Results of workers not returned properly - snow - debug

Question

I'm using the snow package in R to execute a function on a SOCK cluster with multiple machines(3) running on Linux OS. I tried to run the code with both parLapply and clusterApply.

In case of any error at the worker level, the results of the worker nodes are not returned properly to master making it very hard to debug. I'm currently logging every heartbeat of the worker nodes independently using futile.logger. It seems as if the results are properly computed. But when I tried to print the result at the master node (After receiving the output from workers) I get an error which says, Error in checkForRemoteErrors(val): 8 nodes produced errors; first error: missing value where TRUE/FALSE needed.

Is there any way to debug the results of the workers more deeply?

Answer 1

The checkForRemoteErrors function is called by parLapply and clusterApply to check for task errors, and it will throw an error if any of the tasks failed. Unfortunately, although it displays the error message, it doesn't provide any information about what worker code caused the error. But if you modify your worker/task function to catch errors, you can retain some extra information that may be helpful in determining where the error occurred.

For example, here's a simple snow program that fails. Note that it uses outfile='' when creating the cluster so that output from the program is displayed, which by itself is a very useful debugging technique:

library(snow)
cl <- makeSOCKcluster(2, outfile='')
problem <- function(i) {
  if (NA)
    j <- 999
  else
    j <- i
  2 * j
}
r <- parLapply(cl, 1:2, problem)

When you execute this, you see the error message from checkForRemoteErrors and some other messages, but nothing that tells you that the if statement caused the error. To catch errors when calling problem, we define workerfun:

workerfun <- function(i) {
  tryCatch({
    problem(i)
  },
  error=function(e) {
    print(e)
    stop(e)
  })
}

Now we execute workerfun with parLapply instead of problem, first exporting problem to the workers:

clusterExport(cl, c('problem'))
r <- parLapply(cl, 1:2, workerfun)

Among the other messages, we now see

<simpleError in if (NA) j <- 999 else j <- i: missing value where TRUE/FALSE needed>

which includes the actual if statement that generated the error. Of course, it doesn't tell you the file name and line number of the expression, but it's often enough to let you solve the problem.

Answer 2

check the range of your observations. how the observation varies. I have noticed that when there are lots of decimal places 4, 5,6 , it throws glm.nb off. To solve this i just round the observations to 2 decimal places.