Page Comparison

...

Info
A text editor (such as `nano`, `vim`, `emacs`, etc.) could have been used to create the `.Rprofile` file as well.

Some packages depend on other libraries and are harder to be installed locally. For example, sf is a package to deal with spatial (GIS) data. It depends on geos, gdal, and proj. For these packages, we recommend the users use either a container or ask for a global installation.

Global package install

Please submit a ticket with the packages you would like installed and the R version, and the administrators will install it for you.

...

Code Block

language	r

module load r/4.2.2
module load openmpi/4.1.4
R
.libPaths("~/rlibs") # assuming you are installing your 
                     # packages at the ~/rlibs folder
install.packages("Rmpi", lib = "~/rlibs", repo = "https://cloud.r-project.org/",
                 configure.args = "--with-mpi=/gpfs/sharedfs1/admin/hpc2.0/apps/openmpi/4.1.4/")
install.packages("snow", lib = "~/rlibs", repo = "https://cloud.r-project.org/")

To submit a MPI slurm job, we created the submit-mpi.slurm file (see code below). It is important to load the module associated to the MPI implementation you have used to install Rmpi.

Code Block

#!/bin/bash
#SBATCH -p general
#SBATCH -n 30

source /etc/profile.d/modules.sh
module purge
module load r/4.2.2 openmpi/4.1.4

# If MPI tells you that forking is bad uncomment the line below 
# export OMPI_MCA_mpi_warn_on_fork=0

Rscript mpi.R

Now create the mpi.R script:

Code Block

language	r

library(parallel)

.libPaths("~/rlibs")

hello_world <- function() {
    ## Print the hostname and MPI worker rank.
    paste(Sys.info()["nodename"],Rmpi::mpi.comm.rank(), sep = ":")
}

cl <- makeCluster(Sys.getenv()["SLURM_NTASKS"], type = "MPI")
clusterCall(cl, hello_world)
stopCluster(cl)

Run the script with:

Code Block
sbatch submit-mpi.slurm

In your slurm output you will see a message from each of the MPI workers.

Read R's built-in "parallel" package documentation for tips on parallel programming in R: https://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf

RGDAL R package

The GDAL library is available as a global HPC module. The GDAL versions installed on HPC are through the SPACK package manager suite.

More information about the SPACK package manager is located here:

Spack Package Manager

The RGDAL package is available in the global R module version on HPC, but to call/load rgdal successfully the following lines would need to be entered before calling R and loading the rgdal library.

...

language	r

...

OpenMPI/5.0.2 and r/4.4.0:

Code Block

module load gdla/3.8.4 cuda/11.6 r/4.4.0
R
> .libPaths("~/rlibs")
> install.packages("Rmpi", lib = "~/rlibs", repo = "https://cloud.r-project.org/", configure.args = c("--with-Rmpi-include=/gpfs/sharedfs1/admin/hpc2.0/apps/openmpi/5.0.2/include", "--with-Rmpi-libpath=/gpfs/sharedfs1/admin/hpc2.0/apps/openmpi/5.0.2/lib", "--with-Rmpi-type=OPENMPI", "--with-mpi=/gpfs/sharedfs1/admin/hpc2.0/apps/openmpi/5.0.2"))

OpenMPI/5.0.5 and r/4.4.1

Code Block

module load gdal/3.9.2 r/4.4.1
R
> .libPaths("~/rlibs")
> install.packages("Rmpi", lib = "~/rlibs", type = "source", repo = "https://cloud.r-project.org/", configure.args = c("--with-Rmpi-include=/gpfs/sharedfs1/admin/hpc2.0/apps/openmpi/5.0.5/include", "--with-Rmpi-libpath=/gpfs/sharedfs1/admin/hpc2.0/apps/openmpi/5.0.5/lib", "--with-Rmpi-type=OPENMPI", "--with-mpi=/gpfs/sharedfs1/admin/hpc2.0/apps/gdalopenmpi/35.6.1/spack/share/spack/setup-env.sh

spack load gdal

R-INLA R package

The R-INLA R package also depends on GDAL.

The R-INLA package can be install locally under a user’s account with the following steps:

#1

If a conda base environment activated, the environment would need to deactivated to install R-INLA without conflicting with conda:

Code Block
(base) [netidhere@node ~]$ conda deactivate

#2

Perform the following module loads:

Code Block
[netidhere@node ~]$ module load gsl/2.7 cuda/11.6 udunits freetype/2.12.1 gdal/3.8.4 r/4.4.0

#3

After gdal is loaded, R can be called to install a local version of INLA

R-INLA needs the remotes command from either devtools or standalone to be able to install successfully as R-INLA is not in the CRAN repository.

If devtools is not locally installed, devtools would need to be installed first before R-INLA can be installed:

Code Block
> .libPaths("~/rlibs") > install.packages("devtools", lib = "~/rlibs", type = "source", repo = "https://cloud.r-project.org/")

Devtools can take a long time to install due to being a very large package.

If devtools crashes and fails to install dependencies, the remotes R package can be directly installed instead of devtools with the following command:

install.packages("remotes", lib = "~/rlibs", type = "source", repo = "https://cloud.r-project.org/")

#4

Install SF R package:

Code Block

> .libPaths("~/rlibs")
> install.packages("sf", lib = "~/rlibs", type = "source", configure.args = c("--with-sqlite3-lib=/gpfs/sharedfs1/admin/hpc2.0/apps/sqlite/3.45.2/lib", "--with-proj-lib=/gpfs/sharedfs1/admin/hpc2.0/apps/proj/9.4.0/lib64"), repo = "https://cloud.r-project.org/")

The libraries for sqlite3 and proj can change depending if gdal updates and gdal is built with newer versions.

Before each install, you might need to quit out of R and reload R with a fresh environment.

#5

R-INLA:

Code Block

[netidhere@node ~]$ R

> .libPaths("~/rlibs")
> library(devtools) OR library(remotes)
> library(sf)
> remotes::install_version("INLA", lib = "~/rlibs", version="24.02.09",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)

A specific version of R-INLA can be installed, in the above example the stable 24.02.09 version of INLA will be installed and the library will install under a local library directory called ~/rlibs.

When R prompts to updated existing packages when installing R-INLA, say option 3 to not update packages.

If a specific version is not needed, then the following command can be entered to install the stable version of R-INLA:

Code Block
remotes::install_version("INLA", lib = "~/rlibs",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)

The install should be successful once the install finishes.

#4

To upgrade the current INLA version, the following command can be entered within R after loading the INLA R package:

Code Block
> library(INLA) > inla.upgrade() INLA : Version 18.07.12 installed in /home/netidhere/rlibs Version 24.02.09 available at https://inla.r-inla-download.org/R/stable Update? (Yes/no/cancel) y

At the time of this update, version 24.02.09 is the latest supported stable version.

Once INLA gets installed (specific version or upgraded), conda can be reactivated and INLA should load and run successfully in R

#5

Code Block

> library(INLA)
Loading required package: Matrix
Loading required package: sp
This is INLA_24.02.09 built 2024-02-09 03:35:28 UTC.
 - See www.r-inla.org/contact-us for how to get help.
 - List available models/likelihoods/etc with inla.list.models()
 - Use inla.doc(<NAME>) to access documentation
>

#6

If INLA runs into glibc errors, the following command can be a workaround:

Code Block

The followup steps will be:

1. inla.upgrade(testing=T) to get the most recent testing version.
2. Close R using quit() and select no. This forces an update with R
3. Re-load R, load the INLA R package, then install a binary version with the command:
inla.binary.install()
Select CentOS 7 when prompted or Fedora

RCurl with sftp functionality

Code Block

module load libiconv/1.17 udunits gdal/3.6.0 r/4.2.2

source /gpfs/sharedfs1/admin/hpc2.0/apps/gdal/3.6.0/spack/share/spack/setup-env.sh

spack load gdal

module load libcurl/8.6.0

R

> .libPaths("~/rlibs")

> install.packages("RCurl", lib = "~/rlibs", repo = "https://cloud.r-project.org/")

> library(RCurl)
>
> curlVersion()$protocols
 [1] "dict"    "file"    "ftp"     "ftps"    "gopher"  "gophers" "http"
 [8] "https"   "imap"    "imaps"   "mqtt"    "pop3"    "pop3s"   "rtsp"
[15] "scp"     "sftp"    "smb"     "smbs"    "smtp"    "smtps"   "telnet"
[22] "tftp"

SF R package

After building the gdal dependency tree from source, the SF R package has issues pulling from the paths set by the modules loaded on HPC for sqlite3 and proj.

To bypass the issue, certain configure flags need to be set within the R install.packages command that is used to install the SF package.

To install the SF R package under a local HPC directory the following modules would need to be loaded and the following R command to be used:

Code Block

module load udunits gdal/3.8.4 r/4.3.2

R
> .libPaths("~/rlibs")
> install.packages("sf", lib = "~/rlibs", type = "source", configure.args = c("--with-sqlite3-lib=/gpfs/sharedfs1/admin/hpc2.0/apps/sqlite/3.45.2/lib", "--with-proj-lib=/gpfs/sharedfs1/admin/hpc2.0/apps/proj/9.4.0/lib64"), repo = "https://cloud.r-project.org/")

The above install.packages command should be successful.

...

0.5"))

To submit a MPI slurm job, we created the submit-mpi.slurm file (see code below). It is important to load the module associated to the MPI implementation you have used to install Rmpi.

Code Block

#!/bin/bash
#SBATCH -p general
#SBATCH -n 30

source /etc/profile.d/modules.sh
module purge
module load r/4.2.2 openmpi/4.1.4

# If MPI tells you that forking is bad uncomment the line below 
# export OMPI_MCA_mpi_warn_on_fork=0

Rscript mpi.R

Now create the mpi.R script:

Code Block

language	r

library(parallel)

.libPaths("~/rlibs")

hello_world <- function() {
    ## Print the hostname and MPI worker rank.
    paste(Sys.info()["nodename"],Rmpi::mpi.comm.rank(), sep = ":")
}

cl <- makeCluster(Sys.getenv()["SLURM_NTASKS"], type = "MPI")
clusterCall(cl, hello_world)
stopCluster(cl)

Run the script with:

Code Block
sbatch submit-mpi.slurm

In your slurm output you will see a message from each of the MPI workers.

Read R's built-in "parallel" package documentation for tips on parallel programming in R: https://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf

RCurl with sftp functionality

Code Block

module load libiconv/1.17 udunits gdal/3.6.0 r/4.2.2

source /gpfs/sharedfs1/admin/hpc2.0/apps/gdal/3.6.0/spack/share/spack/setup-env.sh

spack load gdal

module load libcurl/8.6.0

R

> .libPaths("~/rlibs")

> install.packages("RCurl", lib = "~/rlibs", repo = "https://cloud.r-project.org/")

> library(RCurl)
>
> curlVersion()$protocols
 [1] "dict"    "file"    "ftp"     "ftps"    "gopher"  "gophers" "http"
 [8] "https"   "imap"    "imaps"   "mqtt"    "pop3"    "pop3s"   "rtsp"
[15] "scp"     "sftp"    "smb"     "smbs"    "smtp"    "smtps"   "telnet"
[22] "tftp"

SF R package

After building the gdal dependency tree from source, the SF R package has issues pulling from the paths set by the modules loaded on HPC for sqlite3 and proj.

To bypass the issue, certain configure flags need to be set within the R install.packages command that is used to install the SF package.

SF has replaced rgdal due to rgdal being deprecated.

SF is recommended going forward.

To install the SF R package under a local HPC directory the following modules would need to be loaded and the following R command to be used:

Code Block

module load udunits gdal/3.8.4 r/4.3.2

R
> .libPaths("~/rlibs")
> install.packages("sf", lib = "~/rlibs", type = "source", configure.args = c("--with-sqlite3-lib=/gpfs/sharedfs1/admin/hpc2.0/apps/sqlite/3.45.2/lib", "--with-proj-lib=/gpfs/sharedfs1/admin/hpc2.0/apps/proj/9.4.0/lib64"), repo = "https://cloud.r-project.org/")

The above install.packages command should be successful.

Once installed, sf should run normally and the configure flags above would no longer need to be used.

R-INLA R package

The R-INLA R package also depends on GDAL.

The R-INLA package can be install locally under a user’s account with the following steps:

#1

If a conda base environment activated, the environment would need to deactivated to install R-INLA without conflicting with conda:

Code Block
(base) [netidhere@node ~]$ conda deactivate

#2

Perform the following module loads:

Code Block
[netidhere@node ~]$ module load gsl/2.7 cuda/11.6 udunits freetype/2.12.1 gdal/3.8.4 r/4.4.0

#3

After gdal is loaded, R can be called to install a local version of INLA

R-INLA needs the remotes command from either devtools or standalone to be able to install successfully as R-INLA is not in the CRAN repository.

If devtools is not locally installed, devtools would need to be installed first before R-INLA can be installed:

Code Block
> .libPaths("~/rlibs") > install.packages("devtools", lib = "~/rlibs", type = "source", repo = "https://cloud.r-project.org/")

Devtools can take a long time to install due to being a very large package.

If devtools crashes and fails to install dependencies, the remotes R package can be directly installed instead of devtools with the following command:

install.packages("remotes", lib = "~/rlibs", type = "source", repo = "https://cloud.r-project.org/")

#4

Install SF R package if not already installed:

Code Block

> .libPaths("~/rlibs")
> install.packages("sf", lib = "~/rlibs", type = "source", configure.args=c("--with-sqlite3-lib=/gpfs/sharedfs1/admin/hpc2.0/apps/sqlite/3.45.2/lib", "--with-proj-lib=/gpfs/sharedfs1/admin/hpc2.0/apps/proj/9.4.0/lib64"), repo = "https://cloud.r-project.org/")

The libraries for sqlite3 and proj can change depending if gdal updates and gdal is built with newer versions.

Before each install, you might need to quit out of R and reload R with a fresh environment.

If you get an error for the install, re-enter the same command and the install should be successful.

#5

R-INLA:

Code Block

[netidhere@node ~]$ R
> .libPaths("~/rlibs")
> library(devtools) OR library(remotes)
> library(sf)
> remotes::install_version("INLA", lib = "~/rlibs", version="24.02.09",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)

A specific version of R-INLA can be installed, in the above example the stable 24.02.09 version of INLA will be installed and the library will install under a local library directory called ~/rlibs.

When R prompts to updated existing packages when installing R-INLA, say option 3 to not update packages.

If a specific version is not needed, then the following command can be entered to install the stable version of R-INLA:

Code Block
remotes::install_version("INLA", lib = "~/rlibs",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)

The install should be successful once the install finishes.

#4

To upgrade the current INLA version, the following command can be entered within R after loading the INLA R package:

Code Block
> library(INLA) > inla.upgrade() INLA : Version 18.07.12 installed in /home/netidhere/rlibs Version 24.02.09 available at https://inla.r-inla-download.org/R/stable Update? (Yes/no/cancel) y

At the time of this update, version 24.02.09 is the latest supported stable version.

Once INLA gets installed (specific version or upgraded), conda can be reactivated and INLA should load and run successfully in R

#5

Code Block

> library(INLA)
Loading required package: Matrix
Loading required package: sp
This is INLA_24.02.09 built 2024-02-09 03:35:28 UTC.
 - See www.r-inla.org/contact-us for how to get help.
 - List available models/likelihoods/etc with inla.list.models()
 - Use inla.doc(<NAME>) to access documentation
>

#6

If INLA runs into glibc errors, the following command can be a workaround:

Code Block

The followup steps will be:
1. inla.upgrade(testing=T) to get the most recent testing version.
2. Close R using quit() and select no. This forces an update with R
3. Re-load R, load the INLA R package, then install a binary version with the command:
inla.binary.install()
Select CentOS 7 when prompted or Fedora

Versions Compared

Old Version 28

New Version Current

Key

Global package install

RGDAL R package

R-INLA R package

RCurl with sftp functionality

SF R package

RCurl with sftp functionality

SF R package

R-INLA R package