doparallel

问题 This question already has answers here : How can I print when using %dopar% (6 answers) Closed 3 years ago . I'm using doParallel to do fairly long parallel processing with foreach . Rather than most examples I see, where a computationally-intensive but input-light code is fed into the loop, I'm using foreach to coordinate the simultaneous processing of a number of large, independent datasets. So inside the loop, I'm using metadata to read in a file from disk, operate on it, and write back

问题 This question already has answers here : How can I print when using %dopar% (6 answers) Closed 3 years ago . I'm using doParallel to do fairly long parallel processing with foreach . Rather than most examples I see, where a computationally-intensive but input-light code is fed into the loop, I'm using foreach to coordinate the simultaneous processing of a number of large, independent datasets. So inside the loop, I'm using metadata to read in a file from disk, operate on it, and write back

问题 Anyone knows how to get all the random numbers different in the following code? E.g. with doRNG package? I don't care about reproducibility. Edit: Duplicates by pure chance are accepted. rm(list = ls()) set.seed(666) cat("\014") library(plyr) library(dplyr) library(doRNG) # ====== Data Preparation ====== dt = data.frame(id = 1:10, part = rep("dt",10), HG = c(1,3,6,NA,NA,2,NA,NA,NA,NA), random = NA) # ====== Set Parallel Computing ====== library(foreach) library(doParallel) cl = makeCluster(3,

问题 I am having trouble exporting a data frame to %dopar% in foreach package. It works if I use %do% together with registerDoSEQ() , but with registerDoParallel() I always get: Error in { : task 1 failed - "object 'kyphosis' not found" Here is a reproducible example using kyphosis data from rpart package. I am trying to parallelize stepwise regression a little: library(doParallel) library(foreach) library(rpart) invars <- c('Age', 'Number', 'Start') n_vars <- 2 vars <- length(invars) iter <-

问题 I run a simulation using foreach and doParallel and struggling with random numbers (named random in the code). In a nutshell: I simulate a football league, randomly generating winners of all the matches and corresponding results. In dt_base no match was played, in dt_ex1 and dt_ex2 results of 4 matches are known already. All unknown results should be simulated. In the League Simulation Code at the bottom of this post I set 1000 simulations, split into 100 chunks (the forloop is used to send

问题 On a Linux computer, following doParallel's vignette, I use doParallel::registerDoParallel() and then I use options(cores = N) where N is the number of cores I want to use with foreach . I can verify with foreach::getDoParWorkers() that when I change the option cores , it automatically changes the number of cores used by foreach . Yet, on Windows 10 (latest versions of R and packages), this option doesn't seem to have any effect as changing its value doesn't change the value of foreach:

问题 I am attempting to create an all in one parallel processing caret function for training caret models with different inputs. I want the function to be its own process independant of all other calls. The function that I have developed so far seems to be reproducible with some models and not with others. For example, below I train a gbm on the iris data set = fail to reproduce. Then train a rpart model = reproduce (aside from time difference). Is my function sound? Is it okay to specify the

问题 I am trying to optimize my code to run glms multiple times, and I would like to leverage parallelization, either with foreach or some other more efficient way. As you can see; the for loop takes about 800 secs to run 270000 glms; while foreach with dopar unintuitively takes for ever (It either crashes or I force it to stop after a couple of hours). Thanks for your help. Jinesh library(data.table) library(parallel) library(doParallel) library(foreach) scen_bin <- expand.grid(n = c(10, 20, 30),

问题 I am trying to optimize my code to run glms multiple times, and I would like to leverage parallelization, either with foreach or some other more efficient way. As you can see; the for loop takes about 800 secs to run 270000 glms; while foreach with dopar unintuitively takes for ever (It either crashes or I force it to stop after a couple of hours). Thanks for your help. Jinesh library(data.table) library(parallel) library(doParallel) library(foreach) scen_bin <- expand.grid(n = c(10, 20, 30),

问题 How can I export the global environment for the beginning of each parallel simulation in foreach? The following code is part of a function that is called to run the simulations. num.cores <- detectCores()-1 cluztrr <- makeCluster(num.cores) registerDoParallel(cl = cluztrr) sim.result.list <- foreach(r = 1:simulations, .combine = list, .multicombine = TRUE, ) %dopar% { #...tons of calculations using many variables... list(vals1, vals2, vals3) } stopCluster(cluztrr) Is it necessary to use