Celeste.jl

I see what you mean, but from my perspective what this argument is missing is the context that a large and important subset of the scientific community has never moved to Python to begin with. In other words, there is an even crankier and older community for which Julia may be the first actual change in a very long time. This is particularly the case in HPC, where everyone still uses Fortran, C, or C++, and has never moved nor ever will move to Python because Python is fundamentally unsuited to these workloads. But in some cases, Julia is [1]. The best differential equation solvers (e.g., SUNDIALS [2] for a modern example) have been written in FORTRAN for the last 50 years. If Julia can challenge Fortran as the go-to language for this type of work (e.g. DifferentialEquations.jl [3] and SciML), that would hardly count as excessive churn. [

I don't feel qualified to speculate on the implications for astronomy because that's not my field. However, Celeste is indeed a generative model and you can draw an image sample from the Celeste model and compare it to the original (there's a few examples in the Celeste issue tracker - https://github.com/jeff-regier/Celeste.jl/issues/625 is one I found on a quick look but there's more), so I think it qualifies in that sense. Of course you may want a more sophisticated model than Celeste for a complete model of the sky.

This is totally fair. Even the Julia creators regularly mention "stay with existing tools for now, if you are happy." Julia is an increasingly attractive option for greenfield, high-performance development, but for folks who aren't doing a lot of that, a more stable language and mature ecosystem is obviously important. Julia and many libraries can still require some elbow-grease, and the maturity and coverage varies between fields. However, this is rapidly changing as folks do new research in Julia and build up everything else they need along the way. The current situation is very similar to SciPy in the mid-2000s, though with perhaps a somewhat different mix of backgrounds. Here are a few examples: https://github.com/JuliaOpt https://github.com/jeff-regier/Celeste.jl https://github.com/dlfivefifty https://github.com/JuliaInv http://docs.