TorhamDev / dockerizing-python-project

1. Create a Dockerfile with the following content:

    # Set base image
    FROM python:3.9

    # Set working directory in container
    WORKDIR /app

    # Install required packages
    COPY ./requirements.txt .
    RUN pip install --no-cache-dir -r requirements.txt

    # Copy project files to container
    COPY . .

    # Expose required ports
    EXPOSE 8000

    # Set default command to run the app
    CMD ["python", "app.py"]
   

2. In your project directory, create a new file called requirements.txt listing all the Python packages that your project depends on or simply use virtualenv and run pip freeze > requirements.txt.

3. Run the following command in your project directory to build the Docker image:

   docker build -t myproject .

   This command tells Docker to build the image, tag it with the name myproject, and use the current directory (.) as the build context.

4. After building the image, you can start a container by running the following command:

   docker run -p 8000:8000 myproject

   This command tells Docker to start a container based on the myproject image, map port 8000 on the host to port 8000 in the container, and run the default command specified in the Dockerfile.

5. Now you should be able to access your Python application by navigating to http://localhost:8000 in your web browser.

That's it! Dockerizing a Python project can be a straightforward process with just a few steps. Of course, depending on your specific project requirements, you may need to modify the Dockerfile or docker run command to suit your needs.

Step-By-Step [detailed]

Create a Dockerfile with the following content:

# Set base image
FROM python:3.9

This line tells Docker to use the official Python 3.9 base image as a starting point for our container. We can use any other valid base image if our project requires a different runtime environment.

# Set working directory in container
WORKDIR /app

This line sets the working directory inside the container to /app. We'll use this directory to copy our project files and run the application.

# Install required packages
COPY ./requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

This line installs the required packages defined in our requirements.txt file inside the container. pip is the Python package manager, and the --no-cache-dir flag tells it not to use any cache files since we're building a new image.

this line should be before copying the source code because when an upper layer is changed docker won't use the cached layer for pip and keeps downloading packges even if requirements.txt isn't changed.

# Copy project files to container
COPY . .

This line tells Docker to copy all of the files in our local directory to the /app directory in the container.

# Expose required ports
EXPOSE 8000

This line tells Docker to expose port 8000 in our container. We'll use this port to access our Python application.

# Set default command to run the app
CMD ["python", "app.py"]

This line sets the default command to run when the container starts. In this case, it's running python app.py. You can replace app.py with the name of your main Python file.

Run the following command in your project directory to build the Docker image:

docker build -t myproject .

This line tells Docker to build an image with the tag myproject based on the Dockerfile in the current directory (.). Docker will process every line of the Dockerfile to build a new image with our project and all its dependencies installed.

After building the image, you can start a container by running the following command:

docker run -p 8000:8000 myproject

This line tells Docker to start a new container based on the myproject image we just built and map port 8000 of the container to port 8000 on the host. We can then access our Python application on http://localhost:8000.

Optionally, you can add the -d flag at the end of this command to run the container in detached mode.

Finally, you can stop the running container using the following command:

docker stop <container-id>

You can get the container ID by running the docker ps command.

That's the basic process for dockerizing a Python project! Remember, some Python projects may require more complex setup, so you may need to modify the Dockerfile or the docker run command according to your specific needs.