  We introduce an ODE solver for the PyTorch ecosystem that can solve multiple
ODEs in parallel independently from each other while achieving significant
performance gains. Our implementation tracks each ODE's progress separately and
is carefully optimized for GPUs and compatibility with PyTorch's JIT compiler.
Its design lets researchers easily augment any aspect of the solver and collect
and analyze internal solver statistics. In our experiments, our implementation
is up to 4.3 times faster per step than other ODE solvers and it is robust
against within-batch interactions that lead other solvers to take up to 4 times
as many steps.
  Code available at https://github.com/martenlienen/torchode
