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Aggressive and accurate control of complex dynamical systems, such as soft robots, is especially challenging due to the difficulty of obtaining an accurate and tractable model for real-time control. Learned dynamic models are incredibly useful because they do not require derivation of an analytical model, they can represent complex, nonlinear behavior directly from data, and they can be evaluated quickly on graphics-processing units (GPUs). In this paper, we present an open-source Python library to further current research in model-based control of soft robot systems. Our library for Modeling of Learned Dynamics (MoLDy), is designed to generate learned forward models of complex systems through a data-driven approach to hyperparameter optimization and learned model training. Included in the MoLDy library, we present an open-source version of NEMPC (Nonlinear Evolutionary Model Predictive Control), a previously published control algorithm validated on soft robots. We demonstrate the ability of MoLDy and NEMPC to accurately perform modelbased control on a physical pneumatic continuum joint. We also present a benchmarking study on the effect of the loss metric used in model training on control performance. The results of this paper serve to guide other researchers in creating learned dynamic models of novel systems and using them in closed-loop control tasks.