A Dockerfile is a reproducible packaging plan for an application: it defines the base image, filesystem layers, dependencies, configuration, exposed port, and startup command. This turns a Python service into an executable image that can run consistently across deployment environments. section 3.2.2
A practical Dockerfile commonly assembles an application in this order:
- Select a base image with FROM and establish the application directory with WORKDIR. section 3.2.2
- Install operating-system and Python dependencies with RUN, copying requirements before application files. section 3.2.2
- Set runtime configuration with EXPOSE, ENV, and build-time ARG values. section 3.2.2
- Make the startup script executable and define it with ENTRYPOINT. section 3.2.2
Listing 3.2 An example Dockerfile for Python applications
FROM python:3.10-slim-buster #1
WORKDIR /app #2
RUN apt-get update && \
apt-get install --no-install-recommends -y \
build-essential \
&& apt-get clean && rm -rf /tmp/* /var/tmp/*
COPY requirements.txt /app/requirements.txt #3
RUN pip3 install --upgrade pip #3
RUN pip3 install --no-cache-dir -r requirements.txt #3
COPY . /app #4
EXPOSE 8083
ENV PYTHONPATH="/app" #5
ARG environment #5
ENV ENVIRONMENT $environment #5
RUN chmod +x /app/entrypoint.sh #6
ENTRYPOINT ["/app/entrypoint.sh"] #6
Use the complete Python service Dockerfile as a concrete template for layering dependencies, configuration, and startup behavior. section 3.2.2
Build, run, and publish a service image
Build a tagged image with `docker build . -t hello-joker:v1`, run it with `docker run -it -p 8081:8083 hello-joker:v1`, and verify the service through the published host port. Tags such as `v1` distinguish deployable versions; after logging in, tag and push the image to a container registry. section 3.2.3
For Kubeflow notebooks, a multistage Dockerfile can combine a base image, code-server or Jupyter runtime, dependencies, and compatibility configuration. Optimize its layers and image size before organizational deployment. section 7.1.5
