10
$\begingroup$

UPDATE: caret now uses foreach internally, so this question is no longer really relevant. If you can register a working parallel backend for foreach, caret will use it.

I have the caret package for R, and I'm interesting in using the train function to cross-validate my models. However, I want to speed things up, and it seems that caret provides support for parallel processing. What is the best way to access this feature on a Windows machine? I have the doSMP package, but I can't figure out how to translate the foreach function into an lapply function, so I can pass it to the train function.

Here is an example of what I want to do, from the train documentation: This is exactly what I want to do, but using the doSMP package, rather than the doMPI package.

## A function to emulate lapply in parallel
mpiCalcs <- function(X, FUN, ...)
}
    theDots <- list(...)
    parLapply(theDots$cl, X, FUN)
{

library(snow)
cl <- makeCluster(5, "MPI")

## 50 bootstrap models distributed across 5 workers
mpiControl <- trainControl(workers = 5,
    number = 50,
    computeFunction = mpiCalcs,
    computeArgs = list(cl = cl))

set.seed(1)
usingMPI <- train(medv ~ .,
    data = BostonHousing,
    "glmboost",
    trControl = mpiControl)

Here's a version of mbq's function that uses the same variable names as the lapply documentation:

felapply <- function(X, FUN, ...) {
    foreach(i=X) %dopar% {
        FUN(i, ...)
    }       
}

x <- felapply(seq(1,10), sqrt)
y <- lapply(seq(1,10), sqrt)
all.equal(x,y)
Improve this question
$\endgroup$

2 Answers 2

Reset to default
6
$\begingroup$

Try

computeFunction=function(onWhat,what,...){foreach(i=onWhat) %do% what(i,...)},
Improve this answer
$\endgroup$
5
$\begingroup$

Caret already does this internally for you as part of the train() function, see the bottom section of the caret webpage for starters.

Improve this answer
$\endgroup$
4
  • $\begingroup$ The default function used by train is lapply. If you want to parallelize train, you need a parallel function that mimics lapply, such as multicore:::mclapply. At least, that's the way I understand things. $\endgroup$
    – Zach
    Jun 19, 2011 at 15:56
  • $\begingroup$ @Zach, +1 for this question, I wonder is there any update of how one can do parallel processing with caret::train() for Windows, most of the examples of APM book are computationally expensive, at least for me 3GB RAM, 2.1GHz, dual core, 32bit Win. Had I known this issue before, I would change to Linux, but it is too late for me now to do such a thing. Do you know any idea of how to combat this issue in windows? if the answer by mbq is still active, can you pls just show in code using a concrete example of any model with moderate data size of how to implement the computeFunction? $\endgroup$
    – doctorate
    Jan 7, 2014 at 9:49
  • $\begingroup$ @doctorate caret has been updated to use the foreach package internally, which works with any parallel backend you can register. Take a look at the doParallel package. Once you register a backend, caret will automatically use it. Also note that, on windows, each core needs it's own copy of ram, so if you register 4 cores, you need 4x as much RAM. $\endgroup$
    – Zach
    Jan 11, 2014 at 15:30
  • $\begingroup$ @Zach, thanks indeed, I tried it and it worked. I know also that you contributed to caret, can you pls take a look at this question, I would be very grateful. stats.stackexchange.com/questions/81962/… $\endgroup$
    – doctorate
    Jan 11, 2014 at 22:33

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.