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This post is the draft of the vignette for a new R package by o2r team members Matthias and Daniel. Find the original file in the package repository on GitHub.
- 1. Introduction
- 2. Creating a Dockerfile
- 3. Including resources
- 4. Image metadata
- 5. Further customization
- 6. CLI
- 7. Challenges
- 8. Conclusions and future work
- Metadata
1. Introduction
Even though R is designed for open and reproducible research, users who want to share their work with others are facing challenges. Sharing merely the R script or R Markdown document should warrant reproducibility, but many analyses rely on additional resources and specific third party software as well. An R script may < !--more-->produce unexpected results or errors when executed under a different version of R or another platform. Reproduciblility is only assured by providing complete setup instructions and resources. Long-term reproducibility can be achieved by either regular maintenance of the code, i.e. keeping it always working with the latest package versions from CRAN. It can be supported by packages such as packrat and platforms such as MRAN, which provide means to capture a specific combination of R packages. An alternative to updating or managing packages explicitly is providing the full runtime environment in its original state, using virtual machines or software containers.
The R extension package containerit aims to facilitate the latter
approach by making reproducible and archivable research with containers
easier. The development is supported by the DFG-funded project Opening
Reproducible Research (o2r, https://o2r.info). containerit relies on
Docker and automatically generates a container
manifest, or “recipe”, with setup instructions to recreate a runtime
environment based on a given R session, R script, R Markdown file or
workspace directory. The resulting
Dockerfile can
not only be read and understood by humans, but also be interpreted by
the Docker engine to create a software container containing all the R
packages and their system dependencies. This way all requirements of an
R workflow are packaged in an executable format.
The created Dockerfiles are based on the
Rocker project (Rocker on
Docker Hub,
introduction).
Using the stack of version-stable Rocker images, it is possible to match
the container’s R version with the local R installation or any R version
the user requires. containerit executes the provided input workspace
or file first locally on the host machine in order to detect all
dependencies. For determining external software dependencies of attached
packages, containerit relies (a) on the sysreqs
database and makes use of the corresponding
web API and R package, and (b) on internally defined rule sets for
challenging configurations.
The Dockerfile created by containerit can then be used to build a
Docker image. Running the image will start an R session that closely
resembles the creating systems runtime environment. The image can be
shared and archived and works anywhere with a compatible Docker version.
To build images and run containers, the package integrates with the
harbor package and adds a few
convenience functions for interacting with Docker images and containers.
For concrete details on reading, loading, or installing the exact
versions of R packages including their system dependencies/libraries,
this project focuses on the geospatial domain. containerit uses the
package
futile.logger
to provide information to the user at a configurable level of detail,
see futile.logger
documentation.
In the remainder of this vignette, we first introduce the main usage
scenarios for containerit and document current challenges as well as
directions for future work.
2. Creating a Dockerfile
2.1 Basics
The easiest way to generate a Dockerfile is to run an analysis in an
interactive R session and create a Dockerfile for this session by
loading containerit and calling the dockerfile()– method with
default parameters. As shown in the example below, the result can be
pretty-printed and written to a file. If no file argument is supplied
to write(), the Dockerfile is written to the current working directory
as ./Dockerfile, following the typical naming convention of Docker.
When packaging any resources, it is essential that the R working directory is the same as the build context, to which the Dockerfile refers. All resources must be located below this directory so that they can be refered to by relative paths (e.g. for copy instructions). This must also be considered when packaging R scripts that use relative paths, e.g. for reading a file or sourcing another R script.
2.2 Packaging an interactive session
library("containerit")
##
## Attaching package: 'containerit'
## The following object is masked from 'package:base':
##
## Arg
# do stuff, based on demo("krige")
library("gstat")
library("sp")
data(meuse)
coordinates(meuse) = ~x+y
data(meuse.grid)
gridded(meuse.grid) = ~x+y
v <- variogram(log(zinc)~1, meuse)
m <- fit.variogram(v, vgm(1, "Sph", 300, 1))
plot(v, model = m)
# create Dockerfile representation
dockerfile_object <- dockerfile()
## INFO [2017-05-30 14:49:20] Trying to determine system requirements for the package(s) 'sp, gstat, knitr, Rcpp, intervals, lattice, FNN, spacetime, zoo, digest, rprojroot, futile.options, backports, magrittr, evaluate, stringi, futile.logger, xts, rmarkdown, lambda.r, stringr, yaml, htmltools' from sysreq online DB
## INFO [2017-05-30 14:49:21] Adding CRAN packages: sp, gstat, knitr, Rcpp, intervals, lattice, FNN, spacetime, zoo, digest, rprojroot, futile.options, backports, magrittr, evaluate, stringi, futile.logger, xts, rmarkdown, lambda.r, stringr, yaml, htmltools
## INFO [2017-05-30 14:49:21] Created Dockerfile-Object based on sessionInfo
The representation of a Dockerfile in R is an instance of the S4 class
Dockerfile.
dockerfile_object ## An object of class "Dockerfile" ## Slot "image": ## An object of class "From" ## Slot "image": ## [1] "rocker/r-ver" ## ## Slot "postfix": ## An object of class "Tag" ## [1] "3.4.0" ## ## ## Slot "maintainer": ## An object of class "Label" ## Slot "data": ## $maintainer ## [1] "daniel" ## ## ## Slot "multi_line": ## [1] FALSE ## ## ## Slot "instructions": ## [[1]] ## An object of class "Run_shell" ## Slot "commands": ## [1] "export DEBIAN_FRONTEND=noninteractive; apt-get -y update" ## [2] "apt-get install -y pandoc \\\n\tpandoc-citeproc" ## ## ## [[2]] ## An object of class "Run" ## Slot "exec": ## [1] "install2.r" ## ## Slot "params": ## [1] "-r 'https://cloud.r-project.org'" "sp" ## [3] "gstat" "knitr" ## [5] "Rcpp" "intervals" ## [7] "lattice" "FNN" ## [9] "spacetime" "zoo" ## [11] "digest" "rprojroot" ## [13] "futile.options" "backports" ## [15] "magrittr" "evaluate" ## [17] "stringi" "futile.logger" ## [19] "xts" "rmarkdown" ## [21] "lambda.r" "stringr" ## [23] "yaml" "htmltools" ## ## ## [[3]] ## An object of class "Workdir" ## Slot "path": ## [1] "/payload/" ## ## ## ## Slot "cmd": ## An object of class "Cmd" ## Slot "exec": ## [1] "R" ## ## Slot "params": ## [1] NA
The printout below shows the rendered Dockerfile. Its instructions follow a pre-defined order:
- define the base image
- define the maintainer label
- install system dependencies and external software
- install the R packages themselves
- set the working directory
- copy instructions and metadata labels (see examples in later sections)
CMDinstruction (final line) defines the default command when running the container
Note that the maintainer label as well as the R version of the base image are detected from the runtime environment, if not set to different values manually.
print(dockerfile_object)
FROM rocker/r-ver:3.4.0
LABEL maintainer="daniel"
RUN export DEBIAN_FRONTEND=noninteractive; apt-get -y update \
&& apt-get install -y pandoc \
pandoc-citeproc
RUN ["install2.r", "-r 'https://cloud.r-project.org'", "sp", "gstat", "knitr", "Rcpp", "intervals", "lattice", "FNN", "spacetime", "zoo", "digest", "rprojroot", "futile.options", "backports", "magrittr", "evaluate", "stringi", "futile.logger", "xts", "rmarkdown", "lambda.r", "stringr", "yaml", "htmltools"]
WORKDIR /payload/
CMD ["R"]
Instead of printing out to the console, you can also write to a file:
write(dockerfile_object, file = tempfile(fileext = ".dockerfile")) ## INFO [2017-05-30 14:49:21] Writing dockerfile to /tmp/Rtmp25OKLi/file1a9726e56459.dockerfile
2.3 Packaging an external session
Packaging an interactive session has the disadvantage that unnecessary
dependencies might be added to the Dockerfile and subsequently to the
container. For instance the package futile.logger is a dependency of
containerit, and it will be added to the container because it was
loaded into the same session were the analyses was executed. It cannot
be removed by default, because other packages in the session might use
it as well (even unintentionally in case of generic methods). Therefore,
it is safer not to tamper with the current session, but to run the
analysis in an isolated vanilla session, which does not have
containerit in it. The latter will batch-execute the commands in a
seperate instance of R and retrieves an object of class sessionInfo.
The session info is then used as input to dockerfile(). This is also
how dockerfile() works internally when packaging either expressions,
scripts or R markdown files.
The following code creates a Dockerfile for a list of expressions in a vanilla session.
exp <- c(expression(library(sp)),
expression(data(meuse)),
expression(mean(meuse[["zinc"]])))
session <- clean_session(exp, echo = TRUE)
## INFO [2017-05-30 14:49:21] Creating an R session with the following arguments:
## R --silent --vanilla -e "library(sp)" -e "data(meuse)" -e "mean(meuse[[\"zinc\"]])" -e "info <- sessionInfo()" -e "save(list = \"info\", file = \"/tmp/Rtmp25OKLi/rdata-sessioninfo1a9714893e92\")"
dockerfile_object <- dockerfile(from = session)
## INFO [2017-05-30 14:49:23] Trying to determine system requirements for the package(s) 'sp, lattice' from sysreq online DB
## INFO [2017-05-30 14:49:24] Adding CRAN packages: sp, lattice
## INFO [2017-05-30 14:49:24] Created Dockerfile-Object based on sessionInfo
print(dockerfile_object)
FROM rocker/r-ver:3.4.0
LABEL maintainer="daniel"
RUN ["install2.r", "-r 'https://cloud.r-project.org'", "sp", "lattice"]
WORKDIR /payload/
CMD ["R"]
2.4 Packaging an R script
R scripts are packaged by just supplying the file path or paths to the
arguement from of dockerfile(). They are automatically copied into
the container’s working directory. In order to run the R script on
start-up, rather than an interactive R session, a CMD instruction can be
added by providing the value of the helper function CMD_Rscript() as
an argument to cmd.
# create simple script file
scriptFile <- tempfile(pattern = "containerit_", fileext = ".R")
writeLines(c('library(rgdal)',
'nc <- rgdal::readOGR(system.file("shapes/", package="maptools"), "sids", verbose = FALSE)',
'proj4string(nc) <- CRS("+proj=longlat +datum=NAD27")',
'plot(nc)'), scriptFile)
# use a custom startup command
scriptCmd <- CMD_Rscript(basename(scriptFile))
# create Dockerfile for the script
dockerfile_object <- dockerfile(from = scriptFile, silent = TRUE, cmd = scriptCmd)
print(dockerfile_object)
FROM rocker/r-ver:3.4.0
LABEL maintainer="daniel"
RUN export DEBIAN_FRONTEND=noninteractive; apt-get -y update \
&& apt-get install -y gdal-bin \
libgdal-dev \
libproj-dev
RUN ["install2.r", "-r 'https://cloud.r-project.org'", "rgdal", "sp", "lattice"]
WORKDIR /payload/
COPY [".", "."]
CMD ["R", "--vanilla", "-f", "containerit_1a977e2dcdea.R"]
2.5 Packaging an R Markdown file
Similarly to scripts, R Markdown files can be passed to the from
argument. In the following example, a vignette from the Simple Features
package sf is packaged in a container. To render the document at
startup, the Dockerfile’s CMD instruction must be changed. To do this,
the cmd argument passed to dockerfile() is constructed using the
function CMD_Render. Note that, as shown in the Dockerfile, the GDAL
library has to be build from source for sf to work properly, because a
quite recent version of GDAL is required. This adaptation of the
installation instruction is based on an internal ruleset for the package
sf.
response <- file.copy(from = system.file("doc/sf3.Rmd",package = "sf"),
to = temp_workspace, recursive = TRUE)
vignette <- "sf3.Rmd"
dockerfile_object <- dockerfile(from = vignette, silent = TRUE, cmd = CMD_Render(vignette))
## Loading required namespace: sf
print(dockerfile_object)
FROM rocker/r-ver:3.4.0
LABEL maintainer="daniel"
RUN export DEBIAN_FRONTEND=noninteractive; apt-get -y update \
&& apt-get install -y gdal-bin \
libgeos-dev \
libproj-dev \
libudunits2-dev \
make \
pandoc \
pandoc-citeproc \
wget
WORKDIR /tmp/gdal
RUN wget http://download.osgeo.org/gdal/2.1.3/gdal-2.1.3.tar.gz \
&& tar zxf gdal-2.1.3.tar.gz \
&& cd gdal-2.1.3 \
&& ./configure \
&& make \
&& make install \
&& ldconfig \
&& rm -r /tmp/gdal
RUN ["install2.r", "-r 'https://cloud.r-project.org'", "dplyr", "sf", "Rcpp", "assertthat", "digest", "rprojroot", "R6", "DBI", "backports", "magrittr", "evaluate", "units", "rlang", "stringi", "rmarkdown", "udunits2", "stringr", "yaml", "htmltools", "knitr", "tibble"]
WORKDIR /payload/
COPY ["sf3.Rmd", "sf3.Rmd"]
CMD ["R", "--vanilla", "-e", "rmarkdown::render(\"sf3.Rmd\", output_format = rmarkdown::html_document())"]
2.6 Packaging a workspace directory
A typical case expected to be interesting for containerit users is
packaging a local directory with a collection of data and code files. If
providing a directory path to the dockerfile() function, the package
searches for the first occurence of an R script, or otherwise the first
occurence of an R markdown file. It then proceeds to package this file
along with all other resources in the directory, as shown in the next
section.
3. Including resources
Analyses in R often rely on external files and resources that are
located located in the workspace. When scripts or R markdown files are
packaged, they are copied by default into the same location relative to
the working directory. The argument copy influences how dockefile()
behaves in this matter. It can either have the values script (default
behaviour), script_dir (copies the complete directory in which the
input file is located), or a custom list of files and directories inside
the current working directory
response <- file.copy(from = system.file("simple_test_script_resources/",
package = "containerit"),
to = temp_workspace, recursive = TRUE)
dockerfile_object <- dockerfile("simple_test_script_resources/",
copy = "script_dir",
cmd = CMD_Rscript("simple_test_script_resources/simple_test.R"))
print(dockerfile_object)
FROM rocker/r-ver:3.4.0
LABEL maintainer="daniel"
WORKDIR /payload/
COPY ["simple_test_script_resources", "simple_test_script_resources/"]
CMD ["R", "--vanilla", "-f", "simple_test_script_resources/simple_test.R"]
Including R objects works similar to resources, using the argument
save_image. The argument can be set to TRUE to save all objects of
the current workspace to an .RData file, which is then copied to the
container’s working directory and loaded on startup (based on
save.image()).
df <- dockerfile(save_image = TRUE)
print(df)
FROM rocker/r-ver:3.4.0
LABEL maintainer="daniel"
RUN export DEBIAN_FRONTEND=noninteractive; apt-get -y update \
&& apt-get install -y gdal-bin \
libgeos-dev \
libproj-dev \
libudunits2-dev \
make \
pandoc \
pandoc-citeproc \
wget
WORKDIR /tmp/gdal
RUN wget http://download.osgeo.org/gdal/2.1.3/gdal-2.1.3.tar.gz \
&& tar zxf gdal-2.1.3.tar.gz \
&& cd gdal-2.1.3 \
&& ./configure \
&& make \
&& make install \
&& ldconfig \
&& rm -r /tmp/gdal
RUN ["install2.r", "-r 'https://cloud.r-project.org'", "sp", "gstat", "knitr", "Rcpp", "magrittr", "units", "lattice", "rjson", "FNN", "udunits2", "stringr", "xts", "DBI", "lambda.r", "futile.logger", "htmltools", "intervals", "yaml", "rprojroot", "digest", "sf", "futile.options", "evaluate", "rmarkdown", "stringi", "backports", "spacetime", "zoo"]
WORKDIR /payload/
COPY ["./.RData", "./"]
CMD ["R"]
Alternatively, a object names as well as other arguments can be passed
as a list, which then are passed to the save() function.
require(fortunes)
## Loading required package: fortunes
rm(list = ls())
calculation <- 41 + 1
frtn <- fortunes::fortune()
original_sessionInfo <- sessionInfo()
df <- dockerfile(silent = TRUE,
save_image = list("original_sessionInfo", "frtn"))
print(df)
FROM rocker/r-ver:3.4.0
LABEL maintainer="daniel"
RUN export DEBIAN_FRONTEND=noninteractive; apt-get -y update \
&& apt-get install -y gdal-bin \
libgeos-dev \
libproj-dev \
libudunits2-dev \
make \
pandoc \
pandoc-citeproc \
wget
WORKDIR /tmp/gdal
RUN wget http://download.osgeo.org/gdal/2.1.3/gdal-2.1.3.tar.gz \
&& tar zxf gdal-2.1.3.tar.gz \
&& cd gdal-2.1.3 \
&& ./configure \
&& make \
&& make install \
&& ldconfig \
&& rm -r /tmp/gdal
RUN ["install2.r", "-r 'https://cloud.r-project.org'", "fortunes", "sp", "gstat", "knitr", "Rcpp", "magrittr", "units", "lattice", "rjson", "FNN", "udunits2", "stringr", "xts", "DBI", "lambda.r", "futile.logger", "htmltools", "intervals", "yaml", "rprojroot", "digest", "sf", "futile.options", "evaluate", "rmarkdown", "stringi", "backports", "spacetime", "zoo"]
WORKDIR /payload/
COPY ["./payload.RData", "./payload.RData"]
CMD ["R"]
4. Image metadata
Metadata can be added to Docker images using Label
instructions.
Label instructions are key-value pairs of arbitrary content. A dublicate
key overwrites existing ones. Although it is up to the user how many
labels are created, it is recommended to bundle them into one Label
instruction in the Dockerfile. Each use of the Label() function
creates a seperate instruction in the Dockerfile.
As shown in section 2, the maintainer label is set by default to the top
as the dockerfile and contains the username of the current host system.
The maintainer can be changed with the maintainer argument of
dockerfile():
labeled_dockerfile <- dockerfile(from = clean_session(), maintainer = "Jon_Doe@example.com")
Labels can be applied to the existing Dockerfile object using the
addInstructions() function, which adds any newly created instructions
to the end of the Dockerfile but before the CMD statement. The Label()
constructor can be used for creating labels of arbitrary content and
works similar to creating named lists in R.
# A simple label that occupies one line:
label1 <- Label(key1 = "this", key2 = "that", otherKey = "content")
addInstruction(labeled_dockerfile) <- label1
#label with fixed namespace for all keys
label2 <- Label("name"="A name", "description" = "A description", label_ns = "my.label.ns.")
# A multiline label with one key/value pair per line
label3 <- Label("info.o2r.name" = "myProject_ImageName", "org.label-schema.name"="ImageName",
"yet.another_labelname"="true", multi_line = TRUE)
addInstruction(labeled_dockerfile) <- list(label2, label3)
Metadata according to the Label Schema
conventions can be created with a function constructed by the helper
factory LabelSchemaFactory().
Label_LabelSchema <- LabelSchemaFactory() label <- Label_LabelSchema(name = "ImageName", description = "Description of the image", build_date = Sys.time()) addInstruction(labeled_dockerfile) <- label
You can also put session information, using either base R or devtools,
into a label as plain text or as json:
addInstruction(labeled_dockerfile) <- Label_SessionInfo(session = clean_session()) addInstruction(labeled_dockerfile) <- Label_SessionInfo(session = devtools::session_info(), as_json = TRUE)
The resulting Dockerfile with all the labels:
print(labeled_dockerfile)
FROM rocker/r-ver:3.4.0
LABEL maintainer="Jon_Doe@example.com"
WORKDIR /payload/
LABEL key1="this" key2="that" otherKey="content"
LABEL my.label.ns.name="A name" my.label.ns.description="A description"
LABEL info.o2r.name="myProject_ImageName" \
org.label-schema.name="ImageName" \
yet.another_labelname="true"
LABEL org.label-schema.schema-version="1.0.0-rc.1" \
org.label-schema.build-date="2017-05-30T14:49:39+0200" \
org.label-schema.name="ImageName" \
org.label-schema.description="Description of the image"
LABEL R.session-info="R version 3.4.0 (2017-04-21)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 16.04.2 LTS\n\nMatrix products: default\nBLAS: /usr/lib/libblas/libblas.so.3.6.0\nLAPACK: /usr/lib/lapack/liblapack.so.3.6.0\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C \n [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_US.UTF-8 \n [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_US.UTF-8 \n [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C \n [9] LC_ADDRESS=C LC_TELEPHONE=C \n[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C \n\nattached base packages:\n[1] stats graphics grDevices utils datasets methods base \n\nloaded via a namespace (and not attached):\n[1] compiler_3.4.0"
LABEL R.session-info="{\"platform\":{\"version\":\"R version 3.4.0 (2017-04-21)\",\"system\":\"x86_64, linux-gnu\",\"ui\":\"X11\",\"language\":\"en\",\"collate\":\"en_US.UTF-8\",\"tz\":\"Europe/Berlin\",\"date\":\"2017-05-30\"},\"packages\":{\"package\":[\"backports\",\"base\",\"compiler\",\"containerit\",\"datasets\",\"DBI\",\"devtools\",\"digest\",\"evaluate\",\"FNN\",\"fortunes\",\"futile.logger\",\"futile.options\",\"graphics\",\"grDevices\",\"grid\",\"gstat\",\"htmltools\",\"intervals\",\"knitr\",\"lambda.r\",\"lattice\",\"magrittr\",\"memoise\",\"methods\",\"Rcpp\",\"rjson\",\"rmarkdown\",\"rprojroot\",\"sf\",\"sp\",\"spacetime\",\"stats\",\"stringi\",\"stringr\",\"tools\",\"udunits2\",\"units\",\"utils\",\"withr\",\"xts\",\"yaml\",\"zoo\"],\"*\":[\"\",\"*\",\"\",\"*\",\"*\",\"\",\"\",\"\",\"\",\"\",\"*\",\"\",\"\",\"*\",\"*\",\"\",\"*\",\"\",\"\",\"*\",\"\",\"\",\"\",\"\",\"*\",\"\",\"\",\"\",\"\",\"\",\"*\",\"\",\"*\",\"\",\"\",\"\",\"\",\"\",\"*\",\"\",\"\",\"\",\"\"],\"version\":[\"1.0.5\",\"3.4.0\",\"3.4.0\",\"0.2.0\",\"3.4.0\",\"0.6-1\",\"1.13.1\",\"0.6.12\",\"0.10\",\"1.1\",\"1.5-4\",\"1.4.3\",\"1.0.0\",\"3.4.0\",\"3.4.0\",\"3.4.0\",\"1.1-5\",\"0.3.6\",\"0.15.1\",\"1.16\",\"1.1.9\",\"0.20-35\",\"1.5\",\"1.1.0\",\"3.4.0\",\"0.12.11\",\"0.2.15\",\"1.5\",\"1.2\",\"0.4-3\",\"1.2-4\",\"1.2-0\",\"3.4.0\",\"1.1.5\",\"1.2.0\",\"3.4.0\",\"0.13\",\"0.4-4\",\"3.4.0\",\"1.0.2\",\"0.9-7\",\"2.1.14\",\"1.8-0\"],\"date\":[\"2017-01-18\",\"2017-04-21\",\"2017-04-21\",\"2017-05-30\",\"2017-04-21\",\"2017-04-01\",\"2017-05-13\",\"2017-01-27\",\"2016-10-11\",\"2013-07-31\",\"2016-12-29\",\"2016-07-10\",\"2010-04-06\",\"2017-04-21\",\"2017-04-21\",\"2017-04-21\",\"2017-03-12\",\"2017-04-28\",\"2015-08-27\",\"2017-05-18\",\"2016-07-10\",\"2017-03-25\",\"2014-11-22\",\"2017-04-21\",\"2017-04-21\",\"2017-05-22\",\"2014-11-03\",\"2017-04-26\",\"2017-01-16\",\"2017-05-15\",\"2016-12-22\",\"2016-09-03\",\"2017-04-21\",\"2017-04-07\",\"2017-02-18\",\"2017-04-21\",\"2016-11-17\",\"2017-04-20\",\"2017-04-21\",\"2016-06-20\",\"2014-01-02\",\"2016-11-12\",\"2017-04-12\"],\"source\":[\"CRAN (R 3.4.0)\",\"local\",\"local\",\"local\",\"local\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"local\",\"local\",\"local\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"cran (@1.16)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.3.3)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"local\",\"cran (@0.12.11)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"local\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"local\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"local\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\",\"CRAN (R 3.4.0)\"]}}"
CMD ["R"]
5. Further customization
The dockerfile() function allows further customization regarding the R
version or the used base image (cf. Rocker stack). Note that while
choosing an R version for the Dockerfile explicitly is possible, the
session to generate the required information (i.e. which packages are
attached etc.) is still running the R version of the generating machine.
The following examples show usage of these options and the respective
FROM statements in the Dockerfile.
df_custom <- dockerfile(from = NULL, r_version = "3.1.0", silent = TRUE) print(df_custom@image) FROM rocker/r-ver:3.1.0 df_custom <- dockerfile(from = NULL, image = "rocker/geospatial", silent = TRUE) print(df_custom@image) FROM rocker/geospatial df_custom <- dockerfile(from = NULL, image = "rocker/verse:3.0.0", silent = TRUE)@image print(df_custom@image) [1] "rocker/verse"
6. CLI
A command line interface to the package functions is also available for Linux based on docopt.R. This allows integration into workflows and tools written in other programming languages than R.
You can make the command containerit available on your maching by
linking the R script file delivered with the package as follows:
ln -s $(Rscript -e "cat(system.file(\"cli/container_it.R\", package=\"containerit\"))") /usr/local/bin/containerit
CLI Examples:
containerit --help # runs the first R markdown or R script file locally # prints Dockerfile without writing a file containerit dir -p --no-write # Packages R-script # saves a workspace image (-i parameter) # Writes Dockerfile (overwrite with -f) # execute the script on start-up containerit file -ifp --cmd-R-file path/example.R # Creates an empty R session with the given R commands # Set R version of the container to 3.3.0 containerit session -p -e "library(sp)" -e "demo(meuse, ask=FALSE)" --r_version 3.3.0
7. Challenges
We encountered several challenges during containerit’s development.
First and foremost, a well known limitation is that R packages don’t
define system dependencies and do not provide explicit versions for R
package dependencies. The sysreqs package is a promising approach
towards handling system requirements, but so far lists package names but
does not provide version information. The
shinyapps-package-dependencies
demonstrate a (currently system dependent) alternative. The high value
of R might well lie in the fact that “packages currently on CRAN” should
work well with each other.
An unmet challenge so far is the installation of specific versions of
external libraries (see
issue). A
package like sf relies on well-tested and powerful system libraries,
see sf::sf_extSoftVersion(), which ideally should be matched in the
created container.
And of course users may do things that containerit cannot capture from
the session state “after the analysis is completed”, such as detaching
packages or removing relevant files, and unknown side-effects might
occur.
All software is presumed to be installed and run on the host system. Although it is possible to use deviating versions of R or even create Dockerfiles using sessionInfo-objects created on a different host, this may lead to unexpected errors because the setup cannot be tested locally.
8. Conclusions and future work
containerit alows to create and costumize Dockerfiles with minimal
effort, which are suitable for packaging R analyses in the persistant
runtime environment of a software container. So far, we were able to
reproduce complete R sessions regarding loaded and attached packages and
mitigate some challenges towards reproducible computational research.
Although we are able to package different versions of R, we still do not fully support the installation of specific versions of R packages and external software libraries, which R itself does not support. This should be tested in the future by evaluating version-stable package repositories like MRAN and GRAN or utility packages such as packrat – see the GitHub issues for the status of these plans or provide your own ideas there.
Related to installing specific versions is support for other package repositories, such as Bioconductor, git, BitBucket, or even local files. For now, it is recommended that users have all software up-to-date when building a software container, as the latest version are installed from CRAN during the image build, to have matching package versions between the creation runtime environment and the container. All Dockerfiles and instructions are adjusted to the Rocker image stack and assume a Debian/Linux operating system. As we are not yet supporting the build of Docker images from scratch, we are restricted to this setup.
The package is a first prototype available via GitHub. While a
publication on CRAN is a goal, it should be preceded by feedback from
the user community and ideally be accompanied by related packages, such
as harbor, being available on
CRAN, too. The prototype of containerit was developed and tested only
on Ubuntu/Linux, which should be extended before releasing a stable
version on CRAN.
As part of the o2r project, it is planned to integrate containerit in
a web service for creating archivable
research in form of Executable Research Compendia
(ERC). Making containerit
itself easier to use for end-users is a secondary but worthwhile goal, for example by
building a graphical user interface for metadata creation. Country
locales are also not supported yet. We may want to support other
container OS (e.g. windows container or other Linux distributions) or
even containerization solutions such as
Singularity or the Open Container
Initiative’s (OCI) Image
Format.
Feedback and contributions are highly welcome on GitHub or o2r_project on Twitter.
Metadata
sessionInfo() ## R version 3.4.0 (2017-04-21) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 16.04.2 LTS ## ## Matrix products: default ## BLAS: /usr/lib/libblas/libblas.so.3.6.0 ## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0 ## ## locale: ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C ## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_US.UTF-8 ## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_US.UTF-8 ## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C ## [9] LC_ADDRESS=C LC_TELEPHONE=C ## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] fortunes_1.5-4 sp_1.2-4 gstat_1.1-5 containerit_0.2.0 ## [5] knitr_1.16 ## ## loaded via a namespace (and not attached): ## [1] Rcpp_0.12.11 rstudioapi_0.6 magrittr_1.5 ## [4] devtools_1.13.1 units_0.4-4 lattice_0.20-35 ## [7] rjson_0.2.15 FNN_1.1 udunits2_0.13 ## [10] stringr_1.2.0 tools_3.4.0 xts_0.9-7 ## [13] grid_3.4.0 DBI_0.6-1 withr_1.0.2 ## [16] lambda.r_1.1.9 futile.logger_1.4.3 htmltools_0.3.6 ## [19] intervals_0.15.1 yaml_2.1.14 rprojroot_1.2 ## [22] digest_0.6.12 sf_0.4-3 futile.options_1.0.0 ## [25] memoise_1.1.0 evaluate_0.10 rmarkdown_1.5 ## [28] stringi_1.1.5 compiler_3.4.0 backports_1.0.5 ## [31] spacetime_1.2-0 zoo_1.8-0
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