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We introduce SPFlow, an open-source Python library providing a simpleinterface to inference, learning and manipulation routines for deep andtractable probabilistic models called Sum-Product Networks (SPNs). The libraryallows one to quickly create SPNs both from data and through a domain specificlanguage (DSL). It efficiently implements several probabilistic inferenceroutines like computing marginals, conditionals and (approximate) most probableexplanations (MPEs) along with sampling as well as utilities for serializing,plotting and structure statistics on an SPN. Moreover, many of the algorithmsproposed in the literature to learn the structure and parameters of SPNs arereadily available in SPFlow. Furthermore, SPFlow is extremely extensible andcustomizable, allowing users to promptly distill new inference and learningroutines by injecting custom code into a lightweight functional-oriented APIframework. This is achieved in SPFlow by keeping an internal Pythonrepresentation of the graph structure that also enables practical compilationof an SPN into a TensorFlow graph, C, CUDA or FPGA custom code, significantlyspeeding-up computations.

SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks