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Batch processing pyro models so cc This function is fit to observed data points, one fit per object @fonnesbeck as i think he’ll be interested in batch processing bayesian models anyway
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I want to run lots of numpyro models in parallel I have a dataset of l objects 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. 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 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 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
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 I’m seeking advice on improving runtime performance of the below numpyro model
