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This tutorial builds a small Flask app, packages it as a Docker image, and runs it locally in a container. It is groundwork for Kubernetes, not a cluster deployment: there is no Kubernetes Deployment or Service in these steps.
What you will build
The app returns a greeting assembled from environment variables. You will create the Python files, build an image, and run a container with a customized greeting. The corrected examples below fix three issues in the original tutorial: the GEETING typo, the misspelled Dockerfile name, and invalid dependency pin syntax. The original Day 07 tutorial is useful as a starting point, but its Flask development server and debug setting are for local learning rather than production.
Create the Flask application
Set up the project files
Create a directory for the app and add these files:
app.py
requirements.txt
DockerfileIn app.py, define a route that reads the correctly spelled GREETING variable along with APP_ENV:
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import os
from flask import Flask
app = Flask(__name__)
@app.route("/")
def home():
greeting = os.environ.get("GREETING", "Hello")
environment = os.environ.get("APP_ENV", "development")
return f"{greeting} from Flask in {environment}"
if __name__ == "__main__":
app.run(host="0.0.0.0", port=5001, debug=True)Binding to 0.0.0.0 lets the server accept connections through the container’s network interface. The debug-enabled Flask development server is appropriate only for a local exercise; use a production-grade server and suitable runtime settings for a real application.
Declare the dependency
Python package requirements use comparison specifiers. For an exact version pin, use two equals signs:
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Flask==3.0.3This corrects the single-equals form in the source example, which is not valid requirement pin syntax.
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Build a Docker image
Write the Dockerfile
Save this content in a file named exactly Dockerfile:
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FROM python:3.12-alpine
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY app.py .
EXPOSE 5001
CMD ["python", "app.py"]FROM selects the base image; the Python Alpine image here is one option, not a universal recommendation. A minimal image can reduce included software, but base-image choice also needs to suit application and package compatibility. Tags may point to different image contents over time. Pinning a digest can improve repeatability, while making updates an intentional maintenance step.
WORKDIR sets the working directory, and COPY adds project files to the image. RUN executes while Docker builds the image, so the dependency installation result is included in a build layer. EXPOSE documents the container port; it does not publish that port on your host.
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Understand the container command
CMD supplies a default command when a container starts. The example uses exec form, written as a JSON array, so Docker invokes Python directly without implicitly starting a shell. Shell form runs through a shell, which changes how shell features such as variable expansion work and affects process and signal handling details. Docker documents the forms and their behavior in its Dockerfile reference.
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Build the image
From the project directory, run:
docker build -t flask-greeting:1.0 .The final period tells Docker to use the current directory as the build context. The -t option gives the resulting image a name and tag. If you choose a separate Dockerfile, pass its actual filename with -f; for example, a file named Dockerfile.alpine would use:
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docker build -f Dockerfile.alpine -t flask-greeting:1.0 .Run the app locally in a container
Start a container and map host port 5001 to container port 5001:
docker run --rm -p 5001:5001 -e GREETING="Hello" -e APP_ENV="local" flask-greeting:1.0Open http://localhost:5001/. The response should include “Hello” and “local.” Change the value passed to -e GREETING=... to customize the greeting. The Docker -p option makes a host-to-container port mapping for this local run; it is separate from the image’s EXPOSE metadata.
What changes when you move to Kubernetes?
A Deployment manages the workload
Building and running the image with Docker does not deploy it to Kubernetes. In Kubernetes, a Deployment declares the desired state for an application workload and manages its Pods. The cluster must be able to retrieve the image, so the image reference in the Deployment must point to an image available to that cluster. Consult the Kubernetes Deployments documentation for how Deployments manage Pods and reconcile actual state with the declared state.
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For a Kubernetes workload, set variables such as GREETING and APP_ENV in the Pod configuration using env or envFrom. Values supplied there override variables baked into the image. The Kubernetes environment-variable guide shows the Pod configuration approach. A ConfigMap is intended for ordinary configuration; credentials belong in a Secret as the lesson expands to sensitive settings.
Networking is a separate step
Docker’s -p 5001:5001 does not configure Kubernetes networking. If clients need to reach the app in a cluster, create a Kubernetes Service or use another access method suited to the environment. After applying a Deployment, verify its rollout and inspect Pod status as distinct checks; a running local Docker container is not evidence that a Kubernetes workload is healthy.
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