Docker for ML workloads in MLOps - Commands & Configuration

Introduction

Docker helps package your machine learning code, libraries, and environment into one container. This solves the problem of running ML models consistently on any computer or server without setup issues.

When you want to share your ML model with others and ensure it runs the same way on their machines.

When you need to deploy an ML model to a cloud server without worrying about missing dependencies.

When you want to test your ML code in a clean environment that matches production.

When you want to run multiple ML experiments with different library versions without conflicts.

When you want to automate ML training and deployment in a CI/CD pipeline.

Config File - Dockerfile

Dockerfile

FROM python:3.10-slim

WORKDIR /app

COPY requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

COPY . ./

CMD ["python", "train.py"]

This Dockerfile starts from a lightweight Python 3.10 image.

It sets the working directory to /app inside the container.

It copies the requirements.txt file and installs Python packages needed for ML.

It copies all your ML code into the container.

Finally, it runs the training script train.py when the container starts.

Commands

This command builds a Docker image named 'ml-training-image' from the Dockerfile in the current folder. It packages your ML code and dependencies into one image.

Terminal

docker build -t ml-training-image .

Expected Output

Sending build context to Docker daemon  12.3MB

Step 1/6 : FROM python:3.10-slim

 ---> 123abc456def

Step 2/6 : WORKDIR /app

 ---> Using cache

 ---> 789def012abc

Step 3/6 : COPY requirements.txt ./

 ---> Using cache

 ---> 345ghi678jkl

Step 4/6 : RUN pip install --no-cache-dir -r requirements.txt

 ---> Running in abc123def456

Collecting numpy

Installing collected packages: numpy

Successfully installed numpy-1.24.2

Removing intermediate container abc123def456

 ---> 567mno890pqr

Step 5/6 : COPY . ./

 ---> 234stu567vwx

Step 6/6 : CMD ["python", "train.py"]

 ---> Running in def789ghi012

Removing intermediate container def789ghi012

 ---> 890yz123abc

Successfully built 890yz123abc

Successfully tagged ml-training-image:latest

→

-t - Assigns a name and optionally a tag to the image

This command runs the container from the 'ml-training-image' image. The --rm flag removes the container after it finishes to keep your system clean.

Terminal

docker run --rm ml-training-image

Expected Output

Training started...

Epoch 1/10

Loss: 0.45

Epoch 2/10

Loss: 0.30

Training completed successfully.

→

--rm - Automatically removes the container when it exits

This command lists all Docker images on your system so you can verify your ML image was created.

Terminal

docker images

Expected Output

REPOSITORY          TAG       IMAGE ID       CREATED         SIZE

ml-training-image   latest    890yz123abc    2 minutes ago   150MB

Key Concept

If you remember nothing else from this pattern, remember: Docker packages your ML code and environment together so it runs the same everywhere.

Common Mistakes

Not including all required ML libraries in requirements.txt

The container will miss needed packages and your code will fail to run.

List all Python dependencies your ML code needs in requirements.txt before building the image.

Running docker run without the --rm flag during testing

Containers accumulate and use disk space if not removed after running.

Use --rm to automatically clean up containers after they finish.

Not copying the ML code files into the Docker image

The container will have no code to run, causing errors.

Use COPY commands in the Dockerfile to include your ML scripts and files.

Summary

Write a Dockerfile to specify the Python environment and ML dependencies.

Build a Docker image with your ML code and libraries using 'docker build'.

Run the ML training inside a container with 'docker run' to ensure consistency.

Use 'docker images' to check your built images and manage them.