BwUniCluster2.0/Software/R/Glmnet - Revision history

K Kuck: /* Install glmnet */

2024-10-30T08:48:42Z

Install glmnet

← Older revision		Revision as of 10:48, 30 October 2024
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	# Install the glmnet package from within R session		# Install the glmnet package from within R session
	R -q		R -q

	R> install.packages("RcppEigen", configure.vars = "CXXFLAGS='-O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option'")

	R> install.packages("glmnet")		R> install.packages("glmnet")

2024-10-28T17:28:33Z

Install glmnet

← Older revision		Revision as of 19:28, 28 October 2024
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	# Install the glmnet package from within R session		# Install the glmnet package from within R session
	R -q		R -q

			R> install.packages("RcppEigen", configure.vars = "CXXFLAGS='-O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option'")

	R> install.packages("glmnet")		R> install.packages("glmnet")

2024-10-28T16:48:59Z

2024-10-28T16:48:37Z

← Older revision		Revision as of 18:48, 28 October 2024
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			== Install <code>glmnet</code> ==
⚫	# Load the R software module~~, e.g.~~
			<pre>
⚫	module load math/R/4.1.2
		⚫	# Load the R software module:
		⚫	module load math/R/4.4.1-mkl-2022.2.1-gnu-13.3

		⚫	# Install the glmnet package from within R session
	# Prepare .R directory (if it does not already exists)
		⚫	R -q
	mkdir -p ~/.R

		⚫	R> install.packages("glmnet")
	# Write the following environment variables to Makevars
			</pre>
	# Skip the 2nd and 3rd commands below if Makevars already consist these variables (1st command shows content of Makevars)
	cat ~/.R/Makevars
	echo "CXX17=icpc" >> ~/.R/Makevars
	echo "CXX17FLAGS=-O3 -fPIC -std=c++17 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp" >> ~/.R/Makevars

			== Test the installation ==
			As a quick test of the installation of <code>glmnet</code> the following lines of code can be run:

			<pre>

⚫	# Install the glmnet package from within R session
⚫	R -q
⚫	> install.packages("glmnet"~~, dependencies=TRUE~~)

	# Run a quick test		# Run a quick test
	> library(glmnet)		> library(glmnet)

2024-10-28T16:32:06Z

Preparations

← Older revision		Revision as of 18:32, 28 October 2024
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	CXX17FLAGS += -std=c++17		CXX17FLAGS += -std=c++17
	CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option		CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option
			</pre>

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

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	salloc -n 1 -t 30 -p dev_single		salloc -n 1 -t 30 -p dev_single
	</pre>		</pre>


	# Load the R software module, e.g.		# Load the R software module, e.g.

2024-10-28T16:28:45Z

← Older revision		Revision as of 18:28, 28 October 2024
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	= General information =		= General information =
	<code>glmnet</code> is an <code>R</code> package that fits generalized linear and similar models via penalized maximum likelihood, particularly, lasso and elastic-net.		<code>glmnet</code> is an <code>R</code> package that fits generalized linear and similar models via penalized maximum likelihood, particularly, lasso and elastic-net.


	= Installation instructions =		= Installation instructions =

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

2024-10-28T16:28:35Z

← Older revision		Revision as of 18:28, 28 October 2024
Line 1:		Line 1:
	<span style="color:red"><b>Note that the instructions provided below refer to R 4.2.1 (but not R 4.3.3)! ~~We are currently updating our guides for R 4.3.3.~~ </b></span>		<span style="color:red"><b>Note that the instructions provided below refer to R 4.4.1 (but not R 4.2.1)!</b></span>

	= General information =		= General information =
			<code>glmnet</code> is an <code>R</code> package that fits generalized linear and similar models via penalized maximum likelihood, particularly, lasso and elastic-net.
	glmnet is a R library for lasso and elastic-net regularized generalized linear models


	= Installation instructions =		= Installation instructions =

			== Preparations ==
	Consider starting an interactive job for compiling. Copy and paste the following to your shell.
			Installing <code>glmnet</code> involves compiling source code. Therefore, ensure that the following flags are set in <code>$HOME/.R/Makevars</code>:

	<pre>		<pre>
			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>

			Since installing <code>glmnet</code> involves compiling, we start an <b>interactive session</b> on one of the compute nodes:
			<pre>
			salloc -n 1 -t 30 -p dev_single
			</pre>

	# Load the R software module, e.g.		# Load the R software module, e.g.
	module load math/R/4.1.2		module load math/R/4.1.2

2024-06-18T07:51:54Z

← Older revision		Revision as of 09:51, 18 June 2024
Line 1:		Line 1:
			<span style="color:red"><b>Note that the instructions provided below refer to R 4.2.1 (but not R 4.3.3)! We are currently updating our guides for R 4.3.3. </b></span>

	= General information =		= General information =
	glmnet is a R library for lasso and elastic-net regularized generalized linear models		glmnet is a R library for lasso and elastic-net regularized generalized linear models

2023-10-24T08:22:53Z

Installation instructions

← Older revision		Revision as of 10:22, 24 October 2023
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	# Skip the 2nd and 3rd commands below if Makevars already consist these variables (1st command shows content of Makevars)		# Skip the 2nd and 3rd commands below if Makevars already consist these variables (1st command shows content of Makevars)
	cat ~/.R/Makevars		cat ~/.R/Makevars
	echo "~~CXX14~~=icpc" >> ~/.R/Makevars		echo "CXX17=icpc" >> ~/.R/Makevars
	echo "~~CXX14FLAGS~~=-O3 -fPIC -std=c++14 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp" >> ~/.R/Makevars		echo "CXX17FLAGS=-O3 -fPIC -std=c++17 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp" >> ~/.R/Makevars



	# Install the glmnet package from within R session		# Install the glmnet package from within R session

2023-03-14T12:41:43Z