Comments (6)
This usually occurs when one of the tasks quits unexpectedly
> bplapply(1:4, function(i) if (i == 2) q())
Error in reducer$value.cache[[as.character(idx)]] <- values :
wrong args for environment subassignment
In addition: Warning message:
In parallel::mccollect(wait = FALSE, timeout = 1) :
1 parallel job did not deliver a result
either because you are asking for too much memory (nworkers * memory used per worker > memory available on the computer) or because of a bug in the FUN
being used. It is impossible to tell without a 'reproducible example'.
How much memory does each FUN
use?
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i don’t know the memory used per worker ,but i see the total memory uesd is smaller than the tatal memory that can be uesd.Besides,i tried 1 nworkers.It works well,so i have no idea that why the error comes up(it seems that the code has no bug).
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You could approximately measure use with
gc(reset = TRUE)
result <- FUN(<first element>)
gc()
and look at 'max used (Mb)'. Treat this as a lower bound. If you are using a laptop, then 20 cores is likely to use up all cores and all memory for moderately sized data -- usually workers should be at most the number of cores available, e.g., via parallel::detectCores()
.
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i run it on server,and i use 10 nworkers,it still happens.
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Until Martin is back online, I recommend trying with nworkers = 2
. That'll help narrow in on the problem.
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If you are using bplapply()
directly, and if you are using MulticoreParam()
, then maybe some insight can come from using mclapply()
instead
> fun = function(i) { message(i); if (i == 2) q(); i }
> bplapply(1:2, fun)
1
Error in reducer$value.cache[[as.character(idx)]] <- values :
wrong args for environment subassignment
In addition: Warning message:
In parallel::mccollect(wait = FALSE, timeout = 1) :
1 parallel job did not deliver a result
versus (in a new session) where messages from the second process before abort are echoed to the terminal
> mclapply(1:2, fun)
1
2
Error in serialize(data, node$con) : error writing to connection
[[1]]
[1] 1
[[2]]
NULL
Warning message:
In mclapply(1:2, function(i) { :
scheduled core 2 did not deliver a result, all values of the job will be affected
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Related Issues (20)
- Error in serialize(data, node$con, xdr = FALSE) : ignoring SIGPIPE signal HOT 5
- Performance (speed) degradation in MulticoreParam with default force.G = TRUE HOT 3
- Nested parallellization question HOT 6
- stop.on.error = FALSE for DoParam doesn't work as expected HOT 2
- Troubles with bplapply within function (using SnowParam on Windows) HOT 6
- MulticoreParam bplapply unable to restart upon interrupt HOT 1
- Increase depth of traceback beyond `tryCatch()` for bp* functions - possible enhancement HOT 1
- The running time isn't reduced when using bplapply()? HOT 3
- BiocParallel errors HOT 4
- Handle worker abort better HOT 1
- move Rmpi to Enhances: HOT 1
- "foreach" %in% loadedNamespaces() instead of "package:foreach" %in% search()? HOT 1
- BiocParallel errors:could not find function ".OLD_read_block" HOT 11
- BiocParallel : long vectors are not supported in .C() HOT 5
- BiocParallel for parallelization in BEER: Error and GPU Compatibility HOT 8
- BatchtoolsParam fails to propagate errors in bpiterate HOT 2
- Extremely minor: typo in docs? man/MulticoreParam-class.Rd HOT 1
- BiocParallel socketConnection error HOT 5
- strategy of tasks in MulticoreParam HOT 4
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