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.

Start a uv instance

This image includes uv tools you can invoke directly:

• uv - The main uv command-line tool
• uvx - Run Python tools and scripts

The following example shows how to run uv --help in a Docker container. You can use this pattern to run any of the uv tools included in this image.

Run the following command.

docker run --rm dhi.io/uv:<tag> uv --help

Common uv use cases

Initialize a new Python project

Create a new Python project with uv:

docker run --rm -it \
  -v "$PWD:/workspace:rw" \
  dhi.io/uv:<tag> \
  uv init my-project

Install dependencies

Use uv to install Python packages in your project:

docker run --rm -it \
  -v "$PWD:/workspace:rw" \
  dhi.io/uv:<tag> \
  uv add requests pandas

Create and manage virtual environments

Create a virtual environment for your Python project:

docker run --rm -it \
  -v "$PWD:/workspace:rw" \
  dhi.io/uv:<tag> \
  uv venv

Run Python scripts

Execute Python scripts using uvx:

docker run --rm -it \
  -v "$PWD:/workspace:rw" \
  dhi.io/uv:<tag> \
  uvx ruff check .

Install Python tools

Install and run Python tools without creating a virtual environment:

docker run --rm -it \
  -v "$PWD:/workspace:rw" \
  dhi.io/uv:<tag> \
  uv tool install ruff

Sync dependencies from lockfile

Synchronize your environment with a lockfile for reproducible builds:

docker run --rm -it \
  -v "$PWD:/workspace:rw" \
  dhi.io/uv:<tag> \
  uv sync

Use in a multi-stage Dockerfile

Use the uv image to manage Python dependencies in your application's Dockerfile:

FROM dhi.io/python:<tag>-dev AS builder

COPY --from=dhi.io/uv:<tag> /usr/local/bin/uv \
    /usr/local/bin/uvx /usr/local/bin/

WORKDIR /app
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen

FROM dhi.io/python:<tag>

WORKDIR /app
COPY --from=builder /app/.venv /app/.venv
COPY . .

ENV PATH="/app/.venv/bin:$PATH"
CMD ["python", "main.py"]

Non-hardened images vs Docker Hardened Images

Key differences

Why no shell or package manager?

Docker Hardened Images prioritize security through minimalism:

• Reduced attack surface: Fewer binaries mean fewer potential vulnerabilities
• Immutable infrastructure: Runtime containers shouldn't be modified after deployment
• Compliance ready: Meets strict security requirements for regulated environments

The hardened images intended for runtime don't contain a shell nor any tools for debugging. Common debugging methods for applications built with Docker Hardened Images include:

• Docker Debug⁠ to attach to containers
• Docker's Image Mount feature to mount debugging tools
• Ecosystem-specific debugging approaches

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.

For example, you can use Docker Debug:

docker debug <container-name>

or mount debugging tools with the Image Mount feature:

docker run --rm -it --pid container:my-uv \
  --mount=type=image,source=dhi.io/busybox,destination=/dbg,ro \
  dhi.io/uv:<tag> /dbg/bin/sh

Image variants

Docker Hardened Images come in different variants depending on their intended use.

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 the 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 variant 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

FIPS variants

FIPS variants include fips in the variant name and tag. They come in both runtime and build-time variants. These variants use cryptographic modules that have been validated under FIPS 140, a U.S. government standard for secure cryptographic operations.

For example, usage of MD5 fails in FIPS variants. To verify FIPS compliance, check the cryptographic module version in use by your uv instance.

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 Python projects using uv, you'll typically want to use the dev variant to install 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 Python 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.

   Only images tagged as dev typically have package managers. You should use a multi-stage Dockerfile to install the packages. Install the packages in the build stage that uses a dev image. Then, if needed, copy any necessary artifacts to the runtime stage that uses a non-dev image.

   For Alpine-based images, you can use apk to install packages. For Debian-based images, you can use apt-get to install packages.

Troubleshoot 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 Hardened Images is to use 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.

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.

Working with Python virtual environments

When using uv in a multi-stage build, ensure that the virtual environment created by uv is properly copied to your runtime stage. The virtual environment is typically located at .venv in your project directory. You'll also need to update your PATH environment variable to include the virtual environment's bin directory.