In bhaskarvk/docker: Wraps Docker Python SDK

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

description <- readLines(system.file("DESCRIPTION", package='docker'))
rvers <- stringr::str_match(grep("R \\(", description, value = TRUE), "[0-9]{1,4}\\.[0-9]{1,4}\\.[0-9]{1,4}")[1,1]
version <- gsub(" ", "", gsub("Version:", "", grep("Version:", description, value = TRUE)))

`docker`: An R Package for Docker

The docker R package provides access to the docker API to programmatically control a docker engine from R. The docker engine could be running either locally or remotely. The docker package uses the reticulate R package to invoke the Python SDK for docker. Using the Python SDK allows the docker package to have a very minimal foot-print and yet allow complete docker API access. The package has been tested against docker engine running on Linux and Microsoft Windows and should also work under MacOS.

Controlling a docker engine from withing R can be useful for ...

Writing automated unit/integration tests for R packages against running services in docker containers. The package author can start a docker container, execute the tests, and tear the container down, all from withing the R code of the test.
Another use case is easy access to containerized services as part of a researchers reproducible research workflow. Having access to a containerized service ensures that the research code executes identically regardless of where it's run.

Installation

Release version

install.packages('docker')

OR development version

if(!require(devtools)) {
  install.packages("devtools")
}
devtools::install_github('bhaskarvk/docker')

Setup

Before you can use this package you need to have Python 2.x (>=2.7) or Python 3.x (>=3.4) with the docker Python module which provides Python SDK for docker. A simple way to do this is using a virtual environment. virtualenvwrapper makes setting up Python modules quite painless.

Once you have installed Python and setup virtualenvwrapper you need to create a new virtual environment and install the docker Python module in it.

mkvirtualenv --python=/usr/bin/python3 docker
workon docker
pip install docker

# Test the SDK againsts a locally running docker
# You should have a locally running docker for this.
python -c 'import docker; print(docker.from_env().version())'

You should see something like below, provided you had a locally running docker engine.

{'Os': 'linux', 'Arch': 'amd64', 'KernelVersion': '4.10.0-24-generic', 'GitCommit': '02c1d87', 'Version': '17.06.0-ce', 'BuildTime': '2017-06-23T21:19:04.990631145+00:00', 'MinAPIVersion': '1.12', 'GoVersion': 'go1.8.3', 'ApiVersion': '1.30'}

Example

reticulate::use_virtualenv("docker")
library(docker)
client <- docker$from_env()
s <- client$containers$run("alpine", 'echo -n "Hello World!"', remove=TRUE)
print(s$decode("UTF-8"))

Usage

After you have a successful 'Hellow World!' displayed above, you can call every API supported by the Python SDK using reticulate. Please consult the documents for the SDK and the reticulate R package in the links given above.