We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs). The library allows one to quickly create SPNs both from data and through a domain specific language (DSL). It efficiently implements several probabilistic inference routines like computing marginals, conditionals and (approximate) most probable explanations (MPEs) along with sampling as well as utilities for serializing, plotting and structure statistics on an SPN. Moreover, many of the algorithms proposed in the literature to learn the structure and parameters of SPNs are readily available in SPFlow. Furthermore, SPFlow is extremely extensible and customizable, allowing users to promptly distill new inference and learning routines by injecting custom code into a lightweight functional-oriented API framework. This is achieved in SPFlow by keeping an internal Python representation of the graph structure that also enables practical compilation of an SPN into a TensorFlow graph, C, CUDA or FPGA custom code, significantly speeding-up computations.

SPFlow是一个开源Python库，提供深度和可计算概率模型称为Sum-Product Networks（SPNs）的推理、学习和操作例程的简便接口。它不仅实现了几种概率推理例程，还提供了序列化、绘图和结构统计的实用程序。此外，SPFlow还支持各种学习算法，并具有扩展性和可自定义性，可让用户通过自定义代码注入轻量级面向API结构快速提取新的推理和学习例程。

SPFlow：一个易于扩展的库，用于使用Sum-Product网络进行深度概率学习