Prerequisites

All examples in this guide use the public image. If you’ve mirrored the repository for your own use (for example, to your Docker Hub namespace), update your commands to reference the mirrored image instead of the public one.

For example:

• Public image: dhi.io/<repository>:<tag>
• Mirrored image: <your-namespace>/dhi-<repository>:<tag>

For the examples, you must first use docker login dhi.io to authenticate to the registry to pull the images.

What's included in this mlflow image

This Docker Hardened mlflow image includes:

• mlflow CLI and server binaries (mlflow command)
• A minimal Python runtime configured to run mlflow
• Hardened runtime configuration (nonroot runtime, reduced attack surface)

Start a mlflow image

Below are common ways to run the mlflow server from the hardened image. The mlflow server exposes the tracking UI and API (default port 5000).

Entrypoint behavior

This image uses mlflow as its runtime entrypoint. Pass mlflow subcommands directly in docker run and Docker Compose examples:

• Use server, not mlflow server
• Use models build-docker, not mlflow models build-docker

Basic usage

$ docker run -d --name mlflow-server -p 5000:5000 \
  dhi.io/mlflow:3 \
  server \
  --backend-store-uri sqlite:///mlflow.db \
  --default-artifact-root file:///opt/mlflow/data/mlruns \
  --host 0.0.0.0 \
  --port 5000

This starts a simple single-node tracking server with a local SQLite backend and local artifact folder (suitable for development and evaluation only).

With Docker Compose (recommended for production-like setups)

services:
  postgres:
    image: dhi.io/postgres:18
    environment:
      POSTGRES_USER: mlflow
      POSTGRES_PASSWORD: mlflow
      POSTGRES_DB: mlflowdb
    volumes:
      - mlflow-db:/var/lib/postgresql/data

  minio:
    image: dhi.io/minio:0
    environment:
      MINIO_ROOT_USER: minioadmin
      MINIO_ROOT_PASSWORD: minioadmin
    command:
      - server
      - /data
    ports:
      - "9000:9000"
    volumes:
      - mlflow-minio:/data

  mlflow:
    image: dhi.io/mlflow:3
    container_name: mlflow-server
    ports:
      - "5000:5000"
    environment:
      AWS_ACCESS_KEY_ID: minioadmin
      AWS_SECRET_ACCESS_KEY: minioadmin
      MLFLOW_S3_ENDPOINT_URL: http://minio:9000
    command:
      - server
      - --backend-store-uri
      - postgresql+psycopg2://mlflow:mlflow@postgres:5432/mlflowdb
      - --default-artifact-root
      - s3://mlflow-artifacts
      - --host
      - 0.0.0.0
      - --port
      - "5000"
    depends_on:
      - postgres
      - minio

volumes:
  mlflow-db:
  mlflow-minio:

Common mlflow use cases

• Basic single-node tracking server (development): SQLite backend and local artifact root. Not suitable for production.

• Production tracking server: Postgres (or other SQL DB) as backend store and S3/MinIO as artifact store. Run behind a reverse proxy (nginx) for TLS and authentication.

• Model packaging and serving: The models subcommands are available in the image. For detailed workflows and model deployment patterns, refer to the upstream MLflow documentation at mlflow.org⁠.

Non-hardened images vs. Docker Hardened Images

The hardened mlflow image focuses on security (minimal runtime, nonroot user, reduced packages). The main migration difference is that the runtime image uses mlflow as its entrypoint, so container commands must pass mlflow subcommands directly.

Image variants

Docker Hardened Images come in different variants depending on their intended use. Image variants are identified by their tag.

• Runtime variants are designed to run your application in production. These images are intended to be used either directly or as the FROM image in the final stage of a multi-stage build. These images typically:

  • Run as a nonroot user
  • Do not include a shell or a package manager
  • Contain only the minimal set of libraries needed to run the app

• Build-time variants typically include dev in the tag name and are intended for use in the first stage of a multi-stage Dockerfile. These images typically:

  • Run as the root user
  • Include a shell and package manager
  • Are used to build or compile applications

To view the image variants and get more information about them, select the Tags tab for this repository, and then select a tag.

• FIPS variants include fips in the tag. They layer the OpenSSL FIPS provider and a dhi/python runtime tagged for FIPS (-debian13-fips) so TLS and other OpenSSL-backed crypto use FIPS-validated modules. Application-level Python cryptography (for example via PyPI packages) depends on those libraries; behavior may differ from non-FIPS images for algorithms that are not allowed under FIPS policy.

Migrate to a Docker Hardened Image

To migrate your application to a Docker Hardened Image, you must update your Dockerfile. At minimum, you must update the base image in your existing Dockerfile to a Docker Hardened Image. This and a few other common changes are listed in the following table of migration notes.

The following steps outline the general migration process.

1. Find hardened images for your app.

   A hardened image may have several variants. Inspect the image tags and find the image variant that meets your needs.

2. Update the base image in your Dockerfile.

   Update the base image in your application's Dockerfile to the hardened image you found in the previous step. For framework images, this is typically going to be an image tagged as dev because it has the tools needed to install packages and dependencies.

3. For multi-stage Dockerfiles, update the runtime image in your Dockerfile.

   To ensure that your final image is as minimal as possible, you should use a multi-stage build. All stages in your Dockerfile should use a hardened image. While intermediary stages will typically use images tagged as dev, your final runtime stage should use a non-dev image variant.

4. Install additional packages

   Docker Hardened Images contain minimal packages in order to reduce the potential attack surface. You may need to install additional packages in your Dockerfile. Inspect the image variants to identify which packages are already installed.

Troubleshooting migration

The following are common issues that you may encounter during migration.

General debugging

The hardened images intended for runtime don't contain a shell nor any tools for debugging. The recommended method for debugging applications built with Docker Debug (https://docs.docker.com/reference/cli/docker/debug/⁠) to attach to these containers. Docker Debug provides a shell, common debugging tools, and lets you install other tools in an ephemeral, writable layer that only exists during the debugging session.

Permissions

By default image variants intended for runtime, run as the nonroot user. Ensure that necessary files and directories are accessible to the nonroot user. You may need to copy files to different directories or change permissions so your application running as the nonroot user can access them.

Privileged ports

Non-dev hardened images run as a nonroot user by default. As a result, applications in these images can't bind to privileged ports (below 1024) when running in Kubernetes or in Docker Engine versions older than 20.10. To avoid issues, configure your application to listen on port 1025 or higher inside the container, even if you map it to a lower port on the host. For example, docker run -p 80:8080 my-image will work because the port inside the container is 8080, and docker run -p 80:81 my-image won't work because the port inside the container is 81.

No shell

By default, image variants intended for runtime don't contain a shell. Use dev images in build stages to run shell commands and then copy any necessary artifacts into the runtime stage. In addition, use Docker Debug to debug containers with no shell.

Entry point

Docker Hardened Images may have different entry points than images such as Docker Official Images. Use docker inspect to inspect entry points for Docker Hardened Images and update your Dockerfile if necessary.