Docker containerization in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Dockerfile RUN command

What will be the output when building this Dockerfile snippet?

ML Python

FROM python:3.8-slim
RUN echo "Hello from Docker" > /message.txt
RUN cat /message.txt

A Hello from Docker

B Hello from Docker\nHello from Docker

C No output

D Error: /message.txt not found

💡 Hint

RUN commands execute during build and output their results in the build logs.

The RUN command executes shell commands during image build. The first RUN writes the message, the second RUN reads and outputs it.

❓ Model Choice

intermediate

Choosing the best Docker base image for ML model deployment

You want to deploy a TensorFlow model with GPU support in a Docker container. Which base image is the best choice?

A ubuntu:20.04

B python:3.9-slim

C tensorflow/tensorflow:latest-gpu

D node:16-alpine

💡 Hint

Look for images that include TensorFlow and GPU support.

The tensorflow/tensorflow:latest-gpu image includes TensorFlow with GPU support pre-installed, making it ideal for deploying GPU models.

❓ Hyperparameter

advanced

Optimizing Docker container for faster ML training

Which Dockerfile instruction helps reduce image size and speeds up ML training container startup?

A Combining RUN commands with && to reduce layers

B Installing all packages in separate RUN commands

C Using multiple RUN commands separately

D Using COPY after RUN commands

💡 Hint

Fewer layers in Docker images reduce size and improve startup.

Combining RUN commands with && creates fewer layers, reducing image size and improving container startup time.

🔧 Debug

advanced

Debugging Docker container failing to access GPU

You built a Docker container for ML training with GPU support, but inside the container, the GPU is not detected. What is the most likely cause?

A The host machine does not have a GPU

B The container was not run with the --gpus flag

C The Docker image does not have CUDA libraries installed

D The Dockerfile uses python:3.8-slim base image

💡 Hint

GPU access requires special runtime flags when running containers.

To access GPUs inside a container, you must run it with the --gpus flag; otherwise, the container cannot see the GPU hardware.

❓ Metrics

expert

Measuring Docker container startup time impact on ML inference latency

You want to measure how Docker container startup time affects ML model inference latency in production. Which approach is best?

A Ignore container startup time as it does not affect inference latency

B Include container startup time in latency measurement for each inference request

C Measure container startup time separately and add average to inference latency

D Measure inference latency only after container is fully started

💡 Hint

Inference latency should reflect only the model prediction time during normal operation.

Inference latency metrics typically exclude container startup time because startup happens once, not per request.

Practice

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

easy

A. It replaces the need for training data

B. It automatically improves model accuracy

C. It packages code and environment together for consistent runs

D. It speeds up GPU computations directly

Solution

1. Step 1: Understand Docker's purpose

   Docker bundles your ML code with all software and libraries needed.

2. Step 2: Identify the benefit for ML projects

   This bundling ensures your code runs the same on any machine, avoiding environment issues.

3. Final Answer:

   It packages code and environment together for consistent runs -> Option C

4. Quick Check:

   Docker = consistent environment [OK]

Hint: Docker bundles code + environment for consistency [OK]

Common Mistakes:

• Thinking Docker improves model accuracy
• Believing Docker replaces training data
• Assuming Docker speeds up hardware directly

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 build ml_container ml_image

B. docker start ml_image --name ml_container

C. docker create ml_container ml_image

D. docker run --name ml_container ml_image

Solution

1. Step 1: Recall Docker run command usage

   The docker run command starts a container from an image and can assign a name with --name.

2. Step 2: Check each option

   docker run --name ml_container ml_image correctly uses docker run --name container image. Others misuse commands: start needs container ID, create creates but doesn't start, build builds images.

3. Final Answer:

   docker run --name ml_container ml_image -> Option D

4. Quick Check:

   docker run + --name = start container [OK]

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

Common Mistakes:

• Using 'docker start' with image name instead of container
• Confusing 'docker create' with 'docker run'
• Using 'docker build' to start containers

3. Given this Dockerfile snippet for an ML project:

FROM python:3.8-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 container?

medium

A. It only copies files but does not install dependencies

B. It installs dependencies and runs train.py inside the container

C. It runs train.py on the host machine, not inside container

D. It fails because CMD syntax is incorrect

Solution

1. Step 1: Analyze Dockerfile commands

   The Dockerfile uses FROM python:3.8-slim to get Python, sets working directory, copies requirements, installs them, copies code, then runs train.py.

2. Step 2: Understand container build and run

   Building creates an image with dependencies installed. Running executes python train.py inside the container environment.

3. Final Answer:

   It installs dependencies and runs train.py inside the container -> Option B

4. Quick Check:

   Dockerfile installs + runs script inside container [OK]

Hint: Dockerfile RUN installs, CMD runs script inside container [OK]

Common Mistakes:

• Thinking dependencies install on host, not container
• Believing CMD runs outside container
• Assuming CMD syntax is invalid here

4. You wrote a Dockerfile but when building the image, you get an error: ERROR: Could not open requirements.txt. What is the most likely cause?

medium

A. requirements.txt is not in the build context directory

B. Python version in FROM is too old

C. CMD syntax is wrong

D. Docker daemon is not running

Solution

1. Step 1: Understand build context and COPY command

   The COPY command copies files from the build context (folder where docker build runs). If requirements.txt is missing there, COPY fails.

2. Step 2: Check error cause

   Error says requirements.txt can't be opened, so likely it's not in the folder where build runs, causing failure.

3. Final Answer:

   requirements.txt is not in the build context directory -> Option A

4. Quick Check:

   Missing file in build context = COPY error [OK]

Hint: Ensure files are in build folder before docker build [OK]

Common Mistakes:

• Blaming Python version for file not found
• Assuming CMD syntax causes file errors
• Not checking if Docker daemon is running

5. You want to share your ML model training environment with a teammate. Which Docker approach ensures they get the exact same setup including Python version, libraries, and your training script?

hard

A. Create a Dockerfile specifying Python version, dependencies, and copy training code

B. Send them your Python script and ask them to install dependencies manually

C. Use a virtual environment on your machine and share it via email

D. Only share the trained model file without environment details

Solution

1. Step 1: Identify how to package environment and code

   A Dockerfile lets you specify exact Python version, libraries, and copies your code, creating a reproducible image.

2. Step 2: Compare options for sharing environment

   Manual installs or virtual envs are error-prone and not portable. Sharing only model misses environment needed for training.

3. Final Answer:

   Create a Dockerfile specifying Python version, dependencies, and copy training code -> Option A

4. Quick Check:

   Dockerfile = reproducible ML environment [OK]

Hint: Use Dockerfile to share full ML environment [OK]

Common Mistakes:

• Thinking manual installs guarantee same environment
• Believing virtualenv can be emailed and run anywhere
• Ignoring environment when sharing model files