parallel-processing

问题 I wrote a function to run R parallel, but it doesn't seem to work. The code is ''' rm(list=ls()) square<-function(x){ library(Iso) y=ufit(x,lmode<-2,x<-c(1:length(x)),type="b")[[2]] return(y) } num<-c(1,2,1,4) cl <- makeCluster(getOption("cl.cores",2)) clusterExport(cl,"square") results<-parLapply(cl,num,square) stopCluster(cl) ''' and the error is: Error in checkForRemoteErrors(val) : 2 nodes produced errors; first error: cannot open the connection I think a possible reason is that I used

问题 How to call global function speedDistribution from the parLapply function? speedDistribution <- function(speed) { return(quantile(speed, seq(0.2, 1, by = 0.20))) } estimateFeatures <- function(trips,target) { cl <- makeCluster( 4 ) features = NULL features = parLapply(cl, 1:length(trips), function(z){ z <- as.data.frame(z) speed <- 3.6 * sqrt(diff(z$x)^2 + diff(z$y)^2) s <- speed[!speed > mean(speed) + sd(speed) * 5] features = c(speedDistribution(s),target) return(cbind(features, rep(z, nrow

问题 How to call global function speedDistribution from the parLapply function? speedDistribution <- function(speed) { return(quantile(speed, seq(0.2, 1, by = 0.20))) } estimateFeatures <- function(trips,target) { cl <- makeCluster( 4 ) features = NULL features = parLapply(cl, 1:length(trips), function(z){ z <- as.data.frame(z) speed <- 3.6 * sqrt(diff(z$x)^2 + diff(z$y)^2) s <- speed[!speed > mean(speed) + sd(speed) * 5] features = c(speedDistribution(s),target) return(cbind(features, rep(z, nrow

问题 I have a script that looks like a more complicated version of this: import ray var1 = 0 var2 = 0 @ray.remote def create(n): global var1 global var2 for i in range(10): var1 += 1 var2 += 1 def create2(): tasks = [create.remote(i) for i in range(20)] ray.get(tasks) create2() This errors because Ray does not allow global variables to be used in the traditional way. How can I get around this? 回答1: Q : "How can I get around this?" One solution would be to create a method .add1() for either of

问题 I am currently using the parallel package in R and I am trying to make by work reproducible by setting seeds. However, if you set the seed before creating the cluster and performing the tasks you want in parallel, for some reason, it doesn't make it reproducible. I think I need to set the seed for each core when I make the cluster. I have made a small example here to illustrate my problem: library(parallel) # function to generate 2 uniform random numbers runif_parallel <- function() { # make

问题 I am currently using the parallel package in R and I am trying to make by work reproducible by setting seeds. However, if you set the seed before creating the cluster and performing the tasks you want in parallel, for some reason, it doesn't make it reproducible. I think I need to set the seed for each core when I make the cluster. I have made a small example here to illustrate my problem: library(parallel) # function to generate 2 uniform random numbers runif_parallel <- function() { # make

问题 I am currently using the parallel package in R and I am trying to make by work reproducible by setting seeds. However, if you set the seed before creating the cluster and performing the tasks you want in parallel, for some reason, it doesn't make it reproducible. I think I need to set the seed for each core when I make the cluster. I have made a small example here to illustrate my problem: library(parallel) # function to generate 2 uniform random numbers runif_parallel <- function() { # make

问题 I am currently using the parallel package in R and I am trying to make by work reproducible by setting seeds. However, if you set the seed before creating the cluster and performing the tasks you want in parallel, for some reason, it doesn't make it reproducible. I think I need to set the seed for each core when I make the cluster. I have made a small example here to illustrate my problem: library(parallel) # function to generate 2 uniform random numbers runif_parallel <- function() { # make

问题 I am currently using the parallel package in R and I am trying to make by work reproducible by setting seeds. However, if you set the seed before creating the cluster and performing the tasks you want in parallel, for some reason, it doesn't make it reproducible. I think I need to set the seed for each core when I make the cluster. I have made a small example here to illustrate my problem: library(parallel) # function to generate 2 uniform random numbers runif_parallel <- function() { # make

问题 I am currently using the parallel package in R and I am trying to make by work reproducible by setting seeds. However, if you set the seed before creating the cluster and performing the tasks you want in parallel, for some reason, it doesn't make it reproducible. I think I need to set the seed for each core when I make the cluster. I have made a small example here to illustrate my problem: library(parallel) # function to generate 2 uniform random numbers runif_parallel <- function() { # make