search for: doparallel

Hi, I'm trying to submit my first package which depends on doParallel: Depends: R (>= 3.0), igraph, doParallel Running hadley devtools: devtools::check() and devtools::release() result in no problem (no ERROR nor NOTE on Linux, Mac and Windows). However, when in use the devtools::build_win() command, it results in the following note: * checking R code for poss...

On 05/03/2015 6:16 PM, Brian G. Peterson wrote: > On Thu, 2015-03-05 at 18:03 -0500, Etienne Lord wrote: >> Hi, >> >> I'm trying to submit my first package which depends on doParallel: >> >> Depends: R (>= 3.0), igraph, doParallel > > add foreach to your Depends. That should resolve the error you're > seeing. Please don't do that. Use Imports. Duncan Murdoch > > >> Running hadley devtools: devtools::check() and devtools::releas...

Thanks for the quick reply. Adding all the dependencies of doParallel (foreach, parallel, iterators) in the DESCRIPTION and in import statements in NAMESPACE resolved the build_win problems. Don't know why this is required for Windows build. Thanks again. 2015-03-05 18:22 GMT-05:00 Duncan Murdoch <murdoch.duncan at gmail.com>: > On 05/03/2015 6:16 PM,...

...cores I have available to me (e.g. eight cores, eight chunks). I spent much of the day today trying to wrap myself around an expedient way to do this on a windows system in R (seems like the 'best' way to do this on systems that support forking is much more settled), and picked foreach and doParallel based on the package documentation for foreach; I'm open to other suggestions. Having tried it out though, it doesn't seem to be doing anything to speed up the task - indeed, it takes more time! Also, compiling the functions results in the 'winner' in terms of the serial and (I hop...

Hi, all-- I've been working on a doParallel package to provide a foreach parallel backend for the new parallel package, but I am having trouble building the vignette. On my system, if I use the following minimal vignette (call it buggyVignette.Rnw): \documentclass[a4paper]{article} \title{Sweave Example 1} \author{Rich Calaway}...

...) Get a more advanced textbook. E.g. could you recommend Gentleman, "R for Bioinformatics"? 2) Because textbooks are limited and become obsolete fast, I can focus on learning state-of-the-art packages, but for that I need to find a list of most useful general purpose packages (foreach, doParallel, etc) that is updated in real time. Does such list exist? Your recommendations are very welcome. Thanks, Nik

Buenas, Quiero ver si he paralelizado correctamente el proceso. Tengo dos dataframes, A y B y quiero calcular la distancia euclídea de todas las filas de A sobre todas las filas de B. Para ello he hecho lo siguiente #cargo las librerias library(foreach) library(doParallel) #establezco el numero de clusters, en mi caso 4, ya que el procesador tiene 4 nucleos cl<-makeCluster(4) registerDoParallel(cl) #Creo los dataframes A<-as.data.frame(matrix(rnorm(50,10,2),ncol=5,nrow=10)) B<-as.data.frame(matrix(rnorm(50,10,2),ncol=5,nrow=10)) #calculo las distanc...

...n: * checking CRAN incoming feasibility ... NOTE Maintainer: 'Jonathan Asher Greenberg <spatial-tools@estarcion.net>' Depends: includes the non-default packages: 'sp' 'raster' 'rgdal' 'mmap' 'abind' 'parallel' 'foreach' 'doParallel' 'rgeos' Adding so many packages to the search path is excessive and importing selectively is preferable. Is this a warning that would need to be fixed pre-CRAN (not really sure how, since I need functions from all of those packages)? Is there a way to import only a single function fr...

...seems to work when %do% is used. I have searched for similar issues, but haven't managed to figure it out. This is on Windows 10 platform. Thanks in advance, Nik # -------------------------------------------------------------- library('gamlss') library('foreach') library('doParallel') registerDoParallel(cores = 4) # Generate data set.seed(314) sample.size <- 30 input.processed.cut <- data.frame(TP = round(runif(sample.size) * 100), FP = round(runif(sample.size) * 100), x = runif(sample.size)) # Fi...

...ook. E.g. could you recommend Gentleman, > "R for Bioinformatics"? > > 2) Because textbooks are limited and become obsolete fast, I can focus on > learning state-of-the-art packages, > but for that I need to find a list of most useful general purpose packages > (foreach, doParallel, etc) that is > updated in real time. Does such list exist? > > Your recommendations are very welcome. > > Thanks, > Nik > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mai...

...meone posted this exact same issue <http://stackoverflow.com/questions/13942202/r-and-shared-memory-for-parallelmclapply> on stackoverflow a couple years ago but it never got answered. Do I have to manually tell mclapply to use shared memory (if so, how?)? Is this type of job better with the doParallel package and foreach approach? Josh Bradley [[alternative HTML version deleted]]

...searched for similar issues, but haven't managed to figure >it out. This is on Windows 10 platform. > >Thanks in advance, >Nik > ># -------------------------------------------------------------- > >library('gamlss') >library('foreach') >library('doParallel') > >registerDoParallel(cores = 4) ># Generate data >set.seed(314) >sample.size <- 30 >input.processed.cut <- data.frame(TP = round(runif(sample.size) * 100), > > FP = round(runif(sample.size) * 100), >...

...ions 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: >>> Us...

...es I am specifying and seems to load balance between all of them. The issue with this is that it doesn't actually seem to perform non-trivial tasks at all anymore. This is an example of testing code I've been using for testing the %dopar% loop. library(iterators) library(foreach) library(doParallel) library(Parallel) nCores <- 4 cl <- makeCluster(nCores) registerDoParallel(cl) trials = 100000 x <- iris[which(iris[,5] != "setosa"),c(1,5)] t2 <- system.time({ r2 <- foreach(icount(trials), .combine=cbind) %dopar% { ind <- sample(100,100,replace= TRUE)...

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,

...I would like to use the full processing power (specifying cores/nodes/memory). The code essentially runs predictions based on a GAM regression and saves the results as a CSV file for multiple sets of data (here I only show two). Is it possible to run this code using HPC packages such as Rmpi/snow/doParallel? Thank you! ##################### library(data.table) library(mgcv) library(reshape2) library(dplyr) library(tidyr) library(lubridate) library(DataCombine) # gam_max_count_wk <- gam(count_pop ~ factor(citycode) + factor(year) + factor(week) + s(lnincome) + s(tmax) + s(hmax),data=cont,na.action=...

...se 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: ?doSNOW? >...

...39;, which carries out the Lilliefors correction to the Kolmogorov-Smirnoff (K-S) test for use in (one-sample) goodness-of-fit tests. Aside from several minor changes, the biggest change is that the Monte Carlo algorithm now supports parallel implementation, using the platform-independent 'doParallel' and 'foreach' infrastructure. For complex distributions (such as Weibull, gamma, and mixture of normals), running in parallel can significantly reduce computation time. It's well-established that it inappropriate to use the K-S test when sample statistics are used to estimate p...

...39;, which carries out the Lilliefors correction to the Kolmogorov-Smirnoff (K-S) test for use in (one-sample) goodness-of-fit tests. Aside from several minor changes, the biggest change is that the Monte Carlo algorithm now supports parallel implementation, using the platform-independent 'doParallel' and 'foreach' infrastructure. For complex distributions (such as Weibull, gamma, and mixture of normals), running in parallel can significantly reduce computation time. It's well-established that it inappropriate to use the K-S test when sample statistics are used to estimate p...

I have this code which is working library("checkpoint") library("gtrendsR") library("doParallel") cl<-makeCluster(4) registerDoParallel(cl) gconnect(usr = "email at gmail.com", psw = "password", verbose = FALSE) names <- c("apple","shit", "android", "rocks") formula <- function(x){ x0 <- gtrends(x, res = "7d&q...