Docker for ML reproducibility in MLOps - Mini Project: Build & Apply

Docker for ML reproducibility

📖 Scenario: You are a data scientist working on a machine learning project. You want to make sure your code runs the same way on any computer. This helps your team avoid problems when sharing your work.Docker is a tool that packages your code, libraries, and settings into one container. This container works the same everywhere.

🎯 Goal: Build a simple Docker setup that runs a Python script for training a machine learning model. You will create a Dockerfile, add a configuration, run the training script inside the container, and see the output.

📋 What You'll Learn

Create a Python script named train.py that prints a message about training.

Write a Dockerfile that uses Python 3.12 image and copies train.py inside.

Add a configuration variable inside train.py to set the number of training epochs.

Run the training script inside the Docker container and print the training message with epochs.

💡 Why This Matters

🌍 Real World

Data scientists use Docker to share ML projects that run the same on any computer or cloud.

💼 Career

Knowing Docker helps ML engineers and DevOps teams ensure reproducible and reliable ML workflows.

1

Create the training script

Create a Python file named train.py with a variable message set to the string "Training model..." and a print statement that prints message.

MLOps

# Your code here

Hint

Use a variable called message and set it to the exact string. Then print it.

2

Write the Dockerfile

Create a file named Dockerfile that starts with FROM python:3.12-slim, copies train.py into the container, and sets the command to run python train.py.

MLOps

message = "Training model..."
print(message)

# Your Dockerfile content here

Hint

Use the official Python 3.12 slim image. Copy the script and set the working directory. Use CMD to run the script.

3

Add training epochs configuration

In train.py, add a variable epochs set to 5. Change the print statement to print f"Training model for {epochs} epochs..." using an f-string.

MLOps

message = "Training model..."
print(message)

# Add epochs variable and update print statement
# Your code here

Hint

Remove the old message variable. Use epochs = 5 and an f-string in print.

4

Run the Docker container and show output

Run the Docker container using the command docker build -t ml-train . to build the image, then docker run ml-train to run it. Write a print statement in your script that outputs exactly Training model for 5 epochs....

MLOps

epochs = 5
print(f"Training model for {epochs} epochs...")

# Run the container and print output
# Your code here

Hint

Make sure your print statement matches the output exactly. Build and run the Docker container as instructed.

Practice

1. What is the main benefit of using Docker for machine learning projects?

easy

A. It replaces the need for writing ML code.

B. It automatically improves the accuracy of ML models.

C. It ensures the ML code runs the same way on any machine.

D. It speeds up the training process by using GPUs only.

Solution

1. Step 1: Understand Docker's purpose in ML

   Docker packages the code and environment so it runs identically anywhere.

2. Step 2: Compare options

   Only 'It ensures the ML code runs the same way on any machine.' describes this reproducibility benefit correctly.

3. Final Answer:

   It ensures the ML code runs the same way on any machine. -> Option C

4. Quick Check:

   Docker = consistent environment [OK]

Hint: Docker = same environment everywhere [OK]

Common Mistakes:

• Thinking Docker improves model accuracy automatically
• Believing Docker replaces ML coding
• Assuming Docker only speeds up training

2. Which of the following is the correct way to start a Docker container from an image named ml-image?

easy

A. docker run ml-image

B. docker start ml-image

C. docker build ml-image

D. docker create ml-image

Solution

1. Step 1: Identify the command to run a container

   The docker run command starts a container from an image.

2. Step 2: Understand other commands

   docker start starts stopped containers, docker build creates images, docker create creates containers but does not start them.

3. Final Answer:

   docker run ml-image -> Option A

4. Quick Check:

   Run container = docker run [OK]

Hint: Use 'docker run' to start containers from images [OK]

Common Mistakes:

• Using 'docker start' to run new containers
• Confusing 'docker build' with running containers
• Using 'docker create' without starting container

3. Given this Dockerfile snippet:

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt ./
RUN pip install -r requirements.txt
COPY . ./
CMD ["python", "train.py"]

What happens when you build and run this Docker image?

medium

A. The container installs dependencies and runs train.py automatically.

B. The container only copies files but does not install dependencies.

C. The container runs train.py without installing dependencies.

D. The container fails because requirements.txt is missing.

Solution

1. Step 1: Analyze Dockerfile steps

   The Dockerfile sets Python 3.12, copies requirements.txt, installs dependencies, copies code, then runs train.py.

2. Step 2: Understand build and run behavior

   Building installs dependencies; running executes train.py automatically as CMD defines the command.

3. Final Answer:

   The container installs dependencies and runs train.py automatically. -> Option A

4. Quick Check:

   Dockerfile CMD runs train.py after setup [OK]

Hint: CMD runs script after dependencies installed [OK]

Common Mistakes:

• Assuming dependencies are not installed
• Thinking CMD runs during build, not run
• Believing files are not copied before run

4. You wrote a Dockerfile but when running the container, your ML code fails with "ModuleNotFoundError". What is the most likely cause?

medium

A. You forgot to copy your code files into the image.

B. You did not expose the correct port in Dockerfile.

C. You used the wrong base image version.

D. You did not install the required Python packages.

Solution

1. Step 1: Understand ModuleNotFoundError meaning

   This error means Python cannot find a required package or module.

2. Step 2: Identify cause related to Dockerfile

   Not installing required packages (missing pip install) causes this error, not copying code or ports.

3. Final Answer:

   You did not install the required Python packages. -> Option D

4. Quick Check:

   ModuleNotFoundError = missing packages [OK]

Hint: Missing packages cause ModuleNotFoundError [OK]

Common Mistakes:

• Blaming missing code files instead of packages
• Confusing port exposure with module errors
• Assuming base image version always causes this

5. You want to ensure your ML training runs reproducibly with Docker, including specific Python version, dependencies, and data files. Which Dockerfile snippet best achieves this?

hard

A. FROM ubuntu:latest
RUN apt-get update
COPY train.py ./
CMD ["python", "train.py"]

B. FROM python:3.12
WORKDIR /app
COPY requirements.txt ./
RUN pip install -r requirements.txt
COPY data/ ./data/
COPY train.py ./
CMD ["python", "train.py"]

C. FROM python:latest
COPY train.py ./
CMD ["python", "train.py"]

D. FROM python:3.12
RUN pip install numpy
CMD ["python", "train.py"]

Solution

1. Step 1: Check for full environment setup

   FROM python:3.12 WORKDIR /app COPY requirements.txt ./ RUN pip install -r requirements.txt COPY data/ ./data/ COPY train.py ./ CMD ["python", "train.py"] sets Python 3.12, installs dependencies from requirements.txt, copies data and code, then runs training.

2. Step 2: Compare other options

   The other options miss dependencies, data files, or use generic Python versions, risking non-reproducibility.

3. Final Answer:

   FROM python:3.12 WORKDIR /app COPY requirements.txt ./ RUN pip install -r requirements.txt COPY data/ ./data/ COPY train.py ./ CMD ["python", "train.py"] -> Option B

4. Quick Check:

   Complete setup = reproducibility [OK]

Hint: Copy code, data, install deps, set Python version [OK]

Common Mistakes:

• Skipping dependency installation
• Not copying data files needed for training
• Using generic or latest Python versions