如何在没有集群变量的情况下关闭R并行集群？

使用parallelR包,我可以像这样并行运行:

library(parallel)
cl <- makeCluster(2) # Create a cluster with 2 workers
... # do some parallel stuff
stopCluster(cl)

但是,cl引用群集的变量可能会丢失,例如从失败的函数运行时:

do.something <- function() {
    library(parallel)
    cl <- makeCluster(detectCores())
    parLapply(cl, 1:10, function(x) {
        stop("An error occured")
    })
    stopCluster(cl)
}
do.something()

在这里,stopCluster还没有被执行.当发生这种情况时,我会让工作人员继续运行,如下所示ps:

501 53300  9225   0  2:16PM ttys003    0:00.27 /opt/local/Library/Frameworks/R.framework/Resources/bin/exec/R
501 53390     1   0  2:19PM ttys003    0:00.16 /opt/local/Library/Frameworks/R.framework/Resources/bin/exec/R --slave --no-restore -e parallel:::.slaveRSOCK() --args MASTER=localhost PORT=11099 OUT=/dev/null TIMEOUT=2592000 XDR=TRUE
501 53399     1   0  2:19PM ttys003    0:00.16 /opt/local/Library/Frameworks/R.framework/Resources/bin/exec/R --slave --no-restore -e parallel:::.slaveRSOCK() --args MASTER=localhost PORT=11099 OUT=/dev/null TIMEOUT=2592000 XDR=TRUE
501 53408     1   0  2:19PM ttys003    0:00.16 /opt/local/Library/Frameworks/R.framework/Resources/bin/exec/R --slave --no-restore -e parallel:::.slaveRSOCK() --args MASTER=localhost PORT=11099 OUT=/dev/null TIMEOUT=2592000 XDR=TRUE
501 53417     1   0  2:19PM ttys003    0:00.16 /opt/local/Library/Frameworks/R.framework/Resources/bin/exec/R --slave --no-restore -e parallel:::.slaveRSOCK() --args MASTER=localhost PORT=11099 OUT=/dev/null TIMEOUT=2592000 XDR=TRUE

当然,我可以kill逐个手动设置从站,或者重新启动R.但有时它可能不实用,例如,如果R的多个实例正在运行它们自己的池.有什么办法可以cl在丢失时阻止他们进入R内吗？人们通常如何处理这种情况？

即使存在错误,也有一些机制可以使代码始终运行:

try

将容易出错的部分包含在一个try或一个tryCatch块内.然后,您可以检查结果以查看是否存在错误.

do.something <- function() {
    library(parallel)
    cl <- makeCluster(detectCores())
    result <- try({
        parLapply(cl, 1:10, function(x) {
            stop("An error occured")
        })
    })
    if(inherits(result, "try-error"))
        print("there was an error!")
    stopCluster(cl)
    result
}

on.exit

on.exit调用内部的代码将始终在函数结束时运行,无论是干净还是由于错误.

do.something <- function() {
    library(parallel)
    cl <- makeCluster(detectCores())
    on.exit(stopCluster(cl))
    parLapply(cl, 1:10, function(x) {
        stop("An error occured")
    })
}