cloudposterior

Stop waiting for MCMC. Start shipping posteriors.

Run PyMC models on cloud VMs without changing your sampling code. One extra line gives you cloud compute, automatic caching, and phone notifications — while pm.sample() stays exactly the same.

import cloudposterior as cp

with cp.cloud(model, remote=True):
    idata = pm.sample(draws=5000, chains=8)  # 8 cores in the cloud, zero config

Why

You’ve built a hierarchical model. It’s beautiful. But sampling takes 45 minutes on your laptop, your fans sound like a jet engine, and you can’t use your machine for anything else. cloudposterior fixes that:

• Ship sampling to the cloud with one line — your model runs on a VM with as many cores and as much RAM as it needs.
• Never re-run the same model twice — results are cached automatically, so re-running a cell returns the posterior instantly.
• Monitor from anywhere — live progress notifications on your phone while the model samples.

All three work independently — use any combination, or just the caching.

Install

pip install cloudposterior        # pulls in PyMC + ArviZ
pip install modal && modal setup  # optional, for cloud execution

Next steps

• Getting started — install and your first cloud sample
• Examples — cloud execution, caching, parallel fitting, monitoring
• API reference — cloud, sample, map, and configuration