一个作业中的错误会污染mclapply [英] An error in one job contaminates others with mclapply

问题描述

当mclapply(X, FUN)遇到某些X值的错误时，该错误会传播到X的某些(但不是全部)其他值:

When mclapply(X, FUN) encounters errors for some of the values of X, the errors propagate to some (but not all) of the other values of X:

require(parallel)
test <- function(x) if(x == 3) stop() else x
mclapply(1:3, test, mc.cores = 2)

#[[1]]
#[1] "Error in FUN(c(1L, 3L)[[2L]], ...[cut]
#
#[[2]]
#[1] 2
#
#[[3]]
#[1] "Error in FUN(c(1L, 3L)[[2L]], ... [cut]

#Warning message:
#In mclapply(1:3, test, mc.cores = 2) :
#  scheduled core 1 encountered error in user code, all values of the job will be affected

如何阻止这种情况发生?

How can I stop this happening?

推荐答案

诀窍是设置mc.preschedule = FALSE

mclapply(1:3, test, mc.cores = 2, mc.preschedule = FALSE)
#[[1]]
#[1] 1

#[[2]]
#[1] 2

#[[3]]
#[1] "Error in FUN(X[[nexti]], ...[cut]
#Warning message:
#In mclapply(1:3, test, mc.cores = 2, mc.preschedule = FALSE) :
#  1 function calls resulted in an error

之所以起作用，是因为默认情况下，mclapply似乎将X划分为mc.cores个组，并将矢量化版本的FUN应用于每个组.结果，如果组中的任何成员产生错误，则该组中的所有值都将产生相同的错误(但其他组中的值不受影响).

This works because by default mclapply seems to divide X into mc.cores groups and applies a vectorized version of FUN to each group. As a result if any member of the group yields an error, all values in that group will yield the same error (but values in other groups are unaffected).

设置mc.preschedule = FALSE有不利影响，并且可能无法重现伪随机数序列，其中相同作业始终按顺序接收相同的数字，请参见标题随机数<下的?mcparallel. /em>.

Setting mc.preschedule = FALSE has adverse effects and may make it impossible to reproduce a sequence of pseudo-random numbers where the same job always receives the same number in the sequence, see ?mcparallel under the heading Random numbers.

这篇关于一个作业中的错误会污染mclapply的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！