chkptstanr-package.RdFit Bayesian models in Stan (Carpenter et al. 2017)
with checkpointing, that is, the ability to stop the MCMC sampler at will,
and then pick right back up where the MCMC sampler left off. Custom Stan models
can be fitted, or the popular package brms (B昼㹣rkner 2017)
can be used to generate the Stan code. This package is fully compatible with the
R packages brms,
posterior,
cmdstanr, and
bayesplot.
There are a variety of use cases for chkptstanr, including (but not limited to) the following:
The primary motivation for developing chkptstanr is to reduce the cost of fitting models with Stan when using, say, AWS, and in particular by taking advantage of so-called spot instances. These instances are "a cost-effective choice if you can be flexible about when your applications run and if your applications can be interrupted [emphasis added]" (AWS website).
chkptstanr thus allows for taking advantage of spot instances by enabling "interruptions" during model fitting. This can reduce the cost by 90 %.
Stan allows for fitting complex models. This often entails iteratively improving the model to ensure that the MCMC algorithm has converged. Typically this requires waiting until the model has finished sampling, and then assessing MCMC diagnostics (e.g., R-hat).
chkptstanr can be used to make iterative model building more efficient, e.g., by having the ability to pause sampling and examine the model (e.g., convergence diagnostics), and then deciding to stop sampling or to continue on.
Computationally intensive models can sometimes take several days to finish up. When using a personal computer, this can take up all the computing resources.
chkptstanr can be used with scheduling, such that the model is fitted during certain windows (e.g., at night, weekends, etc.)
Those familiar with Bayesian methods will know all too well that a model can take longer than expected. This can be problematic when there is another task that needs to be completed, because one is faced with waiting it out or stopping the model (and loosing all of the progress).
chkptstanr makes it so that models can be conveniently stopped if need be, while not loosing any of the progress.
B昼㹣rkner P (2017).
“brms: An R package for Bayesian multilevel models using Stan.”
Journal of statistical software, 80, 1--28.
Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, Brubaker M, Guo J, Li P, Riddell A (2017).
“Stan: A probabilistic programming language.”
Journal of statistical software, 76(1).