习框架。