Trying to use furrr for parallel processing but can only see one processor working

I'm working within hosted RStudio on a Linux EC2 server with 4 processors. In the past when I've used packages such as XGBoost or foreach, which use parallel processing, I have been able to watch the terminal and see all 4 processors light up within linux with top and pressing 1.

When I do that now here's what I see:
12 PM

There's only one processor working. I expected to see multiple processors at work like I have in the past.

Here's my code block. Hoping someone can recognize what I'm not doing right here? This is my first time using furrr.

library(pscl) # hurdle and zero inflated models
library(furrr) # parallel processing
plan(multicore) # also tried multiprocess but no change, only one processor seems to run 

# otherwise error:
# Error in getGlobalsAndPackages(expr, envir = envir, tweak = tweakExpression,  : 
#   The total size of the 9 globals that need to be exported for the future expression (‘{; ...future.f.env <- environment(...future.f); if (!is.null(...future.f.env$`~`)) {; if (is_bad_rlang_tilde(...future.f.env$`~`)) {; ...future.f.env$`~` <- base::`~`; }; ...; .out; }); }’) is 1.76 GiB. This exceeds the maximum allowed size of 1.46 GiB (option 'future.globals.maxSize'). The three largest globals are ‘...future.x_ii’ (1.76 GiB of class ‘list’), ‘is_bad_rlang_tilde’ (15.05 KiB of class ‘function’) and ‘’ (6.61 KiB of class ‘function’).
options(future.globals.maxSize = 2000 * 1024^2)

# create train test split
pdata_split <- initial_split(pdata, 0.9)
training_data <- training(pdata_split)
testing_data <- testing(pdata_split)

# cross validation folds
pdata_cv <- vfold_cv(training_data, 5, strata = spend_30d) %>% 
  # create training and validation sets within each fold
  mutate(train = map(splits, ~training(.x)),
         test = map(splits, ~testing(.x))) %>% 
  # hurdle model for each fold
  mutate(hurdle_model = furrr::future_map(train, 
                            ~hurdle(formula = spend_30d ~., 
                                    data = .x,
                                    dist = "negbin")))
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I got it working by moving plan(multiprocess) to be directly above the first line of the dply chain of commands starting pdata_cv <- vfold_cv(training_data, 5, strata = spend_30d) %>%

Would be nice to understand why this is though.

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About muticore, you can check if this is supported in your setup


It won't work with RStudio for example.

Also, you did not specify any workers so the default is parallel::availableCores(constraints = "multicore") which depend on your setup.

If you don't support multicore, multisession will use some R processes - you can check the open process when you run your code to see if this is currently working.

I don't know moving the plan call closer to the pipe can help... :man_shrugging:

Hope it helps.

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