search for: dosnow

Dear list, When I run an R CMD check --as-cran on my package (pROC) I get the following note: > Uses the superseded package: ?doSNOW? The fact that it uses the doSNOW package is correct as I have the following example in an .Rd file: > #ifdef windows > if (require(doSNOW)) { > registerDoSNOW(cl <- makeCluster(2, type = "SOCK")) > ci(roc2, method="bootstrap", parallel=TRUE) > \don...

...el API functions in > those packages should work unchanged (apart from changing any > references to their namespaces to a reference to \pkg{parallel}, > and links explicitly to \CRANpkg{multicore} or \CRANpkg{snow} on help > pages). > > So you should replace your dependency on doSNOW with doParallel, which > is the equivalent foreach adapter for the parallel package. > > Martyn > > On Mon, 2015-02-09 at 23:08 +0100, Xavier Robin wrote: >> Dear list, >> >> When I run an R CMD check --as-cran on my package (pROC) I get the >> following note:...

Hi all, Within a foreach loop with doSNOW, we cant call functions which come from the non-default package. We need to load(require/library) the package once more within the foreach loop. Anyone knows why would happen like this? Is it caused by the snow package and something happened when "snow" parallelize the job? Other than lo...

...written to use the higher-level API functions in those packages should work unchanged (apart from changing any references to their namespaces to a reference to \pkg{parallel}, and links explicitly to \CRANpkg{multicore} or \CRANpkg{snow} on help pages). So you should replace your dependency on doSNOW with doParallel, which is the equivalent foreach adapter for the parallel package. Martyn On Mon, 2015-02-09 at 23:08 +0100, Xavier Robin wrote: > Dear list, > > When I run an R CMD check --as-cran on my package (pROC) I get the > following note: > > Uses the superseded package...

...wing? I have a foreach loop to generate random samples. If I run the exact code below in normal r (2.14.1) it works as expected, but if I run it from revolution 4.2.0 each loop returns the same numbers. The only way I can get revolution to give different numbers is using 1 instead of 8 in registerDoSNOW(makeCluster(8, type = "SOCK")) but that seems to defeat the point. library(foreach) library(doSNOW) registerDoSNOW(makeCluster(8, type = "SOCK")) getDoParWorkers() getDoParName() getDoParVersion() mySamples <- foreach (jj = 1:4, .combine=cbind) %dopar% { return(samp...

...t of time and I'd like to do this in parallel, if possible. If it's the disk reading speed that is the bottleneck I guess I should not expect an improvement, but perhaps it's the processor. So I want to try this out. I'm working on windows 7 with R 2.15.1 and the latest foreach and doSNOW packages. See sessionInfo() below. Thanks in advance for any inputs! # With lapply it works (i.e. each .tar.gz file is decompressed into several directories with the files of interest inside) lapply(tar.files.vector, FUN=untar) # It also works with foreach in serial mode: foreach(i=1:length(tar....

Shouldn't -n be 4 in the bsub command? One master+3 slaves. This was required for snowfall, but I think doSNOW is similar. Hope it helps mario On 16-Dec-10 23:09, Marius Hofert wrote: > Dear expeRts, > > I try to get a minimal example for parallel computing via "foreach" + "doSNOW" to run on a computer cluster (Brutus from ETH Zurich)....

Whas missing the R in the command line: "mpirun -n --hostfile /home/hostfile R --no-save -f rtest.R" Hope this helps mario On 13-Jan-11 22:08, Justin Moriarty wrote: > Hi, > Just wanted to share a working example of doSNOW and foreach for an openMPI cluster. The function eddcmp() is just an example and returns some inocuous warnings. The example first has each node return its nodename, then runs an example comparing dopar, do and a for loop. In the directory containing rtest.R it is run from the command line with:...

Dear R help, I am trying to set up a cluster for parallel computing on a machine running 64-bit windows XP, using the doSNOW package on R 2.14.1. However, the command makeCluster() consistently makes the computer hang. No R processes are started, no CPU consumed, R just becomes unresponsive. ESC still terminates the command, but I cannot start the cluster. There seems to be quite a lot of posts on R-help with this or a s...

...e 5 is running on: MyMaster > mpichhosts() master slave1 slave2 slave3 slave4 "localhost" "localhost" "localhost" "localhost" "localhost" > mpi.universe.size() [1] 4 > mpi.close.Rslaves() [1] 1 library(doSNOW) But every time I try to set up a cluster via cluster <- makeCluster(4, type = "MPI") My computer hangs up and I have to close the R session. Any advice how I get this running? Thanks in advance > sessionInfo() R version 3.0.1 (2013-05-16) Platform: x86_64-w64...

...1) of size 5 is running on: MyMaster > mpichhosts() master slave1 slave2 slave3 slave4 "localhost" "localhost" "localhost" "localhost" "localhost" > mpi.universe.size() [1] 4 > mpi.close.Rslaves() [1] 1 library(doSNOW) But every time I try to set up a cluster via cluster <- makeCluster(4, type = "MPI") My computer hangs up and I have to close the R session. Any advice how I get this running? Thanks in advance > sessionInfo() R version 3.0.1 (2013-05-16) Platform: x86_64-w64-mingw32/x64 (64-b...

I am trying to do parallel programming and I tried this library(doSNOW) library(foreach) testfunc<-function(x){ x<-x+1 x } noc<-2 cl <- makeCluster(do.call(rbind,rep(list("localhost"),noc)), type = "SOCK") registerDoSNOW(cl) clusterExport(cl=cl,c("testfunc.r")) testl<-foreach(pp=1:2) %dopar% { testfunc(pp) } And this...

Hello, I know of some various methods out there to utilize multiple processors but am not sure what the best solution would be. First some things to note: I'm running dependent simulations, so direct parallel coding is out (multicore, doSnow, etc). I'm on Windows, and don't know C. I don't plan on learning C or any of the *nix languages. My main concern deals with Multiple analyses on large data sets. By large I mean that when I'm done running 2 simulations R is using ~3G of RAM, the remaining ~3G is chewed up when I t...

Hello, I was using snow to parallel-process some code in R-2.10 (32-bit windows. ). The code is as follows: require(foreach) require(doSNOW) cl <- makeCluster(6, type='SOCK') registerDoSNOW(cl) bl2 <- foreach(i=icount(length(unqmrno))) %dopar% { (some code here) } stopCluster(cl) When I run the same code in Windows R-2.11 (either 32-bit or 64-bit), R hangs at cl<-makeCluster(6, type='SOCK') and no R processe...

...ot;red button" in the R Console is dark red [normal behavior if R is running], so R tries to do something, but cannot succeed. CPU goes up to 100%... Same happens if I start the job from the command line via R CMD BATCH. What's going on? Is this a known issue? Cheers, Marius library(doSNOW) library(Rmpi) library(rlecuyer) library(foreach) cl <- makeCluster(mpi.universe.size(), type ="MPI") tmp <- clusterSetupRNG(cl, seed=rep(1,6)) registerDoSNOW(cl) counter <- 0 res <- foreach(k = 1:1000) %do% { counter <- counter + 1 } tmp <- stopCluster(cl) u...

...rk the "clients" are doing is substantial enough to overwhelm the communication overhead. And there are many ways to accomplish the same task more or less efficiently (for example, doing blocks of tasks in chunks rather than passing each one as an individual job). But more to the point, doSNOW works just fine on an SMP, no problem, it doesn't require a cluster. Jay <<example code omitted>> Not only is the sequential foreach much slower than the simple for-loop (as least in this particular instance), but I am not quite sure how to make foreach run parallel. Where would...

Hello group, I am experimenting with parallel processing on my quad core Win 7 32 bit machine. Using these packages for the first time. I can see all my processor running at full performance when I use a smaller dataset require(snow) require(doSNOW) require(foreach) #change the 8 to however many cores\phys processors you have on your machine cl.tmp = makeCluster(rep("localhost",4), type="SOCK") registerDoSNOW(cl.tmp) optData.df <- head(pristine,100000) system.time( optData.df$impliedVol <- foreach(i=1:NROW(optData...

...kages (windows xp 32bit R). . 1. Suppose I would like to use package *foreach*, can I use current Sys.time as a seed? Although I can get the time up to1e-6 second precesion, the code below dose not work well on a local machine with two cores. ################# library(foreach) library(snow) library(doSNOW) testfun=function(){ options(digits.secs=6) set.seed(slave.seed <- substring(Sys.time(), 21, 26)) a <- rnorm(5,0,1) return(a) } maxkk <- 5 cl <- makeCluster(5, type = "SOCK") registerDoSNOW(cl)...

...Error in .lec.SetPackageSeed(seed) (from #11) : | Seed[0] >= -930997252, Seed is not set. `---- What's the problem? How can I construct a valid seed for l'Ecuyer's rng with just the information in .Random.seed? Thanks & Cheers, Marius Here is the minimal example: require(doSNOW) require(foreach) doForeach <- function(n, seed=1, type="MPI") { ## create cluster object cl <- snow::makeCluster(parallel::detectCores(), type=type) on.exit(snow::stopCluster(cl)) ## shut down cluster and terminate execution environment registerDoSNOW(cl) ## regist...

...' cluster, it generally cannot rely on its own side effects to report progress. I've found three approaches to progress bars for parallel processes on CRAN: - Importing 'snow' (not 'parallel') internals like sendCall and implementing parallel processing on top of them (doSNOW). This has the downside of having to write higher-level code from scratch using undocumented inferfaces. - Splitting the workload into length(cluster)-sized chunks and processing them in separate parLapply() calls between updating the progress bar (pbapply). This approach trades off p...