@stencila/dockter
Advanced tools
:sparkles: Help us choose a better name for this project! :sparkles:
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.
🦄 Dockter is in early development. Features that are not yet implemented are indicated by unicorn emoji. Usually they have a link next to them, like this 🦄 #2, indicating the relevent 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.
Dockter scans your project folder and builds a 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, generate a .Dockerfile and use that.
Dockter currently handles R and Python source code (with Node.js support planned). A project can have a mix of these languages.
If the folder contains a R package DESCRIPTION file then Dockter will build an image with the R packages listed under Imports installed. e.g.
Package: myrproject
Version: 1.0.0
Date: 2017-10-01
Imports:
ggplot2
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.
Dockter checks if any of your dependenies (or dependencies of dependencies, or dependencies of...) requires system packages (e.g. libxml-dev) and installs those too. No more trial and error of build, fail, add dependency, repeat... cycles!
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 🦄 #5 scan all the folder's .py files for import statements and create a .requirements.txt file for you.
If the folder contains a 🦄 #7 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 🦄 #8 scan all the folder's .js files for import or require statements and create a .package.json file for you.
If the folder contains any 🦄 #9 .ipynb files, Dockter will scan the code cells in those files for any Python import or R library statements and extract a list of package dependencies. It will also 🦄 #10 add Jupyter to the built Docker image.
If you have built a Docker image 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 filesystems, 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 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,
pandas==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 can 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,
pandas==0.23.1
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))
<snip>
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
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 schema.org and CodeMeta.
For example, It will parse a Dockerfile into a schema.org 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": [
"https://r-forge.r-project.org/projects/car/",
"https://CRAN.R-project.org/package=car",
"http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/index.html"
],
"authors": [
{
"name": "John Fox",
"familyNames": [
"Fox"
],
"givenNames": [
"John"
]
},
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 🦄 #36 include comments on what each section does and are a good wat 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 build: Dockter manage builds use 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.
Download the command line interface (CLI) as a pre-compiled, standalone binary for Windows, MacOS or Linux from the releases page.
For example, on Linux, you can download and install Dockter using the following command (change the version to the latest):
curl -sL https://github.com/stencila/dockter/releases/download/v0.2.4/dockter-linux -o ~/.local/bin/dockter && chmod +x ~/.local/bin/dockter
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
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:
lubridate::now()
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 meatadata on your project and all of its dependencies
.Dockerfile: A Dockerfile generated from .environ.jsonld
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
You can use Docker to run the created image. Or use Dockter's execute command to compile, build and run your image in one step:
> dockter execute
2018-10-23 00:58:39
We 💕 contributions! All contributions: ideas 💡, bug reports 🐛, documentation 🗎, code 💾. See CONTRIBUTING.md for more details.
There are several other projects that create Docker images from source code and/or requirements files including:
alibaba/derrickjupyter/repo2dockerGueils/whaleso2r-project/containeritopenshift/source-to-imageViDA-NYU/reprozipDockter 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 (🦄 well, right now just R).
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 when requirements often evolve
by default, but optionally, installs Stencila packages so that Stencila client interfaces can execute code in the container.
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 likely to be 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
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:
detectivemodulefinderrequirementsWhy 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.
Why is Dockter implemented in Typescript?
We chose Typescript because it's type-checking and type-annotations reduce the number of runtime errors and improves developer experience.
I'd love to help out! Where do I start?
See CONTRIBUTING.md (OK, so this isn't asked that frequently. But it's worth a try eh :woman_shrugging:.)
Dockter was inspired by similar tools for researchers including binder, repo2docker and containerit. It relies on many great open source projects, in particular:
FAQs
A Docker image builder for researchers
The npm package @stencila/dockter receives a total of 12 weekly downloads. As such, @stencila/dockter popularity was classified as not popular.
We found that @stencila/dockter demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
The Socket Research Team uncovered a malicious Python package typosquatting the popular 'fabric' SSH library, silently exfiltrating AWS credentials from unsuspecting developers.
Security News
At its inaugural meeting, the JSR Working Group outlined plans for an open governance model and a roadmap to enhance JavaScript package management.
Security News
Research
An advanced npm supply chain attack is leveraging Ethereum smart contracts for decentralized, persistent malware control, evading traditional defenses.