Debugging Python in Docker: A Tutorial for Learners

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# Introduction

 
Docker has simplified how we develop, ship, and run purposes by offering constant environments throughout totally different methods. Nevertheless, this consistency comes with a trade-off: debugging turns into deceptively advanced for freshmen when your purposes — together with Python purposes — are working inside Docker containers.

For these new to Docker, debugging Python purposes can really feel like making an attempt to repair a automotive with the hood welded shut. You understand one thing’s unsuitable, however you may’t fairly see what’s occurring inside.

This beginner-friendly tutorial will educate you methods to get began with debugging Python in Docker.

# Why is Debugging in Docker Totally different?

 
Earlier than we dive in, let’s perceive why Docker makes debugging difficult. If you’re working Python domestically in your machine, you may:

• See error messages instantly
• Edit information and run them once more
• Use your favourite debugging instruments
• Verify what information exist and what’s in them

However when Python runs inside a Docker container, it is usually trickier and fewer direct, particularly in case you’re a newbie. The container has its personal file system, its personal setting, and its personal working processes.

# Setting Up Our Instance

 
Let’s begin with a easy Python program that has a bug. Don’t be concerned about Docker but; let’s first perceive what we’re working with.

Create a file known as app.py:

def calculate_sum(numbers):
    whole = 0
    for num in numbers:
        whole += num
        print(f"Including {num}, whole is now {whole}")
    return whole

def important():
    numbers = [1, 2, 3, 4, 5]
    outcome = calculate_sum(numbers)
    print(f"Closing outcome: {outcome}")
    
    # This line will trigger our program to crash!
    division_result = 10 / 0
    print(f"Division outcome: {division_result}")

if __name__ == "__main__":
    important()

When you run this usually with python3 app.py, you will see it calculates the sum accurately however then crashes with a “division by zero” error. Simple to identify and repair, proper?

Now let’s see what occurs when this straightforward utility runs inside a Docker container.

# Creating Your First Docker Container

 
We have to inform Docker methods to bundle our Python program. Create a file known as `Dockerfile`:

FROM python:3.11-slim

WORKDIR /app

COPY app.py .

CMD ["python3", "app.py"]

Let me clarify every line:

• FROM python:3.11-slim tells Docker to start out with a pre-made Linux system that already has Python put in
• WORKDIR /app creates an `/app` folder contained in the container and units it because the working listing
• COPY app.py . copies your app.py file out of your laptop into the `/app` folder contained in the container
• CMD ["python3", "app.py"] tells Docker what command to run when the container begins

Now let’s construct and run this container:

docker construct -t my-python-app .
docker run my-python-app

You may see the output, together with the error, however then the container stops and exits. This leaves you to determine what went unsuitable contained in the remoted container.

# 1. Operating an Interactive Debugging Session

 
The primary debugging ability you want is studying methods to get inside a working container and examine for potential issues.

As an alternative of working your Python program instantly, let’s begin the container and get a command immediate inside it:

docker run -it my-python-app /bin/bash

Let me break down these new flags:

• -i means “interactive” — it retains the enter stream open so you may sort instructions
• -t allocates a “pseudo-TTY” — principally, it makes the terminal work correctly
• /bin/bash overrides the traditional command and provides you a bash shell as an alternative

Now that you’ve a terminal contained in the container, you may run instructions like so:

# See what listing you are in
pwd

# Checklist information within the present listing
ls -la

# Have a look at your Python file
cat app.py

# Run your Python program
python3 app.py

You may additionally see the error:

root@fd1d0355b9e2:/app# python3 app.py
Including 1, whole is now 1
Including 2, whole is now 3
Including 3, whole is now 6
Including 4, whole is now 10
Including 5, whole is now 15
Closing outcome: 15
Traceback (most up-to-date name final):
  File "/app/app.py", line 18, in 
    important()
  File "/app/app.py", line 14, in important
    division_result = 10 / 0
                      ~~~^~~
ZeroDivisionError: division by zero

Now you may:

• Edit the file proper right here within the container (although you will want to put in an editor first)
• Discover the setting to know what’s totally different
• Take a look at small items of code interactively

Repair the division by zero error (perhaps change `10 / 0` to `10 / 2`), save the file, and run it once more.

The issue is mounted. If you exit the container, nonetheless, you lose monitor of adjustments you made. This brings us to our subsequent method.

# 2. Utilizing Quantity Mounting for Dwell Edits

 
Would not it’s good in case you may edit information in your laptop and have these adjustments routinely seem contained in the container? That is precisely what quantity mounting does.

docker run -it -v $(pwd):/app my-python-app /bin/bash

The brand new half right here is -v $(pwd):/app:

• $(pwd) outputs the present listing path.
• :/app maps your present listing to /app contained in the container.
• Any file you alter in your laptop instantly adjustments contained in the container too.

Now you may:

1. Edit app.py in your laptop utilizing your favourite editor
2. Contained in the container, run python3 app.py to check your adjustments
3. Maintain enhancing and testing till it really works

This is a pattern output after altering the divisor to 2:

root@3790528635bc:/app# python3 app.py
Including 1, whole is now 1
Including 2, whole is now 3
Including 3, whole is now 6
Including 4, whole is now 10
Including 5, whole is now 15
Closing outcome: 15
Division outcome: 5.0

That is helpful since you get to make use of your acquainted enhancing setting in your laptop and the very same setting contained in the container as effectively.

# 3. Connecting a Distant Debugger from Your IDE

 
When you’re utilizing an Built-in Growth Surroundings (IDE) like VS Code or PyCharm, you may truly join your IDE’s debugger on to code working inside a Docker container. This offers you the complete energy of your IDE’s debugging instruments.

Edit your `Dockerfile` like so:

FROM python:3.11-slim

WORKDIR /app

# Set up the distant debugging library
RUN pip set up debugpy

COPY app.py .

# Expose the port that the debugger will use
EXPOSE 5678

# Begin this system with debugger assist
CMD ["python3", "-m", "debugpy", "--listen", "0.0.0.0:5678", "--wait-for-client", "app.py"]

What this does:

• pip set up debugpy installs Microsoft’s debugpy library.
• EXPOSE 5678 tells Docker that our container will use port 5678.
• The CMD begins our program by means of the debugger, listening on port 5678 for a connection. No adjustments to your Python code are wanted.

Construct and run the container:

docker construct -t my-python-app .
docker run -p 5678:5678 my-python-app

The -p 5678:5678 maps port 5678 from contained in the container to port 5678 in your laptop.

Now in VS Code, you may arrange a debug configuration (in .vscode/launch.json) to hook up with the container:

{
    "model": "0.2.0",
    "configurations": [
        {
            "name": "Python: Remote Attach",
            "type": "python",
            "request": "attach",
            "connect": {
                "host": "localhost",
                "port": 5678
            }
        }
    ]
}

If you begin debugging in VS Code, it’ll connect with your container, and you may set breakpoints, examine variables, and step by means of code similar to you’d with native code.

# Frequent Debugging Issues and Options

 
⚠️ “My program works on my laptop however not in Docker”

This normally means there is a distinction within the setting. Verify:

• Python model variations.
• Lacking dependencies.
• Totally different file paths.
• Surroundings variables.
• File permissions.

⚠️ “I can not see my print statements”

• Use python -u to keep away from output buffering.
• Be sure to’re working with -it if you would like interactive output.
• Verify in case your program is definitely working as supposed (perhaps it is exiting early).

⚠️ “My adjustments aren’t displaying up”

• Be sure to’re utilizing quantity mounting (-v).
• Verify that you just’re enhancing the correct file.
• Confirm the file is copied into the container.

⚠️ “The container exits instantly”

• Run with /bin/bash to examine the container’s state.
• Verify the error messages with docker logs container_name.
• Be sure that your CMD within the Dockerfile is appropriate.

# Conclusion

 
You now have a fundamental toolkit for debugging Python in Docker:

1. Interactive shells (docker run -it ... /bin/bash) for exploring and fast fixes
2. Quantity mounting (-v $(pwd):/app) for enhancing in your native file system
3. Distant debugging for utilizing your IDE’s full capabilities

After this, you may strive utilizing Docker Compose for managing advanced purposes. For now, begin with these easy methods. Most debugging issues may be solved simply by getting contained in the container and poking round.

The secret is to be methodical: perceive what needs to be occurring, determine what is definitely occurring, after which bridge the hole between the 2. Completely satisfied debugging!
 
 

Bala Priya C is a developer and technical author from India. She likes working on the intersection of math, programming, knowledge science, and content material creation. Her areas of curiosity and experience embrace DevOps, knowledge science, and pure language processing. She enjoys studying, writing, coding, and occasional! Presently, she’s engaged on studying and sharing her information with the developer neighborhood by authoring tutorials, how-to guides, opinion items, and extra. Bala additionally creates partaking useful resource overviews and coding tutorials.