They are. Lots of numerical code benefits from GPU and lots of numerical code benefits from derivatives. Simulations, solvers, numerical optimization, good old fashioned statistics.

Libraries like this enable differentiable programming, which lets you backprop through more than just neural networks. For instance, people have built a differentiable raytracer and plugged a physics engine into reinforcement learning to accelerate training.

https://en.wikipedia.org/wiki/Differentiable_programming

Idk about using these libraries, but its almost impossible to find generic graph libraries that aren't designed around either ML or alternatively scheduling batches. One such example is my own, https://github.com/timkpaine/tributary

Interesting library & idea, almost like its own programming paradigm when you abstract away all the specificity for building software or running ETL jobs or whatever.

But this is a completely different kind of graph. The graphs being discussed here are differentiable DAGs of mathematical computations.

Is it that different? https://github.com/timkpaine/tributary/blob/main/docs/exampl...

Now that is definitely interesting. And you have some notion of "differentiability" for all of your various sources, sinks, and transforms?

That said, Tensorflow and Pytorch are both very much general purpose numerical computing libraries. You don't have to use them for neural networks.

We use them for computational imaging reconstruction in electron microscopy.