Error in biomod2 package while analyzing large dataset in R

Question

I am encountering an error while using the biomod2 package in R to analyze a dataset consisting of 1000 species and over 10 thousand records. The error message I received is as follows:

"18 nodes produced errors; first error: attempt to set an attribute on NULL"

Along with this error, I also received a warning message stating:

"In searchCommandline(parallel, cpus = cpus, type = type, socketHosts = socketHosts, : Unknown option on commandline: --file"

I am unsure about the cause of this error and how to resolve it. Interestingly, the analysis works with a smaller dataset containing about 20 species, but it fails when applied to the entire dataset.

'{**## build species modelling wrapper ----**
biomod2_wrapper <- function(sp){
  cat("\n> species : ", sp)

  ## get occurrences points
  sp_dat <- data[data$species == sp, ]

  **## formating the data**
  sp_format <- 
    BIOMOD_FormatingData(
      resp.var = rep(1, nrow(sp_dat)), 
      expl.var = stk_current,
      resp.xy = sp_dat[, c("longitude", "latitude")],
      resp.name = sp,
      PA.strategy = "random", 
      PA.nb.rep = 2, 
      PA.nb.absences = 1000
    )

  **## print formatting summary**
  sp_format

  **## save image of input data summary**
  if(!exists(sp)) dir.create(sp)
  pdf(paste(sp, "/", sp ,"_data_formated.pdf", sep=""))
  try(plot(sp_format))
  dev.off()

  **## define models options**
  sp_opt <- BIOMOD_ModelingOptions()

  **## model species**
  sp_model <- BIOMOD_Modeling( 
    sp_format, 
    models = c('GBM', 'RF'), 
    models.options = sp_opt, 
    NbRunEval = 2, 
    DataSplit = 70, 
    Yweights = NULL, 
    VarImport = 3, 
    models.eval.meth = c('ROC','TSS'),
    SaveObj = TRUE,
    rescal.all.models = FALSE,
    do.full.models = FALSE,
    modeling.id = "demo2"
  )
  var_imp <- get_variables_importance(sp_model) 
  write.csv(var_imp, paste0(sp, "/", sp , "_evaluations.csv"))
  model.eva <- get_evaluations(sp_model) #get model evaluations of calibrate data


  pdf(paste0(sp, "/", sp , "_models_scores.pdf"))
  try(gg1 <- models_scores_graph(sp_model, metrics = c("TSS", "ROC"), by = 'models', plot = FALSE))
  try(gg2 <- models_scores_graph(sp_model, metrics = c("TSS", "ROC"), by = 'data_set', plot = FALSE))
  try(gg3 <- models_scores_graph(sp_model, metrics = c("TSS", "ROC"), by = 'cv_run', plot = FALSE))
  try(grid.arrange(gg1, gg2, gg3))
  dev.off()

  **## build ensemble models**
  sp_ens_model <- 
    BIOMOD_EnsembleModeling(
      modeling.output = sp_model,
      em.by = 'all',
      eval.metric = 'TSS',
      eval.metric.quality.threshold = 0.4,
      models.eval.meth = c('TSS','ROC'),
      prob.mean = FALSE,
      prob.mean.weight = TRUE,
      VarImport = 0
    )

  write.csv(get_evaluations(sp_model),file=paste0(sp,"/", sp ,"EM_evaluationscores.csv"),row.names=T)


  proj_scen <- c("LGM", "MID", "current")


  for(scen in proj_scen){
    cat("\n> projections of ", scen)

    **## single model projections**
    sp_proj <- 
      BIOMOD_Projection(
        modeling.output = sp_model,
        new.env = get(paste0("stk_", scen)),
        #new.env = proj.ext,
        proj.name = scen,
        selected.models = 'all',
        binary.meth = "TSS",
        filtered.meth = NULL,
        compress = TRUE,
        build.clamping.mask = FALSE,
        do.stack = FALSE,
        output.format = ".img"
      )

    **## ensemble model projections**
    sp_ens_proj <- 
      BIOMOD_EnsembleForecasting(
        EM.output = sp_ens_model,
        projection.output = sp_proj,
        binary.meth = "TSS",
        compress = TRUE,
        do.stack = FALSE,
        output.format = ".img"
      )
  }


  return(paste0(sp," modelling completed !"))
}


**## launch the species modelling wrapper over the species list**
if(require(snowfall)){

  sfInit(parallel = TRUE, cpus = 5) 
  sfExportAll()
  sfLibrary(biomod2)

  sf_out <- sfLapply(spp_to_model, biomod2_wrapper)

  sfStop()
} else { ## sequential computation
  for (sp in spp_to_model){
    biomod2_wrapper(sp)
  }
}} 

'''R'