I am running MCMC in with the NUTS sampler. I cannot save all the samples in memory. Now I want to sample in memory as a buffer, write the samples to disk and continue sampling from the last state.

Is this possible?

I am running MCMC in with the NUTS sampler. I cannot save all the samples in memory. Now I want to sample in memory as a buffer, write the samples to disk and continue sampling from the last state.

Is this possible?

Hi @ritchie46, it would be easier to do it using `dev`

version in the following way

```
import tqdm
import torch
import pyro
import pyro.distributions as dist
from pyro.infer.mcmc import NUTS
from pyro.infer.mcmc.util import initialize_model
dim = 3
data = torch.randn(2000, dim)
true_coefs = torch.arange(1., dim + 1.)
labels = dist.Bernoulli(logits=(true_coefs * data).sum(-1)).sample()
def model(data):
coefs_mean = torch.zeros(dim)
coefs = pyro.sample('beta', dist.Normal(coefs_mean, torch.ones(dim)))
y = pyro.sample('y', dist.Bernoulli(logits=(coefs * data).sum(-1)), obs=labels)
return y
init_params, potential_fn, transforms, _ = initialize_model(model, (data,), {},
jit_compile=True, skip_jit_warnings=True)
hmc_kernel = NUTS(model=None, potential_fn=potential_fn, transforms=transforms)
hmc_kernel.initial_params = init_params
hmc_kernel.setup(warmup_steps=1000) # initialize warmup adapter
collection = []
for i in tqdm.trange(2000):
z = hmc_kernel.sample(None) # sample unconstrained values
if i >= 1000:
# transform unconstrained values to constrained values
for name, transform in transforms.items():
z[name] = transform.inv(z[name])
collection.append(z) # or save collection to disk
```