Brancher vs PySpark: What are the differences?

Developers describe Brancher as "A Python package for differentiable probabilistic inference". It is a user-centered Python package for differentiable probabilistic inference. It allows to design and train differentiable Bayesian models using stochastic variational inference. It is based on the deep learning framework PyTorch. On the other hand, PySpark is detailed as "The Python API for Spark". It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

Brancher and PySpark can be primarily classified as "Data Science" tools.

Brancher is an open source tool with 163 GitHub stars and 29 GitHub forks. Here's a link to Brancher's open source repository on GitHub.