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Batch processing pyro models so cc (there might be some unnecessary memory duplication going on in this step?) are there any “quick fixes” to reduce the memory footprint of mcmc @fonnesbeck as i think he’ll be interested in batch processing bayesian models anyway

I want to run lots of numpyro models in parallel I assume upon trying to gather all results 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.

This would appear to be a bug/unsupported feature If you like, you can make a feature request on github (please include a code snippet and stack trace) However, in the short term your best bet would be to try to do what you want in pyro, which should support this. Hi everyone, i am very new to numpyro and hierarchical modeling

There is another prior (theta_part) which should be centered around theta_group I am trying to use lognormal as priors for both 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… The following operation failed in the torchscript interpreter Traceback of torchscript (most recent call last) Tensor however, if i hardcode sigma=1.0, the code runs

Apologies for the rather long post This is the gmm code that works when i fit with both hmc and svi. I am running nuts/mcmc (on multiple cpu cores) for a quite large dataset (400k samples) for 4 chains x 2000 steps

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