A production-ready Docker image with Gunicorn and Uvicorn workers for Python web applications, using Poetry for dependency management.
uvicorn-gunicorn-poetry is a Docker image designed to run Python web applications in production. It combines Gunicorn as a process manager with Uvicorn ASGI workers and uses Poetry for dependency management, providing a standardized and optimized environment for frameworks like FastAPI. The image supports multiple Python versions and CPU architectures, making it versatile for various deployment scenarios.
Developers and DevOps engineers deploying Python web applications, especially those using ASGI frameworks like FastAPI, who need a production-ready Docker image with built-in dependency management and process supervision.
It offers a pre-configured, best-practices Docker image that simplifies deployment by integrating Gunicorn, Uvicorn, and Poetry, reducing setup time and ensuring consistency across environments. Its focus on production-grade configurations and multi-architecture support makes it a reliable choice for scalable applications.
Docker image with Gunicorn using Uvicorn workers for running Python web applications. Uses Poetry for managing dependencies and setting up a virtual environment. Supports AMD64 and ARM64 CPU architectures.
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Poetry handles Python package dependencies and sets up a virtual environment automatically, reducing setup complexity as highlighted in the README.
Combines Gunicorn for process management with Uvicorn workers, optimized for micro-frameworks like FastAPI, ensuring efficient request handling.
Supports AMD64 and ARM64 CPU architectures along with multiple Python versions (3.10-3.12), making it versatile for diverse deployment environments.
Environment variables allow fine-tuning of logging, worker processes, and server mechanics, following best practices for production-grade containerization.
Requires a specific file layout without a src folder, which can conflict with existing project structures and add migration overhead.
Optimized for Kubernetes with Prometheus, but defaults to one worker, potentially limiting performance for high-traffic applications not using Kubernetes.
Forces the use of Poetry for dependency management, which may not align with teams standardized on pip or other tools, adding learning curve.