How to use this image

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/ollama:<tag>
• Mirrored image: <your-namespace>/dhi-ollama:<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 Ollama image

This Docker Hardened Image ships the ollama binary at /usr/bin/ollama and its inference helper, the llama-server binary (with the ggml/llama shared libraries), at /usr/lib/ollama. The image serves the Ollama HTTP API on port 11434. The default tags run CPU inference. The -cuda flavor (Debian 13 only) adds the CUDA backend for NVIDIA GPUs; ROCm, Vulkan, and Jetson backends are not included.

Start Ollama

$ docker run --rm -p 11434:11434 \
  dhi.io/ollama:<version>

Verify the server is running:

$ curl http://localhost:11434/
Ollama is running

Pull and run a model against the running server (from another terminal, using the upstream ollama CLI or the HTTP API):

$ curl http://localhost:11434/api/pull -d '{"model": "llama3.2"}'

$ curl http://localhost:11434/api/generate -d '{
  "model": "llama3.2",
  "prompt": "Why is the sky blue?"
}'

Persist models across container restarts

Ollama stores downloaded models under $HOME/.ollama/models (HOME=/home/ollama in this image) and generates a registry-auth keypair at $HOME/.ollama/id_ed25519 on first run. Neither path is declared as a volume in the image metadata, so mount a volume at /home/ollama to persist models and the keypair across container restarts.

The upstream ollama/ollama image runs as root with HOME=/root, so it stores models under /root/.ollama. If you're migrating an existing deployment, re-point any volume mounted at /root/.ollama to /home/ollama, otherwise the mount won't match this image's HOME and your existing models will not be found.

$ docker run --rm -p 11434:11434 \
  -v ollama-data:/home/ollama \
  dhi.io/ollama:<version>

Run with an NVIDIA GPU

The -cuda flavor ships the CUDA 13.2 backend and runtime libraries. The host needs the NVIDIA driver and the NVIDIA Container Toolkit. Pass --gpus all to expose GPU devices to the container.

$ docker run --rm --gpus all -p 11434:11434 \
  dhi.io/ollama:<version>-debian13-cuda

Without --gpus all, or on a host without an NVIDIA GPU, the -cuda flavor falls back to CPU inference. The -cuda-fips and -cuda-fips-dev tags combine the CUDA backend with the FIPS variant described below.

Docker Hardened Image (DHI) vs Official Docker image

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

• 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.

• The cuda flavor includes cuda in the tag and adds the CUDA backend plus the NVIDIA CUDA runtime libraries for GPU inference. It is available for Debian 13 in runtime, build-time, and FIPS variants and requires the NVIDIA Container Toolkit on the host.

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

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. To view if a package manager is available for an image variant, select the Tags tab for this repository. To view what packages are already installed in an image variant, select the Tags tab for this repository, and then select a tag.

   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.

Troubleshooting 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.

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, including a mounted volume at /home/ollama if you need models to persist.

Entry point

Docker Hardened Images may have different entry points than the upstream Ollama image. Use docker inspect to inspect entry points and update your deployment if necessary.