parallel-foreach

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 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 When I run the code below, R crashes. If I comment out the tuneGrid line in the call to train, there is no crash. I've tried this with another dataset, and still crash R. Crash message is R Session Aborted R encountered a fatal error The session was terminated Start new session. The code is: library(splines) library(foreach) library(gam) library(lattice) library(ggplot2) library(caret) # crashes when I uncomment the tuneGrid = tuneGrid line Set_seed_seed <- 100 data_set <- diamonds[, c(1, 5,

来源： https://stackoverflow.com/questions/36826341/saving-output-from-parallel-jobs-in-r-into-one-file

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 Some R packages have functions that can do their work in parallel if multiple cores are available - for example, the rstan package can run multiple MCMC chains in parallel. When I run a number of Stan processes in parallel to each other using, e.g., doSNOW and foreach , I'd like my code to operate in parallel at both levels*. Instead, the Stan processes get farmed out to my workers and seem to run their chains in sequence there, as if once they've been assigned to a core they can't see the

问题 I am currently trying parallel computing in R. I am trying to train a logistic ridge model , and I currently have 4 Cores on my computer. I would like to split my data set equally into 4 pieces, and use each core to train model (on the training data) and save the result of each core into a single vector . the problem is that i have no clue how to do it, right now I tried to parallel with the foreach package, but the problem is the each core sees the same training data. here is the code with

问题 I ran into an issue trying to use %dopar% and foreach() together with an R6 class. Searching around, I could only find two resources related to this, an unanswered SO question and an open GitHub issue on the R6 repository. In one comment (i.e., GitHub issue) an workaround is suggested by reassigning the parent_env of the class as SomeClass$parent_env <- environment() . I would like to understand what exactly does environment() refer to when this expression (i.e., SomeClass$parent_env <-

问题 Progress has been made on getting the parallel processing part working but saving the vector with the fetch distances is not working properly. The error I get is df_Test_Fetch <- data.frame(x_lake_length) Error in data.frame(x_lake_length) : object 'x_lake_length' not found write.table(df_Test_Fetch,file="C:/tempTest_Fetch.csv",row.names=TRUE,col.names=TRUE, sep=",") Error in is.data.frame(x) : object 'df_Test_Fetch' not found I have tried altering the code below so that the foreach step is