Error in { : task 1 failed - "could not find function "ranger"" >

I was able to run the following code without any problems:

# first code: works fine
library(dplyr)
library(ranger)

original_data = rbind( data_1 = data.frame( class = 1, height = rnorm(10000, 180,10), weight = rnorm(10000, 90,10), salary = rnorm(10000,50000,10000)),  data_2 = data.frame(class = 0, height = rnorm(100, 160,10), weight = rnorm(100, 100,10), salary = rnorm(100,40000,10000)) )

original_data$class = as.factor(original_data$class)
original_data$id = 1:nrow(original_data)

test_set=  rbind(original_data[ sample( which( original_data$class == "0" ) , replace = FALSE , 30 ) , ], original_data[ sample( which( original_data$class == "1" ) , replace = FALSE, 2000 ) , ])

train_set = anti_join(original_data, test_set)

# Step 2: Create "Balanced" Random Subsets:

results <- list()
for (i in 1:100)
   
{
   iteration_i = i
   
    sample_i =  rbind(train_set[ sample( which( train_set$class == "0" ) , replace = TRUE , 50 ) , ], train_set[ sample( which( train_set$class == "1" ) , replace = TRUE, 60 ) , ])
   
    results_tmp = data.frame(iteration_i, sample_i)
    results_tmp$iteration_i = as.factor(results_tmp$iteration_i)
   results[[i]] <- results_tmp
   
}

results_df <- do.call(rbind.data.frame, results)

X<-split(results_df, results_df$iteration)

 invisible(lapply(seq_along(results),
       function(i,x) {assign(paste0("train_set_",i),x[[i]], envir=.GlobalEnv)},
       x=results))

# Step 3: Train Models on Each Subset:
wd = getwd()
results_1 <- list()

for (i in 1:100){
     
    model_i <- ranger(class ~  height + weight + salary, data = X[[i]], probability = TRUE)
    saveRDS(model_i, paste0("wd", paste("model_", i, ".RDS")))
    results_1[[i]] <- model_i   
}

# Step 4: Combine All Models and Use Combined Model to Make Predictions on the Test Set:
results_2 <- list()
for (i in 1:100){
predict_i <- data.frame(predict( results_1[[i]], data = test_set)$predictions)


predict_i$id = 1:nrow(predict_i)
 results_2[[i]] <- predict_i
   
}

final_predictions = aggregate(.~ id, do.call(rbind, results_2), mean)

I am now trying to run the same code (Step 2, Step 3, Step 4) in parallel - here is my attempt:

# second code: does not work fine
library(doParallel)
library(foreach)

registerDoParallel(cores = detectCores())
foreach(i = 1:100) %dopar% {
        # Step 2: Create "Balanced" Random Subsets:

results <- list()
for (i in 1:100)
   
{
   iteration_i = i
   
    sample_i =  rbind(train_set[ sample( which( train_set$class == "0" ) , replace = TRUE , 50 ) , ], train_set[ sample( which( train_set$class == "1" ) , replace = TRUE, 60 ) , ])
   
    results_tmp = data.frame(iteration_i, sample_i)
    results_tmp$iteration_i = as.factor(results_tmp$iteration_i)
   results[[i]] <- results_tmp
   
}

results_df <- do.call(rbind.data.frame, results)

X<-split(results_df, results_df$iteration)

 invisible(lapply(seq_along(results),
       function(i,x) {assign(paste0("train_set_",i),x[[i]], envir=.GlobalEnv)},
       x=results))

# Step 3: Train Models on Each Subset:
wd = getwd()
results_1 <- list()

for (i in 1:100){
     
    model_i <- ranger(class ~  height + weight + salary, data = X[[i]], probability = TRUE)
    saveRDS(model_i, paste0("wd", paste("model_", i, ".RDS")))
    results_1[[i]] <- model_i   
}

# Step 4: Combine All Models and Use Combined Model to Make Predictions on the Test Set:
results_2 <- list()
for (i in 1:100){
predict_i <- data.frame(predict( results_1[[i]], data = test_set)$predictions)


predict_i$id = 1:nrow(predict_i)
 results_2[[i]] <- predict_i
   
}

final_predictions = aggregate(.~ id, do.call(rbind, results_2), mean)
    
}

stopImplicitCluster()

This is giving me the following error:

Error in { : task 1 failed - "could not find function "ranger""

I am not sure why this error is being produced, seeing as I have loaded the "ranger" library.

My Question: Can someone please show me what I am doing wrong and how can I make the second code run like the first code?

Thanks!

Note : After adding the suggestion made by @Waldi, the code doesn't produce an error, but is taking a very long time to run. Does anyone have any recommendations on how to improve this?