Our great sponsors
-
diffeqpy
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
With Python you have to write packages in some other language anyways, so you might as well do that with Julia. One of the reasons for getting all of this precompilation going is to eventually ship precompiled system images with things like https://github.com/SciML/diffeqpy, effectively using Julia as a replacement for where C/Fortran is traditionally used there. If I can make that pipeline smooth, then I think Julia as a Python package building source will be a good option for a lot of folks. Right now it's a very manual, but it could easily improve with a bit of tooling.
Related posts
- DifferentialEquations compilation issue in Julia 1.6
- 2023 was the year that GPUs stood still
- Why Fortran is a scientific powerhouse
- How much useful are Runge-Kutta methods of order 9 and higher within double-precision arithmetic/floating point accuracy?
- Interpolant Coefficients for the BS5 Runge-Kutta method