This work introduces SpaiNN, an open-source Python software for performing surface hopping molecular dynamics simulations utilizing machine learning predicted energies, forces, nonadiabatic couplings or dipole moment vectors. By the integration of the nonadiabatic molecular dynamics program SHARC 3.0 with the neural network potentials provided by SchNetPack 2.0, SpaiNN provides a flexible and modular tool for training and predicting excited state properties involving multiple electronic (singlet) states us-ing rotationally-invariant (SchNet) or -equivariant (PaiNN) representation of molecules. Moreover, its modular architecture allows for straightforward adaption of modules and the development of new ones. The method is accessible on GitHub 56 and is accompanied by a comprehensive documentation including tutorials, which facilitate its integration into research.SpaiNN incorporates three core modules to i) generate databases or convert existing databases into the SpaiNN database format, and to interface ii) SchNetPack and iii) SHARC.