similar to: Bug in parallel / mclapply

I've been using mclapply and have encountered situations where it gives errors or returns incorrect results. Here's a minimal example, which gives the error on R 2.15.2 on Mac and Linux: library(parallel) f <- function(x) NULL mclapply(1, f, mc.preschedule = FALSE, mc.cores = 1) # Error in sum(sapply(res, inherits, "try-error")) : # invalid 'type' (list) of argument

On Mar 30, 2015, at 4:40 PM, Valerie Obenchain <vobencha at fredhutch.org> wrote: > On 03/25/2015 07:48 PM, Simon Urbanek wrote: >> On Mar 25, 2015, at 3:46 PM, Valerie Obenchain <vobencha at fredhutch.org> wrote: >> >>> Hi Simon, >>> >>> I'm having trouble with nested parallel workers, specifically, forking inside socket connections.

Hello, Consider this: 1) library(parallel) res <- mclapply(1:2, stop) #Warning message: #In mclapply(1:2, stop) : # all scheduled cores encountered errors in user code is(res[[1]], 'try-error') #[1] FALSE 2) library(parallel) res <- mclapply(1:2, stop, mc.preschedule=FALSE) #Warning message: #In mclapply(1:2, stop, mc.preschedule = FALSE) : # 2 function calls resulted in an

QUESTION: Is it possible to query number of active jobs running after launching them with parallel::mcparallel()? For example, if I launch 3 jobs using: > library(parallel) > f <- lapply(1:3, FUN=mcparallel) then I can inspect them as: > str(f) List of 3 $ :List of 2 ..$ pid: int 142225 ..$ fd : int [1:2] 8 13 ..- attr(*, "class")= chr [1:3] "parallelJob"

Hi Simon, I'm having trouble with nested parallel workers, specifically, forking inside socket connections. When mclapply is called inside a SOCK, PSOCK or FORK worker I get an error in unserialize(). cl <- makeCluster(1, "SOCK") fun = function(i) { library(parallel) mclapply(1:2, sqrt) } Failure occurs after multiple calls to clusterApply: > clusterApply(cl, 1,

Hi Tomas, Thanks for your prompt reply and your offer to help. I might need to get back to this since I am not too experienced in debugging these kinds of issues. Anyway, I gave it a try and I think I have found the immediate cause: I installed the debug symbols (r-base-core-dbg), placed https://github.com/wch/r-source/blob/tags/R-3-5-2/src/library/parallel/src/fork.c in cwd and changed the

On Mar 25, 2015, at 3:46 PM, Valerie Obenchain <vobencha at fredhutch.org> wrote: > Hi Simon, > > I'm having trouble with nested parallel workers, specifically, forking inside socket connections. > You simply can't by definition - when you fork *all* the workers share the same connection inherited from the parent, so you cannot use any I/O operations that you

Hi again, One important correction of my first message: I misinterpreted the output. Actually in that R session 2 input files were processed one after the other in a loop. The first (with 88 parts went fine). The second (with 85 parts) produced the sendMaster errors and failed. If (in a new session via Rscript) I only process the second input file it will work. The other observations on R vs

On 03/25/2015 07:48 PM, Simon Urbanek wrote: > On Mar 25, 2015, at 3:46 PM, Valerie Obenchain <vobencha at fredhutch.org> wrote: > >> Hi Simon, >> >> I'm having trouble with nested parallel workers, specifically, forking inside socket connections. >> > > You simply can't by definition - when you fork *all* the workers share the same connection

On 20/06/2015 22:21, Henrik Bengtsson wrote: > QUESTION: > Is it possible to query number of active jobs running after launching > them with parallel::mcparallel()? > > For example, if I launch 3 jobs using: > >> library(parallel) >> f <- lapply(1:3, FUN=mcparallel) > > then I can inspect them as: > >> str(f) > List of 3 > $ :List of 2 >

On 5/3/19 3:04 PM, Gergely Dar?czi wrote: > On Thu, May 2, 2019 at 7:24 PM Tomas Kalibera <tomas.kalibera at gmail.com> wrote: >> On 5/1/19 12:25 AM, Gergely Dar?czi wrote: >>> Dear All, >>> >>> I'm running into issues with calling mccollect on a list containing NULL >>> using R 3.6 (this used to work in 3.5.3): >>> >>> jobs

Hi all, With the help of Tomas, I was able to track the issue down: Prior to R v3.6.0 the parallel package passes an uninitialized variable as the file descriptor argument to the close system call. In my particular R session this uninitialized variable (reproducibly) was holding the value 7, which corresponded to the file descriptor of the write end of the pipe the second child would use to

Hi all, With the help of Tomas, I was able to track the issue down: Prior to R v3.6.0 the parallel package passes an uninitialized variable as the file descriptor argument to the close system call. In my particular R session this uninitialized variable (reproducibly) was holding the value 7, which corresponded to the file descriptor of the write end of the pipe the second child would use to

On 1/10/20 7:33 AM, Henrik Bengtsson wrote: > I'd like to pick up this thread started on 2019-04-11 > (https://hypatia.math.ethz.ch/pipermail/r-devel/2019-April/077632.html). > Modulo all the other suggestions in this thread, would my proposal of > being able to disable forked processing via an option or an > environment variable make sense? I don't think R should be doing

Dear All, I'm running into issues with calling mccollect on a list containing NULL using R 3.6 (this used to work in 3.5.3): jobs <- lapply( list(NULL, 'foobar'), function(x) mcparallel(identity(x))) mccollect(jobs, wait = FALSE, timeout = 0) #> Error in names(res) <- pnames[match(s, pids)] : #> 'names' attribute [2] must be the same length as the vector

The RStudio GUI was just one example. AFAIK, and please correct me if I'm wrong, another example is where multi-threaded code is used in forked processing and that's sometimes unstable. Yes another, which might be multi-thread related or not, is https://stat.ethz.ch/pipermail/r-devel/2018-September/076845.html: res <- parallel::mclapply(urls, function(url) { download.file(url,

On Thu, May 2, 2019 at 7:24 PM Tomas Kalibera <tomas.kalibera at gmail.com> wrote: > > On 5/1/19 12:25 AM, Gergely Dar?czi wrote: > > Dear All, > > > > I'm running into issues with calling mccollect on a list containing NULL > > using R 3.6 (this used to work in 3.5.3): > > > > jobs <- lapply( > > list(NULL, 'foobar'), >

On Fri, Jan 10, 2020 at 7:23 PM Simon Urbanek <simon.urbanek at r-project.org> wrote: > > Henrik, > > the example from the post works just fine in CRAN R for me - the post was about homebrew build so it's conceivably a bug in their libraries. I think it works now, because Apple switched to a different SSL library for libcurl. It usually crashes or fails on older macOS

On Fri, Jan 10, 2020 at 11:23 AM Simon Urbanek <simon.urbanek at r-project.org> wrote: > > Henrik, > > the example from the post works just fine in CRAN R for me - the post was about homebrew build so it's conceivably a bug in their libraries. Thanks for ruling that example out. > That's exactly why I was proposing a more general solution where you can simply define

I stumbled upon the following: f <- parallel::mcparallel(raw(0L)) parallel::mccollect(f) # $`77083` # NULL but f <- parallel::mcparallel(raw(1L)) parallel::mccollect(f) # Error in unserialize(r) : read error traceback() # 2: unserialize(r) # 1: parallel::mccollect(f) (restarting because the above appears to corrupt the R session) f <- parallel::mcparallel(raw(2L))