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Running Open WebUI Successfully with Docker Model Runner

Running your own local Large Language Models (LLMs) offers significant advantages, including privacy and faster response times. However, managing the models and interacting with them can sometimes feel technical. This is where a user interface like Open WebUI becomes invaluable. Combined with a dedicated Model Runner, such as Ollama, and the power of Docker, you can set up a robust and user-friendly local AI chat environment quickly and efficiently.

Using Docker for this setup provides several key benefits. It ensures consistency across different systems by packaging the application and its dependencies into containers. This eliminates “it works on my machine” problems, simplifies installation, and makes it easy to manage updates or even run multiple configurations side-by-side. The Model Runner handles the heavy lifting of serving the LLMs, while Open WebUI provides a beautiful and intuitive interface for downloading, managing, and chatting with them.

To get started, you’ll need Docker and Docker Compose installed on your system. Docker Compose is essential as it allows you to define and run multiple Docker containers (in this case, the Open WebUI service and the Model Runner service) with a single configuration file and command.

The core of the setup involves creating a docker-compose.yml file. This file tells Docker Compose which services to run, what images to use, how they should be configured, and how they should communicate. Crucially, you will define volumes for both services. Volumes are critical for data persistence. The Open WebUI service needs a volume (e.g., mapped from ./data on your host) to store user accounts, chat history, and settings. The Model Runner service needs a separate volume (e.g., mapped from ./ollama_library) to store the actual LLM files you download. Mapping these directories from your host machine ensures that your data and models are not lost when the containers are stopped or updated.

Before running Docker Compose, it’s good practice to create the host directories that your volumes will map to (e.g., mkdir data ollama_library). Once your docker-compose.yml file is configured with the necessary services, ports (typically exposing Open WebUI on a port like 3000), and volumes, you can start the services using a single command in your terminal from the directory containing the file: docker compose up -d. The -d flag runs the containers in detached mode, leaving your terminal free. You can verify that the containers are running using docker compose ps.

Once the containers are up and running, you can access the Open WebUI interface through your web browser, usually at http://localhost:3000. On your first visit, you will likely be prompted to create an administrator account. After logging in, you can navigate the interface. The power comes from connecting to the Model Runner container (which Open WebUI does internally via the Docker network). You can then use the Open WebUI interface to browse and download available LLMs directly from repositories compatible with your Model Runner (like the Ollama model library).

After downloading a model, simply select it within Open WebUI to start a new chat session. You can manage multiple models, switch between them effortlessly, and leverage the user-friendly chat interface which often includes features like markdown support, code highlighting, and chat history management – all running entirely on your local machine.

To stop the services when you’re finished, navigate back to the directory containing your docker-compose.yml file and run the command: docker compose down. This will gracefully stop and remove the containers, though your persistent data in the volumes (./data and ./ollama_library) will remain untouched, ready for the next time you run docker compose up -d.

Setting up Open WebUI with a Model Runner using Docker provides an incredibly efficient and manageable way to run LLMs locally. It abstracts away complex dependencies and configurations, allowing you to focus on interacting with the models through a clean, powerful web interface. This method is highly recommended for anyone looking to dive into local AI without the setup headaches.

Source: https://collabnix.com/how-to-successfully-run-open-webui-with-docker-model-runner/

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