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

Docker for ML Workloads

📖 Scenario: You are a data scientist who wants to share a machine learning model with your team. To make sure everyone can run the model easily, you decide to use Docker. Docker will package your model and its environment so it works the same on any computer.

🎯 Goal: Build a simple Docker setup that packages a Python script for a machine learning model. You will create the Python script, write a Dockerfile to set up the environment, build the Docker image, and run the container to see the model output.

📋 What You'll Learn

Create a Python script named model.py that prints a simple message simulating a model prediction.

Write a Dockerfile that uses a Python base image and copies model.py into the container.

Build a Docker image named ml-model from the Dockerfile.

Run a Docker container from the ml-model image and display the output.

💡 Why This Matters

🌍 Real World

Docker helps data scientists share ML models easily by packaging code and environment together. This avoids 'it works on my machine' problems.

💼 Career

Understanding Docker is essential for ML engineers and data scientists to deploy models reliably and collaborate with teams.

1

Create the Python script for the ML model

Create a file named model.py with a Python print statement that outputs exactly: "Model prediction: 42".

MLOps

# Create model.py with a print statement
# Your code here

Hint

Use the print() function to display the message.

2

Write the Dockerfile to set up the environment

Create a Dockerfile that uses the Python 3.12 base image, copies model.py into the container, and sets the default command to run python model.py.

MLOps

# Write Dockerfile content below
# Your code here

Hint

Use FROM to specify the base image, COPY to add files, WORKDIR to set the folder, and CMD to run the script.

3

Build the Docker image named ml-model

Run the Docker build command to create an image named ml-model using the current directory as context.

MLOps

# Run the docker build command below
# Your code here

Hint

Use docker build -t ml-model . to build the image with the tag ml-model.

4

Run the Docker container and display the output

Run a Docker container from the ml-model image using the command docker run ml-model and display the output.

MLOps

# Run the docker container below
# Your code here

Hint

Use docker run ml-model to start the container and see the printed message.

Practice

1. What is the main benefit of using Docker for ML workloads?

easy

A. It provides a graphical interface for ML model training.

B. It automatically improves the accuracy of ML models.

C. It replaces the need for data preprocessing.

D. It packages the ML project with all dependencies to run anywhere.

Solution

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

   Docker packages the ML project with all needed tools and code, ensuring consistency.

2. Step 2: Identify the main benefit

   This packaging allows the ML workload to run the same way on any machine without setup issues.

3. Final Answer:

   It packages the ML project with all dependencies to run anywhere. -> Option D

4. Quick Check:

   Docker ensures consistent ML environment = D [OK]

Hint: Docker bundles code and tools for consistent runs anywhere [OK]

Common Mistakes:

• Thinking Docker improves model accuracy
• Believing Docker replaces data preprocessing
• Assuming Docker provides a GUI for training

2. Which of the following is the correct syntax to start a Docker container named ml_container from an image called ml_image?

easy

A. docker start ml_image --name ml_container

B. docker create ml_image ml_container

C. docker run --name ml_container ml_image

D. docker build ml_container ml_image

Solution

1. Step 1: Recall Docker run command syntax

   The command to start a container with a name is: docker run --name [container_name] [image_name].

2. Step 2: Match the correct syntax

   docker run --name ml_container ml_image matches this syntax exactly, starting a container named ml_container from ml_image.

3. Final Answer:

   docker run --name ml_container ml_image -> Option C

4. Quick Check:

   docker run --name container image = B [OK]

Hint: Use 'docker run --name' to start named containers [OK]

Common Mistakes:

• Using docker start instead of docker run to create container
• Confusing docker build with running containers
• Wrong order of arguments in command

3. Given this Dockerfile snippet for an ML project:

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 run docker build -t ml_train . followed by docker run ml_train?

medium

A. The container only copies files but does not run train.py.

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

C. The build command fails due to missing CMD syntax.

D. The container runs but does not install dependencies.

Solution

1. Step 1: Analyze Dockerfile commands

   The Dockerfile installs Python 3.12, sets /app as working directory, copies requirements.txt, installs dependencies, copies all files, then sets command to run train.py.

2. Step 2: Understand build and run behavior

   docker build creates an image with dependencies installed. docker run starts a container that runs train.py automatically as CMD is set.

3. Final Answer:

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

4. Quick Check:

   Dockerfile CMD runs train.py after build and run = A [OK]

Hint: CMD runs train.py after build and run commands [OK]

Common Mistakes:

• Thinking CMD is ignored during run
• Assuming build fails without explicit entrypoint
• Believing dependencies install at run time

4. You wrote this Dockerfile for your ML project:

FROM python:3.12
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD python train.py

When building the image, you get an error: pip: command not found. What is the likely cause?

medium

A. The base image python:3.12 does not include pip by default.

B. The COPY command is incorrect and did not copy requirements.txt.

C. The CMD syntax is wrong and causes build failure.

D. The WORKDIR is set after COPY, causing path issues.

Solution

1. Step 1: Check base image contents

   Some python base images do not include pip by default, causing 'pip: command not found' error.

2. Step 2: Verify other commands

   COPY and WORKDIR are correct; CMD syntax is valid for shell form. The error points to missing pip in base image.

3. Final Answer:

   The base image python:3.12 does not include pip by default. -> Option A

4. Quick Check:

   Missing pip in base image causes error = A [OK]

Hint: Check if base image includes pip before installing packages [OK]

Common Mistakes:

• Blaming COPY command for pip error
• Thinking CMD syntax causes build error
• Ignoring base image contents

5. You want to optimize your Dockerfile for faster ML model training iterations by caching dependencies. Which change helps achieve this?

hard

A. Copy only requirements.txt and run pip install before copying the rest of the code.

B. Copy all files first, then run pip install to include all dependencies.

C. Run pip install after CMD to delay installation.

D. Use docker run to install dependencies each time the container starts.

Solution

1. Step 1: Understand Docker layer caching

   Docker caches layers. If requirements.txt changes, only pip install layer rebuilds, speeding up builds.

2. Step 2: Apply caching best practice

   Copying requirements.txt and installing dependencies before copying other code avoids reinstalling packages when code changes.

3. Final Answer:

   Copy only requirements.txt and run pip install before copying the rest of the code. -> Option A

4. Quick Check:

   Separate requirements.txt copy for caching = C [OK]

Hint: Copy requirements.txt first to cache pip install layer [OK]

Common Mistakes:

• Copying all files before pip install causing cache misses
• Running pip install after CMD which never executes during build
• Installing dependencies at container start wasting time