  Optimal transport tools (OTT-JAX) is a Python toolbox that can solve optimal
transport problems between point clouds and histograms. The toolbox builds on
various JAX features, such as automatic and custom reverse mode
differentiation, vectorization, just-in-time compilation and accelerators
support. The toolbox covers elementary computations, such as the resolution of
the regularized OT problem, and more advanced extensions, such as barycenters,
Gromov-Wasserstein, low-rank solvers, estimation of convex maps, differentiable
generalizations of quantiles and ranks, and approximate OT between Gaussian
mixtures. The toolbox code is available at
\texttt{https://github.com/ott-jax/ott}
