TulikaC explains recent upgrades for building and deploying Python apps on Azure App Service for Linux, covering modern tools like pyproject.toml, uv, Poetry, and offering improved DevOps workflows.

What’s New for Python on App Service for Linux: pyproject.toml, uv, and More

Modern Python development and deployment on Azure App Service for Linux has become easier and more robust, thanks to a series of upgrades described by TulikaC. This post covers:

  • Support for pyproject.toml with uv and Poetry: Enabling reproducible, modern Python builds.
  • Continued setup.py support: Backward compatibility for classic workflows.
  • Refreshed .bashrc experience: Improving SSH sessions in Azure App Service Linux containers.
  • Ready-to-use GitHub Actions sample workflows: Simplifying automation for various Python project types.

Key Upgrades

1. pyproject.toml with uv and Poetry

  • uv is a new, fast Python project and package manager written in Rust, delivering rapid dependency resolution and robust workflows (Astral uv docs).
  • If your repository contains both pyproject.toml and uv.lock, Azure App Service for Linux now triggers automated uv builds for repeatable environments without extra configuration.
  • pyproject.toml (per PEP 621) standardizes Python project metadata and dependencies for tools like uv and Poetry.
  • Quickstart steps:
    1. pip install uv
    2. uv init (creates pyproject.toml and a sample main.py)
    3. Add dependencies with uv add <package>, e.g., uv add flask.
    4. Lock dependencies and run code using uv run app.py.

  • A minimal pyproject.toml example for a Flask app:

    [project]
name = "uv-pyproject"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.14"
dependencies = ["flask>=3.1.2",]
  • App Service’s default startup is app.py. If your script is main.py, either rename or specify a startup command (e.g., uv run uvicorn main:app --host 0.0.0.0 --port 8000).
  • During deployment, you’ll see logs confirming uv environment creation and dependency syncing.
  • Projects using Poetry (with pyproject.toml and poetry.lock) are supported equally (Poetry reference).
  • For a working uv sample, check the lowlight-enhancer-uv project.

2. setup.py Support

  • The setup.py format (classic Setuptools packaging) remains fully supported for projects that haven’t switched to pyproject.toml.

  • Minimal example:

    # setup.py
from setuptools import setup, find_packages

setup(
    name="flask-app",
    version="0.1.0",
    packages=find_packages(exclude=("tests",)),
    python_requires=">=3.14",
    install_requires=["Flask>=3.1.2",],
    include_package_data=True,
)
  • During deployment, App Service builds virtual environments and installs dependencies as specified in setup.py.
  • For Setuptools configuration options, consult the Setuptools user guide and keywords reference.

3. Enhanced SSH/Bash Shell Experience

  • Logging into the App Service Linux container via SSH now presents:
    • Clear runtime version information
    • Links to general and Python-specific documentation
    • Instance Name/Id for diagnostics
    • Clean shell startup with error handling improvements (no more confusing auto-activate errors)
  • More shell usability options and quality-of-life updates are planned.

4. GitHub Actions Sample Workflows

Ready-to-use sample workflows for deploying Python apps are available, supporting all major build/deploy scenarios:

To use:

  1. Copy the relevant YAML workflow into your repo’s .github/workflows/ directory.
  2. Set authentication (OIDC with azure/login is recommended).
  3. Fill in required workflow inputs (app name, resource group, any required image/sidecar parameters).
  4. Commit and run the workflow via push or on-demand.

For more on automated deployments, see Microsoft Learn’s deployment with GitHub Actions docs.

What’s Next

Azure App Service for Linux is continuing to deliver new upgrades for Python, aiming for faster builds, better support for AI/ML workloads, and enhanced diagnostics.

Summary

TulikaC’s update helps developers:

  • Use modern Python build tools with Azure App Service for Linux
  • Automate deployments using robust CI/CD pipelines with GitHub Actions
  • Troubleshoot and manage container shell experience with greater ease

Further Reading

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