fast, full-stack Python (uv & Docker)

python

docker

Author

Cody

Published

November 25, 2024

Using uv and Docker to develop and deploy simple, scalable, full-stack Python applications.

In this post, we’ll look at my Dockerfile:

# dkdc Dockerfile
FROM ghcr.io/astral-sh/uv:latest AS uv
FROM python:3.12-slim
# set the environment variables
ENV PATH=/root/.local/bin:$PATH
# set the working directory
WORKDIR /app
# copy the files
COPY readme.md /app/readme.md
COPY pyproject.toml /app/pyproject.toml
COPY src /app/src
# install the Python packages
RUN --mount=type=cache,target=/root/.cache/uv \
    --mount=from=uv,source=/uv,target=./uv \
    ./uv pip install '.' --system --upgrade

I use it to develop simple, scalable, full-stack Python applications built with:

Typer: CLI
Ibis: table management and queries
- SQLite and/or Postgres: OLTP database
- DuckDB: OLAP database (views on top of the OLTP tables)
Shiny for Python: GUI
- Plotly: plots

Then, deploy them with ease (and minimal build times) locally, on a Raspberry Pi, and on a cloud VM. Of course, other open source software and Python packages are used along the way (and you can use whatever you like!).

Some miscellaneous notes:

order: the Dockefile is ordered so that frequently changing parts are toward the bottom
cache: the big point is the caching line, which I stole from somewhere (here?)
pyproject.toml: the pyproject.toml is needed as it defines the installation of the package (and CLI)
readme.md: the readme.md is needed because it’s referenced in the pyproject.toml
src: this is the source code, in something like src/my_package
pinning verions: in general I’m not pinning (or using a uv lockfile), but I would obviously recommend doing so for production workloads and having a process for updating dependencies
system installation: we use system Python instead of creating a Python virtual environment (which uv makes easy) because we’re already in a virtual container (often itself in a virtual machine)

Combining this with a few justfile commands makes development and deployment very easy. The simplicity of Python and tools that cover the full stack (CLI, database, GUI) make it a great choice for rapidly prototyping scalable applications.