Long-running steps – running the simulator, training a neural density estimator – report progress through one mechanism, so a fit that simulates and then trains shows the same style of bar for both phases. Each bar reports the elapsed time and an ETA extrapolated from the work done so far.
If the progressr package is installed, neuralsbi emits standard
progressr updates, so progressr::handlers() and
progressr::with_progress() control reporting exactly as they do for any
other progressr-aware package:
library(progressr)
handlers(global = TRUE)
handlers("cli") # or "progress", "txtprogressbar", "rstudio"
fit <- npe(prior, simulator, n_simulations = 10000)Without progressr installed (or when it is installed but no handler is
registered) neuralsbi draws its own bar, which needs no extra packages.
Reporting is controlled by options(neuralsbi.progress = ):
"auto"(default) – report in interactive sessions, stay quiet in scripts,R CMD check, and knitr.TRUE– always report, interactive or not.FALSE– never report."builtin"– always report with the built-in bar, ignoring progressr.
Training progress
While a neural estimator trains, one progress step is one epoch. The total
is not known in advance because training stops early, so the bar targets the
epoch at which training would stop if the validation loss never improved
again (best epoch + patience). The target moves out whenever the
validation loss improves, so the ETA is a running estimate, not a promise.
See also
nsbi_parallel for running the simulator across cores.