Previously named poetry-dockerize-plugin

Key features:

• Automatically generate a docker image from your uv/poetry application.
• PEP-621 compliant.
• 100% configurable. You can configure the image by adding a section in the pyproject.toml configuration file.

1. Install it as a dev dependency:

uv add dockerpyze --dev

2. Configure entrypoint in pyproject.toml:

[tool.dpy]
entrypoint = "uv run <your-script>"

3. Now straight to the point:

uv run dockerpyze
>No .dockerignore found, using a good default one 😉
>Building image: dockerpyze:latest 🔨
Successfully built images: ✅  (0.3s)
  - dockerpyze:latest

4. Tell your friends about this library 😉

0. Move your project to uv... well if you can't do it, you can still use poetry.
1. Install the freaking plugin:

poetry self add dockerpyze@latest

2. Configure entrypoint in pyproject.toml:

[tool.dpy]
entrypoint = "poetry run <your-script>"

3. Now straight to the point:

poetry dockerpyze
>No .dockerignore found, using a good default one 😉
>Building image: dockerpyze:latest 🔨
Successfully built images: ✅  (0.3s)
  - dockerpyze:latest

4. Tell your friends about this library 😉 (and then switch to uv)

To customize some options, you can add a [tool.dpy] section in your pyproject.toml file. For example to change the image name:

[tool.dpy]
name = "myself/myproject-app"

You can also pass any option via environment variable by prefixing the key with DPY_. For example, to set the entrypoint you can use the DPY_ENTRYPOINT environment variable:

export DPY_ENTRYPOINT="python -m myapp"
uv run dockerpyze

or use a .env file which will be loaded by the plugin:

echo "DPY_ENTRYPOINT=python -m myapp" > .env
poetry dockerpyze

For dicts such as env and labels, you can set multiple values by adding multiple variables:

export DPY_ENV_MY_VAR="my_value"
export DPY_ENV_MY_OTHER_VAR="my_other_value"
export DPY_LABELS_MY_LABEL="label1"
poetry dockerpyze

You just need to run the quickstart command in your GitHub Actions workflow:

name: Build and publish latest

on:
  push:
    branches: main

jobs:
  login:
    runs-on: ubuntu-latest
    steps:
        - name: Check out the repo
          uses: actions/checkout@v3

        - name: "Setup: Python 3.11"
          uses: actions/setup-python@v4

        - name: Install uv
          run: python -m pip install uv

        - name: Build and package
          run: |
            uv sync
            uv run ruff 
            uv run pytest
            uv run dockerpyze

        - name: Login to Docker Hub
          uses: docker/login-action@v3
          with:
            username: ${{ secrets.DOCKERHUB_USERNAME }}
            password: ${{ secrets.DOCKERHUB_TOKEN }}

        - name: Push to Docker Hub
          run: docker push my-app:latest

This examples shows a complete configuration of the docker image:

[tool.dpy]
name = "alternative-image-name"
python = "3.12"
base-image = "python:3.12-slim"
tags = ["latest-dev"]
entrypoint = ["python", "-m", "whatever"]
ports = [5000]
env = {"MY_APP_ENV" = "dev"}
labels = {"MY_APP_LABEL" = "dev"}
apt-packages = ["curl"]
extra-run-instructions = ["RUN curl https://huggingface.co/transformers/"]

# Only for build docker layer
build-apt-packages = ["gcc"]
extra-build-instructions = ["RUN poetry config http-basic.foo <username> <password>"]
build-poetry-install-args = ["-E", "all", "--no-root"]

• name customizes the docker image name.
• python python version to use. If not specified, will try to be extracted from tool.poetry.dependencies.python. Default is 3.11
• base-image customizes the base image. If not defined, the default base image is python:<python-version>-slim-bookworm.
• tags declares a list of tags for the image.
• entrypoint customizes the entrypoint of the image. If not provided, the default entrypoint is retrieved from the packages configuration.
• ports exposes ports
• env declares environment variables inside the docker image.
• labels append labels to the docker image. Default labels are added following the opencontainers specification.
• apt-packages installs apt packages inside the docker image.
• extra-run-instructions adds extra instructions to the docker run (after poetry install). Any modification to the filesystem will be kept after the poetry install.

For the build step:

• build-apt-packages installs apt packages inside the build docker container.
• extra-build-instructions adds extra instructions to the docker build (before poetry install). Any modification to the filesystem will be lost after the poetry install. If you need to add files to the image, use the extra-run-instructions.
• build-poetry-install-args adds additional arguments to the poetry install command in the build step.

All command line options provided by the dockerpyze may be accessed by typing:

uv run dockerpyze --help
poetry dockerpyze --help

To troubleshoot the plugin, you can use the --debug flag to get more information about the execution.

poetry dockerpyze --debug

The build is broken and --debug is completely useless? I get it. You can generate the Dockerfile and manually build it to have more control over the problem.

uv run dockerpyze --generate
docker build Dockerfile .

It's totally fine to use the --generate flag to generate the initial Dockerfile and then customize it. I don't mind.

This project is licensed under the terms of the MIT license.

1. Open an issue
2. (optional) Open a pull request and I'll merge it, maybe.