Imposing explicit constraints are new and increasingly trendy in deep learning, stimulated by, e.g., trustworthy AI that performs robust optimization over complicated perturbation sets and scientific applications that need to respect physical laws and constraints. However, it can be hard to reliably solve constrained deep learning problems without optimization expertise. Existing deep learning frameworks do not admit constraints. General-purpose optimization packages can handle constraints but …