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Running R on Cypress
R Modules
As of July 31st, 2017 there are five versions of R installed on Cypress in the modules
- R/3.1.2(default)
- R/3.2.4
- R/3.2.5-intel
- R/3.3.1-intel
- R/3.4.1-intel
Running R Interactively
For Workshop
If you use a temporary workshop account, do this.
export MY_PARTITION=workshop export MY_QUEUE=workshop
Start an interactive session using idev
[tulaneID@cypress1 pp-1.6.4]$ idev Requesting 1 node(s) task(s) to normal queue of defq partition 1 task(s)/node, 20 cpu(s)/task, 2 MIC device(s)/node Time: 0 (hr) 60 (min). Submitted batch job 52311 Seems your requst is pending. JOBID=52311 begin on cypress01-035 --> Creating interactive terminal session (login) on node cypress01-035. --> You have 0 (hr) 60 (min). [tulaneID@cypress01-035 pp-1.6.4]$
Load the R module
[tulaneID@cypress01-035 pp-1.6.4]$ module load R/3.1.2 [tulaneID@cypress01-035 pp-1.6.4]$ module list Currently Loaded Modulefiles: 1) git/2.4.1 3) idev 5) R/3.1.2 2) slurm/14.03.0 4) bbcp/amd64_rhel60
Run R in the command line window
[tulaneID@cypress01-035 pp-1.6.4]$R R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-unknown-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. >
Running a R script in Batch mode
You can also submit your R job to the batch nodes (compute nodes) on Cypress. Inside your SLURM script, include a command to load the desired R module. Then invoke the Rscript command on your R script.
#!/bin/bash #SBATCH --qos=normal # Quality of Service #SBATCH --job-name=R # Job Name #SBATCH --time=00:01:00 # WallTime #SBATCH --nodes=1 # Number of Nodes #SBATCH --ntasks-per-node=1 # Number of tasks (MPI processes) #SBATCH --cpus-per-task=1 # Number of threads per task (OMP threads) module load R/3.1.2 Rscript myRscript.R
Running a Parallel R Job
Starting with version 2.14.0, R has offered direct support for parallel computation through the "parallel" package. We will present two examples of running a parallel job of BATCH mode. They differ in the ways in which they communicate the number of cores reserved by SLURM to R. Both are based on code found in "Getting Started with doParallel and foreach" by Steve Weston and Rich Calaway and modified by The University of Chicago Resource Computing Center.
In the first example, we will use the built in R function Sys.getenv( ) to get the SLURM environment variable from the operating system.
#Based on code from the UCRCC website library(doParallel) # use the environment variable SLURM_CPUS_PER_TASK to set the number of cores registerDoParallel(cores=(Sys.getenv("SLURM_CPUS_PER_TASK"))) # Bootstrapping iteration example x <- iris[which(iris[,5] != "setosa"), c(1,5)] iterations <- 10000# Number of iterations to run # Parallel version of code # Note the '%dopar%' instruction part <- system.time({ r <- foreach(icount(iterations), .combine=cbind) %dopar% { ind <- sample(100, 100, replace=TRUE) result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) coefficients(result1) } })[3] # Shows the number of Parallel Workers to be used getDoParWorkers() # Executes the functions part
This script will obtain the number of tasks per node set in our SLURM script and will pass that value to the registerDoParallel( ) function. To implement this we need only set the correct parameters in our SLURM script. Suppose we wanted to use 16 cores. Then the correct script would be
#!/bin/bash #SBATCH --qos=normal # Quality of Service #SBATCH --job-name=R # Job Name #SBATCH --time=00:01:00 # WallTime #SBATCH --nodes=1 # Number of Nodes #SBATCH --ntasks-per-node=1 # Number of Tasks per Node #SBATCH --cpus-per-task=16 # Number of threads per task (OMP threads) module load R/3.1.2 Rscript bootstrap.R $SLURM_CPUS_PER_TASK
The disadvantage of this approach is that it is system specific. If we move our code to a machine that uses PBS-Torque as it's manager (sphynx for example) we have to change our source code. An alternative method that results in a more portable code base uses command line arguments to pass the value of our environment variables into the script.
#Based on code from the UCRCC website library(doParallel) # Enable command line arguments args<-commandArgs(TRUE) # use the environment variable SLURM_CPUS_PER_TASK to set the number of cores registerDoParallel(cores=(as.integer(args[1]))) # Bootstrapping iteration example x <- iris[which(iris[,5] != "setosa"), c(1,5)] iterations <- 10000# Number of iterations to run # Parallel version of code # Note the '%dopar%' instruction part <- system.time({ r <- foreach(icount(iterations), .combine=cbind) %dopar% { ind <- sample(100, 100, replace=TRUE) result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) coefficients(result1) } })[3] # Shows the number of Parallel Workers to be used getDoParWorkers() # Executes the functions part
Note the use of args←commandArgs(TRUE) and of as.integer(args[1]). This allows us to pass in a value from the command line when we call the script and the number of cores will be set to that value. Using the same basic submission script as last time, we need only pass the value of the correct SLRUM environment variable to the script at runtime.
#!/bin/bash #SBATCH --qos=normal # Quality of Service #SBATCH --job-name=R # Job Name #SBATCH --time=00:01:00 # WallTime #SBATCH --nodes=1 # Number of Nodes #SBATCH --ntasks-per-node=1 # Number of Tasks per Node #SBATCH --cpus-per-task=16 # Number of threads per task (OMP threads) module load R/3.1.2 Rscript bootstrapWargs.R $SLURM_CPUS_PER_TASK
Not that since we did not specify an output file, the output will be written to slurm-<JobNumber>.out. For example:
[cmaggio@cypress1 ~]$ sbatch RsubmissionWargs.srun Submitted batch job 52481 [tulaneID@cypress1 ~]$ cat slurm-52481.out Loading required package: foreach Loading required package: iterators Loading required package: parallel [1] "16" elapsed 3.282 [tulaneID@cypress1 ~]$
Installing R Packages into user home directory or lustre sub-directory
If you want to use some R packages that are not yet installed in your desired version of R on Cypress, then you have several alternatives - depending on your desired level of reproducibility.
Alternative 1 - default to home sub-directory
From your R session, you may choose to have R install its packages into a sub-directory under your home directory. By default R will create such a sub-directory whose name corresponds to the R version of your current R session and install your packages there.
> R.version.string
[1] "R version 3.4.1 (2017-06-30)"
> install.packages("copula")
Installing package into ‘/share/apps/spark/spark-2.0.0-bin-hadoop2.6/R/lib’
(as ‘lib’ is unspecified)
Warning in install.packages("copula") :
'lib = "/share/apps/spark/spark-2.0.0-bin-hadoop2.6/R/lib"' is not writable
Would you like to use a personal library instead? (y/n) y
Would you like to create a personal library
~/R/x86_64-pc-linux-gnu-library/3.4
to install packages into? (y/n) y
...
Alternative 2 - specify your lustre sub-directory via exported environment variable
Alternatively, if you prefer to use, say, your lustre sub-directory rather than your home directory, then you may do so via an exported environment variable setting as in the following. The environmental variable R_LIBS_USER points the desired location of user package(s).
First, create a directory and export the environment variable.
mkdir -p /lustre/project/<your-group-name>/R/Library export R_LIB_USER=/lustre/project/<your-group-name>/R/Library
Then run R and install a package. Note that we can use the R function .libPaths() as confirmation of the user library location.
> .libPaths()
[1] "/lustre/project/<your-group-name>/R/Library"
[2] "/share/apps/spark/spark-2.0.0-bin-hadoop2.6/R/lib"
[3] "/share/apps/R/3.4.1-intel/lib64/R/library"
> install.packages("copula")
Installing package into ‘/lustre/project/<your-group-name>/R/Library’
(as ‘lib’ is unspecified)
...
Alternative 3 - specify lustre sub-directory via environment file
Similarly, you may accomplish the above via the same environment variable setting as above but in a local file as in the following.
First, create a directory as above.
mkdir -p /lustre/project/<your-group-name>/R/Library
Then setting R_LIBS_USER in the file ~/.Renviron will tell R a default location.
Note however that setting or unsetting the environment variable R_LIBS_USER in the file ~/.Renviron will override any previously exported value of that same environment variable!
echo 'R_LIBS_USER="/lustre/project/<your-group-name>/R/Library"' > ~/.Renviron
Or use a text editor in order to create and edit the file ~/.Renviron so that the file includes the following line.
R_LIBS_USER="/lustre/project/<your-group-name>/R/Library"
Then run R and install a package. Note again the use of R function .libPaths() as confirmation of the user library location.
> .libPaths()
[1] "/lustre/project/<your-group-name>/R/Library"
[2] "/share/apps/spark/spark-2.0.0-bin-hadoop2.6/R/lib"
[3] "/share/apps/R/3.4.1-intel/lib64/R/library"
> install.packages("copula")
Installing package into ‘/lustre/project/<your-group-name>/R/Library’
(as ‘lib’ is unspecified)
...
Alternative 4 - specify lustre sub-directory via R profile file
Similarly, you may set the sub-directory depending on R major.minor version via the R profile file as in the following.
Edit the file ~/.Rprofile as follows.
majorMinorPatch <- paste(R.version[c("major", "minor")], collapse=".")
majorMinor <- gsub("(.*)\\..*", "\\1", majorMinorPatch)
#print(paste0("majorMinor=", majorMinor))
myLibPath <- paste0("/lustre/project/<your-group-name>/R/Library/", majorMinor)
dir.create(myLibPath, showWarnings = FALSE)
#print(paste0("myLibPath=", myLibPath))
newLibPaths <- c(myLibPath, .libPaths())
.libPaths(newLibPaths)
Note that setting the R library trees directly via the R function .libPaths() in the file ~/.Rprofile can thus either override or append to that of any previously set value of R_LIBS_USER!
Then run R and install a package. Note again the use of R function .libPaths() as confirmation of the user library location.
> .libPaths()
[1] "/lustre/project/<your-group-name>/R/Library/3.4"
[2] "/share/apps/spark/spark-2.0.0-bin-hadoop2.6/R/lib"
[3] "/share/apps/R/3.4.1-intel/lib64/R/library"
> install.packages("copula")
Installing package into ‘/lustre/project/<your-group-name>/R/Library/3.4’
(as ‘lib’ is unspecified)
...
Alternative 5 - specify lustre sub-directory via R code
As for yet another alternative, you can accomplish the above entirely in your R code via the following. First, create a directory as before.
mkdir -p /lustre/project/<your-group-name>/R/Library
Then run R and install a package, but note that you must also specify the location from which to load the package in the ensuing call to the R function library().
> myLib := "/lustre/project/<your-group-name>/R/Library"
> install.packages("copula",lib=myLib)
...
> library(copula, lib.loc=myLib)
