Docker for ML workloads in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

Output of Docker command with ML model container

You run this command to list running Docker containers for your ML model deployment:

docker ps --filter "ancestor=ml-model:latest" --format "{{.Names}}: {{.Status}}"

What is the expected output if one container named model-serving-1 is running for 5 minutes?

A model-serving-1: Created 5 minutes ago

B No containers found

C ml-model:latest: Up 5 minutes

D model-serving-1: Up 5 minutes

💡 Hint

The command filters containers by image and formats output to show container names and status.

The command lists running containers from the image 'ml-model:latest' and shows their names and uptime. 'Up 5 minutes' means the container is running for 5 minutes.

❓ Configuration

intermediate

Correct Dockerfile snippet for ML dependencies

Which Dockerfile snippet correctly installs Python 3.12 and the ML package scikit-learn in a Debian-based image?

A

FROM python:3.12
RUN pip install sklearn

B

FROM debian
RUN apt-get install python3.12 scikit-learn

C

FROM python:3.12-slim
RUN pip install scikit-learn

D

FROM python:3.12
RUN apt-get install scikit-learn

💡 Hint

Use official Python images and pip for Python packages.

Option C uses the official Python 3.12 slim image and installs scikit-learn via pip, which is the correct way to install Python ML packages in Docker.

❓ Troubleshoot

advanced

Diagnosing Docker container crash for ML model

Your ML model container crashes immediately after starting. The Docker logs show:

ModuleNotFoundError: No module named 'tensorflow'

Which Dockerfile change will fix this error?

A Add <code>ENV TENSORFLOW_HOME=/usr/local/tensorflow</code>

B Add <code>RUN pip install tensorflow</code> before the command that runs the model

C Change base image to <code>tensorflow/tensorflow:latest</code>

D Add <code>RUN apt-get install tensorflow</code>

💡 Hint

The error means the Python package tensorflow is missing.

Installing tensorflow via pip inside the Dockerfile ensures the Python environment has the required package. Changing base image might work but is not guaranteed. apt-get does not install Python packages.

🔀 Workflow

advanced

Correct order to build and deploy ML Docker container

Arrange these steps in the correct order to build and deploy an ML model Docker container:
1. Push image to Docker registry
2. Write Dockerfile with ML dependencies
3. Run container on cloud server
4. Build Docker image locally

A 1,2,3,4

B 2,1,3,4

C 1,3,2,4

D 2,3,1,4

💡 Hint

You must write the Dockerfile before building the image.

First write the Dockerfile, then build the image locally, push it to a registry, and finally run the container on the cloud server.

✅ Best Practice

expert

Best practice for managing large ML model files in Docker

You have a large ML model file (2GB) that changes frequently. What is the best practice to handle this in your Docker workflow?

A Mount the model file as a volume at container runtime to avoid rebuilding image

B Include the model file inside the Docker image to keep everything together

C Download the model file inside the container during build time

D Store the model file in environment variables for easy access

💡 Hint

Think about avoiding rebuilding large images when the model changes.

Mounting the model as a volume allows updating the model without rebuilding the Docker image, saving time and storage.

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