Optimizing PyMC Code for Efficiency for many runs

I have a Python code that utilizes the PyMC library for Bayesian modeling to compare two group performances. This code aims to do this for multiple groups (1000+). However, I’ve noticed that the code’s performance is suboptimal (100+ groups take 24 hours), especially when dealing with a large number of ad groups. I’m looking for advice and suggestions from the community on how to improve the code’s efficiency and make it run faster.
Here is the code I use

for compare_group_infos in zip(ad_group_list, prior_mean_a_list, prior_std_a_list, prior_mean_b_list, prior_std_b_list, obs_a_list, obs_b_list):
        ad_group_id, prior_mean_a, prior_std_a, prior_mean_b, prior_std_b, obs_a, obs_b = compare_group_infos
        with pm.Model() as model:
            # Vectorized priors
            ctrl_cpas = pm.TruncatedNormal('ctrl_cpas', mu=prior_mean_a, sigma=prior_std_a, lower=0)
            treat_cpas = pm.TruncatedNormal('treat_cpas', mu=prior_mean_b, sigma=prior_std_b, lower=0)

            # Vectorized delta
            deltas = pm.Deterministic("deltas", treat_cpas - ctrl_cpas)

            # Vectorized observations
            pm.Exponential(f"obs_ctrl", 1 / ctrl_cpas, observed=obs_a)
            pm.Exponential(f"obs_treat", 1 / treat_cpas, observed=obs_b)

            # Sampling
            trace = pm.sample(20000, chains=4)
            print('model complete')
        delta_samples = np.concatenate(trace.posterior.deltas.data[:, 10000:])
        win_ratio = np.mean(delta_samples < 0)
        out_dict[ad_group_id] = {'win_ratio': win_ratio, 'control_cpx': np.mean(obs_a), 'treat_cpx': np.mean(obs_b)}
        print('evaluation done: %s with win_ratio %f' % (ad_group_id, win_ratio))

I previously did some research and plan to try it on GPU or make it parallel following this thread(MCMC for big datasets: faster sampling with JAX and the GPU - PyMC Labs), but I then realized my problem might be different from a standard case, where single run seems ok to be finished within 10 mins.

You can try including more groups per model instead of one model per group.

If that and hardware doesn’t cut it, you may need to look for some more creative solutions. Some folks at PyMC Labs did a write-up on work we did in this area here: Bayesian inference at scale: Running A/B tests with millions of observations - PyMC Labs

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Thank you very much for sharing this information; it is very helpful. The post you shared is very similar to my case! Let me try this out.

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