Max Balandat

Thanks for flagging this. We'll have to look into this in a bit more detail. cc @SebastianAment for potential numerical issues with gradient computations. That said, is there a specific...

> Understandably, for many non-analytic acquisition functions this has not been implemented yet (i.e., batch mode). So we do support batch MC acquisition functions in conjunction with a "fully Bayesian...

This would be great to have. More than happy to provide guidance on the implementation. As Eytan said, a generic setup for this would be nice. Maybe there are also...

Fixed by #2290

@esantorella I ran into this when fixing some timeouts, is there any reason for this pipe to be here? Also, tangentially related, do we still need to write the results...

hmm nvm looks like this is needed after all in CI (this ran fine locally when running the module)?

The following code handles this if the noise is passed in as an iterable of noise tensors (one for each of the likelihoods): https://github.com/cornellius-gp/gpytorch/blob/b017b9c3fe4de526f7a2243ce12ce2305862c90b/gpytorch/likelihoods/likelihood_list.py#L31-L35 Does this help?

Is there a particular use case you have for training data with `requires_grad=True`? Or is this an issue that you ran into without deliberately setting this? In general it's not...

> However, the fit_gpytorch method always failed until I figured out that the issue went away until I used with torch.no_grad() Just to make sure there is no confusion here,...

Hi @TobyBoyne, thanks a lot for picking this up! Great to see that you've already made some progress on this. While I think it would make sense to keep the...