Flowtorch
This library would form a permanent home for reusable components for deep probabilistic programming. The library would form and harness a community of users and contributors by focusing initially on complete infra and documentation for how to use and create components.
Overview:
FlowTorch is a PyTorch library developed by Meta Platforms, Inc. It is used for learning and sampling from complex probability distributions using a method called Normalizing Flows. This library allows users to install the source code and provides further information on installation, usage, and contribution.
Features:
- Normalizing Flows: FlowTorch utilizes Normalizing Flows to learn and sample from complex probability distributions.
- Installation Guide: Provides a guide on how to install FlowTorch from the source code.
- Documentation: The FlowTorch website offers comprehensive documentation on installation, library usage, and contribution guidelines.
- Construct and Train Distributions: FlowTorch provides instructions on how to construct and train distributions using the library.
- Contributing: Users can contribute new normalizing flow methods to the FlowTorch library.
- Bug Reporting: FlowTorch provides a platform to report any bugs encountered while using the library.
- Support and Feature Requests: Users can ask general questions and make feature requests on the FlowTorch platform.
- Future Features: FlowTorch provides information on upcoming features planned for the near future.
Summary:
FlowTorch is a PyTorch library developed by Meta Platforms, Inc. It enables users to learn and sample from complex probability distributions using Normalizing Flows. The library offers features such as source code installation, documentation, construction, and training of distributions. Users can also contribute new normalizing flow methods, report bugs, and make feature requests. FlowTorch provides detailed instructions on installation through cloning the source code repository and running the setup command. It aims to provide a comprehensive solution for working with complex probability distributions in a PyTorch environment.