Open mbac opened 3 years ago
The installation of rstanarm
from source can take a good deal of time, since multiple models need to be compiled.
Can you try installing using multiple cores:
Sys.setenv(MAKEFLAGS = "-j6")
remotes::install_github("stan-dev/rstanarm", verbose = TRUE)
And then monitoring the CPU and RAM usage of the VM to see if the installation is processing?
Hi!
I can’t manage to install
rstanarm
in a Google Notebook instance (running R 4.0.5 in Jupyter Lab 3.1.0).The console kernel just hangs few seconds after I run:
No error/warning messages, just hangs. The VM should be relatively fast with 8 CPU cores and 32 GB of RAM.
Can you please help me troubleshoot?