For some reason, I’m getting memory errors when running the following code on a simple regression model:

posterior_samples_regression = pm.sample_posterior_predictive(trace, model = model, samples=500)

This has worked in previous models. The full error code and model are below.

Error Code:

## 100%|██████████| 500/500 [06:02<00:00, 1.10s/it]

MemoryError Traceback (most recent call last)

in

----> 1 posterior_samples_regression = pm.sample_posterior_predictive(trace, model = model, samples=500)~/anaconda3/lib/python3.7/site-packages/pymc3/sampling.py in sample_posterior_predictive(trace, samples, model, vars, size, random_seed, progressbar)

1144 indices.close()

1145

-> 1146 return {k: np.asarray(v) for k, v in ppc_trace.items()}

1147

1148~/anaconda3/lib/python3.7/site-packages/pymc3/sampling.py in (.0)

1144 indices.close()

1145

-> 1146 return {k: np.asarray(v) for k, v in ppc_trace.items()}

1147

1148~/anaconda3/lib/python3.7/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)

499

500 “”"

–> 501 return array(a, dtype, copy=False, order=order)

502

503MemoryError:

Model:

b_input = tt.shared(np.asarray(X_train))

with pm.Model() as model:

`#priors a = pm.Normal('intercept', mu = 0, sd = 5) beta = pm.Normal('betas', mu = 0, sd = 5) sd = pm.HalfCauchy('sd', 5) mu = a + T.dot(beta, b_input) y = pm.Normal('y', mu = mu, sd = sd, observed = Y_train)`

Has anyone else seen this specifically with sampling the posterior?