Hi,
I'm currently playing around with progress bars in BiocParallel - which is a great package! ;-) For demonstration, I'm using the example code from DESeq2::DESeq. library(DESeq2) library(BiocParallel) f <- function(mu) { cnts <- matrix(rnbinom(n=1000, mu=mu, size=1/0.5), ncol=10) cond <- factor(rep(1:2, each=5)) # object construction suppressMessages({ dds <- DESeqDataSetFromMatrix(cnts, DataFrame(cond), ~ cond) dds <- DESeq(dds) }) res <- results(dds) return(res) } and apply 'f' to a range of 'mu' values using 'bplapply'. mu.grid <- 90:120 x <- bplapply(mu.grid, f) Now, switching to serial execution and verbosing progress bp <- registered()$SerialParam bpprogressbar(bp) <- TRUE register(bp) x <- bplapply(mu.grid, f) gives me somehow no progress bar at all. Furthermore, switching to multi-core execution (2 cores) and verbosing progress bp <- registered()$MulticoreParam bpprogressbar(bp) <- TRUE register(bp) x <- bplapply(mu.grid, f) | | |=================================== | |======================================================================| 100% gives me only a very coarse-grained progress bar (updates when 50% of the job is done, and when the complete job = 100% is done). What I actually want to have is a fine-grained progress bar that updates whenever f finishes execution on an element of the vector I am applying over. In "normal" serial R execution, the desired behavior can be illustrated via pb <- txtProgressBar(90, 120, style=3, width=length(mu.grid)) r <- vector(mode="list", length=length(mu.grid)) for(i in mu.grid) { setTxtProgressBar(pb, i) r[[i-89]] <- f(i) } close(pb) Is there a way to obtain something similar using BiocParallel? Thanks, Ludwig -- Dr. Ludwig Geistlinger CUNY School of Public Health > sessionInfo() R version 3.4.2 (2017-09-28) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS High Sierra 10.13.1 Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] parallel stats4 stats graphics grDevices utils datasets [8] methods base other attached packages: [1] BiocParallel_1.12.0 DESeq2_1.18.1 [3] SummarizedExperiment_1.8.0 DelayedArray_0.4.1 [5] matrixStats_0.52.2 Biobase_2.38.0 [7] GenomicRanges_1.30.0 GenomeInfoDb_1.14.0 [9] IRanges_2.12.0 S4Vectors_0.16.0 [11] BiocGenerics_0.24.0 loaded via a namespace (and not attached): [1] genefilter_1.60.0 locfit_1.5-9.1 splines_3.4.2 [4] lattice_0.20-35 colorspace_1.3-2 htmltools_0.3.6 [7] base64enc_0.1-3 blob_1.1.0 survival_2.41-3 [10] XML_3.98-1.9 rlang_0.1.4 DBI_0.7 [13] foreign_0.8-69 bit64_0.9-7 RColorBrewer_1.1-2 [16] GenomeInfoDbData_0.99.1 plyr_1.8.4 stringr_1.2.0 [19] zlibbioc_1.24.0 munsell_0.4.3 gtable_0.2.0 [22] htmlwidgets_0.9 memoise_1.1.0 latticeExtra_0.6-28 [25] knitr_1.17 geneplotter_1.56.0 AnnotationDbi_1.40.0 [28] htmlTable_1.9 Rcpp_0.12.14 acepack_1.4.1 [31] xtable_1.8-2 scales_0.5.0 backports_1.1.1 [34] checkmate_1.8.5 Hmisc_4.0-3 annotate_1.56.1 [37] XVector_0.18.0 bit_1.1-12 gridExtra_2.3 [40] ggplot2_2.2.1 digest_0.6.12 stringi_1.1.6 [43] grid_3.4.2 tools_3.4.2 bitops_1.0-6 [46] magrittr_1.5 RSQLite_2.0 lazyeval_0.2.1 [49] RCurl_1.95-4.8 tibble_1.3.4 Formula_1.2-2 [52] cluster_2.0.6 Matrix_1.2-12 data.table_1.10.4-3 [55] rpart_4.1-11 nnet_7.3-12 compiler_3.4.2 [[alternative HTML version deleted]] _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel