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Dockter: a container image builder for researchers

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Docker is a useful tool for creating reproducible computing environments. But creating truly reproducible Docker images can be difficult - even if you already know how to write a Dockerfile.

Dockter makes it easier for researchers to create Docker images for their research projects. Dockter generates a Dockerfile and builds a image, for your project, based on your source code.


🦄 Features that are planned, but not yet implemented, are indicated by unicorn emoji. Usually they have a link next to them, like this 🦄 #2, indicating the relevant issue where you can help make the feature a reality. It's readme driven development with calls to action to chase after mythical vaporware creatures! So hip.

Builds a Docker image based on your source code

Dockter scans your project and builds a custom Docker image for it. If the the folder already has a Dockerfile, Dockter will build the image from that. If not, Dockter will scan the source code files in the folder and generate one for you. Dockter currently handles R, Python and Node.js source code. A project can have a mix of these languages.


If the folder contains a R package DESCRIPTION file then Dockter will install the R packages listed under Imports into the image. e.g.

Package: myrproject
Version: 1.0.0
Date: 2017-10-01

The Package and Version fields are required in a DESCRIPTION file. The Date field is used to define which CRAN snapshot to use. MRAN daily snapshots began 2014-09-08 so the date should be on or after that.

If the folder does not contain a DESCRIPTION file then Dockter will scan all the R files (files with the extension .R or .Rmd) in the folder for package import or usage statements, like library(package) and package::function(), and create a .DESCRIPTION file for you.


If the folder contains a requirements.txt file, or a 🦄 #4 Pipfile, Dockter will copy it into the Docker image and use pip to install the specified packages.

If the folder does not contain either of those files then Dockter will scan all the folder's .py files for import statements and create a .requirements.txt file for you.


If the folder contains a package.json file, Dockter will copy it into the Docker image and use npm to install the specified packages.

If the folder does not contain a package.json file, Dockter will scan all the folder's .js files for require calls and create a .package.json file for you.


If the folder contains any JATS files (.xml files with <!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) ...), 🦄 #52 Docker will scan reproducible elements defined in the Dar JATS extension for any package import statements (e.g. Python import, R library, or Node.js require) and install the necessary packages into the image.


If the folder contains any Jupyter .ipynb files, 🦄 #9 Dockter will scan the code cells in those files for any package import statements (e.g. Python import, R library, or Node.js require) and install the necessary packages into the image. It will also 🦄 #10 add the necesary Jupyter kernels to the built Docker image.

Automatically determines system requirements

One of the headaches researchers face when hand writing Dockerfiles is figuring out which system dependencies your project needs. Often this involves a lot of trial and error.

Dockter automatically checks if any of your dependencies (or dependencies of dependencies, or dependencies of...) requires system packages and installs those into the image. For example, let's say you have a project with an R script that requires the rgdal package for geospatial analyses,


When you run dockter compile in this project, Dockter will generate a Dockerfile with the following section which installs R, plus the three system dependencies required gdal-bin, libgdal-dev, and libproj-dev:

# This section installs system packages required for your project
# If you need extra system packages add them here.
RUN apt-get update \
 && DEBIAN_FRONTEND=noninteractive apt-get install -y \
      gdal-bin \
      libgdal-dev \
      libproj-dev \
      r-base \
 && apt-get autoremove -y \
 && apt-get clean \
 && rm -rf /var/lib/apt/lists/*

For R, Dockter does this by querying the database. For Python, Dockter includes a mapping of system requirements of packages that users can contribute to.

No more trial and error of build, fail, add dependency, repeat... cycles!

Faster re-installation of language packages

If you have built a Docker image before, you'll know that it can be frustrating waiting for all your project's dependencies to reinstall when you simply add or remove one of them.

The reason this happens is that, due to Docker's layered filesystem, when you update a requirements file, Docker throws away all the subsequent layers - including the one where you previously installed your dependencies. That means that all those packages need to get reinstalled.

Dockter takes a different approach. It leaves the installation of language packages to the language package managers: Python's pip , Node.js's npm, and R's install.packages. These package managers are good at the job they were designed for - to check which packages need to be updated and to only update them. The result is much faster rebuilds, especially for R packages, which often involve compilation.

Dockter does this by looking for a special # dockter comment in a Dockerfile. Instead of throwing away subsequent image layers, it executes all instructions after this comment in the same layer - thus reusing packages that were previously installed.

Here's a simple motivating example. It's a Python project with a requirements.txt file which specifies that the project depends upon pandas which, to ensure reproducibility, is pinned to version 0.23.0,


The project also has a Dockerfile which specifies which Python version we want to use, copies requirements.txt into the image, and uses pip to install the packages:

FROM python:3.7.0

COPY requirements.txt .
RUN pip install -r requirements.txt

You could build a Docker image for that project using Docker,

docker build .

Docker will download the base Python image (if you don't yet have it), download five packages (pandas and it's four dependencies) and install them. This took over 9 minutes when we ran it.

Now, let's say that we want to get the latest version of pandas and increment the version in the requirements.txt file,


When we do docker build . again to update the image, Docker notices that the requirements.txt file has changed and so throws away that layer and all subsequent ones. This means that it will download and install all the necessary packages again, including the ones that we previously installed. For a more contrived illustration of this, simply add a space to one of the lines in the requirements.txt file and notice how the package install gets repeated all over again.

Now, let's add a special # dockter comment to the Dockerfile before the COPY directive,

FROM python:3.7.0

# dockter

COPY requirements.xt .
RUN pip install -r requirements.txt

The comment is ignored by Docker but tells dockter to run all subsequent instructions in a single filesystem layer,

dockter build .

Now, if you change the requirements.txt file, instead of reinstalling everything again, pip will only reinstall what it needs to - the updated pandas version. The output looks like:

Step 1/1 : FROM python:3.7.0
 ---> a9d071760c82
Successfully built a9d071760c82
Successfully tagged dockter-5058f1af8388633f609cadb75a75dc9d:system
Dockter 1/2 : COPY requirements.txt requirements.txt
Dockter 2/2 : RUN pip install -r requirements.txt
Collecting pandas==0.23.1 (from -r requirements.txt (line 1))

Successfully built pandas
Installing collected packages: pandas
  Found existing installation: pandas 0.23.0
    Uninstalling pandas-0.23.0:
      Successfully uninstalled pandas-0.23.0
Successfully installed pandas-0.23.1

Generates structured meta-data for your project

Dockter uses JSON-LD as it's internal data structure. When it parses your project's source code it generates a JSON-LD tree using a vocabularies from and CodeMeta.

For example, It will parse a Dockerfile into a SoftwareSourceCode node extracting meta-data about the Dockerfile.

Dockter also fetches meta data on your project's dependencies, which could be used to generate a complete software citation for your project.

  "name": "myproject",
  "datePublished": "2017-10-19",
  "description": "Regression analysis for my data",
  "softwareRequirements": [
      "description": "\nFunctions to Accompany J. Fox and S. Weisberg,\nAn R Companion to Applied Regression, Third Edition, Sage, in press.",
      "name": "car",
      "urls": [
      "authors": [
          "name": "John Fox",
          "familyNames": [
          "givenNames": [

Easy to pick up, easy to throw away

Dockter is designed to make it easier to get started creating Docker images for your project. But it's also designed not to get in your way or restrict you from using bare Docker. You can easily, and individually, override any of the steps that Dockter takes to build an image.

  • Code analysis: To stop Dockter doing code analysis and take over specifying your project's package dependencies, just remove the leading '.' from the .DESCRIPTION, .requirements.txt or .package.json file that Dockter generates.

  • Dockerfile generation: Dockter aims to generate readable Dockerfiles that conform to best practices. They include comments on what each section does and are a good way to start learning how to write your own Dockerfiles. To stop Dockter generating a .Dockerfile, and start editing it yourself, just rename it to Dockerfile.

  • Image building: Dockter manages incremental builds using a special comment in the Dockerfile, so you can stop using Dockter altogether and build the same image using Docker (it will just take longer if you change you project dependencies).



Dockter is available as pre-compiled, standalone command line tool (CLI), or as a Node.js package. In both cases, if you want to use Dockter to build Docker images, you will need to install Docker if you don't already have it.



To install the latest release of the dockter command line tool, download for the latest release and place it somewhere on your PATH.


To install the latest release of the dockter command line tool to /usr/local/bin just,

curl -L | bash

Or, if you'd prefer to do things manually, download dockter-macos-x64.tar.gz for the latest release and then,

tar xvf dockter-macos-x64.tar.gz
sudo mv -f dockter /usr/local/bin # or wherever you like


To install the latest release of the dockter command line tool to ~/.local/bin/ just,

curl -L | bash

Or, if you'd prefer to do things manually, or place Dockter elewhere, download dockter-linux-x64.tar.gz for the latest release and then,

tar xvf dockter-linux-x64.tar.gz
mv -f dockter ~/.local/bin/ # or wherever you like


If you want to integrate Dockter into another application or package, it is also available as a Node.js package :

npm install @stencila/dockter


The command line tool has three primary commands compile, build and execute. To get an overview of the commands available use the --help option i.e.

dockter --help

To get more detailed help on a particular command, also include the command name e.g

dockter compile --help

Compile a project

The compile command compiles a project folder into a specification of a software environment. It scans the folder for source code and package requirement files, parses them, and creates an .environ.jsonld file. This file contains the information needed to build a Docker image for your project.

For example, let's say your project folder has a single R file, main.R which uses the R package lubridate to print out the current time:


Let's compile that project and inspect the compiled software environment. Change into the project directory and run the compile command.

dockter compile

You should find three new files in the folder created by Dockter:

  • .DESCRIPTION: A R package description file containing a list of the R packages required and other meta-data

  • .envrion.jsonld: A JSON-LD document containing structure meta-data on your project and all of its dependencies

  • .Dockerfile: A Dockerfile generated from .environ.jsonld

To stop Dockter generating any of these files and start editing it yourself, remove the leading . from the name of the file you want to take over creating.

Build a Docker image

Usually, you'll compile and build a Docker image for your project in one step using the build command. This command runs the compile command and builds a Docker image from the generated .Dockerfile (or handwritten Dockerfile):

dockter build

After the image has finished building you should have a new docker image on your machine, called rdate:

> docker images
REPOSITORY        TAG                 IMAGE ID            CREATED              SIZE
rdate             latest              545aa877bd8d        About a minute ago   766MB

If you want to build your image with bare Docker rename .Dockerfile to Dockerfile and run docker build . instead. This might be a good approach when you have finished the exploratory phase of your project (i.e. there is litte or no churn in your package dependencies) and want to create a more final image.

🛈 Docker images can get very large (2-3 GB is not unusual for an image with R and/or Python and associated packages). You might want to occasionally do a clean up of 'dangling' images using docker image prune to save disk space. See the Docker documentation for more on cleaning up unused images and containers.

Execute a Docker image

You can use Docker to run the created image. Or use Dockter's execute command to compile, build and run your image in one:

> dockter execute
2018-10-23 00:58:39

Dockter's execute also mounts the folder into the container and sets the users and group ids. This allows you to read and write files into the project folder from within the container. It's equivaluent to running Docker with these arguments:

docker run --rm --volume $(pwd):/work --workdir=/work --user=$(id -u):$(id -g) <image>

Docter who?

Dockter compiles a meta-data tree of all the packages that your project relies on. Use the who command to get a list of the authors of those packages:

> dockter who
Roger Bivand (rgdal, sp), Tim Keitt (rgdal), Barry Rowlingson (rgdal), Edzer Pebesma (sp)

Use the depth option to restrict the listing to a particular depth in the dependency tree. For example, to list the authors of the packages that your project directly relies upon use:

> dockter who --depth=1


  • Feb 2019: release v1.0
  • Apr 2019: release v1.1


We 💕 contributions! All contributions: ideas 🤔, examples 💡, bug reports 🐛, documentation 📖, code 💻, questions 💬. See for more details.

This project follows the all-contributors specification. Thanks 🙏 to these wonderful people who have contributed so far 💖!

Remi Rampin

🐛 💻 🤔


💻 🤔

Aleksandra Pawlik

💻 💡 🐛

Nokome Bentley

💻 ⚠️

Giorgio Sironi

👀 🐛 🤔 💬

Bruno Vieira

💻 🤔 ⚠️

See also

There are several other projects that create Docker images from source code and/or requirements files including:

Dockter is similar to repo2docker, containerit, and reprozip in that it is aimed at researchers doing data analysis (and supports R) whereas most other tools are aimed at software developers (and don't support R). Dockter differs to these projects principally in that it:

  • performs static code analysis for multiple languages to determine package requirements.

  • uses package databases to determine package system dependencies and generate linked meta-data (containerit does this for R).

  • quicker installation of language package dependencies (which can be useful during research projects where dependencies often change).

  • by default, but optionally, installs Stencila packages so that Stencila client interfaces can execute code in the container.

The approach taken in Dockter to building Docker images is a mix of Dockerfile generation, as in repo2docker, and code injection and incremental builds as in source-to-image.

reprozip and its extension reprounzip-docker may be a better choice if you want to share your existing local environment as a Docker image with someone else.

containerit might suit you better if you only need support for R and don't want managed packaged installation.

repo2docker is probably a better choice if you want to run Jupyter notebooks or RStudio in your container and don't need source code scanning to detect your requirements.

source-to-image might suit you better if your focus is on web development (e.g. Ruby, Node.js) and want a more stable, feature complete implementation of incremental builds.

If you don't want to build a Docker image and just want a tool that helps determining the package dependencies of your source code check out:


Why go to the effort of generating a JSON-LD intermediate representation instead of writing a Dockerfile directly?

Having an intermediate representation of the software environment allows this data to be used for other purposes (e.g. software citations, publishing, archiving). It also allows us to reuse much of this code for build targets other than Docker (e.g. Nix) and sources other than code files (e.g. a GUI).

Why is Dockter a Node.js package?

We've implemented this as a Node.js package for easier integration into Stencila's Node.js based desktop and cloud deployments. We already had familiarity with using dockerode the Node.js package that we use to talk to Docker for incremental builds and container execution.

Why is Dockter implemented in Typescript?

Typescript's type-checking and type-annotations can reduce the number of runtime errors and improves developer experience. For this particular project, we wanted to use the Typescript type definitions for SoftwarePackage, CreativeWork, Person etc that are defined in stencila/schema.

Why didn't you use, and contribute to, an existing project rather than creating a new tool

When existing projects don't take the approach or provide the features you want, it's often a difficult decision to make whether to invest the time to understand and refactor an existing code base or to start fresh. In this case, we chose to start fresh for the reasons and differences outlined above. We felt it would take too much refactoring of existing projects to shoehorn in the approach we wanted to take. We also wanted to be able to reuse much of the code developed here in a sister project, Nixster, which aims to make it easier for researchers to build Nix environments.

I'd love to help out! Where do I start?

See (OK, so this isn't asked that frequently. But it's worth a try eh 🤷‍♀️.)


Dockter was inspired by, and combines ideas from, several similar tools including binder, repo2docker, source-to-image and containerit. It relies on many great open source projects, in particular:

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