tugboat-cli 0.1.2

Quickly containerize your Python and R (or hybrid) projects with minimal configuration. Simple utilities to generate a Dockerfile from an analysis directory, build the corresponding Docker image, push the image to DockerHub, and publicly share the project via Binder.

tugboat

Quickly containerize your Python and R (and hybrid) projects with minimal configuration. tugboat provides simple utilities to generate a Dockerfile from an analysis directory, build the corresponding Docker image, push the image to DockerHub, and publicly share the project via Binder.

tugboat automatically detects all the packages necessary to replicate your analysis and will generate a Dockerfile that contains an exact copy of your analysis directory with all the essential Python/R packages installed. tugboat uses uv and renv for Python and R dependency management, respectively. As a result, projects that already utilize uv or renv are tugboat compatible with no additional setup.

tugboat may be of use, for example, when preparing a replication package for research. With tugboat, you can take a directory on your local computer and quickly generate a corresponding Dockerfile and Docker image that contains all the code and the necessary software to reproduce your findings.

Installation

Install tugboat from PyPI:

pip install tugboat-cli

or install tugboat from GitHub:

pip install git+https://github.com/dmolitor/tugboat-cli

Usage

The tugboat CLI has three primary commands; one to create a Dockerfile from your analysis directory, one to build the corresponding Docker image, and one to make your project ready to share and run in an online, interactive compute environment via Binder.

Create the Dockerfile

The primary command from tugboat is create. This command converts your analysis directory into a Dockerfile that includes all your code and essential Python packages.

This command scans all files in the analysis directory, detects all Python/R packages, and installs these packages in the resulting Docker image. It also copies the entire contents of the analysis directory into the Docker image. For example, if your analysis directory is named incredible_analysis, the corresponding location of your code and data files in the generated Docker image will be /incredible_analysis.

For the most common use-cases, there are a couple of arguments in this command that are particularly important:

• --exclude/-e: A files or sub-directory in your analysis directory that should NOT be included in the Docker image. This is particularly important when you have, for example, a sub-directory with large data files that would make the resulting Docker image extremely large if included. You can tell tugboat to exclude this sub-directory and then simply mount it to a Docker container as needed. This argument can be provided as many times as necessary.

• --from: The base Docker image to build from. By default, tugboat will try to pick this intelligently. This allows the user to specify a base image that includes e.g. external software that is essential for the analysis, such as Stata.

Below I'll outline a couple examples.

# The simplest scenario where your analysis directory is your current
# working directory, you are fine with the default base "python:3.x-slim"
# Docker image, and you want to include all files/directories:
tugboat create

# Suppose your analysis directory is actually a sub-directory of your
# main project directory:
tugboat create ./sub-directory

# Suppose that you specifically need a Docker base image that has uv
# installed. To do this, we will explicitly specify a different Docker
# base image using the `FROM` argument.
tugboat create --from ghcr.io/astral-sh/uv:latest .

# Finally, suppose that we want to include all files except a couple
# particularly data-heavy sub-directories:
tugboat create -e data/big_directory_1 -e data/big_directory_2 .

Build the Docker image

Once the Dockerfile has been created, we can build the Docker image with the build command. By default this will assume the Dockerfile is located in the current working directory. This function assumes a little knowledge about Docker; if you aren't sure where to start, this is a great starting point.

The following example will do the simplest thing and will build the image locally.

tugboat build --image-name awesome_analysis

Suppose that, like above, your analysis directory is a sub-directory of your main project directory:

tugboat build -d ./sub-directory/Dockerfile --build-context ./sub-directory -n awesome_analysis

Push to DockerHub

If, instead of just building the Docker image locally, you want to build the image and then push to DockerHub, you can make a couple small additions to the code above:

tugboat build \
  -d ./sub-directory/Dockerfile \
  --build-context ./sub-directory \
  -n awesome_analysis \
  --dh-username "$DH_USERNAME" \
  --dh-password "$DH_PASSWORD" \
  --push

Note: If you choose to push, you also need to provide your DockerHub username and password. Typically you don't want to pass these in directly and should instead use environment variables (or a similar method) instead. tugboat will automatically use the environment variables DOCKERHUB_USERNAME and DOCKERHUB_PASSWORD if they are available.

Share your project via Binder

Binder lets others instantly launch and interact with your project in a live, cloud-based environment with no local setup required. tugboat will prepare your project to be shared with Binder. The process is easy; simply prep your directory for Binder with the binderize command:

[!NOTE] Your analysis directory must be a GitHub repository.

binderize -b main ./

By default this will add a Binder badge to your README.md file if it already has a section for badges:

Added badge to /.../README.md

If your README file does not have a section for badges, it will automatically save the badge to your clipboard and you will need to manually insert it into the README.

Add the following to your README.md file:

<!-- badges: start -->
[![Launch RStudio Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/{username}/{repo}/{branch}?urlpath=rstudio)
<!-- badges: end -->

After running tugboat binderize you will see the following message:

Your repository has been configured for Binder.
[x] Commit and push all changes
[x] Launch Binder at: https://mybinder.org/v2/gh/{username}/{repo}/{branch}?urlpath=rstudio

You must commit and push all changes before visiting the Binder link, otherwise it will likely fail. Binder can automatically detect changes to the repository and will rebuild as necessary, ensuring that the Binder repository stays up to date.

Usage - Python package

tugboat is equally easy to use programmatically via the package interface. The following code translates the CLI usage above into the equivalent Python code.

from dotenv import load_dotenv
from tugboat_cli import binderize, build, create
import os

load_dotenv()

# Create the Dockerfile
create(
    project=".",
    exclude=["data/big_directory_1", "data/big_directory_2"]
)

# Build the image
build(
    image_name="awesome_analysis",
    push=True,
    dh_username=os.env["DH_USERNAME"],
    dh_password=os.env["DH_PASSWORD"]
)

# Prep for sharing on Binder
binderize(branch="main")

Examples

For some very simple worked examples, see the examples/ directory.

Python and R packages

tugboat is built on Python- and R- specific packages that are available at tugboat-py and tugboat, respectively.