Docker Deep Dive: Reproducible & Portable ML Systems#

Images and Containers#

• Docker Image: A read-only template with instructions for creating containers.
• Docker Container: A running instance of an image. It’s the actual environment where your application executes.

When you run a container from an image, Docker adds a thin writable layer on top of the read-only layers of the image, making changes possible as the container executes.

Docker Hub and Registries#

Docker Hub is a cloud-based repository where you can find thousands of pre-built images for various technologies—Ubuntu, Python, TensorFlow, PyTorch, and more. There are also other registries such as GitHub Container Registry, Google Container Registry, and private self-hosted registries.

Basic Commands#

A few essential Docker commands you’ll need:

Command	Description
docker pull	Download an image from a registry
docker images	List all local images
docker run	Create and run a container from an image
docker ps	List running containers
docker ps -a	List all containers (running and stopped)
docker stop <container_id_or_name>	Stop a running container
docker rm <container_id_or_name>	Remove a stopped container
docker rmi <image_id_or_name>	Remove an image
docker build -t <image_name>: .	Build an image from a Dockerfile
docker exec -it <container_id_or_name> sh	Run a shell in a running container (interactive)

Example:

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# Pull an official Python image
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docker pull python:3.9-slim
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# Run a container from that image
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docker run -it --name my_python_container python:3.9-slim bash

In the above snippet, python:3.9-slim is an image tag that indicates the version (3.9) and a “slim�?variant optimized for minimal size.

Dockerfile Anatomy#

A Dockerfile is a text file containing instructions used to build Docker images. Here’s a simplified example to illustrate the structure:

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# Start from a base image
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FROM python:3.9-slim
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# Set working directory inside the container
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WORKDIR /app
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# Copy local files to the container
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COPY requirements.txt requirements.txt
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# Install dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy the rest of the application code
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COPY . .
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# Specify the default command
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CMD ["python", "train.py"]

Pinning Dependencies#

When building ML applications, it’s crucial to pin your Python dependencies to specific versions. This ensures consistent results. For instance, your requirements.txt might look like this:

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numpy==1.21.0
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pandas==1.3.0
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torch==1.9.0
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scikit-learn==0.24.2

Pinning dependencies helps you avoid unexpected breakages when libraries are updated.

Installing Python Packages and Frameworks#

You can install Python packages via pip inside the Dockerfile:

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RUN pip install --no-cache-dir numpy==1.21.0 pandas==1.3.0 torch==1.9.0 scikit-learn==0.24.2

Using --no-cache-dir reduces image size by preventing pip from saving downloaded packages in cache.

Multi-Stage Builds#

Multi-stage builds let you keep your final image clean and minimal. Usually, you can compile or process resources in an initial build stage and then copy only the necessary artifacts to the final image.

Example:

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# Stage 1: Build
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FROM python:3.9-slim AS builder
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WORKDIR /app
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COPY . .
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RUN pip install --no-cache-dir -r requirements.txt
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# Stage 2: Final
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FROM python:3.9-slim
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WORKDIR /app
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# Copy the installed Python packages from the builder stage
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COPY --from=builder /usr/local/lib/python3.9/site-packages /usr/local/lib/python3.9/site-packages
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COPY --from=builder /app /app
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CMD ["python", "train.py"]

By separating stages, the final image remains smaller because it doesn’t include intermediate caches or build tools.

Overview of Docker Compose#

Docker Compose allows you to define multi-container applications with a YAML file. In complex ML workflows, you might have:

• A container for Jupyter Notebook or a web interface.
• A database container storing metadata.
• A message queue container (e.g., RabbitMQ) coordinating tasks.

All of these services can be managed under a single docker-compose.yaml file.

Example Docker Compose File for ML#

Below is a minimal Docker Compose example with two services: a Jupyter Notebook (ml-notebook) and a Redis cache (redis-cache).

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version: "3.8"
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services:
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  ml-notebook:
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    image: jupyter/tensorflow-notebook
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    ports:
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      - "8888:8888"
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    volumes:
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      - .:/home/jovyan/work
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    environment:
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      - GRANT_SUDO=yes
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    command: start-notebook.sh --NotebookApp.token=''
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  redis-cache:
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    image: redis:6.2-alpine
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    ports:
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      - "6379:6379"

In this configuration:

• ml-notebook uses a pre-built Jupyter image with TensorFlow. Any local files are bind-mounted to the container’s /home/jovyan/work.
• redis-cache is an in-memory data store for caching or message queuing tasks relevant to your ML workflow.

You can start these services with:

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docker-compose up -d

The -d flag runs them in the background.

Scaling Services#

For CPU-bound or I/O-bound ML workflows, Docker Compose can spin up multiple instances of a service:

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docker-compose up --scale ml-notebook=3

This command will create three instances of the ml-notebook service, useful if you want to parallelize tasks like data preprocessing. Note that truly scaling for large tasks often involves container orchestration platforms, but Docker Compose is a great starting point.

Bridged Networking#

By default, Docker containers connect to a “bridge�?network. Each container gets its own IP, and they can communicate with each other by referencing container names when on the same network.

Host Networking#

You can expose your container’s network directly on the host by using --network=host on Linux. This is generally not recommended for security but can be beneficial for performance or for certain applications that need direct network access.

Custom Networks#

Creating a custom network allows you to isolate groups of containers:

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docker network create my_ml_network
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docker run --network my_ml_network -d ...

This approach is useful in multi-container ML projects where you need tighter control over inter-container communication.

Least Privilege Principle#

By default, containers often run as root inside the container. This is convenient but risky. It’s best practice to:

1. Create a non-root user in your Dockerfile.
2. Switch to that user before running your application.

Example:

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FROM python:3.9-slim
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RUN useradd -m mluser
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USER mluser

Secrets Management#

Never store secrets (API keys, tokens) in your Dockerfile or environment variables. Use Docker secrets (in Docker Swarm/Kubernetes) or a secrets management service (Vault, AWS Secrets Manager).

Image Vulnerability Scanning#

Tools like trivy and Docker’s built-in scanning services can detect vulnerabilities by scanning images for known CVEs (Common Vulnerabilities and Exposures). Regular scanning is vital in production setups.

Optimizing Docker Image Size#

Large images slow down both builds and deployments. Here are some tips for optimization:

1. Use smaller base images: python:3.9-slim is smaller than python:3.9.
2. Multi-stage builds: As shown earlier, compile or install only what you need, then copy the results into a minimal base image.
3. Clean up caches: Remove caches or temporary files (e.g., apt-get clean for Debian-based images).
4. Leverage .dockerignore: Exclude unnecessary files (e.g., .git, large data sets) from your build context.

Docker for Microservices Architectures#

You can break down your ML pipeline into multiple microservices:

• A model training service (perhaps triggered nightly).
• A data preprocessing service.
• An inference service that exposes predictions via a REST API.
• A logging and monitoring service.

Docker’s isolation and Compose or orchestration platforms make it simpler to manage these as separate, independently deployable services.

Deploying Containers at Scale (Intro to Swarm & Kubernetes)#

When your ML project grows in complexity or needs high availability, container orchestrators step in:

• Docker Swarm: Docker’s built-in clustering solution. Good for smaller projects or simpler setups.
• Kubernetes: A powerful, world-leading container orchestration platform. It manages container scaling, networking, load balancing, secrets, config maps, and more.

While Docker Compose is excellent for local development and small-scale projects, Kubernetes or Docker Swarm are more robust for large-scale deployments.