[ChrisRackauckas]

This tool already exists in Python and R. See https://cran.r-project.org/web/packages/diffeqr/index.html and https://anaconda.org/conda-forge/diffeqpy. Julia has language bindings that make it simple enough to bind to other high level languages that these projects are maintained by the core team and supports lots of the library, including forms of automatic differentiation and GPU kernel generation. See for example the bindings that allow for usage within PyTorch https://github.com/SciML/juliatorch and the Python-based Collab notebooks showing the GPU usage https://colab.research.google.com/drive/1bnQMdNvg0AL-LyPcXBi....

With Julia v1.12's small binary generation, we plan to release forms via binaries with C ABIs over the next year as well.

Are those sufficient or should we consider supporting other deployments?