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= General information =
= General information =


rstan provides the R interface for [https://mc-stan.org/ Stan], a platform for statistical modeling and high-performance statistical computation.
<code>rstan</code> provides the R interface for [https://mc-stan.org/ Stan], a platform for statistical modeling and high-performance statistical computation.


To be compatible with our R installation, we recommend to install the development version of rstan.
To be compatible with our R installation, we recommend to install the development version of rstan.


= Installation =
= Installation instructions =


Please enter the following code, presented in the boxes below, directly in your shell/command line on bwUniCluster
Please enter the following code, presented in the boxes below, directly in your shell/command line on '''bwUniCluster2.0'''.


== Preparations ==
== Preparations ==
Installing <code>glmnet</code> involves compiling source code. Therefore, ensure that the following flags are set in <code>$HOME/.R/Makevars</code>:
Prepare .R directory (if it does not already exists). This is then filled with information how R should compile the packages ('compilation flags'). These are written (and can be reviewed) into the (text) file Makevars.

If you already have a .R/Makevars file, check whether these flags are already set.


<pre>
<pre>
cat ~/.R/Makevars
cat ~/.R/Makevars

CXX14=g++
CXX17=g++
CXX14FLAGS += -std=c++14
CXX17FLAGS += -std=c++17
CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option
</pre>
</pre>


In this case, skip the following block of five commands. If this is not the case, enter the following commands in your shell (command line).
If necessary, the appropriate compiler flags can be set by running the following lines of code:

<pre>
<pre>
mkdir -p ~/.R
mkdir -p ~/.R
echo "CXX14=icpc" >> ~/.R/Makevars
echo "CXX14=g++" > ~/.R/Makevars
echo "CXX17=icpc" >> ~/.R/Makevars
echo "CXX17=g++" >> ~/.R/Makevars
echo "CXX14FLAGS=-O3 -fPIC -std=c++14 -wd308 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp" >> ~/.R/Makevars
echo "CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option" >> ~/.R/Makevars
echo "CXX17FLAGS=-O3 -fPIC -std=c++17 -wd308 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp" >> ~/.R/Makevars
echo "CXX14FLAGS += -std=c++14" >> ~/.R/Makevars
echo "CXXFLAGS += -wd308" >> ~/.R/Makevars
echo "CXX17FLAGS += -std=c++17" >> ~/.R/Makevars
echo "PKG_CXXFLAGS += -std=c++14 -wd308" >> ~/.R/Makevars
</pre>
</pre>


Since installing <code>glmnet</code> involves compiling, we start an <b>interactive session</b> on one of the compute nodes:
The installation of rstan and its dependencies makes use other libraries (TBB, Threading Building Blocks) which need to be loaded before installation (and R, of course).

<pre>
<pre>
⚫
salloc -n 1 -t 30 -p dev_single
module purge
⚫
module load math/R/4.1.2
module load devel/tbb/2021.4.0
</pre>
</pre>



The installation process of rstan and its dependencies (Rcpp) needs input of how to use the TBB module. This is provided via specific environment variables. We also need to provide a further configuration information to install the R package v8 rstan depends on.
Within the interactive session, load the R 4.4.1 module and provide configuration information to install the R package <code>v8</code>, rstan depends on.


<pre>
<pre>
# Load the R software module:
export TBB_LIB=$TBB_LIB_DIR
⚫
module load math/R/4.4.1-mkl-2022.2.1-gnu-13.3
export TBB_INC=$TBB_INC_DIR

export TBB_INTERFACE_NEW='true'
# Provide configuration information for the v8 R package
export DOWNLOAD_STATIC_LIBV8=1
export DOWNLOAD_STATIC_LIBV8=1
</pre>
</pre>


== Installing the R package(s) [takes roughly 20 min] ==
== Installing the R package(s) [takes roughly 20 min] ==
Now, start <code>R</code> and install <code>RStan</code> using the lines of code below:
We can now install rstan (in its development version) and its dependencies within a R session

Consider starting an interactive job for compiling. You need at most 5000MB total memory, e.g. start

<pre>
⚫
salloc -t 30 -n 1 --mem=5000 -p dev_single
</pre>

Now, start R and install


<pre>
<pre>
Line 67: Line 60:
</pre>
</pre>


== Testing the installation ==
== Testing the installation ==


To check whether the installation worked, make a test run in R
To check whether the installation worked, make a test run in R
Line 74: Line 67:
> example(stan_model, package = "rstan", run.dontrun = TRUE)
> example(stan_model, package = "rstan", run.dontrun = TRUE)
</pre>
</pre>

= Preparations to use the rstan package =
To run its applications, rstan compiles new scripts. For this, it again uses TBB, so you need to set the corresponding environment variables whenever you use rstan - as well as to load the R and TBB modules.


For this, we recommend to add the modules

<pre>
module load math/R/4.1.2
module load devel/tbb/2021.4.0
</pre>


as well as the three export commands

<pre>
export TBB_LIB=$TBB_LIB_DIR
export TBB_INC=$TBB_INC_DIR
export TBB_INTERFACE_NEW='true'
</pre>

to your [[BwUniCluster_2.0_Slurm_common_Features#sbatch_Examples | batch job scripts]] that use rstan or to run them directly in the command line if you use an [[BwUniCluster_2.0_Batch_Queues | interactive session]].

Latest revision as of 18:08, 30 October 2024

General information

rstan provides the R interface for Stan, a platform for statistical modeling and high-performance statistical computation.

To be compatible with our R installation, we recommend to install the development version of rstan.

Installation instructions

Please enter the following code, presented in the boxes below, directly in your shell/command line on bwUniCluster2.0.

Preparations

Installing glmnet involves compiling source code. Therefore, ensure that the following flags are set in $HOME/.R/Makevars: 

cat ~/.R/Makevars

CXX14=g++
CXX17=g++
CXX14FLAGS += -std=c++14
CXX17FLAGS += -std=c++17
CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option

If necessary, the appropriate compiler flags can be set by running the following lines of code: 

mkdir -p ~/.R
echo "CXX14=g++" > ~/.R/Makevars
echo "CXX17=g++" >> ~/.R/Makevars
echo "CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option" >> ~/.R/Makevars
echo "CXX14FLAGS += -std=c++14" >> ~/.R/Makevars
echo "CXX17FLAGS += -std=c++17" >> ~/.R/Makevars

Since installing glmnet involves compiling, we start an interactive session on one of the compute nodes: 

salloc -n 1 -t 30  -p dev_single


Within the interactive session, load the R 4.4.1 module and provide configuration information to install the R package v8, rstan depends on. 

# Load the R software module:
module load math/R/4.4.1-mkl-2022.2.1-gnu-13.3

# Provide configuration information for the v8 R package
export DOWNLOAD_STATIC_LIBV8=1

Installing the R package(s) [takes roughly 20 min]

Now, start R and install RStan using the lines of code below: 

R -q
> install.packages("remotes")
> install.packages("RcppParallel")
> install.packages("V8")
> remotes::install_github("hsbadr/rstan/StanHeaders@develop", force = TRUE)
> remotes::install_github("hsbadr/rstan/rstan/rstan@develop", force = TRUE)

Testing the installation

To check whether the installation worked, make a test run in R 

> library(rstan)
> example(stan_model, package = "rstan", run.dontrun = TRUE)
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