NUTS sampler gets "bad initial energy" only with parallel sampling

I have a model that I am sampling from as follows:

    with m.model:
        m.trace = pm.sample(cores=1, chains=1)

This works fine (well, there are divergences, so not fine, but it works).
But when I remove the cores=1, chains=1, I get a bad initial energy error:

Multiprocess sampling (4 chains in 4 jobs)
NUTS: [weights, err_sd, medium influences, AND Output, βod2, βod1, βtemp2, βtemp1]
Sampling 4 chains:   0%|          | 0/4000 [00:00<?, ?draws/s]/usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/core/ RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
/usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/core/ RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
/usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/core/ RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
/usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/core/ RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)

Bad initial energy, check any log probabilities that are inf or -inf, nan or very small:
Series([], )

Any idea what could be causing this? Is this because the parallel sampling version has multiple different starting points?
This seems particularly unfortunate, because the way I have been debugging such errors is to use only a single chain and a single core, to simplify things.

It may be because the parallel sampling has multiple different starting points.

Try adding init='adapt_diag' when you call pm.sample . See here for more.

Also, why is that empty Series in there? Looks like that’s error_logp.to_string(), which leaves me pretty confused:

I’m also confused because that error_logp comes from:

error_logp = check_test_point.loc[
                (np.abs(check_test_point) >= 1e20) | np.isnan(check_test_point)

But I have been running check_test_point on the model before sampling and don’t see any problems:

βtemp1                       -0.92
βtemp2                        2.08
βod1                         -0.92
βod2                         -0.23
AND Output_interval__        -4.27
medium influences           -19.35
err_sd_log__               -880.76
weights_stickbreaking__      -3.20
obs                       -2744.15
Name: Log-probability of test_point, dtype: float64

I suspect that this might be because the error condition is triggered by this:

       start = self.integrator.compute_state(q0, p0)

        if not np.isfinite(

I believe we have had a discussion about this to the effect that it might not just be the point that has an infinite logp, but that the gradient might be involved, too. I know @junpenglao has posted about this (and how to check the value of the gradient in the debugger) but after about 30 minutes of keyword-searching, I just cannot find the post.

Thanks. I will have a shot at this.

This seems to be a problem with the search and my keyword guessing. Here’s the key post.