Docker for ML workloads in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the base image for a Dockerfile used in ML workloads.

MLOps

FROM [1]

Drag options to blanks, or click blank then click option'

Aalpine:3.12

Bubuntu:latest

Cpython:3.9-slim

Dnode:14

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a base image without Python installed.

Using a Node.js image for Python ML workloads.

The FROM instruction sets the base image. For ML workloads, a Python image like python:3.9-slim is commonly used.

2fill in blank

medium

Complete the Dockerfile command to copy your ML model files into the container.

MLOps

COPY [1] /app/model/

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Amodel/

Bsrc/

Cdata/

Dscripts/

Attempts:

3 left

💡 Hint

Common Mistakes

Copying the wrong directory like src/ or data/.

Forgetting to copy the model files into the container.

The COPY command copies files from your local model/ directory into the container's /app/model/ directory.

3fill in blank

hard

Fix the error in the Dockerfile command to install Python dependencies from requirements.txt.

MLOps

RUN pip install [1] requirements.txt

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A-r

B-e

C--upgrade

D--no-cache-dir

Attempts:

3 left

💡 Hint

Common Mistakes

Using flags that do not specify a requirements file.

Omitting the flag and causing pip to treat requirements.txt as a package name.

The pip install command requires the -r flag to install packages listed in a requirements file.

4fill in blank

hard

Fill both blanks to expose port 5000 and set the command to run the ML app.

MLOps

EXPOSE [1]
CMD ["python", "[2]"]

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A5000

Bapp.py

Cmain.py

D8080

Attempts:

3 left

💡 Hint

Common Mistakes

Exposing the wrong port like 8080.

Running a wrong or non-existent Python script.

Port 5000 is commonly used for ML web apps. The command runs app.py using Python.

5fill in blank

hard

Fill all three blanks to create a Docker volume, mount it, and set environment variable for data path.

MLOps

VOLUME ["[1]"]
RUN mkdir -p [2]
ENV DATA_PATH=[3]

Drag options to blanks, or click blank then click option'

A/data

B/mnt/data

C/app/data

D/var/data

Attempts:

3 left

💡 Hint

Common Mistakes

Using different paths for volume and environment variable.

Not creating the directory before mounting.

Using /data as the volume path, creating it, and setting DATA_PATH to /data keeps data consistent inside the container.