Should I use Poetry in production dockerfile?-CodePudding

I have a web app built with a framework like FastAPI or Django, and my project uses Poetry to manage the dependencies.

I didn't find any topic similar to this.

The question is: should I install poetry in my production dockerfile and install the dependencies using the poetry, or should I export the requirements.txt and just use pip inside my docker image?

Actually, I am exporting the requirements.txt to the project's root before deploy the app and just using it inside the docker image.

My motivation is that I don't need the "complexity" of using poetry inside a dockerfile, since the requirements.txt is already generated by the poetry and use it inside the image will generate a new step into docker build that can impact the build speed.

However, I have seen much dockerfiles with poetry installation, what makes me think that I am doing a bad use of the tool.

CodePudding user response：

It is generally not recommended to use Poetry in a production Dockerfile because Poetry is a package manager and dependency management tool that is primarily intended for development environments.

In a production environment, it is generally a good practice to minimize the number of dependencies and tools that are required to run your application. Using Poetry in a production Dockerfile would add additional complexity and potentially increase the size of the Docker image.

Instead of using Poetry in a production Dockerfile, you can use a requirements file (such as requirements.txt) to specify the Python packages that your application depends on, and install those packages using pip. This will allow you to keep your production Dockerfile simple and focused on running your application.

Here's an example of how you might do this:

FROM python:3.8-slim

# Copy the requirements file and install the packages
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy the application code
COPY . .

# Run the application
CMD ["python", "app.py"]

This Dockerfile installs the necessary Python packages using pip, and then copies and runs the application code. This approach is simple and efficient, and is well-suited for use in a production environment.