I am using the CSIS
class to learn amortized posterior hypotheses for a vision problem, and running inference on a new observation becomes too slow when num_inference_samples
increases. Basically, running my generative model requires rendering a scene. There is any way of evaluating posterior hypotheses in parallel? Because the _traces()
method that computes posterior hypotheses and the correspondent log_weight
runs with for i in range(self.num_samples):
. Thanks in advance!