Docker for ML workloads in MLOps - Step-by-Step Execution

Process Flow - Docker for ML workloads

Write Dockerfile

↓

Build Docker Image

↓

Run Container with ML Code

↓

Container Executes ML Task

↓

Output Results / Logs

↓

Stop Container / Save Model

This flow shows how you create a Docker image for ML, run it as a container to execute ML tasks, and then get results or save models.

Execution Sample

MLOps

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt ./
RUN pip install -r requirements.txt
COPY . ./
CMD ["python", "train.py"]

This Dockerfile sets up a Python environment, installs ML dependencies, copies code, and runs training.

Process Table

Step	Action	Details	Result
1	Read Dockerfile	FROM python:3.12-slim	Base image set to Python 3.12 slim
2	Set working directory	WORKDIR /app	Working directory inside container is /app
3	Copy requirements.txt	COPY requirements.txt ./	requirements.txt copied to /app
4	Install dependencies	RUN pip install -r requirements.txt	Python packages installed
5	Copy source code	COPY . ./	All local files copied to /app
6	Set command	CMD ["python", "train.py"]	Container will run train.py on start
7	Build image	docker build -t ml-train .	Docker image 'ml-train' created
8	Run container	docker run ml-train	Container starts and runs training script
9	Training executes	train.py runs ML training	Model trains and outputs logs
10	Container stops	Training completes	Container exits after job done

💡 Training script finishes, container stops automatically

Status Tracker

Variable	Start	After Step 4	After Step 8	Final
Docker Image	None	Created with Python and dependencies	Used to start container	Image remains for reuse
Container State	Not running	Not running	Running training script	Stopped after training
Model Output	None	None	Being generated	Saved or logged after training

Key Moments - 3 Insights

Why do we copy requirements.txt and run pip install before copying all code?

Because installing dependencies first uses Docker cache efficiently, so if code changes but dependencies don't, Docker skips reinstalling packages (see execution_table steps 3 and 4).

What happens when the container finishes running the training script?

The container stops automatically after the CMD command completes (see execution_table step 10). You must save outputs outside the container if needed.

Why use a slim Python base image?

A slim image is smaller and faster to download, making builds and deployments quicker without unnecessary files (see execution_table step 1).

Visual Quiz - 3 Questions

Look at the execution table, at which step is the Docker image created?

A Step 9

B Step 4

C Step 7

D Step 2

💡 Hint

Check the 'Result' column for the step mentioning 'Docker image created'

At which step does the container start running the ML training script?

A Step 6

B Step 8

C Step 10

D Step 3

💡 Hint

Look for the step where the container 'starts and runs training script'

If you change only the source code but not requirements.txt, which step's action can Docker skip during rebuild?

A Install dependencies

B Copy source code

C Copy requirements.txt

D Set working directory

💡 Hint

Refer to key moment about Docker cache and execution_table steps 3 and 4

Concept Snapshot

Docker for ML workloads:
- Write Dockerfile with base Python image
- Copy requirements.txt and install dependencies first
- Copy ML code and set CMD to run training
- Build image with 'docker build'
- Run container with 'docker run' to execute ML task
- Container stops when training finishes
- Save outputs outside container if needed

Full Transcript

This visual execution shows how Docker helps run machine learning workloads. First, you write a Dockerfile starting from a Python base image. Then you copy the requirements.txt file and install dependencies to use Docker's cache efficiently. Next, you copy your ML code and set the command to run your training script. You build the Docker image, which packages everything needed. Running the container starts the training script inside an isolated environment. When training finishes, the container stops automatically. To keep your model or logs, save them outside the container. This process makes ML workloads portable and consistent across machines.

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