PrototypeML: A Neural Network Integrated Design and Development Environment
This addresses the problem of error-prone and time-consuming neural network development for researchers, industry practitioners, and educators, though it is incremental as it builds on existing frameworks.
The paper tackles the gap between visual design and code implementation of neural networks by introducing PrototypeML, a development environment that provides an intuitive visual interface integrated with PyTorch, reducing design time and automating coding tasks.
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