tugboat-py 0.1.1

Simple utilities to generate a Dockerfile from a directory or project, build the corresponding Docker image, push the image to DockerHub, and publicly share the project via Binder.

tugboat

A simple Python package to generate a Dockerfile and corresponding Docker image from an analysis directory. tugboat also prepares your analysis repository to be shared via Binder.

tugboat uses the pigar package to automatically detect all the packages necessary to replicate your analysis and will generate a Dockerfile that contains an exact copy of your entire directory with all the packages installed. tugboat transforms an unstructured analysis folder into a requirements.txt file and constructs a Docker image that includes all your essential R packages based on this file. tugboat utilizes uv under the hood; as a result, projects that already utilize uv should be directly 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-py

or install tugboat from GitHub:

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

Usage

tugboat has three primary functions; 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 function from tugboat is create(). This function converts your analysis directory into a Dockerfile that includes all your code and essential Python packages.

This function scans all files in the current analysis directory, attempts to detect all Python 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 function that are particularly important:

• project: This argument tells tugboat which directory is the one to generate the Dockerfile from. You can set this value yourself, or you can just use the default value. By default, tugboat uses the working directory to determine the analysis directory.
• exclude: A list of files or sub-directories 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.

Below I'll outline a couple examples.

from tugboat import create

# 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:
create()

# Suppose your analysis directory is actually a sub-directory of your
# main project directory:
create(project="./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.
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:
create(exclude=["data/big_directory_1", "data/big_directory_2"])

Build the Docker image

Once the Dockerfile has been created, we can build the Docker image with the build() function. 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.

build(image_name="awesome_analysis")

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

build(
    dockerfile="./sub-directory",
    build_context="./sub-directory",
    image_name="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:

import os
from dotenv import load_dotenv
from tugboat import build

load_dotenv()

build(
    dockerfile="./sub-directory",
    build_context="./sub-directory",
    image_name="awesome_analysis",
    push=True,
    dh_username=os.environ["DOCKERHUB_USERNAME"],
    dh_password=os.environ["DOCKERHUB_USERNAME"]
)

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.

Share your project via Binder

Binder lets others instantly launch and interact with your R 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 simple:

• First, create the Dockerfile from your analysis directory:

  create(
      project=".",
      exclude=["data/big_directory_1", "data/big_directory_2"]
  )
  

• Then, prep your directory for Binder. Your analysis directory must be a GitHub repository:

  binderize(branch="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 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.

R package

This package has a sibling R package!