@fonnesbeck as i think he’ll be interested in batch processing bayesian models anyway Is there documentation i have missed about kernel coding that you can point me to. I want to run lots of numpyro models in parallel
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I created a new post because
This post uses numpyro instead of pyro i’m doing sampling instead of svi i’m using ray instead of dask that post was 2021 i’m running a simple neal’s funnel.
Hi, i’m trying to write a manual guide for a model I have a 2d array of parameters that i’ve defined like this Model and guide shapes disagree at site ‘z_2’ Torch.size ( [2, 2]) vs torch.size ( [2]) anyone has the clue, why the shapes disagree at some point
Here is the z_t sample site in the model Z_loc here is a torch tensor wi… I’m seeking advice on improving runtime performance of the below numpyro model I have a dataset of l objects
This function is fit to observed data points, one fit per object
I am running nuts/mcmc (on multiple cpu cores) for a quite large dataset (400k samples) for 4 chains x 2000 steps I assume upon trying to gather all results (there might be some unnecessary memory duplication going on in this step?) are there any “quick fixes” to reduce the memory footprint of mcmc I’m learning numpyro and to build my skills i’m trying to implement a metropolis kernel that uses a model instead of a potential
I’ve cobbled something together that seems to work, at least on simple examples, and i’m looking for feedback about how to do this more robustly and more in the numpyro style
