Error in unserialize(node$con) : MultisessionFuture (future_lapply-4) failed to receive results from cluster RichSOCKnode #4 (PID 436932 on localhost ‘localhost’). The reason reported was ‘error reading from connection’. Post-mortem diagnostic: No process exists with this PID, i.e. the localhost worker is no longer alive.

[Yunuuuu]

(Please use https://github.com/HenrikBengtsson/future/discussions for Q&A)
Hi, thanks for your great R package future which really convenient to run long-long task.

Describe the bug

A clear and concise description of what the bug is.

options(future.globals.onReference = "error")
[R]> future::plan("multisession", workers = 10L)
[R]> rrho_res <- biomisc::run_rrho(
         bca_diff_res,
         gender_diff_res,
         stepsize = 100L
     )
[R]> rrho_perm_res <- biomisc::rrho_correct_pval(
         rrho_res,
         method = "permutation", perm = 200L
     )
Error in unserialize(node$con) :                                                 
  MultisessionFuture (future_lapply-4) failed to receive results from cluster RichSOCKnode #4 (PID 436932 on localhost ‘localhost’). The reason reported was ‘error reading from connection’. Post-mortem diagnostic: No process exists with this PID, i.e. the localhost worker is no longer alive. The total size of the 14 globals exported is 15.76 MiB. The three largest globals are ‘rrho_obj’ (15.47 MiB of class ‘list’), ‘rrho_hyper_overlap’ (143.94 KiB of class ‘function’) and ‘progression’ (91.02 KiB of class ‘function’)

I begin to run this function with future::plan("multicore", workers = 10L), it also gave similar error infos as belows, so I tried above multisession as indicated in #474

options(future.globals.onReference = "error")
[R]> future::plan("multicore", workers = 10L)
[R]> rrho_res <- biomisc::run_rrho(
         bca_diff_res,
         gender_diff_res,
         stepsize = 100L
     )
[R]> rrho_perm_res <- biomisc::rrho_correct_pval(
         rrho_res,
         method = "permutation", perm = 200L
     )
Error: Failed to retrieve the result of MulticoreFuture (future_lapply-1) from the forked worker (on localhost; PID 452943). Post-mortem diagnostic: No process exists with this PID, i.e. the forked localhost worker is no longer alive. The total size of the 14 globals exported is 15.76 MiB. The three largest globals are ‘rrho_obj’ (15.47 MiB of class ‘list’), ‘rrho_hyper_overlap’ (143.94 KiB of class ‘function’) and ‘progression’ (91.02 KiB of class ‘function’)
In addition: Warning message:
In mccollect(jobs = jobs, wait = TRUE) :
  1 parallel job did not deliver a result

the rrho_correct_pval is a long function deposited in https://github.com/Yunuuuu/biomisc/blob/81948d2e5e2bab5a4cf76fd76e8ab4a096192efd/R/run_rrho.R#L787

I put the main future function here:

        p <- progressr::progressor(steps = perm)
        perm_hyper_metric <- future.apply::future_lapply(
            seq_len(perm), function(i) {
                hyper_res <- rrho_hyper_overlap(
                    names(rrho_obj$rrho_data$list1)[
                        sample.int(length(rrho_obj$rrho_data$list1), replace = FALSE)
                    ],
                    names(rrho_obj$rrho_data$list2)[
                        sample.int(length(rrho_obj$rrho_data$list2), replace = FALSE)
                    ],
                    stepsize = rrho_obj$stepsize,
                    .parallel = FALSE
                )
                p(message = sprintf("Permuatating %d times", i))
                rrho_metrics(hyper_res, log_base = rrho_obj$log_base)
            },
            future.globals = TRUE,
            future.seed = TRUE
        )

Reproduce example

Actually, the biomisc::run_rrho also use future_lapply but it won't often gave a error (it'll also gave error randomly, I cannot reproduce biomisc::run_rrho error message, but I run biomsc::rrho_correct_pval after biomisc::run_rrho which will often reproduce above error (use my own data: gene expression array data)).

But when I use a artificial data (I cannot make error occur, so I can't give reboust example code to reproduce this error)

Expected behavior

Run without error

Session information

Please share your session information after the error has occurred so that we also see which packages and versions are involved;

> sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libmkl_rt.so;  LAPACK version 3.8.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=zh_CN.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C       

time zone: Asia/Shanghai
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] compiler_4.3.0        parallelly_1.35.0     cli_3.6.1            
 [4] tools_4.3.0           parallel_4.3.0        future.apply_1.10.0  
 [7] listenv_0.9.0         Rcpp_1.0.10           codetools_0.2-19     
[10] progressr_0.13.0-9002 data.table_1.14.9     biomisc_0.0.0.9000   
[13] jsonlite_1.8.4        digest_0.6.31         globals_0.16.2       
[16] rlang_1.1.1           future_1.32.0   
…

…
> future::futureSessionInfo()
*** Package versions
future 1.32.0, parallelly 1.35.0, parallel 4.3.0, globals 0.16.2, listenv 0.9.0

*** Allocations
availableCores():
system  nproc 
    24     24 
availableWorkers():
$nproc
 [1] "localhost" "localhost" "localhost" "localhost" "localhost" "localhost"
 [7] "localhost" "localhost" "localhost" "localhost" "localhost" "localhost"
[13] "localhost" "localhost" "localhost" "localhost" "localhost" "localhost"
[19] "localhost" "localhost" "localhost" "localhost" "localhost" "localhost"

$system
 [1] "localhost" "localhost" "localhost" "localhost" "localhost" "localhost"
 [7] "localhost" "localhost" "localhost" "localhost" "localhost" "localhost"
[13] "localhost" "localhost" "localhost" "localhost" "localhost" "localhost"
[19] "localhost" "localhost" "localhost" "localhost" "localhost" "localhost"


*** Settings
- future.plan=<not set>
- future.fork.multithreading.enable=<not set>
- future.globals.maxSize=<not set>
- future.globals.onReference=‘error’
- future.resolve.recursive=<not set>
- future.rng.onMisuse=<not set>
- future.wait.timeout=<not set>
- future.wait.interval=<not set>
- future.wait.alpha=<not set>
- future.startup.script=<not set>

*** Backends
Number of workers: 10
List of future strategies:
1. multicore:
   - args: function (..., workers = 10L, envir = parent.frame())
   - tweaked: TRUE
   - call: future::plan("multicore", workers = 10L)

*** Basic tests
Main R session details:
     pid     r sysname           release
1 455700 4.3.0   Linux 5.19.0-41-generic
                                                           version nodename
1 #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
  machine   login    user effective_user
1  x86_64 user001 user001        user001
Worker R session details:
   worker    pid     r sysname           release
1       1 472740 4.3.0   Linux 5.19.0-41-generic
2       2 472741 4.3.0   Linux 5.19.0-41-generic
3       3 472742 4.3.0   Linux 5.19.0-41-generic
4       4 472743 4.3.0   Linux 5.19.0-41-generic
5       5 472744 4.3.0   Linux 5.19.0-41-generic
6       6 472745 4.3.0   Linux 5.19.0-41-generic
7       7 472746 4.3.0   Linux 5.19.0-41-generic
8       8 472747 4.3.0   Linux 5.19.0-41-generic
9       9 472748 4.3.0   Linux 5.19.0-41-generic
10     10 472749 4.3.0   Linux 5.19.0-41-generic
                                                            version nodename
1  #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
2  #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
3  #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
4  #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
5  #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
6  #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
7  #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
8  #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
9  #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
10 #42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2  host001
   machine   login    user effective_user
1   x86_64 user001 user001        user001
2   x86_64 user001 user001        user001
3   x86_64 user001 user001        user001
4   x86_64 user001 user001        user001
5   x86_64 user001 user001        user001
6   x86_64 user001 user001        user001
7   x86_64 user001 user001        user001
8   x86_64 user001 user001        user001
9   x86_64 user001 user001        user001
10  x86_64 user001 user001        user001
Number of unique worker PIDs: 10 (as expected)

[HenrikBengtsson]

Hello. It's clear that something causes the parallel workers to completely crash, regardless of you running 'multicore' or 'multisession' workers. Since it happens to both backends, it's likely to be independent of the parallel backend. If you tried with plan(future.callr::callr), you'd probably see the same there.

Depending on your Linux setup, it could be that you're running out of memory, and Linux decides to terminate your workers. If you've seen some error from Linux like "Out of Memory (OOM) killer", then that's the reason.

Regardless, I'd suggest that you try with fewer workers to see if you can reproduce the crash. Try with workers = 2, or even a single one, which you can get by workers = I(1) [note the I()]. If that works for you, try to increase the number of workers until you experience the problem. Pay attention to the system's memory usage, e.g. look at top or htop.

If it still crashes with workers = I(1), then see if it even workers when running sequentially, i.e. plan(sequential). It could be that it crashes then too. You could even check that cases the first thing you do.