PrototypeML: A Neural Network Integrated Design and Development Environment

Abstract

Neural network architectures are most often conceptually designed and described in visual terms, but are implemented by writing error-prone code. PrototypeML is a machine learning development environment that bridges the dichotomy between the design and development processes: it provides a highly intuitive visual neural network design interface that supports (yet abstracts) the full capabilities of the PyTorch deep learning framework, reduces model design and development time, makes debugging easier, and automates many framework and code writing idiosyncrasies. In this paper, we detail the deep learning development deficiencies that drove the implementation of PrototypeML, and propose a hybrid approach to resolve these issues without limiting network expressiveness or reducing code quality. We demonstrate the real-world benefits of a visual approach to neural network design for research, industry and teaching. Available at https://PrototypeML.com

10 pages, 6 figures. Submitted to NeurIPS 2020. More details available at https://PrototypeML.com

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Deep Residual Learning for Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• DyNet: The Dynamic Neural Network Toolkit
• Chainer: A Deep Learning Framework for Accelerating the Research Cycle
• Fabrik: An Online Collaborative Neural Network Editor