Website | Documentation | Contribution Guide
MXFusion is a library for integrating probabilistic modelling with deep learning.
With MXFusion Modules you can use state-of-the-art inference techniques for specialized probabilistic models without needing to implement those techniques yourself. MXFusion helps you rapidly build and test new methods at scale, by focusing on the modularity of probabilistic models and their integration with modern deep learning techniques.
MXFusion uses MXNet as its computational platform to bring the power of distributed, heterogenous computation to probabilistic modelling.
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It currently supports modelling of directed probabilistic models, deep learning integration through MXNet, and Variational Inference methods. Gaussian Processes are soon to come.
MXFusion's primary dependencies are MXNet >= 1.2 and Networkx >= 2.1. See requirements.
MXFusion is tested on Python 3.5+ on MacOS and Amazon Linux.
If you just want to use MXFusion and not modify the source, you can install through pip:
pip install mxfusion
To install MXFusion from source, after cloning the repository run the following from the top-level directory:
pip install .
We welcome your contributions and questions and are working to build a responsive community around MXFusion. Feel free to file an Github issue if you find a bug or want to request a new feature.
Have a look at our contributing guide, thanks for the interest!
Points of contact for MXFusion are:
- Eric Meissner (@meissnereric)
- Zhenwen Dai (@zhenwendai)
MXFusion is licensed under Apache 2.0. See LICENSE.