Optimal Transport Tools
A Python toolbox for solving optimal transport problems with JAX-powered computational efficiency.
What is Optimal Transport Tools?
OTT is a Python toolbox for solving optimal transport problems, which involve finding optimal ways to move mass between probability distributions. It provides efficient implementations of algorithms like Sinkhorn and Greenkhorn, with JAX acceleration for computational performance. The library enables applications in machine learning, computer vision, and data science where measuring distances between distributions is essential.
Machine learning researchers and practitioners working with distributional data, computational geometry problems, or applications requiring Wasserstein distances. Data scientists and engineers needing efficient optimal transport implementations for large-scale problems.
Developers choose OTT for its JAX-powered computational efficiency, clean API design, and focus on differentiability—making it ideal for integration with modern machine learning pipelines. It offers a balance between research flexibility and production-ready performance.
Overview
OTT is a Python library that provides efficient implementations of algorithms for solving optimal transport problems. It focuses on computational performance through JAX acceleration, making it suitable for large-scale applications in machine learning and data science.
Key Features
- JAX-powered computations — Leverages JAX for automatic differentiation, vectorization, and GPU/TPU acceleration
- Comprehensive algorithm suite — Implements Sinkhorn, Greenkhorn, and other optimal transport solvers
- Differentiable transport — Supports end-to-end differentiable pipelines for integration with neural networks
- Geometric tools — Provides utilities for Wasserstein distances, barycenters, and Gromov-Wasserstein alignment
- Modular design — Clean API with separable components for custom algorithm development
Philosophy
OTT prioritizes computational efficiency and differentiability while maintaining a clean, research-friendly API that balances flexibility with performance.
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