Fully reproducible parallel models using caret

One easy way to run fully reproducible model in parallel mode using the caret package is by using the seeds argument when calling the train control. Here the above question is resolved, check the trainControl help page for further infos.

library(doParallel); library(caret)#create a list of seed, here change the seed for each resamplingset.seed(123)#length is = (n_repeats*nresampling)+1seeds <- vector(mode = "list", length = 11)#(3 is the number of tuning parameter, mtry for rf, here equal to ncol(iris)-2)for(i in 1:10) seeds[[i]]<- sample.int(n=1000, 3)#for the last modelseeds[[11]]<-sample.int(1000, 1) #control list myControl <- trainControl(method='cv', seeds=seeds, index=createFolds(iris$Species)) #run model in parallel cl <- makeCluster(detectCores()) registerDoParallel(cl) model1 <- train(Species~., iris, method='rf', trControl=myControl) model2 <- train(Species~., iris, method='rf', trControl=myControl) stopCluster(cl) #compare all.equal(predict(model1, type='prob'), predict(model2, type='prob'))[1] TRUE

So caret uses the foreach package to parallelize. There is most likely a way to set the seed at each iteration, but we would need to setup more options in train.

Alternatively, you could create a custom modeling function that mimics the internal one for random forests and set the seed yourself.

Max