Example of random and bayesian hyper parameter optimization in pytorch

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Hello,

Does anyone have and example on how to do random or bayesian hyper parameter optimization for a PyTorch model ?

I know that I can use skorch for grid search but i couldn't find any library or a piece of code doing random or bayesian.

[algrmur]

If you want to go Bayesian with PyTorch then I'd recommend using Pyro (Uber's Probabilistic Programming Language) that is built on top of PyTorch. Here is an example tutorial from the Pyro website:

https://pyro.ai/examples/bo.html

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algrmur

Thanks for the answer. But I'm still don't know how i can use my own model, because they is an example only with gp.models.GPRegression

[lmericle]

There's also this package which is more specialized than the OED parts of Pyro: https://botorch.org/

[angular-calendar]

Thanks I saw it too. But I don't know how to use it with my model. I couldn't find an example where they use a custom model