Hi, I’m new to this forum. While trying to use Sparse variational GP (and later on Deep GP) with Pyro, I had some questions. Would appreciate if somebody can help!
I’m working with large datasets that cannot be loaded into memory, so I am using the mini-batch training by following examples from here Inferences for Deep Gaussian Process models in Pyro | fehiepsi's blog. However, the GP API in Pyro still requires X and y for model initialization, where X and y are the feature and label of the entire dataset. I looked into the code, it seems X and y are being used for “conditional” pyro.contrib.gp.models.vsgp — Pyro documentation.
I was wondering if the initial X and y we provide matter? Can we simply provide some dummy X and y and just later set data in each mini-batch training epoch? What are the recommended practices? Thank you in advance.