You want to avoid having to continually update the Dockerfile to install necessary system-level dependencies when you add a new Python package. When in doubt, start with a *-slim flavor, especially in development mode, as you're building your application. Refer to The best Docker base image for your Python application and Using Alpine can make Python Docker builds 50× slower for more on why it's best to avoid using Alpine-based base images. As a result, you may end up having to build the binaries yourself, which can increase the image size (depending on the required system-level dependencies) and the build times (due to having to compile from the source). While the Alpine flavor, based on Alpine Linux, is the smallest, it can often lead to increased build times if you can't find compiled binaries that work with it. Since only the final stage is used to create the image, the dependencies and tools associated with building your application are discarded, leaving a lean and modular production-ready image. For example, you can have a stage for compiling and building your application, which can then be copied to subsequent stages. Multi-stage Docker builds allow you to break up your Dockerfiles into several stages. Take advantage of multi-stage builds to create leaner, more secure Docker images. Use a Shared Memory Mount for Gunicorn Heartbeat.Lint and Scan Your Dockerfiles and Images.Understand the Difference Between ENTRYPOINT and CMD.Cache Python Packages to the Docker Host.Order Dockerfile Commands Appropriately.
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