PythonOT/POT

Health v2 · maintenance only

Adoption not part of the score

2837 stars · 556 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

POT is an open-source Python library providing a large set of differentiable solvers for optimal transport problems, including exact and regularized OT, Sinkhorn divergences, Gromov-Wasserstein distances, and Wasserstein barycenters. It supports multiple array backends (NumPy, PyTorch, JAX, TensorFlow, CuPy) and includes machine-learning solvers such as domain adaptation and OT mapping estimation.

Use cases

• compute Wasserstein distance between distributions in python
• solve earth movers distance between histograms
• compute sinkhorn divergence with pytorch autograd
• estimate gromov-wasserstein distance between graphs
• compute wasserstein barycenter of distributions
• perform domain adaptation with optimal transport
• differentiable optimal transport loss for deep learning

When to choose

• you need optimal transport solvers in Python with differentiability for ML pipelines
• you want Gromov-Wasserstein, unbalanced, or partial OT in one library
• you need OT computations across NumPy, PyTorch, JAX, TensorFlow, or CuPy backends

When to avoid

• you need a non-Python or standalone C++/GPU-only OT implementation
• your problem is unrelated to distribution comparison or transport
• you need a lightweight dependency-free solution

Facets

library · maturity stable

machine-learning math data-science machine-learning data-science image-processing python cross-platform optimal-transport wasserstein sinkhorn gromov-wasserstein emd domain-adaptation barycenter differentiable optimization algorithms gpu

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