# Creating a Singularity Container to Run HuggingFace Transformers Models in R

Singularity is a container engine alternative to Docker. Singularity containers are well suited for the requirements of High Performance Computing (HPC) workloads.

A container contains all code as well as all its dependencies so that the an application runs reliably on different computers (or different computing environments). It can be used to run on servers or as a way to ensure computational reproducibility (that the code run on other systems, and in the future). For an introduction to the concept of containers see Computational Reproducibility via Containers in Psychology. Below is code to build a Singularity container for setting up transformers language models from HuggingFace and running the `text`-package.

## Code to build a singularity container with HuggingFace models in R

    Bootstrap: docker
    From: ubuntu:20.04

    %environment
      export LANG=C.UTF-8 LC_ALL=C.UTF-8
      export XDG_RUNTIME_DIR=/tmp/.run_$(uuidgen)

    %post
        # Install
        apt-get -y update

        export R_VERSION=4.2.2
        echo "export R_VERSION=${R_VERSION}" >> $SINGULARITY_ENVIRONMENT

         # Install R
         apt-get update
         apt-get install -y --no-install-recommends software-properties-common dirmngr  wget uuid-runtime
         wget -qO- https://cran.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | \
           tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc
         add-apt-repository \
           "deb https://cran.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/"
         apt-get install -y --no-install-recommends \
         r-base=${R_VERSION}* \
         r-base-core=${R_VERSION}* \
         r-base-dev=${R_VERSION}* \
         r-recommended=${R_VERSION}* \
         r-base-html=${R_VERSION}* \
         r-doc-html=${R_VERSION}* \
         libcurl4-openssl-dev \
         libharfbuzz-dev \
         libfribidi-dev \
         libgit2-dev \
         libxml2-dev \
         libfontconfig1-dev \
         libssl-dev \
         libxml2-dev \
         libfreetype6-dev \
         libpng-dev \
         libtiff5-dev \
         libjpeg-dev

         # Add a default CRAN mirror
         echo "options(repos = c(CRAN = 'https://cran.R-project.com/'), download.file.method = 'libcurl')" >> /usr/lib/R/etc/Rprofile.site

         # Fix R package libpaths (helps RStudio Server find the right directories)
         mkdir -p /usr/lib64/R/etc
         echo "R_LIBS_USER='/usr/lib64/R/library'" >> /usr/lib64/R/etc/Renviron
         echo "R_LIBS_SITE='${R_PACKAGE_DIR}'" >> /usr/lib64/R/etc/Renviron
         # Clean up
         rm -rf /var/lib/apt/lists/*

         # Install python3
         apt-get -y install python3 wget
         apt-get -y clean

         # Install Miniconda
         cd /
         wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
         bash Miniconda3-latest-Linux-x86_64.sh -b -p /miniconda

    /bin/bash <<EOF
         rm Miniconda3-latest-Linux-x86_64.sh
         source /miniconda/etc/profile.d/conda.sh
         conda update -y conda
         # Install reticulate and text
             Rscript -e 'install.packages("pkgdown")'
         Rscript -e 'install.packages("ragg")'
         Rscript -e 'install.packages("textshaping")'
         Rscript -e 'install.packages("reticulate")'
         Rscript -e 'install.packages("devtools")'
         Rscript -e 'install.packages("glmnet")'
         Rscript -e 'install.packages("tidyverse")'
    #     Rscript -e 'install.packages("text")'
         Rscript -e 'devtools::install_github("oscarkjell/text")'
         # Create the Conda environment at a system folder
         Rscript -e 'text::textrpp_install(prompt = FALSE, rpp_version = c("torch==1.11.0", "transformers==4.19.2", "numpy", "nltk"))'
         Rscript -e 'text::textrpp_initialize(save_profile = TRUE, prompt = FALSE, textEmbed_test = TRUE)'
         Rscript -e 'text::textEmbed("hello", model = "distilbert-base-uncased", layers = 5)'
         Rscript -e 'text::textEmbed("hello", model = "roberta-base", layers = 11)'