Manually calculate GP hyperparameter gradients

Due to the slow gradient calculation of gaussian process hyperparameters using autograd, I want to look into implementing my own version of the gradients (for a very limited set of covariance functions).

To do this I looked around and think that guides are what I am looking for.

How would I now define a guide and pass it to the training e.g. using the standard GP regression example? (http://pyro.ai/examples/gp.html)
Just for the sake of the example, let’s say I would just multiply each hyperparameter by 1.1 (i.e. add 10%), how should the guide look like?
Any directions and tips appreciated!

guides are unrelated to custom gradients. custom gradients are implemented using torch machinery and as such having nothing to do with pyro per se, e.g. see this example in gpytorch. i don’t know your use case but it’s unlikely that your custom gradients will win you gigantic speed gains. maybe a factor of 2 but not a factor of 5 or 10.

Thank you for clarifying my misunderstanding!
I will look into it.